Podcasts about cohere

Welcome back to another electrifying episode of Tank Talks! Matt Cohen is joined once again by John Ruffolo to unpack the latest economic and technological headlines. From Canada's growing role in global AI and energy discussions to the latest shifts in public-private partnerships, this episode is packed with high-stakes insights and forward-thinking analysis.Is Canada ready to lead the charge in AI and quantum technology? Can the nation address its looming energy challenges and secure a sustainable economic future? Tune in for an exploration of these questions and more!G7 Summit & Canada's Global Position: A Race for AI Leadership (00:14)The G7 Summit in Alberta saw world leaders make bold commitments to AI and quantum technology, with Canada front and center. But how realistic are these promises? Matt and John dive into the challenges and opportunities ahead as Canada seeks to secure its place as a global leader in innovation and technology.AI Investment and Quantum Computing: Is Canada Ready to Step Up? (00:40)The G7 has pledged $185 million towards AI and quantum growth, but John has concerns about the scale and execution of these investments. Will this funding truly move the needle, or is it just more talk without follow-through? John discusses whether Canada has the right strategy to dominate in these transformative technologies.Energy Challenges: Canada's Struggle for Economic Resilience (03:30)Energy remains Canada's Achilles' heel. As global markets shift and environmental concerns grow, John breaks down Canada's struggle to address its energy needs while maintaining environmental responsibility. How can Canada secure its energy future in a politically and environmentally charged landscape? The conversation digs into what needs to change for the country to thrive.Open Banking: Canada's Slow Progress and Risk of Falling Behind (06:05)Despite promises, Canada is still stumbling on the road to open banking. With no concrete timeline in place, John and Matt discuss the latest developments and why Canada risks falling behind other fintech hubs like the U.S. and the UK. Is Canada's fintech future in peril, or is there hope for change on the horizon?Public-Private Partnerships in AI: A Game-Changer for Canada's Economy? (08:47)Cohere's new partnership with the Canadian and UK governments is raising the stakes for AI innovation in the public sector. As AI gains ground in government services, Matt and John examine how this public-private collaboration could shape Canada's economic future. Are these partnerships the key to unlocking Canada's AI potential?Meta's AI Bet: Is Zuckerberg Playing Catch-Up or Leading the Charge? (14:32)Mark Zuckerberg is throwing down big bets in AI, offering hefty signing bonuses and investing $14 billion into Scale AI. But is this a desperate attempt to catch up with rivals like OpenAI, or a strategic move to solidify Meta's position at the forefront of AI? Matt and John analyze the implications of Zuckerberg's moves and what they mean for Meta's future.Investment Shifts: VC Fund Performance and What It Means for the Tech Landscape (20:01)The latest data on VC fund performance reveals some stark realities. While TVPI (Total Value to Paid-in Capital) shows some life, DPI (Distributions to Paid-in Capital) is still scarce. John and Matt dive into the numbers and discuss what this means for investors, founders, and the future of venture capitalAs global dynamics shift, Canada's role in AI, energy, and investment will be tested like never before. Can the country capitalize on its technological opportunities, or will it get left behind? This episode is a must-listen for anyone interested in understanding how these shifts will shape the future of business, technology, and global leadership.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

What really happened inside Google Brain when the “Attention is All You Need” paper was born? In this episode, Aidan Gomez — one of the eight co-authors of the Transformers paper and now CEO of Cohere — reveals the behind-the-scenes story of how a cold email and a lucky administrative mistake landed him at the center of the AI revolution.Aidan shares how a group of researchers, given total academic freedom, accidentally stumbled into one of the most important breakthroughs in AI history — and why the architecture they created still powers everything from ChatGPT to Google Search today.We dig into why synthetic data is now the secret sauce behind the world's best AI models, and how Cohere is using it to build enterprise AI that's more secure, private, and customizable than anything else on the market. Aidan explains why he's not interested in “building God” or chasing AGI hype, and why he believes the real impact of AI will be in making work more productive, not replacing humans.You'll also get a candid look at the realities of building an AI company for the enterprise: from deploying models on-prem and air-gapped for banks and telecoms, to the surprising demand for multimodal and multilingual AI in Japan and Korea, to the practical challenges of helping customers identify and execute on hundreds of use cases.CohereWebsite - https://cohere.comX/Twitter - https://x.com/cohereAidan GomezLinkedIn - https://ca.linkedin.com/in/aidangomezX/Twitter - https://x.com/aidangomezFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (02:00) The Story Behind the Transformers Paper (03:09) How a Cold Email Landed Aidan at Google Brain (10:39) The Initial Reception to the Transformers Breakthrough (11:13) Google's Response to the Transformer Architecture (12:16) The Staying Power of Transformers in AI (13:55) Emerging Alternatives to Transformer Architectures (15:45) The Significance of Reasoning in Modern AI (18:09) The Untapped Potential of Reasoning Models (24:04) Aidan's Path After the Transformers Paper and the Founding of Cohere (25:16) Choosing Enterprise AI Over AGI Labs (26:55) Aidan's Perspective on AGI and Superintelligence (28:37) The Trajectory Toward Human-Level AI (30:58) Transitioning from Researcher to CEO (33:27) Cohere's Product and Platform Architecture (37:16) The Role of Synthetic Data in AI (39:32) Custom vs. General AI Models at Cohere (42:23) The AYA Models and Cohere Labs Explained (44:11) Enterprise Demand for Multimodal AI (49:20) On-Prem vs. Cloud (50:31) Cohere's North Platform (54:25) How Enterprises Identify and Implement AI Use Cases (57:49) The Competitive Edge of Early AI Adoption (01:00:08) Aidan's Concerns About AI and Society (01:01:30) Cohere's Vision for Success in the Next 3–5 Years

In the last episode of TAB Storytellers season 3, Abi and Jen decided to end with a candid, heartfelt conversation that celebrates their journey, the evolution of TAB (Teaching for Artistic Behavior), and the challenges and triumphs of teaching art in diverse environments. They reflect on their personal and professional growth, share updates about roles, research, and family life, and explore meaningful themes like student agency, teacher identity, and curriculum design. It's a rich and inspiring check-in that reaffirms the community and philosophy behind the TABcast.For more information about TAB, please visit the TAB website: www.teachingfor artisticbehavior.org. Also, you are invited to join us on Mighty Networks (https://teaching-for-artistic-behavior.mn.com/), an online platform dedicated to everything TAB!Here are links to resources or more information that was discussed in the TABcast: Engaging Learners Through Artmaking (Book, 2nd Edition)Fred Tjardes School of Innovation, Greeley, COHere is a link to a lightly edited transcript of this episode. We recognize that there are probably errors and grammatical issues. If anyone with the time or inclination to edit this wants to do so, please email us at storytellers@teachingforartisticbehavior.org

In this episode, I am connecting with Tesa Arcilla. Tesa has never taken the conventional route—personally or professionally. A journalist by training and driven by an insatiable curiosity, she has lived and worked across multiple continents, carving out a career that defies easy labels. From live broadcasts at NBC and CBC to moderating high-stakes political debates for Euronews and the European Commission, she has spent over 20 years asking the tough questions, navigating complex global issues, and making sense of the world's turning points. But beyond the newsrooms and international assignments, Tesa's journey has been defined by bold choices—moving across the world for love, walking away from established career paths, and constantly reinventing herself. She understands what it means to take a leap into the unknown, to bet on yourself even when the path isn't clear, and to thrive outside the traditional mould. Now, she helps others do the same—own their voice, build influence with authenticity, and turn their expertise into impact—without the ‘cringe' of self-promotion. As the host of Choices & Chances, she dives into the defining decisions that shape lives and careers, helping others embrace uncertainty, make bold moves, and step fully into their potential. Whether on stage, moderating a panel, or mentoring thought leaders, Tesa brings a sharp mind, a global perspective, and a passion for real, unfiltered conversations that challenge, inspire, and drive action. Because in the end, life isn't about playing it safe—it's about making the choices that create the chances we truly want. HIGHLIGHTS 00:00:30 - Vulnerability and Boundaries 00:01:56 - Self-Respect and Authenticity 00:02:59 - The Journey of Sharing Personal Stories 00:05:31 - Career as a Foreign Correspondent 00:06:02 - The Privilege of Journalism 00:09:03 - Navigating Stress and Health 00:10:10 - Learning to Listen to the Body 00:12:31 - Highlighting Career Experiences 00:16:18 - The Impact of Political Shows 00:20:16 - The Challenge of Live Broadcasting 00:23:20 - Transitioning to Entrepreneurship 00:26:07 - Recognizing the Right Time for Change 00:29:45 - Unwinding from High-Stress Environments 00:32:38 - Change as a Neutral Concept 00:35:42 - Agency and Choice 00:39:39 - Embracing Uncertainty 00:42:25 - Current Entrepreneurial Endeavours Connect with Tesa Instagram: https://www.instagram.com/tesa.arcilla/ Instagram: https://www.instagram.com/choiceschances/ Website: ​​www.choiceschances.coHere is a freebie on Tesa's website:  https://choiceschances.co/freebie Marsha Vanwynsberghe — NLP Storytelling Trainer, OUTSPOKEN NLP Coaching Certification, Author, Speaker, and Podcaster Download FREE “You Are Supported” Hypnosis and Subliminal Bundle HERE  Join the next cohort of OUTSPOKEN NLP Coaching Certification (kick-off in March 2025) HERE Learn more about changing the Stories We Tell Ourselves Digital Program HERE. Use Code PODCAST to receive 20% off. Code FASTACTION20 Tap the “Follow” button never to miss a show, and if you love the show, please feel free to tag me on social media, share it with a friend, or leave me a rating and review. This helps the show grow! Website: www.marshavanw.comConnect on IG. Click HERESubscribe on YouTube. Click HERE  

In this episode of Production Value Matters, host Matthew Byrne is joined by Isabelle Camp, Global Head of Events at Cohere, to break down what it really takes to design events that deliver measurable business impact. From strategic planning to post-event automation, Isabelle shares the systems, team alignment strategies, and data practices that transform events into revenue engines. Whether you're navigating disconnects between sales and marketing or looking to move beyond vanity metrics, this episode offers a playbook for event pros who want real pipeline results in today's B2B landscape. You'll also hear why creative experiences still matter, how to avoid cookie-cutter formats, and what metrics actually move the needle when it comes to pipeline influence. What you'll learn: How to align event strategy with company-wide business goals Ways to bridge the gap between sales and marketing through events How to track pipeline impact using CRM and automation Why clear pre-event objectives lead to stronger post-event reporting How to balance creativity with data-driven decision-making Strategies for scaling what works—and cutting what doesn't Isabelle Camp brings over a decade of global event marketing experience, from enterprise trade shows to product launches. Now leading the charge at Canada's top AI company, Cohere, she's redefining what success looks like in strategic events and how to achieve it at scale. If you enjoyed this episode, make sure to subscribe, rate, and review on Apple Podcasts and Spotify. Host LinkedIn: linkedin.com/in/matthewbyrnecsep Guest LinkedIn: linkedin.com/isabelle-camp/ For additional resources for #eventprofs visit www.productionvaluematters.com For additional resources for #eventprofs visit www.productionvaluematters.comCheck out our 3 most downloaded episodes:Measuring Value in Your Events: Insights from Jodi CollenEducating Clients and Managing Expectations in Event Production with Fransiska WeckesserThe Intersection of Event Planning and Psychology with Victoria Matey Hosted on Acast. See acast.com/privacy for more information.

Today's show: Microsoft lays off 6,000 employees despite record profits, signaling a ruthless new phase in big tech. Jason, Lon, and Alex discuss what it means for the talent market, why tightening U.S. immigration could cripple startup innovation, and whether AI startup Windsurf is selling too early as OpenAI circles. Plus, Klarna's AI customer service backfires, IPO momentum returns, and Office Hours with airfive founder Jeremy Redman pitches a bold new prepaid SaaS model.Timestamps:(0:00) Episode Teaser(2:40) Why ex-Microsoft staffers are talking smack on Blind(10:08) Atlassian - Head to https://www.atlassian.com/startups/twist to see if you qualify for 50 free seats for 12 months.(15:58) Checking out ElevenLabs' wild new AI soundboard(20:28) Fidelity Private Shares℠ - Visit ⁠https://fidelityprivateshares.com⁠! Mention our podcast and receive 20% off your first-year paid subscription.(24:02) What is Bottom Up TAM and why it isn't dirty like it sounds(30:10) Google Gemini - It uses AI to help you write, code, and create in one interactive space. Try it at gemini.google.com/canvas.(33:52) Cohere missed revenue estimates but things aren't THAT bleak!(37:01) Why America NEEDS highly skilled immigrants(43:12) Working the “I Have a Secret” strategy with Jeremy from AirfiveSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpLinks from episode:EleveLabs Sound Board: https://elevenlabs.io/sound-effects/soundboardLikable: https://likeable.co/airfive: https://airfive.com/Follow Jeremy:X: https://x.com/thejeremyredmanLinkedIn: https://www.linkedin.com/in/thejeremyredman/Follow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:08) Atlassian - Head to https://www.atlassian.com/startups/twist to see if you qualify for 50 free seats for 12 months.(20:28) Fidelity Private Shares℠ - Visit ⁠https://fidelityprivateshares.com⁠! Mention our podcast and receive 20% off your first-year paid subscription.(30:10) Google Gemini - It uses AI to help you write, code, and create in one interactive space. Try it at gemini.google.com/canvas.Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) ChatGPT ranks No. 5 among all websites worldwide 2) ChatGPT is the only website among the top ranked by SimilarWeb that is growing 3) How do chatbots get information if they replace the web? 4) Grok's 'white genocide' messaging campaign 5) What's in a system prompt, with a look inside Grok's 6) The truth about Timothée Chalamet 7) Filing stories directly into ChatGPT? 8) Meta slams into big problems in its Llama AI program 9) Does it matter if scaling is done? 10) IBM survey shows generative ROI is hard to come by despite interest 11) Cohere's revenue trouble 12) Perplexity integrates with Paypal 13) A look at the event calendar ahead --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Grok erzählt zusammenhangslos Verschwörungstheorie über weißen Genozid in Südafrika. Trumps Zölle könnten die US-Konsumlaune ins Wanken bringe. Cohere verfehlt seine Umsatzerwartungen drastisch. CoreWeave übertrifft Erwartungen. Microsofts KI-gestützte Einsparungen werfen Fragen zur Zukunft der Softwareentwicklung auf. Coinbase wurde Opfer von Social Engineering. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:01:50) Reddit Google (00:08:15) Youtube Podcast Charts (00:12:15) US Verbraucherbelastung (00:18:45) xAI Systemprompt Genozid Südafrika (00:32:00) Cohere  (00:38:30) CoreWeave  (00:45:20) AirBnb & Booking (00:56:00) TikTok (00:58:00) Microsoft Entlassungen Softwareentwickler (01:01:20) Coinbase Hack (01:06:15) Trump Apple Indienple Shownotes YouTube veröffentlicht Ranking der Top-Podcasts in den USA – nytimes.com Verbraucher unter Druck durch Trumps Zölle – nytimes.com Grok „weißer Völkermord“ in Südafrika - pivot-to-ai.com, axios.com Grok System Prompt – x.com Sam Altman Tweet – x.com Warum Cohere seine Umsatzprognose um 85% verfehlte – theinformation.com Warum ein KI-Datenzentrum auf der Prärie leer steht – theinformation.com CoreWeave übertrifft Erwartungen – ft.com Airbnb: Köche und Massagen in neuer App – bbc.com EU gegen TikTok wegen Online-Inhaltsregeln – ft.com Microsoft-Entlassungen treffen Softwareingenieure, AI-Einsparungen – bloomberg.com Cyberangriff auf Kryptobörse: Kopfgeld auf Hacker ausgesetzt – manager-magazin.de Trump Apple Indien – ft.com Apple warnt vor EU-Apps ohne App-Store-Zahlungen – theverge.com

In a new season of the Oracle University Podcast, Lois Houston and Nikita Abraham dive into the world of Oracle GoldenGate 23ai, a cutting-edge software solution for data management. They are joined by Nick Wagner, a seasoned expert in database replication, who provides a comprehensive overview of this powerful tool.   Nick highlights GoldenGate's ability to ensure continuous operations by efficiently moving data between databases and platforms with minimal overhead. He emphasizes its role in enabling real-time analytics, enhancing data security, and reducing costs by offloading data to low-cost hardware. The discussion also covers GoldenGate's role in facilitating data sharing, improving operational efficiency, and reducing downtime during outages.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. This time, we're focusing on the fundamentals of Oracle GoldenGate. Oracle GoldenGate helps organizations manage and synchronize their data across diverse systems and databases in real time.  And with the new Oracle GoldenGate 23ai release, we'll uncover the latest innovations and features that empower businesses to make the most of their data. Nikita: Taking us through this is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. He's been doing database replication for about 25 years and has been focused on GoldenGate on and off for about 20 of those years.  01:18 Lois: In today's episode, we'll ask Nick to give us a general overview of the product, along with some use cases and benefits. Hi Nick! To start with, why do customers need GoldenGate? Nick: Well, it delivers continuous operations, being able to continuously move data from one database to another database or data platform in efficiently and a high-speed manner, and it does this with very low overhead. Almost all the GoldenGate environments use transaction logs to pull the data out of the system, so we're not creating any additional triggers or very little overhead on that source system. GoldenGate can also enable real-time analytics, being able to pull data from all these different databases and move them into your analytics system in real time can improve the value that those analytics systems provide. Being able to do real-time statistics and analysis of that data within those high-performance custom environments is really important. 02:13 Nikita: Does it offer any benefits in terms of cost?  Nick: GoldenGate can also lower IT costs. A lot of times people run these massive OLTP databases, and they are running reporting in those same systems. With GoldenGate, you can offload some of the data or all the data to a low-cost commodity hardware where you can then run the reports on that other system. So, this way, you can get back that performance on the OLTP system, while at the same time optimizing your reporting environment for those long running reports. You can improve efficiencies and reduce risks. Being able to reduce the amount of downtime during planned and unplanned outages can really make a big benefit to the overall operational efficiencies of your company.  02:54 Nikita: What about when it comes to data sharing and data security? Nick: You can also reduce barriers to data sharing. Being able to pull subsets of data, or just specific pieces of data out of a production database and move it to the team or to the group that needs that information in real time is very important. And it also protects the security of your data by only moving in the information that they need and not the entire database. It also provides extensibility and flexibility, being able to support multiple different replication topologies and architectures. 03:24 Lois: Can you tell us about some of the use cases of GoldenGate? Where does GoldenGate truly shine?  Nick: Some of the more traditional use cases of GoldenGate include use within the multicloud fabric. Within a multicloud fabric, this essentially means that GoldenGate can replicate data between on-premise environments, within cloud environments, or hybrid, cloud to on-premise, on-premise to cloud, or even within multiple clouds. So, you can move data from AWS to Azure to OCI. You can also move between the systems themselves, so you don't have to use the same database in all the different clouds. For example, if you wanted to move data from AWS Postgres into Oracle running in OCI, you can do that using Oracle GoldenGate. We also support maximum availability architectures. And so, there's a lot of different use cases here, but primarily geared around reducing your recovery point objective and recovery time objective. 04:20 Lois: Ah, reducing RPO and RTO. That must have a significant advantage for the customer, right? Nick: So, reducing your RPO and RTO allows you to take advantage of some of the benefits of GoldenGate, being able to do active-active replication, being able to set up GoldenGate for high availability, real-time failover, and it can augment your active Data Guard and Data Guard configuration. So, a lot of times GoldenGate is used within Oracle's maximum availability architecture platinum tier level of replication, which means that at that point you've got lots of different capabilities within the Oracle Database itself. But to help eke out that last little bit of high availability, you want to set up an active-active environment with GoldenGate to really get true zero RPO and RTO. GoldenGate can also be used for data offloading and data hubs. Being able to pull data from one or more source systems and move it into a data hub, or into a data warehouse for your operational reporting. This could also be your analytics environment too. 05:22 Nikita: Does GoldenGate support online migrations? Nick: In fact, a lot of companies actually get started in GoldenGate by doing a migration from one platform to another. Now, these don't even have to be something as complex as going from one database like a DB2 on-premise into an Oracle on OCI, it could even be simple migrations. A lot of times doing something like a major application or a major database version upgrade is going to take downtime on that production system. You can use GoldenGate to eliminate that downtime. So this could be going from Oracle 19c to Oracle 23ai, or going from application version 1.0 to application version 2.0, because GoldenGate can do the transformation between the different application schemas. You can use GoldenGate to migrate your database from on premise into the cloud with no downtime as well. We also support real-time analytic feeds, being able to go from multiple databases, not only those on premise, but being able to pull information from different SaaS applications inside of OCI and move it to your different analytic systems. And then, of course, we also have the ability to stream events and analytics within GoldenGate itself.  06:34 Lois: Let's move on to the various topologies supported by GoldenGate. I know GoldenGate supports many different platforms and can be used with just about any database. Nick: This first layer of topologies is what we usually consider relational database topologies. And so this would be moving data from Oracle to Oracle, Postgres to Oracle, Sybase to SQL Server, a lot of different types of databases. So the first architecture would be unidirectional. This is replicating from one source to one target. You can do this for reporting. If I wanted to offload some reports into another server, I can go ahead and do that using GoldenGate. I can replicate the entire database or just a subset of tables. I can also set up GoldenGate for bidirectional, and this is what I want to set up GoldenGate for something like high availability. So in the event that one of the servers crashes, I can almost immediately reconnect my users to the other system. And that almost immediately depends on the amount of latency that GoldenGate has at that time. So a typical latency is anywhere from 3 to 6 seconds. So after that primary system fails, I can reconnect my users to the other system in 3 to 6 seconds. And I can do that because as GoldenGate's applying data into that target database, that target system is already open for read and write activity. GoldenGate is just another user connecting in issuing DML operations, and so it makes that failover time very low. 07:59 Nikita: Ok…If you can get it down to 3 to 6 seconds, can you bring it down to zero? Like zero failover time?   Nick: That's the next topology, which is active-active. And in this scenario, all servers are read/write all at the same time and all available for user activity. And you can do multiple topologies with this as well. You can do a mesh architecture, which is where every server talks to every other server. This works really well for 2, 3, 4, maybe even 5 environments, but when you get beyond that, having every server communicate with every other server can get a little complex. And so at that point we start looking at doing what we call a hub and spoke architecture, where we have lots of different spokes. At the end of each spoke is a read/write database, and then those communicate with a hub. So any change that happens on one spoke gets sent into the hub, and then from the hub it gets sent out to all the other spokes. And through that architecture, it allows you to really scale up your environments. We have customers that are doing up to 150 spokes within that hub architecture. Within active-active replication as well, we can do conflict detection and resolution, which means that if two users modify the same row on two different systems, GoldenGate can actually determine that there was an issue with that and determine what user wins or which row change wins, which is extremely important when doing active-active replication. And this means that if one of those systems fails, there is no downtime when you switch your users to another active system because it's already available for activity and ready to go. 09:35 Lois: Wow, that's fantastic. Ok, tell us more about the topologies. Nick: GoldenGate can do other things like broadcast, sending data from one system to multiple systems, or many to one as far as consolidation. We can also do cascading replication, so when data moves from one environment that GoldenGate is replicating into another environment that GoldenGate is replicating. By default, we ignore all of our own transactions. But there's actually a toggle switch that you can flip that says, hey, GoldenGate, even though you wrote that data into that database, still push it on to the next system. And then of course, we can also do distribution of data, and this is more like moving data from a relational database into something like a Kafka topic or a JMS queue or into some messaging service. 10:24 Raise your game with the Oracle Cloud Applications skills challenge. Get free training on Oracle Fusion Cloud Applications, Oracle Modern Best Practice, and Oracle Cloud Success Navigator. Pass the free Oracle Fusion Cloud Foundations Associate exam to earn a Foundations Associate certification. Plus, there's a chance to win awards and prizes throughout the challenge! What are you waiting for? Join the challenge today by visiting visit oracle.com/education. 10:58 Nikita: Welcome back! Nick, does GoldenGate also have nonrelational capabilities?  Nick: We have a number of nonrelational replication events in topologies as well. This includes things like data lake ingestion and streaming ingestion, being able to move data and data objects from these different relational database platforms into data lakes and into these streaming systems where you can run analytics on them and run reports. We can also do cloud ingestion, being able to move data from these databases into different cloud environments. And this is not only just moving it into relational databases with those clouds, but also their data lakes and data fabrics. 11:38 Lois: You mentioned a messaging service earlier. Can you tell us more about that? Nick: Messaging replication is also possible. So we can actually capture from things like messaging systems like Kafka Connect and JMS, replicate that into a relational data, or simply stream it into another environment. We also support NoSQL replication, being able to capture from MongoDB and replicate it onto another MongoDB for high availability or disaster recovery, or simply into any other system. 12:06 Nikita: I see. And is there any integration with a customer's SaaS applications? Nick: GoldenGate also supports a number of different OCI SaaS applications. And so a lot of these different applications like Oracle Financials Fusion, Oracle Transportation Management, they all have GoldenGate built under the covers and can be enabled with a flag that you can actually have that data sent out to your other GoldenGate environment. So you can actually subscribe to changes that are happening in these other systems with very little overhead. And then of course, we have event processing and analytics, and this is the final topology or flexibility within GoldenGate itself. And this is being able to push data through data pipelines, doing data transformations. GoldenGate is not an ETL tool, but it can do row-level transformation and row-level filtering.  12:55 Lois: Are there integrations offered by Oracle GoldenGate in automation and artificial intelligence? Nick: We can do time series analysis and geofencing using the GoldenGate Stream Analytics product. It allows you to actually do real time analysis and time series analysis on data as it flows through the GoldenGate trails. And then that same product, the GoldenGate Stream Analytics, can then take the data and move it to predictive analytics, where you can run MML on it, or ONNX or other Spark-type technologies and do real-time analysis and AI on that information as it's flowing through.  13:29 Nikita: So, GoldenGate is extremely flexible. And given Oracle's focus on integrating AI into its product portfolio, what about GoldenGate? Does it offer any AI-related features, especially since the product name has “23ai” in it? Nick: With the advent of Oracle GoldenGate 23ai, it's one of the two products at this point that has the AI moniker at Oracle. Oracle Database 23ai also has it, and that means that we actually do stuff with AI. So the Oracle GoldenGate product can actually capture vectors from databases like MySQL HeatWave, Postgres using pgvector, which includes things like AlloyDB, Amazon RDS Postgres, Aurora Postgres. We can also replicate data into Elasticsearch and OpenSearch, or if the data is using vectors within OCI or the Oracle Database itself. So GoldenGate can be used for a number of things here. The first one is being able to migrate vectors into the Oracle Database. So if you're using something like Postgres, MySQL, and you want to migrate the vector information into the Oracle Database, you can. Now one thing to keep in mind here is a vector is oftentimes like a GPS coordinate. So if I need to know the GPS coordinates of Austin, Texas, I can put in a latitude and longitude and it will give me the GPS coordinates of a building within that city. But if I also need to know the altitude of that same building, well, that's going to be a different algorithm. And GoldenGate and replicating vectors is the same way. When you create a vector, it's essentially just creating a bunch of numbers under the screen, kind of like those same GPS coordinates. The dimension and the algorithm that you use to generate that vector can be different across different databases, but the actual meaning of that data will change. And so GoldenGate can replicate the vector data as long as the algorithm and the dimensions are the same. If the algorithm and the dimensions are not the same between the source and the target, then you'll actually want GoldenGate to replicate the base data that created that vector. And then once GoldenGate replicates the base data, it'll actually call the vector embedding technology to re-embed that data and produce that numerical formatting for you.  15:42 Lois: So, there are some nuances there… Nick: GoldenGate can also replicate and consolidate vector changes or even do the embedding API calls itself. This is really nice because it means that we can take changes from multiple systems and consolidate them into a single one. We can also do the reverse of that too. A lot of customers are still trying to find out which algorithms work best for them. How many dimensions? What's the optimal use? Well, you can now run those in different servers without impacting your actual AI system. Once you've identified which algorithm and dimension is going to be best for your data, you can then have GoldenGate replicate that into your production system and we'll start using that instead. So it's a nice way to switch algorithms without taking extensive downtime. 16:29 Nikita: What about in multicloud environments?  Nick: GoldenGate can also do multicloud and N-way active-active Oracle replication between vectors. So if there's vectors in Oracle databases, in multiple clouds, or multiple on-premise databases, GoldenGate can synchronize them all up. And of course we can also stream changes from vector information, including text as well into different search engines. And that's where the integration with Elasticsearch and OpenSearch comes in. And then we can use things like NVIDIA and Cohere to actually do the AI on that data.  17:01 Lois: Using GoldenGate with AI in the database unlocks so many possibilities. Thanks for that detailed introduction to Oracle GoldenGate 23ai and its capabilities, Nick.  Nikita: We've run out of time for today, but Nick will be back next week to talk about how GoldenGate has evolved over time and its latest features. And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course to learn more. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:33 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

How do you build strong teams while navigating rapid growth and the rise of AI?In this episode of Success Leaves Clues, Robin and Al sit down with Nora Beatty, VP of People Operations at Cohere, one of Canada's leading AI companies. Nora shares her inspiring career journey—from her early passion for PeopleOps (inspired by her mom) to leadership roles at Oracle, FreshBooks, and now at the forefront of innovation in AI.The conversation explores how AI is reshaping the workforce, the role of PeopleOps in a high-growth startup, and how companies can better support employees in balancing career and family life. Nora also highlights the importance of addressing generational dynamics and being intentional during times of transformation.Key takeaways:✨ AI as an Enabler – AI isn't here to replace employees; it's here to enhance creativity and capability.✨ Growth with Purpose – Rapid expansion requires strategic trade-offs and strong people leadership.✨ Supporting the Whole Employee – Balancing family, career, and mental well-being is a must for long-term success.✨ Generational Awareness – Understanding workplace dynamics across age groups improves collaboration and leadership.✨ Timing is Everything – Bringing in PeopleOps at the right moment can shape a company's future.This episode is a must-listen for leaders, founders, and PeopleOps professionals navigating the evolving intersection of tech, people, and purpose.

Canada-U.S. relations currently experience challenges due to trade tensions. U.S. trade policies affect both economies and create friction. New tariffs raise anticipation about their impact on venture capital investment in Canadian startups. In 2024, Canadian startups raised $6.9 billion in venture capital, a 17% increase from the previous year. Despite funding increases, deal volume declined, with fewer than 700 deals completed in the last year. Major funding rounds in the artificial intelligence sector have driven totals higher, including notable amounts raised by Clio, Tenstorrent, and Cohere. The first quarter of 2024 saw Canadian startups raise $1.6 billion in 128 deals, lower than the previous quarter but higher than the same quarter last year. The effects of new U.S. tariffs on venture activity remain unclear, with potential impacts on hardware and software sectors. Overall, the Canadian venture landscape requires observation as market conditions evolve.Learn more on this news visit us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Discover how Oracle APEX leverages OCI AI services to build smarter, more efficient applications. Hosts Lois Houston and Nikita Abraham interview APEX experts Chaitanya Koratamaddi, Apoorva Srinivas, and Toufiq Mohammed about how key services like OCI Vision, Oracle Digital Assistant, and Document Understanding integrate with Oracle APEX.   Packed with real-world examples, this episode highlights all the ways you can enhance your APEX apps.   Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we looked at how generative AI powers Oracle APEX and in today's episode, we're going to focus on integrating APEX with OCI AI Services. Lois: That's right, Niki. We're going to look at how you can use Oracle AI services like OCI Vision, Oracle Digital Assistant, Document Understanding, OCI Generative AI, and more to enhance your APEX apps. 01:03 Nikita: And to help us with it all, we've got three amazing experts with us, Chaitanya Koratamaddi, Director of Product Management at Oracle, and senior product managers, Apoorva Srinivas and Toufiq Mohammed. In today's episode, we'll go through each Oracle AI service and look at how it interacts with APEX. Apoorva, let's start with you. Can you explain what the OCI Vision service is? Apoorva: Oracle Cloud Infrastructure Vision is a serverless multi-tenant service accessible using the console or REST APIs. You can upload images to detect and classify objects in them. With prebuilt models available, developers can quickly build image recognition into their applications without machine learning expertise. OCI Vision service provides a fully managed model infrastructure. With complete integration with OCI Data Labeling, you can build custom models easily. OCI Vision service provides pretrained models-- Image Classification, Object Detection, Face Detection, and Text Recognition. You can build custom models for Image Classification and Object Detection. 02:24 Lois: Ok. What about its use cases? How can OCI Vision make APEX apps more powerful? Apoorva: Using OCI Vision, you can make images and videos discoverable and searchable in your APEX app.  You can use OCI Vision to detect and classify objects in the images. OCI Vision also highlights the objects using a red rectangular box. This comes in handy in use cases such as detecting vehicles that have violated the rules in traffic images. You can use OCI Vision to identify visual anomalies in your data. This is a very popular use case where you can detect anomalies in cancer X-ray images to detect cancer. These are some of the most popular use cases of using OCI Vision with your APEX app. But the possibilities are endless and you can use OCI Vision for any of your image analysis. 03:29 Nikita: Let's shift gears to Oracle Digital Assistant. Chaitanya, can you tell us what it's all about? Chaitanya: Oracle Digital Assistant is a low-code conversational AI platform that allows businesses to build and deploy AI assistants. It provides natural language understanding, automatic speech recognition, and text-to-speech capabilities to enable human-like interactions with customers and employees. Oracle Digital Assistant comes with prebuilt templates for you to get started.  04:00 Lois: What are its key features and benefits, Chaitanya? How does it enhance the user experience? Chaitanya: Oracle Digital Assistant provides conversational AI capabilities that include generative AI features, natural language understanding and ML, AI-powered voice, and analytics and insights. Integration with enterprise applications become easier with unified conversational experience, prebuilt chatbots for Oracle Cloud applications, and chatbot architecture frameworks. Oracle Digital Assistant provides advanced conversational design tools, conversational designer, dialogue and domain trainer, and native multilingual support. Oracle Digital Assistant is open, scalable, and secure. It provides multi-channel support, automated bot-to-agent transfer, and integrated authentication profile. 04:56 Nikita: And what about the architecture? What happens at the back end? Chaitanya: Developers assemble digital assistants from one or more skills. Skills can be based on prebuilt skills provided by Oracle or third parties, custom developed, or based on one of the many skill templates available. 05:16 Lois: Chaitanya, what exactly are “skills” within the Oracle Digital Assistant framework?  Chaitanya: Skills are individual chatbots that are designed to interact with users and fulfill specific type of tasks. Each skill helps a user complete a task through a combination of text messages and simple UI elements like select list. When a user request is submitted through a channel, the Digital Assistant routes the user's request to the most appropriate skill to satisfy the user's request. Skills can combine multilingual NLP deep learning engine, a powerful dialogflow engine, and integration components to connect to back-end systems.  Skills provide a modular way to build your chatbot functionality. Now users connect with a chatbot through channels such as Facebook, Microsoft Teams, or in our case, Oracle APEX chatbot, which is embedded into an APEX application. 06:21 Nikita: That's fascinating. So, what are some use cases of Oracle Digital Assistant in APEX apps? Chaitanya: Digital assistants streamline approval processes by collecting information, routing requests, and providing status updates. Digital assistants offer instant access to information and documentation, answering common questions and guiding users. Digital assistants assist sales teams by automating tasks, responding to inquiries, and guiding prospects through the sales funnel. Digital assistants facilitate procurement by managing orders, tracking deliveries, and handling supplier communication. Digital assistants simplify expense approvals by collecting reports, validating receipts, and routing them for managerial approval. Digital assistants manage inventory by tracking stock levels, reordering supplies, and providing real-time inventory updates. Digital assistants have become a common UX feature in any enterprise application. 07:28 Want to learn how to design stunning, responsive enterprise applications directly from your browser with minimal coding? The new Oracle APEX Developer Professional learning path and certification enables you to leverage AI-assisted development, including generative AI and Database 23ai, to build secure, scalable web and mobile applications with advanced AI-powered features. From now through May 15, 2025, we're waiving the certification exam fee (valued at $245). So, what are you waiting for? Visit mylearn.oracle.com to get started today. 08:09 Nikita: Welcome back! Thanks for that, Chaitanya. Toufiq, let's talk about the OCI Document Understanding service. What is it? Toufiq: Using this service, you can upload documents to extract text, tables, and other key data. This means the service can automatically identify and extract relevant information from various types of documents, such as invoices, receipts, contracts, etc. The service is serverless and multitenant, which means you don't need to manage any servers or infrastructure. You can access this service using the console, REST APIs, SDK, or CLI, giving you multiple ways to integrate. 08:55 Nikita: What do we use for APEX apps?  Toufiq: For APEX applications, we will be using REST APIs to integrate the service. Additionally, you can process individual files or batches of documents using the ProcessorJob API endpoint. This flexibility allows you to handle different volumes of documents efficiently, whether you need to process a single document or thousands at once. With these capabilities, the OCI Document Understanding service can significantly streamline your document processing tasks, saving time and reducing the potential for manual errors. 09:36 Lois: Ok.  What are the different types of models available? How do they cater to various business needs? Toufiq: Let us start with pre-trained models. These are ready-to-use models that come right out of the box, offering a range of functionalities. The available models are Optical Character Recognition (OCR) enables the service to extract text from documents, allowing you to digitize, scan the documents effortlessly. You can precisely extract text content from documents. Key-value extraction, useful in streamlining tasks like invoice processing. Table extraction can intelligently extract tabular data from documents. Document classification automatically categorizes documents based on their content. OCR PDF enables seamless extraction of text from PDF files. Now, what if your business needs go beyond these pre-trained models. That's where custom models come into play. You have the flexibility to train and build your own models on top of these foundational pre-trained models. Models available for training are key value extraction and document classification. 10:50 Nikita: What does the architecture look like for OCI Document Understanding? Toufiq: You can ingest or supply the input file in two different ways. You can upload the file to an OCI Object Storage location. And in your request, you can point the Document Understanding service to pick the file from this Object Storage location.  Alternatively, you can upload a file directly from your computer. Once the file is uploaded, the Document Understanding service can process the file and extract key information using the pre-trained models. You can also customize models to tailor the extraction to your data or use case. After processing the file, the Document Understanding service stores the results in JSON format in the Object Storage output bucket. Your Oracle APEX application can then read the JSON file from the Object Storage output location, parse the JSON, and store useful information at local table or display it on the screen to the end user. 11:52 Lois: And what about use cases? How are various industries using this service? Toufiq: In financial services, you can utilize Document Understanding to extract data from financial statements, classify and categorize transactions, identify and extract payment details, streamline tax document management. Under manufacturing, you can perform text extraction from shipping labels and bill of lading documents, extract data from production reports, identify and extract vendor details. In the healthcare industry, you can automatically process medical claims, extract patient information from forms, classify and categorize medical records, identify and extract diagnostic codes. This is not an exhaustive list, but provides insights into some industry-specific use cases for Document Understanding. 12:50 Nikita: Toufiq, let's switch to the big topic everyone's excited about—the OCI Generative AI Service. What exactly is it? Toufiq: OCI Generative AI is a fully managed service that provides a set of state of the art, customizable large language models that cover a wide range of use cases. It provides enterprise grade generative AI with data governance and security, which means only you have access to your data and custom-trained models. OCI Generative AI provides pre-trained out-of-the-box LLMs for text generation, summarization, and text embedding. OCI Generative AI also provides necessary tools and infrastructure to define models with your own business knowledge. 13:37 Lois: Generally speaking, how is OCI Generative AI useful?  Toufiq: It supports various large language models. New models available from Meta and Cohere include Llama2 developed by Meta, and Cohere's Command model, their flagship text generation model. Additionally, Cohere offers the Summarize model, which provides high-quality summaries, accurately capturing essential information from documents, and the Embed model, converting text to vector embeddings representation. OCI Generative AI also offers dedicated AI clusters, enabling you to host foundational models on private GPUs. It integrates LangChain and open-source framework for developing new interfaces for generative AI applications powered by language models. Moreover, OCI Generative AI facilitates generative AI operations, providing content moderation controls, zero downtime endpoint model swaps, and endpoint deactivation and activation capabilities. For each model endpoint, OCI Generative AI captures a series of analytics, including call statistics, tokens processed, and error counts. 14:58 Nikita: What about the architecture? How does it handle user input? Toufiq: Users can input natural language, input/output examples, and instructions. The LLM analyzes the text and can generate, summarize, transform, extract information, or classify text according to the user's request. The response is sent back to the user in the specified format, which can include raw text or formatting like bullets and numbering, etc. 15:30 Lois: Can you share some practical use cases for generative AI in APEX apps?  Toufiq: Some of the OCI generative AI use cases for your Oracle APEX apps include text summarization. Generative AI can quickly summarize lengthy documents such as articles, transcripts, doctor's notes, and internal documents. Businesses can utilize generative AI to draft marketing copy, emails, blog posts, and product descriptions efficiently. Generative AI-powered chatbots are capable of brainstorming, problem solving, and answering questions. With generative AI, content can be rewritten in different styles or languages. This is particularly useful for localization efforts and catering to diverse audience. Generative AI can classify intent in customer chat logs, support tickets, and more. This helps businesses understand customer needs better and provide tailored responses and solutions. By searching call transcripts, internal knowledge sources, Generative AI enables businesses to efficiently answer user queries. This enhances information retrieval and decision-making processes. 16:47 Lois: Before we let you go, can you explain what Select AI is? How is it different from the other AI services? Toufiq: Select AI is a feature of Autonomous Database. This is where Select AI differs from the other AI services. Be it OCI Vision, Document Understanding, or OCI Generative AI, these are all freely managed standalone services on Oracle Cloud, accessible via REST APIs. Whereas Select AI is a feature available in Autonomous Database. That means to use Select AI, you need Autonomous Database.  17:26 Nikita: And what can developers do with Select AI? Toufiq: Traditionally, SQL is the language used to query the data in the database. With Select AI, you can talk to the database and get insights from the data in the database using human language. At the very basic, what Select AI does is it generates SQL queries using natural language, like an NL2SQL capability.  17:52 Nikita: How does it actually do that? Toufiq: When a user asks a question, the first step Select AI does is look into the AI profile, which you, as a developer, define. The AI profile holds crucial information, such as table names, the LLM provider, and the credentials needed to authenticate with the LLM service. Next, Select AI constructs a prompt. This prompt includes information from the AI profile and the user's question.  Essentially, it's a packet of information containing everything the LLM service needs to generate SQL. The next step is generating SQL using LLM. The prompt prepared by Select AI is sent to the available LLM services via REST. Which LLM to use is configured in the AI profile. The supported providers are OpenAI, Cohere, Azure OpenAI, and OCI Generative AI. Once the SQL is generated by the LLM service, it is returned to the application. The app can then handle the SQL query in various ways, such as displaying the SQL results in a report format or as charts, etc.  19:05 Lois: This has been an incredible discussion! Thank you, Chaitanya, Apoorva, and Toufiq, for walking us through all of these amazing AI tools. If you're ready to dive deeper, visit mylearn.oracle.com and search for the Oracle APEX: Empowering Low Code Apps with AI course. You'll find step-by-step guides and demos for everything we covered today.  Nikita: Until next week, this is Nikita Abraham… Lois: And Lois Houston signing off! 19:31 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Hey everyone, Alex here

Aidan joined this week's Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what's breaking in the foundation model stack. If you're building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere's agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they've made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea) (0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

Get ready to explore how generative AI is transforming development in Oracle APEX. In this episode, hosts Lois Houston and Nikita Abraham are joined by Oracle APEX experts Apoorva Srinivas and Toufiq Mohammed to break down the innovative features of APEX 24.1. Learn how developers can use APEX Assistant to build apps, generate SQL, and create data models using natural language prompts.   Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome back to another episode of the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and I'm joined by Lois Houston, Director of Innovation Programs. Lois: Hi everyone! In our last episode, we spoke about Oracle APEX and AI. We covered the data and AI -centric challenges businesses are up against and explored how AI fits in with Oracle APEX. Niki, what's in store for today? Nikita: Well, Lois, today we're diving into how generative AI powers Oracle APEX. With APEX 24.1, developers can use the Create Application Wizard to tell APEX what kind of application they want to build based on available tables. Plus, APEX Assistant helps create, refine, and debug SQL code in natural language. 01:16 Lois: Right. Today's episode will focus on how generative AI enhances development in APEX. We'll explore its architecture, the different AI providers, and key use cases. Joining us are two senior product managers from Oracle—Apoorva Srinivas and Toufiq Mohammed. Thank you both for joining us today. We'll start with you, Apoorva. Can you tell us a bit about the generative AI service in Oracle APEX? Apoorva: It is nothing but an abstraction to the popular commercial Generative AI products, like OCI Generative AI, OpenAI, and Cohere. APEX makes use of the existing REST infrastructure to authenticate using the web credentials with Generative AI Services. Once you configure the Generative AI Service, it can be used by the App Builder, AI Assistant, and AI Dynamic Actions, like Show AI Assistant and Generate Text with AI, and also the APEX_AI PL/SQL API. You can enable or disable the Generative AI Service on the APEX instance level and on the workspace level. 02:31 Nikita: Ok. Got it. So, Apoorva, which AI providers can be configured in the APEX Gen AI service? Apoorva: First is the popular OpenAI. If you have registered and subscribed for an OpenAI API key, you can just enter the API key in your APEX workspace to configure the Generative AI service. APEX makes use of the chat completions endpoint in OpenAI. Second is the OCI Generative AI Service. Once you have configured an OCI API key on Oracle Cloud, you can make use of the chat models. The chat models are available from Cohere family and Meta Llama family.  The third is the Cohere. The configuration of Cohere is similar to OpenAI. You need to have your Cohere OpenAI key. And it provides a similar chat functionality using the chat endpoint.  03:29 Lois: What is the purpose of the APEX_AI PL/SQL public API that we now have? How is it used within the APEX ecosystem? Apoorva: It models the chat operation of the popular Generative AI REST Services. This is the same package used internally by the chat widget of the APEX Assistant. There are more procedures around consent management, which you can configure using this package. 03:58 Lois: Apoorva, at a high level, how does generative AI fit into the APEX environment? Apoorva: APEX makes use of the existing REST infrastructure—that is the web credentials and remote server—to configure the Generative AI Service. The inferencing is done by the backend Generative AI Service. For the Generative AI use case in APEX, such as NL2SQL and creation of an app, APEX performs the prompt enrichment.  04:29 Nikita: And what exactly is prompt enrichment? Apoorva: Let's say you provide a prompt saying "show me the average salary of employees in each department." APEX will take this prompt and enrich it by adding in more details. It elaborates on the prompt by mentioning the requirements, such as Oracle SQL syntax statement, and providing some metadata from the data dictionary of APEX. Once the prompt enrichment is complete, it is then passed on to the LLM inferencing service. Therefore, the SQL query provided by the AI Assistant is more accurate and in context.  05:15 Unlock the power of AI Vector Search with our new course and certification. Get more accurate search results, handle complex datasets easily, and supercharge your data-driven decisions. From now to May 15, 2025, we are waiving the certification exam fee (valued at $245). Visit mylearn.oracle.com to enroll. 05:41  Nikita: Welcome back! Let's talk use cases. Apoorva, can you share some ways developers can use generative AI with APEX? Apoorva: SQL is an integral part of building APEX apps. You use SQL everywhere. You can make use of the NL2SQL feature in the code editor by using the APEX Assistant to generate SQL queries while building the apps. The second is the prompt-based app creation. With APEX Assistant, you can now generate fully functional APEX apps by providing prompts in natural language. Third is the AI Assistant, which is a chat widget provided by APEX in all the code editors and for creation of apps. You can chat with the AI Assistant by providing your prompts and get responses from the Generative AI Services. 06:37 Lois: Without getting too technical, can you tell us how to create a data model using AI? Apoorva: A SQL Workshop utility called Create Data Model Using AI uses AI to help you create your own data model. The APEX Assistant generates a script to create tables, triggers, and constraints in either Oracle SQL or Quick SQL format. You can also insert sample data into these tables. But before you use this feature, you must create a generative AI service and enable the Used by App Builder setting. If you are using the Oracle SQL format, when you click on Create SQL Script, APEX generates the script and brings you to this script editor page. Whereas if you are using the Quick SQL format, when you click on Review Quick SQL, APEX generates the Quick SQL code and brings you to the Quick SQL page. 07:39 Lois: And to see a detailed demo of creating a custom data model with the APEX Assistant, visit mylearn.oracle.com and search for the "Oracle APEX: Empowering Low Code Apps with AI" course. Apoorva, what about creating an APEX app from a prompt. What's that process like? Apoorva: APEX 24.1 introduces a new feature where you can generate an application blueprint based on a prompt using natural language. The APEX Assistant leverages the APEX Dictionary Cache to identify relevant tables while suggesting the pages to be created for your application. You can iterate over the application design by providing further prompts using natural language and then generating an application based on your needs. Once you are satisfied, you can click on Create Application, which takes you to the Create Application Wizard in APEX, where you can further customize your application, such as application icon and other features, and finally, go ahead to create your application. 08:53 Nikita: Again, you can watch a demo of this on MyLearn. So, check that out if you want to dive deeper.  Lois: That's right, Niki. Thank you for these great insights, Apoorva! Now, let's turn to Toufiq. Toufiq, can you tell us more about the APEX Assistant feature in Oracle APEX. What is it and how does it work? Toufiq: APEX Assistant is available in Code Editors in the APEX App Builder. It leverages generative AI services as the backend to answer your questions asked in natural language. APEX Assistant makes use of the APEX dictionary cache to identify relevant tables while generating SQL queries. Using the Query Builder mode enables Assistant. You can generate SQL queries from natural language for Form, Report, and other region types which support SQL queries. Using the general assistance mode, you can generate PL/SQL JavaScript, HTML, or CSS Code, and seek further assistance from generative AI. For example, you can ask the APEX Assistant to optimize the code, format the code for better readability, add comments, etc. APEX Assistant also comes with two quick actions, Improve and Explain, which can help users improve and understand the selected code. 10:17 Nikita: What about the Show AI Assistant dynamic action? I know that it provides an AI chat interface, but can you tell us a little more about it?  Toufiq: It is a native dynamic action in Oracle APEX which renders an AI chat user interface. It leverages the generative AI services that are configured under Workspace utilities. This AI chat user interface can be rendered inline or as a dialog. This dynamic action also has configurable system prompt and welcome message attributes.  10:52 Lois: Are there attributes you can configure to leverage even more customization?  Toufiq: The first attribute is the initial prompt. The initial prompt represents a message as if it were coming from the user. This can either be a specific item value or a value derived from a JavaScript expression. The next attribute is use response. This attribute determines how the AI Assistant should return responses. The term response refers to the message content of an individual chat message. You have the option to capture this response directly into a page item, or to process it based on more complex logic using JavaScript code. The final attribute is quick actions. A quick action is a predefined phrase that, once clicked, will be sent as a user message. Quick actions defined here show up as chips in the AI chat interface, which a user can click to send the message to Generative AI service without having to manually type in the message. 12:05 Lois: Thank you, Toufiq and Apoorva, for joining us today. Like we were saying, there's a lot more you can find in the “Oracle APEX: Empowering Low Code Apps with AI” course on MyLearn. So, make sure you go check that out. Nikita: Join us next week for a discussion on how to integrate APEX with OCI AI Services. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 12:28 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

”Apple won't use user data to develop and train AI. And Apple lives in a world where AI is going to be table stakes in any software or hardware moving forward.” Call it counter-programming. A tech palate cleanse for all the election and trade war talk. This week, Rob and Doug tackle the latest AI developments: vibe coding, Cohere's (temporary?) LLM pole position, AI data centres from Telus (powered by Nvidia), and Apple's delayed intelligence. The BetaKit Podcast is presented by The Cyber Challenge, powered by Rogers Cybersecure Catalyst and CCTX—your pathway to new sales, industry connections, and non-dilutive funding. If you're ready to scale, refine, and lead cybersecurity innovation, apply today at www.thecyberchallenge.ca. The BetaKit Podcast is also brought to you by Consensus, where innovators meet investors. This May, crypto's longest-running conference will welcome 20,000 attendees to shape the future of the decentralized digital economy at its inaugural festival in Toronto, Canada's largest tech and financial hub. You can't afford to miss it. Visit go.coindesk.com/betakit to sign up and save 20% off your ticket!   Related links: Something Is Rotten in the State of Cupertino Apple's AI division reportedly starting from scratch   Apple's Craig Federighi Discusses the Future of iPhone AI Apple's Catch-22 Cohere's Command A goes brrrrrrrrr Cohere had to extend the axis a bit Telus, data centres and ‘Sovereign AI' (powered by Nvidia) 25% of Y Combinator's latest startup batch is vibe coding Vibe coding startups are already raising millions @leojr94_ vibe coded his way into a cyber attack (lol)  

In this conversation, Krish Palaniappan delves into the AWS AI series, focusing on Amazon Bedrock and its foundational models. He discusses the differences between serverless models and the Bedrock marketplace, the importance of selecting the right model for specific use cases, and the training and inference processes in AI. The conversation also compares AWS Bedrock with Azure's offerings and emphasizes the complexities of AI architecture in modern development. In this conversation, Krish Palaniappan delves into the complexities of selecting AI models and platforms, particularly focusing on Bedrock and Hugging Face. He discusses the challenges startups face in asset comparisons, the importance of initial architecture in software development, and the evolving landscape of AI tools. The conversation emphasizes the need for a strategic approach to model selection, deployment, and understanding pricing structures, while also highlighting the significance of community engagement in the AI space. Snowpal Products Backends as Services on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AWS Marketplace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Mobile Apps on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠App Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Play Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Web App⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Education Platform⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for Learners and Course Creators

Dr. Max Bartolo from Cohere discusses machine learning model development, evaluation, and robustness. Key topics include model reasoning, the DynaBench platform for dynamic benchmarking, data-centric AI development, model training challenges, and the limitations of human feedback mechanisms. The conversation also covers technical aspects like influence functions, model quantization, and the PRISM project.Max Bartolo (Cohere):https://www.maxbartolo.com/https://cohere.com/commandTRANSCRIPT:https://www.dropbox.com/scl/fi/vujxscaffw37pqgb6hpie/MAXB.pdf?rlkey=0oqjxs5u49eqa2m7uaol64lbw&dl=0TOC:1. Model Reasoning and Verification [00:00:00] 1.1 Model Consistency and Reasoning Verification [00:03:25] 1.2 Influence Functions and Distributed Knowledge Analysis [00:10:28] 1.3 AI Application Development and Model Deployment [00:14:24] 1.4 AI Alignment and Human Feedback Limitations2. Evaluation and Bias Assessment [00:20:15] 2.1 Human Evaluation Challenges and Factuality Assessment [00:27:15] 2.2 Cultural and Demographic Influences on Model Behavior [00:32:43] 2.3 Adversarial Examples and Model Robustness3. Benchmarking Systems and Methods [00:41:54] 3.1 DynaBench and Dynamic Benchmarking Approaches [00:50:02] 3.2 Benchmarking Challenges and Alternative Metrics [00:50:33] 3.3 Evolution of Model Benchmarking Methods [00:51:15] 3.4 Hierarchical Capability Testing Framework [00:52:35] 3.5 Benchmark Platforms and Tools4. Model Architecture and Performance [00:55:15] 4.1 Cohere's Model Development Process [01:00:26] 4.2 Model Quantization and Performance Evaluation [01:05:18] 4.3 Reasoning Capabilities and Benchmark Standards [01:08:27] 4.4 Training Progression and Technical Challenges5. Future Directions and Challenges [01:13:48] 5.1 Context Window Evolution and Trade-offs [01:22:47] 5.2 Enterprise Applications and Future ChallengesREFS:[00:03:10] Research at Cohere with Laura Ruis et al., Max Bartolo, Laura Ruis et al.https://cohere.com/research/papers/procedural-knowledge-in-pretraining-drives-reasoning-in-large-language-models-2024-11-20[00:04:15] Influence functions in machine learning, Koh & Lianghttps://arxiv.org/abs/1703.04730[00:08:05] Studying Large Language Model Generalization with Influence Functions, Roger Grosse et al.https://storage.prod.researchhub.com/uploads/papers/2023/08/08/2308.03296.pdf[00:11:10] The LLM ARChitect: Solving ARC-AGI Is A Matter of Perspective, Daniel Franzen, Jan Disselhoff, and David Hartmannhttps://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf[00:12:10] Hugging Face model repo for C4AI Command A, Cohere and Cohere For AIhttps://huggingface.co/CohereForAI/c4ai-command-a-03-2025[00:13:30] OpenInterpreterhttps://github.com/KillianLucas/open-interpreter[00:16:15] Human Feedback is not Gold Standard, Tom Hosking, Max Bartolo, Phil Blunsomhttps://arxiv.org/abs/2309.16349[00:27:15] The PRISM Alignment Dataset, Hannah Kirk et al.https://arxiv.org/abs/2404.16019[00:32:50] How adversarial examples arise, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madryhttps://arxiv.org/abs/1905.02175[00:43:00] DynaBench platform paper, Douwe Kiela et al.https://aclanthology.org/2021.naacl-main.324.pdf[00:50:15] Sara Hooker's work on compute limitations, Sara Hookerhttps://arxiv.org/html/2407.05694v1[00:53:25] DataPerf: Community-led benchmark suite, Mazumder et al.https://arxiv.org/abs/2207.10062[01:04:35] DROP, Dheeru Dua et al.https://arxiv.org/abs/1903.00161[01:07:05] GSM8k, Cobbe et al.https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k[01:09:30] ARC, François Chollethttps://github.com/fchollet/ARC-AGI[01:15:50] Command A, Coherehttps://cohere.com/blog/command-a[01:22:55] Enterprise search using LLMs, Coherehttps://cohere.com/blog/commonly-asked-questions-about-search-from-coheres-enterprise-customers

Toronto a été le théâtre de plusieurs annonces en intelligence artificielle cette semaine. L'entreprise Moonvalley a dévoilé Marey, un modèle de génération vidéo conçu avec des données acquises sous licence, visant l'industrie du divertissement. Cette approche éthique pourrait influencer le secteur face aux critiques sur l'origine des données d'entraînement. De son côté, Cohere a lancé une IA concurrente à ChatGPT, axée sur les entreprises. Ces avancées marquent la montée en puissance du Canada dans l'IA. J'en discute avec Chloé Sondervorst, réalisatrice et observatrice de l'IA à Radio-Canada

These news snippets from March 14th, 2025, highlight several key developments in the AI landscape, including OpenAI's push for federal AI policy and their concerns regarding state-level regulations and competition with China. The advancements also showcase new AI models from Cohere and Google's Gemini, focusing on enterprise solutions and personalised user experiences respectively. Furthermore, the texts address security risks associated with AI, such as espionage and the potential for misuse in cyberattacks. Finally, the updates touch upon AI integration into existing platforms like Windows and the transformative impact of AI on sectors like healthcare, drug discovery, and global supply chains, alongside the ongoing debate about training AI on copyrighted material.

Google is reinventing search through AI-driven overviews, while Amazon is aggressively pursuing Agentic AI and hybrid reasoning models. Researchers are being recognised for reinforcement learning achievements, and warnings are emerging about emotional attachments to hyper-realistic AI voices. Meanwhile, legal battles surrounding OpenAI's for-profit transition continue, and academic institutions are benefiting from initiatives like OpenAI's NextGenAI. Furthermore, Cohere has launched an impressive multilingual vision model, while incidents such as students using AI to cheat in interviews highlight ongoing ethical challenges.

In this podcast I am joined by a dream guestRohit Bhargava: Host of The Startup Playbook Podcast, The Founder of The Komo ClubWe chat about:Podcasting in 2025 and beyondThe Komo Club GenesisThe Startup Playbook PodcastPlaybook VenturesGuest Rohit's Handles⤵︎ The Komo Club Website here: https://www.thekomoclub.com/Email: rohit@startupplaybook.coHere's Rohit's LinkedIn:https://www.linkedin.com/in/rohbhargava/Heres The Startup Playbook Podcast link: https://open.spotify.com/show/0pIPF1J8KiK0VnCq5XIl03?si=6db67403426c4e0cHost Roohi Kazi's Handles ⤵︎ LinkedIn:   ⁠https://www.linkedin.com/in/roohi-kazi-53174113b/⁠Instagram:  ⁠https://www.instagram.com/roohik2/#⁠Twitter: ⁠https://x.com/roohi_kr⁠E-Mail: bizpodroohi2@gmail.comTO GET FEATURED ON “Business Podcast by Roohi” Email at: bizpodroohi2@gmail.com

Matt Cohen and John Ruffolo talk about the BDC Capital $1B fund, the state of early-stage VC funding in Canada, and the rise of mega-deals dominated by U.S. investors. They also discuss the feasibility of the Quebec City-Toronto high-speed rail project, AI copyright lawsuits, potential Trump-era tariffs, and the future of open banking in Canada.Key TopicsBDC Capital's $1B Growth-Stage Investment Fund (00:42)* BDC Capital announces a $1B investment fund, with:* $500M Growth Venture Fund for direct investments and co-investments.* $450M Growth Equity Partners Program for minority stake investments in mid-market companies.* Concerns were raised by Mark McQueen about lack of early-stage funding* John Ruffolo's take:* Canada's early-stage VC ecosystem is underfunded.* BDC was meant to focus on riskier, early-stage investments, while EDC (Export Development Canada) focused on growth-stage.* Shift towards later-stage funding may leave early-stage startups without necessary capital.Canadian Venture Capital Funding Trends (04:55)* CVCA's 2024 report:* $7.86B invested across 592 deals, up 10% from 2023.* Mega deals ($50M+ rounds) comprised 62% of total VC investments.* Seed-stage funding fell 50% to $510M.* Notable mega-deals:* Clio – $1.24B Series F* Cohere – $616M Series D* Blockstream – $289M convertible note* Waabi – $275M Series B* U.S. investors dominate:* 32% of Canadian VC deals had U.S. investor participation.* Clio's round was entirely U.S.-funded.* John Ruffolo's analysis:* Canada needs stronger domestic venture capital.* U.S. capital will always flow into late-stage companies, but early-stage funding is crucial for long-term ecosystem growth.* Lack of Canadian IPOs in 2024 is a concerning sign.Quebec City-Toronto High-Speed Rail: $90B Boondoggle? (09:17)* Massive infrastructure proposal:* $60B–$90B price tag, with $3.9B allocated to planning alone.* Construction won't begin for at least five years, taking 5–7 years per segment.* Criticisms:* Timing is political (announced right before an election).* Where is the funding coming from? Canada's finances are already stretched.* Route selection is questionable – e.g., Laval getting a stop over Mississauga/Brampton.* John Ruffolo's take:* Financial viability is unclear – pension funds won't invest without guarantees of ridership.* Other priorities (e.g., Arctic infrastructure, national security) are being ignored.* The government should invest in digital infrastructure instead (e.g., full 5G coverage).AI Copyright Lawsuits: Cohere vs. Media Giants (14:35)* Major media coalition (The Atlantic, Forbes, The Guardian, Vox, etc.) sues AI startup Cohere for copyright infringement in New York.* Allegations: Cohere scraped and displayed copyrighted content without permission.* Seeking $150K per work infringed + an injunction against Cohere using their content.* Growing legal pressure on AI companies:* NY Times vs. OpenAI – potentially setting a massive precedent.* Anthropic, Meta, and Thomson Reuters have faced similar lawsuits.* John Ruffolo's view:* Copyright concerns were always an issue for AI models.* AI startups may have to pay into a licensing pool (like the music industry).* Investor risk increasing – legal uncertainties may impact funding for public LLMs.Trump's Potential Tariffs: What Canada Should Do (19:25)* Trump's trade policies likely to return if re-elected, impacting Canadian businesses.* John Ruffolo's recommendations:* Canada must fix internal issues first (e.g., interprovincial trade barriers).* Tariffs won't disappear for at least four years, so businesses must adapt.* Canadian businesses will have to shift profits & operations to the U.S. to remain competitive.The Future of Open Banking in Canada (22:00)* U.S. fintech sector gains a boost as Trump administration removes CFPB regulations.* Chime & Klarna expected to benefit from deregulation.* Canadian Conservatives promise major push for open banking if elected.* Liberals have been slow to act on open banking despite six years of promises.John Ruffolo's perspective:* Open banking will make Canadian banks stronger, not weaker.* Canada must prepare for U.S. competition in financial services.Follow Matt Cohen and Tank Talks here!Podcast production support provided by Agentbee.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

The Canadian company is currently worth $5 billion. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Plus, publishers sue AI startup Cohere for copyright and trademark infringement. And, Jeff Bezos's Blue Origin plans to lay off 10% of its workforce. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Oracle announced the Oracle Fusion Cloud Applications Suite is now available on Oracle EU Sovereign Cloud to enable private and public sector organizations across the European Union. NetSuite announced it is migrating to Oracle Autonomous Database to enable its customers to take advantage of the enhanced security, reliability, and performance of a fully managed Oracle Database in OCI with integrated AI. Salesforce, in collaboration with Hugging Face, Cohere, and Carnegie Mellon University, announced the release of the AI Energy Score, a first-of-its-kind benchmarking tool that enables AI developers and users to evaluate, identify, and compare the energy consumption of AI models.Connect with us!https://www.erpadvisorsgroup.com866-499-8550LinkedIn:https://www.linkedin.com/company/erp-advisors-groupTwitter:https://twitter.com/erpadvisorsgrpFacebook:https://www.facebook.com/erpadvisorsInstagram:https://www.instagram.com/erpadvisorsgroupPinterest:https://www.pinterest.com/erpadvisorsgroupMedium:https://medium.com/@erpadvisorsgroup

Every travel professional knows how frustrating tech can be. In fact, it's one of the most commonly shared roadblocks when building a coaching business online.Thankfully, we now have a solution for that.The Travel Coach Network is proud to announce a partnership with Cohere!In this episode, Sahara Rose DeVore - founder of the Travel Coach Network - interviews Anette Oran, co-founder of Cohere, to share why this partnership is so needed.Register for the masterclass happening on Tuesday, February 18th, 2025 at 1pm Eastern.Click below to register, and for the replay if you find this episode after the masterclass has been recorded:https://app.cohere.live/contribution-view/679019f40740293de23a91e9/b7858b8d-f286-4809-83d6-f1fbb5facff7/aboutIf you've been loving the show, we'd so appreciate it if you could leave a 5-star review on Spotify or Apple Podcasts!And of course, we'd love to see you in our free Facebook Group:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/928430197344106⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Have questions about the Travel Coach Certification Program? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Send me a DM on Instagram over at @travelcoachnetwork.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-------------------TRAVEL COACHING RESOURCESAre you ready to elevate your travel business? To achieve clarity, focus, and success instead of constant confusion?If so, then I'd love to invite you to join the Travel Coach Certification Program.Join the conversation in our Travel Coach Network Global Community. It's our free Facebook Group for aspiring and inspiring travel coaches.If you're brand new to the concept of travel coaching, be sure to grab the Beginner's Guide to Travel Coaching by clicking below.Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thetravelcoachnetwork.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TCN Global Community on Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/travelcoachnetwork⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/thetravelcoachnetwork/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Travel Coach Certification Program: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thetravelcoachnetwork.mykajabi.com/the-travel-coach-program⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Free Beginner's Guide to Travel Coaching:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thetravelcoachnetwork.mykajabi.com/main-email-series-and-workbook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ultimate Travel Business Planner Bundle: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.etsy.com/shop/TravelCoachNetwork?ref=seller-platform-mcnav⁠

In this episode, Matt welcomes Willson Cross, the co-founder and CEO of Borderless AI, to discuss how AI is transforming the global HR and payroll industry. Willson shares his entrepreneurial journey, from founding and selling GoFetch to launching Borderless AI. They explore how AI-driven compliance, payroll, and onboarding are solving key challenges in hiring global teams. Willson also talks about the company's $35M funding, its partnership with Cohere, and how they differentiate from major competitors like Deel and Rippling.About Willson Cross:Willson Cross is the Co-Founder and CEO of Borderless AI, a global payroll platform that uses generative AI to streamline hiring, managing, and paying international employees. Since launching in 2023, the company has raised $27 million from top investors, including Susquehanna and Bernard Arnault. Based in Toronto, Willson leads the team in building AI-powered solutions for the future of work.Before Borderless AI, Willson co-founded GoFetch, Canada's leading pet services marketplace. Starting from his basement in 2015, he grew the company to seven markets, raised $3.5 million, and led a team of 45 before selling the business in 2018. Earlier, he launched UBC Bitcoin Jobs, an online job board that connected university students with cryptocurrency startups, matching over 80 students to 20 companies.Originally from Vancouver, Willson studied economics at New York University before leaving after his third year to pursue startups full-time.⏱ Topics* (1:26) – Willson's background & founding GoFetch* (2:59) – Key lessons from running a bootstrapped startup* (4:55) – The transition to Borderless AI & identifying HR's biggest challenges* (6:33) – Payroll & benefits: The first major opportunities* (6:52) – Building real-time global payroll infrastructure* (7:50) – Meeting co-founder Sean Agarwal & forming a strong partnership* (9:45) – AI's role in HR compliance, payroll & automation* (12:04) – How Cohere's AI models enhance HRGPT* (15:48) – Competing with Deel & Rippling as an AI-native company* (18:19) – Pricing strategy & product differentiation* (19:13) – How AI is transforming HR roles* (20:47) – The shift toward larger early-stage funding rounds* (24:30) – Target customers: Startups & large enterprises* (27:41) – Why Borderless AI chose a full in-office model

Submit your question and we'll answer it in a future episode!Join our Patreon Community!https://www.patreon.com/badassbreastfeedingpodcast Have you ever been called for Jury Duty?  What about being called to Jury Duty as a breastfeeding mother?  What can you do about this?  Listen today as Dianne and Abby discuss a specific situation and give some tips on what to do if you were to get called to serve as a juror for Jury Duty. If you are a new listener, we would love to hear from you.  Please consider leaving us a review on iTunes or sending us an email with your suggestions and comments to badassbreastfeedingpodcast@gmail.com.  You can also add your email to our list and have episodes sent right to your inbox! Things we talked about:Messages with questions [4:24]What is jury duty [10:00]Every state is different [14:08]Abby turned her husband in for jury duty [19:10]FB post from Alabama [22:07]Breastfeeding support is lacking despite recommendations [27:38]Pumping in other countries [32:26]Links to information we discussed or episodes you should check out!https://badassbreastfeedingpodcast.com/episode/exclusive-pumping/https://badassbreastfeedingpodcast.com/episode/pumping-stories-from-badasses/ Set up your consultation with Diannehttps://badassbreastfeedingpodcast.com/consultations/      Check out Dianne's blog here:https://diannecassidyconsulting.com/milklytheblog/Follow our Podcast:https://badassbreastfeedingpodcast.coHere is how you can connect with Dianne and Abby:AbbyTheuring ,https://www.thebadassbreastfeeder.comDianne Cassidy @diannecassidyibclc,  http://www.diannecassidyconsulting.com Music we use:Music: "Levels of Greatness" from "We Used to Paint Stars in the Sky (2012)" courtesy of Scott Holmes at freemusicarchive.org/music/Scott Holmes

Chapters:0:00 Introduction to Aidan Gomez, CEO of Cohere2:12 Childhood and growing up in Canada5:50 Getting into computers and internet10:30 Sending cold emails14:20 How to work with Aidan16:40 The AI paper - "Attention is all you need"18:45 Starting Cohere21:10 Why choose enterprise (vs consumer) as a market24:45 AI strategy28:20 Hallucinations in LLM models30:10 Enterprise software and security implications32:05 Deloitte, Accenture and the impact of generative AI36:40 Will LLM scaling laws hit a plateau?38:50 AGI, reasoning and inference for LLM models41:30 Synthetic data - what is it? Why is it interesting?43:25 Looking ahead - what is the Cohere's strategy?46:00 Cohere's capital structure49:15 Enterprise Use Cases52:10 Advice for founders - adaptability54:15 Thank you Follow Sriram:https://www.instagram.com/sriramk/https://twitter.com/sriramkFollow Aarthi:https://www.instagram.com/aarthir/https://twitter.com/aarthirFollow the podcast:https://www.instagram.com/aarthiandsriramshow/https://twitter.com/aarthisrirampod

Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** TOC 1. LLM Foundations and Learning 1.1 Scale and Learning in Language Models [00:00:00] 1.2 Procedural Knowledge vs Fact Retrieval [00:03:40] 1.3 Influence Functions and Model Analysis [00:07:40] 1.4 Role of Code in LLM Reasoning [00:11:10] 1.5 Semantic Understanding and Physical Grounding [00:19:30] 2. Reasoning Architectures and Measurement 2.1 Measuring Understanding and Reasoning in Language Models [00:23:10] 2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40] 2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10] 2.4 Neural Network Architectures and Tensor Product Representations [00:40:50] 3. AI Agency and Risk Assessment 3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10] 3.2 Defining and Measuring Agency in AI Systems [00:49:50] 3.3 Core Knowledge Systems and Agency Detection [00:54:40] 3.4 Language Models as Agent Models and Simulator Theory [01:03:20] 3.5 AI Safety and Societal Control Mechanisms [01:07:10] 3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20] REFS: [00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning Ruis et al., 2024 https://arxiv.org/abs/2411.12580 [00:03:50] EK-FAC Influence Functions in Large LMs Grosse et al., 2023 https://arxiv.org/abs/2308.03296 [00:13:05] Surfaces and Essences: Analogy as the Core of Cognition Hofstadter & Sander https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 [00:13:45] Wittgenstein on Language Games https://plato.stanford.edu/entries/wittgenstein/ [00:14:30] Montague Semantics for Natural Language https://plato.stanford.edu/entries/montague-semantics/ [00:19:35] The Chinese Room Argument David Cole https://plato.stanford.edu/entries/chinese-room/ [00:19:55] ARC: Abstraction and Reasoning Corpus François Chollet https://arxiv.org/abs/1911.01547 [00:24:20] Systematic Generalization in Neural Nets Lake & Baroni, 2023 https://www.nature.com/articles/s41586-023-06668-3 [00:27:40] Open-Endedness & Creativity in AI Tim Rocktäschel https://arxiv.org/html/2406.04268v1 [00:30:50] Fodor & Pylyshyn on Connectionism https://www.sciencedirect.com/science/article/abs/pii/0010027788900315 [00:31:30] Tensor Product Representations Smolensky, 1990 https://www.sciencedirect.com/science/article/abs/pii/000437029090007M [00:35:50] DreamCoder: Wake-Sleep Program Synthesis Kevin Ellis et al. https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf [00:36:30] Compositional Generalization Benchmarks Ruis, Lake et al., 2022 https://arxiv.org/pdf/2202.10745 [00:40:30] RNNs & Tensor Products McCoy et al., 2018 https://arxiv.org/abs/1812.08718 [00:46:10] Formal Causal Definition of Agency Kenton et al. https://arxiv.org/pdf/2208.08345v2 [00:48:40] Agency in Language Models Sumers et al. https://arxiv.org/abs/2309.02427 [00:55:20] Heider & Simmel's Moving Shapes Experiment https://www.nature.com/articles/s41598-024-65532-0 [01:00:40] Language Models as Agent Models Jacob Andreas, 2022 https://arxiv.org/abs/2212.01681 [01:13:35] Pragmatic Understanding in LLMs Ruis et al. https://arxiv.org/abs/2210.14986

In this episode, Raja Iqbal welcomes Jay Alammar, a renowned educator, researcher, and visual storyteller in machine learning. Jay shares his fascinating journey into simplifying complex AI concepts through visual storytelling and his passion for making AI education accessible to everyone. Raja and Jay discuss the power of visual learning, the role of intuition in understanding AI, and the challenges and opportunities in enterprise AI adoption. Jay also explores how AI is reshaping industries, the importance of tools like Retrieval-Augmented Generation (RAG), and his experiences at Cohere, where he helps organizations harness the power of large language models for real-world applications. This episode is perfect for anyone curious about the evolving world of AI, practical ways to adopt AI in business, and the importance of education in driving innovation.

This episode is sponsored by Oracle. Harness the power of AI without overspending with Oracle Cloud Infrastructure (OCI). Ideal for AI model training, OCI offers 4-8x more bandwidth than competitors at half the cost. Transform your business like Uber and Cohere with OCI.Try it for free at https://oracle.com/gameplanWelcome back to The Game Plan podcast!In this episode, I'm joined by my good friend, best man and fitness legend, @Joe Delaney.We dive into Joe's journey of balancing fatherhood, running a fitness app, and life as a content creator. Joe shares why he stepped away from sponsorships, the challenges of redesigning and growing his app, and his fresh perspective on building a sustainable business.We also get real about the highs and lows of parenthood—from sleepless nights to the pure joy of first laughs—and how he's navigating it all while staying true to his long-term vision.Enjoyed the chat? Don't forget to like, comment, and subscribe for more!Check out the best protein pancakes in the world at Fuel Cakes: https://fuelcakes.com/

Matt Cohen and John Ruffolo discuss the fast-moving events shaping Canada's political and economic landscape. Topics include the fallout from Prime Minister Justin Trudeau's resignation, the complexities of the CRA's proposed capital gains tax adjustments, and the legal challenges tied to Parliament's prorogation. The conversation then pivots to groundbreaking developments in AI, spotlighting RBC's partnership with Cohere to build a generative AI platform. The episode wraps with a critical analysis of the sudden closure of Vancouver-based Bench Accounting and its surprising acquisition.Topics:* (00:45) CRA's enforcement of capital gains tax changes and taxpayer strategies* (02:41) Legislative uncertainty surrounding the federal budget and prorogation* (04:08) Legal arguments challenging prorogation and their implications* (06:04) External perceptions of Canadian governance* (08:22) RBC's partnership with Cohere for AI development* (11:36) Anthropic's funding round and global AI investment trends* (11:52) Bench Accounting's shutdown and its acquisition by employer.comFollow Matt Cohen and Tank Talks here!Podcast production support provided by Agentbee.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Send us a textNEW FUND ANNOUNCEMENT*: The AG Dillon Anduril Pre-IPO Stock Fund is now accepting investors. Anduril Industries is a defense technology company that specializes in building advanced artificial intelligence (AI) and autonomous systems for military and national security purposes. Financial advisors only. Email aaron.dillon@agdillon.com to invest or request fund materials. Note important disclosures at the end of this post.Subscribe to AG Dillon Pre-IPO Stock Research at agdillon.com/subscribe;- Wednesday = secondary market valuations, revenue multiples, performance, index fact sheets- Saturdays = pre-IPO news and insights, webinar replays00:00 - Intro00:07 - Anthropic Targets $60B Valuation with $2B Raise01:33 - Whatnot Hits $4.97B Valuation with $265M Raise02:31 - xAI Reaches $83B Valuation, Launches iOS App for Grok03:55 - SandboxAQ Raises $300M at $5.6B Valuation05:03 - Wiz Prepares for IPO, Valued at $20.5B06:10 - Cohere Launches North, Valued at $5.4B07:38 - Epirus in Talks for $1B Valuation Amid Defense Focus08:38 - Hippocratic AI Raises $141M, Valued at $1.64B09:27 - Pre-IPO Stock Market Weekly Performance10:18 - Pre-IPO Stock Vintage Index Weekly Performance* NOTE: AG Dillon ("AGD") is not affiliated with Anduril. Anduril may require company approval for purchases (aka transfers). AGD has not been pre-approved by Anduril to purchase their stock. AGD purchases pre-IPO stocks in the secondary market and may gain exposure by directly purchasing the stock (on the company's capitalization table) and/or through a third-party fund (aka special purpose vehicle, or SPV).

Topics: Sickness, Prose, Reality, Box Scores, Worldly Knight, Spiritual Knight, Cohere, Themes, Galahad, Continuations, Lanzelet, Vulgate, Chaucer, Purity, Merlin, Monmouth, The Firste Moevere, Original Spelling, Ovid, Round Table, Avalon, Alliteration, Enli...

In this episode of The Cognitive Revolution, Nathan shares a fascinating cross-post from Doom Debates featuring a conversation between Liron Shapira and roon, an influential Twitter Anon from OpenAI's technical staff. They explore crucial insights into how OpenAI's team views AI's future, including discussions on AGI development, alignment challenges, and extinction risks. Join us for this thought-provoking analysis of AI safety and the mindset of those building transformative AI systems. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: GiveWell: GiveWell has spent over 17 years researching global health and philanthropy to identify the highest-impact giving opportunities. Over 125,000 donors have contributed more than $2 billion, saving over 200,000 lives through evidence-backed recommendations. First-time donors can have their contributions matched up to $100 before year-end. Visit https://GiveWell.org, select podcast, and enter Cognitive Revolution at checkout to make a difference today. SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: CHAPTERS: (00:00:00) About the Episode (00:07:18) Introducing roon (00:09:13) roon's Background (00:16:40) roon the Person (Part 1) (00:21:56) Sponsors: GiveWell | SelectQuote (00:24:45) roon the Person (Part 2) (00:26:43) Excitement in AI (00:31:59) Creativity in AI (00:40:18) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:42:36) roon's P(Doom) (00:52:25) AI Risk & Regulation (00:53:51) AI Timelines (01:01:20) Aligned by Default? (01:09:16) Training vs Production (01:14:30) Open Source AI Risk (01:26:25) Goal-Oriented AI (01:34:29) Pause AI? (01:39:46) Dogecoin & Wrap Up (01:41:06) Outro & Call to Action (01:56:38) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast...

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver. Today, we're proud to share Loubna's highly anticipated talk (slides here)!Synthetic DataWe called out the Synthetic Data debate at last year's NeurIPS, and no surprise that 2024 was dominated by the rise of synthetic data everywhere:* Apple's Rephrasing the Web, Microsoft's Phi 2-4 and Orca/AgentInstruct, Tencent's Billion Persona dataset, DCLM, and HuggingFace's FineWeb-Edu, and Loubna's own Cosmopedia extended the ideas of synthetic textbook and agent generation to improve raw web scrape dataset quality* This year we also talked to the IDEFICS/OBELICS team at HuggingFace who released WebSight this year, the first work on code-vs-images synthetic data.* We called Llama 3.1 the Synthetic Data Model for its extensive use (and documentation!) of synthetic data in its pipeline, as well as its permissive license. * Nemotron CC and Nemotron-4-340B also made a big splash this year for how they used 20k items of human data to synthesize over 98% of the data used for SFT/PFT.* Cohere introduced Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress observing gains of up to 56.5% improvement in win rates comparing multiple teachers vs the single best teacher model* In post training, AI2's Tülu3 (discussed by Luca in our Open Models talk) and Loubna's Smol Talk were also notable open releases this year.This comes in the face of a lot of scrutiny and criticism, with Scale AI as one of the leading voices publishing AI models collapse when trained on recursively generated data in Nature magazine bringing mainstream concerns to the potential downsides of poor quality syndata:Part of the concerns we highlighted last year on low-background tokens are coming to bear: ChatGPT contaminated data is spiking in every possible metric:But perhaps, if Sakana's AI Scientist pans out this year, we will have mostly-AI AI researchers publishing AI research anyway so do we really care as long as the ideas can be verified to be correct?Smol ModelsMeta surprised many folks this year by not just aggressively updating Llama 3 and adding multimodality, but also adding a new series of “small” 1B and 3B “on device” models this year, even working on quantized numerics collaborations with Qualcomm, Mediatek, and Arm. It is near unbelievable that a 1B model today can qualitatively match a 13B model of last year:and the minimum size to hit a given MMLU bar has come down roughly 10x in the last year. We have been tracking this proxied by Lmsys Elo and inference price:The key reads this year are:* MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases* Apple Intelligence Foundation Language Models* Hymba: A Hybrid-head Architecture for Small Language Models* Loubna's SmolLM and SmolLM2: a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters on the pareto efficiency frontier.* and Moondream, which we already covered in the 2024 in Vision talkFull Talk on YouTubeplease like and subscribe!Timestamps* [00:00:05] Loubna Intro* [00:00:33] The Rise of Synthetic Data Everywhere* [00:02:57] Model Collapse* [00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks* [00:12:36] DCLM, Nemotron-CC* [00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage* [00:16:17] Smol Models* [00:18:24] On Device Models* [00:22:45] Smol Vision Models* [00:25:14] What's NextTranscript2024 in Synthetic Data and Smol Models[00:00:00] ​[00:00:05] Loubna Intro[00:00:05] Speaker: ​I'm very happy to be here. Thank you for the invitation. So I'm going to be talking about synthetic data in 2024. And then I'm going to be talking about small on device models. So I think the most interesting thing about synthetic data this year is that like now we have it everywhere in the large language models pipeline.[00:00:33] The Rise of Synthetic Data Everywhere[00:00:33] Speaker: I think initially, synthetic data was mainly used just for post training, because naturally that's the part where we needed human annotators. And then after that, we realized that we don't really have good benchmarks to [00:01:00] measure if models follow instructions well, if they are creative enough, or if they are chatty enough, so we also started using LLMs as judges.[00:01:08] Speaker: Thank you. And I think this year and towards the end of last year, we also went to the pre training parts and we started generating synthetic data for pre training to kind of replace some parts of the web. And the motivation behind that is that you have a lot of control over synthetic data. You can control your prompt and basically also the kind of data that you generate.[00:01:28] Speaker: So instead of just trying to filter the web, you could try to get the LLM to generate what you think the best web pages could look like and then train your models on that. So this is how we went from not having synthetic data at all in the LLM pipeline to having it everywhere. And so the cool thing is like today you can train an LLM with like an entirely synthetic pipeline.[00:01:49] Speaker: For example, you can use our Cosmopedia datasets and you can train a 1B model on like 150 billion tokens that are 100 percent synthetic. And those are also of good quality. And then you can [00:02:00] instruction tune the model on a synthetic SFT dataset. You can also do DPO on a synthetic dataset. And then to evaluate if the model is good, you can use.[00:02:07] Speaker: A benchmark that uses LLMs as a judge, for example, MTBench or AlpacaEvil. So I think this is like a really mind blowing because like just a few years ago, we wouldn't think this is possible. And I think there's a lot of concerns about model collapse, and I'm going to talk about that later. But we'll see that like, if we use synthetic data properly and we curate it carefully, that shouldn't happen.[00:02:29] Speaker: And the reason synthetic data is very popular right now is that we have really strong models, both open and closed. It is really cheap and fast to use compared to human annotations, which cost a lot and take a lot of time. And also for open models right now, we have some really good inference frameworks.[00:02:47] Speaker: So if you have enough GPUs, it's really easy to spawn these GPUs and generate like a lot of synthetic data. Some examples are VLM, TGI, and TensorRT.[00:02:57] Model Collapse[00:02:57] Speaker: Now let's talk about the elephant in the room, model [00:03:00] collapse. Is this the end? If you look at the media and all of like, for example, some papers in nature, it's really scary because there's a lot of synthetic data out there in the web.[00:03:09] Speaker: And naturally we train on the web. So we're going to be training a lot of synthetic data. And if model collapse is going to happen, we should really try to take that seriously. And the other issue is that, as I said, we think, a lot of people think the web is polluted because there's a lot of synthetic data.[00:03:24] Speaker: And for example, when we're building fine web datasets here at Guillerm and Hinek, we're interested in like, how much synthetic data is there in the web? So there isn't really a method to properly measure the amount of synthetic data or to save a webpage synthetic or not. But one thing we can do is to try to look for like proxy words, for example, expressions like as a large language model or words like delve that we know are actually generated by chat GPT.[00:03:49] Speaker: We could try to measure the amount of these words in our data system and compare them to the previous years. For example, here, we measured like a, these words ratio in different dumps of common crawl. [00:04:00] And we can see that like the ratio really increased after chat GPT's release. So if we were to say that synthetic data amount didn't change, you would expect this ratio to stay constant, which is not the case.[00:04:11] Speaker: So there's a lot of synthetic data probably on the web, but does this really make models worse? So what we did is we trained different models on these different dumps. And we then computed their performance on popular, like, NLP benchmarks, and then we computed the aggregated score. And surprisingly, you can see that the latest DOMs are actually even better than the DOMs that are before.[00:04:31] Speaker: So if there's some synthetic data there, at least it did not make the model's worse. Yeah, which is really encouraging. So personally, I wouldn't say the web is positive with Synthetic Data. Maybe it's even making it more rich. And the issue with like model collapse is that, for example, those studies, they were done at like a small scale, and you would ask the model to complete, for example, a Wikipedia paragraph, and then you would train it on these new generations, and you would do that every day.[00:04:56] Speaker: iteratively. I think if you do that approach, it's normal to [00:05:00] observe this kind of behavior because the quality is going to be worse because the model is already small. And then if you train it just on its generations, you shouldn't expect it to become better. But what we're really doing here is that we take a model that is very large and we try to distill its knowledge into a model that is smaller.[00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks[00:05:14] Speaker: And in this way, you can expect to get like a better performance for your small model. And using synthetic data for pre-training has become really popular. After the textbooks are all you need papers where Microsoft basically trained a series of small models on textbooks that were using a large LLM.[00:05:32] Speaker: And then they found that these models were actually better than models that are much larger. So this was really interesting. It was like first of its time, but it was also met with a lot of skepticism, which is a good thing in research. It pushes you to question things because the dataset that they trained on was not public, so people were not really sure if these models are really good or maybe there's just some data contamination.[00:05:55] Speaker: So it was really hard to check if you just have the weights of the models. [00:06:00] And as Hugging Face, because we like open source, we tried to reproduce what they did. So this is our Cosmopedia dataset. We basically tried to follow a similar approach to what they documented in the paper. And we created a synthetic dataset of textbooks and blog posts and stories that had almost 30 billion tokens.[00:06:16] Speaker: And we tried to train some models on that. And we found that like the key ingredient to getting a good data set that is synthetic is trying as much as possible to keep it diverse. Because if you just throw the same prompts as your model, like generate like a textbook about linear algebra, and even if you change the temperature, the textbooks are going to look alike.[00:06:35] Speaker: So there's no way you could scale to like millions of samples. And the way you do that is by creating prompts that have some seeds that make them diverse. In our case, the prompt, we would ask the model to generate a textbook, but make it related to an extract from a webpage. And also we try to frame it within, to stay within topic.[00:06:55] Speaker: For example, here, we put like an extract about cardiovascular bioimaging, [00:07:00] and then we ask the model to generate a textbook related to medicine that is also related to this webpage. And this is a really nice approach because there's so many webpages out there. So you can. Be sure that your generation is not going to be diverse when you change the seed example.[00:07:16] Speaker: One thing that's challenging with this is that you want the seed samples to be related to your topics. So we use like a search tool to try to go all of fine web datasets. And then we also do a lot of experiments with the type of generations we want the model to generate. For example, we ask it for textbooks for middle school students or textbook for college.[00:07:40] Speaker: And we found that like some generation styles help on some specific benchmarks, while others help on other benchmarks. For example, college textbooks are really good for MMLU, while middle school textbooks are good for benchmarks like OpenBookQA and Pico. This is like a sample from like our search tool.[00:07:56] Speaker: For example, you have a top category, which is a topic, and then you have some [00:08:00] subtopics, and then you have the topic hits, which are basically the web pages in fine web does belong to these topics. And here you can see the comparison between Cosmopedia. We had two versions V1 and V2 in blue and red, and you can see the comparison to fine web, and as you can see throughout the training training on Cosmopedia was consistently better.[00:08:20] Speaker: So we managed to get a data set that was actually good to train these models on. It's of course so much smaller than FineWeb, it's only 30 billion tokens, but that's the scale that Microsoft data sets was, so we kind of managed to reproduce a bit what they did. And the data set is public, so everyone can go there, check if everything is all right.[00:08:38] Speaker: And now this is a recent paper from NVIDIA, Neumatron CC. They took things a bit further, and they generated not a few billion tokens, but 1. 9 trillion tokens, which is huge. And we can see later how they did that. It's more of, like, rephrasing the web. So we can see today that there's, like, some really huge synthetic datasets out there, and they're public, so, [00:09:00] like, you can try to filter them even further if you want to get, like, more high quality corpses.[00:09:04] Speaker: So for this, rephrasing the web this approach was suggested in this paper by Pratyush, where basically in this paper, they take some samples from C4 datasets, and then they use an LLM to rewrite these samples into a better format. For example, they ask an LLM to rewrite the sample into a Wikipedia passage or into a Q& A page.[00:09:25] Speaker: And the interesting thing in this approach is that you can use a model that is Small because it doesn't, rewriting doesn't require knowledge. It's just rewriting a page into a different style. So the model doesn't need to have like knowledge that is like extensive of what is rewriting compared to just asking a model to generate a new textbook and not giving it like ground truth.[00:09:45] Speaker: So here they rewrite some samples from C4 into Q& A, into Wikipedia, and they find that doing this works better than training just on C4. And so what they did in Nemo Trans CC is a similar approach. [00:10:00] They rewrite some pages from Common Crawl for two reasons. One is to, like improve Pages that are low quality, so they rewrite them into, for example, Wikipedia page, so they look better.[00:10:11] Speaker: And another reason is to create more diverse datasets. So they have a dataset that they already heavily filtered, and then they take these pages that are already high quality, and they ask the model to rewrite them in Question and Answer format. into like open ended questions or like multi choice questions.[00:10:27] Speaker: So this way they can reuse the same page multiple times without fearing like having multiple duplicates, because it's the same information, but it's going to be written differently. So I think that's also a really interesting approach for like generating synthetic data just by rephrasing the pages that you already have.[00:10:44] Speaker: There's also this approach called Prox where they try to start from a web page and then they generate a program which finds how to write that page to make it better and less noisy. For example, here you can see that there's some leftover metadata in the web page and you don't necessarily want to keep that for training [00:11:00] your model.[00:11:00] Speaker: So So they train a model that can generate programs that can like normalize and remove lines that are extra. So I think this approach is also interesting, but it's maybe less scalable than the approaches that I presented before. So that was it for like rephrasing and generating new textbooks.[00:11:17] Speaker: Another approach that I think is really good and becoming really popular for using synthetic data for pre training is basically building a better classifiers. For filtering the web for example, here we release the data sets called fine web edu. And the way we built it is by taking Llama3 and asking it to rate the educational content of web pages from zero to five.[00:11:39] Speaker: So for example, if a page is like a really good textbook that could be useful in a school setting, it would get a really high score. And if a page is just like an advertisement or promotional material, it would get a lower score. And then after that, we take these synthetic annotations and we train a classifier on them.[00:11:57] Speaker: It's a classifier like a BERT model. [00:12:00] And then we run this classifier on all of FineWeb, which is a 15 trillion tokens dataset. And then we only keep the pages that have like a score that's higher than 3. So for example, in our case, we went from 15 trillion tokens to 3. to just 1. 5 trillion tokens. Those are really highly educational.[00:12:16] Speaker: And as you can see here, a fine web EDU outperforms all the other public web datasets by a larger margin on a couple of benchmarks here, I show the aggregated score and you can see that this approach is really effective for filtering web datasets to get like better corpuses for training your LLMs.[00:12:36] DCLM, Nemotron-CC[00:12:36] Speaker: Others also try to do this approach. There's, for example, the DCLM datasets where they also train the classifier, but not to detect educational content. Instead, they trained it on OpenHermes dataset, which is a dataset for instruction tuning. And also they explain like IAM5 subreddits, and then they also get really high quality dataset which is like very information dense and can help [00:13:00] you train some really good LLMs.[00:13:01] Speaker: And then Nemotron Common Crawl, they also did this approach, but instead of using one classifier, they used an ensemble of classifiers. So they used, for example, the DCLM classifier, and also classifiers like the ones we used in FineWebEducational, and then they combined these two. Scores into a, with an ensemble method to only retain the best high quality pages, and they get a data set that works even better than the ones we develop.[00:13:25] Speaker: So that was it for like synthetic data for pre-training.[00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage[00:13:28] Speaker: Now we can go back to post training. I think there's a lot of interesting post training data sets out there. One that was released recently, the agent instructs by Microsoft where they basically try to target some specific skills. And improve the performance of models on them.[00:13:43] Speaker: For example, here, you can see code, brain teasers, open domain QA, and they managed to get a dataset that outperforms that's when fine tuning Mistral 7b on it, it outperforms the original instruct model that was released by Mistral. And as I said, to get good synthetic data, you really [00:14:00] have to have a framework to make sure that your data is diverse.[00:14:03] Speaker: So for example, for them, they always. And then they see the generations on either source code or raw text documents, and then they rewrite them to make sure they're easier to generate instructions from, and then they use that for their like instruction data generation. There's also the Tool3SFT mixture, which was released recently by Allen AI.[00:14:23] Speaker: It's also really good quality and it covers a wide range of tasks. And the way they make sure that this dataset is diverse is by using personas from the persona hub datasets. Which is basically a data set of like I think over a million personas. And for example, in the tool mixture to generate like a new code snippet, they would give like the model persona, for example, a machine learning researcher interested in neural networks, and then ask it to generate like a coding problem.[00:14:49] Speaker: This way you make sure that your data set is really diverse, and then you can further filter the data sets, for example, using the reward models. We also released a dataset called Smalltalk, [00:15:00] and we also tried to cover the wide range of tasks, and as you can see here, for example, when fine tuning Mistral 7b on the dataset, we also outperformed the original Mistral instructs on a number of benchmarks, notably on mathematics and instruction following with ifevil.[00:15:18] Speaker: Another paper that's really interesting I wanted to mention is this one called Multilingual Data Arbitrage by Cohere. And basically they want to generate a data set for post training that is multilingual. And they have a really interesting problem. It's the fact that there isn't like one model that's really good at all the languages they wanted.[00:15:36] Speaker: So what they do is that like they use not just one teacher model, but multiple teachers. And then they have a router which basically sends the prompts they have to all these models. And then they get the completions and they have a reward model that traces all these generations and only keeps the best one.[00:15:52] Speaker: And this is like arbitrage and finance. So well, I think what's interesting in this, it shows that like synthetic data, it doesn't have to come from a single model. [00:16:00] And because we have so many good models now, you could like pull these models together and get like a dataset that's really high quality and that's diverse and that's covers all your needs.[00:16:12] Speaker: I was supposed to put a meme there, but. Yeah, so that was it for like a synthetic data.[00:16:17] Smol Models[00:16:17] Speaker: Now we can go to see what's happening in the small models field in 2024. I don't know if you know, but like now we have some really good small models. For example, Lama 3. 2 1B is. It matches Lama 2. 13b from, that was released last year on the LMSYS arena, which is basically the default go to leaderboard for evaluating models using human evaluation.[00:16:39] Speaker: And as you can see here, the scores of the models are really close. So I think we've made like hugely forward in terms of small models. Of course, that's one, just one data point, but there's more. For example, if you look at this chart from the Quint 2. 5 blog post, it shows that today we have some really good models that are only like 3 billion parameters [00:17:00] and 4 billion that score really high on MMLU.[00:17:03] Speaker: Which is a really popular benchmark for evaluating models. And you can see here that the red, the blue dots have more than 65 on MMLU. And the grey ones have less. And for example, Llama33b had less. So now we have a 3b model that outperforms a 33b model that was released earlier. So I think now people are starting to realize that like, we shouldn't just scale and scale models, but we should try to make them more efficient.[00:17:33] Speaker: I don't know if you knew, but you can also chat with a 3B plus model on your iPhone. For example, here, this is an app called PocketPal, where you can go and select a model from Hugging Face. It has a large choice. For example, here we loaded the 5. 3. 5, which is 3. 8 billion parameters on this iPhone. And we can chat with this and you can see that even the latency is also acceptable.[00:17:57] Speaker: For example, here, I asked it to give me a joke about [00:18:00] NeurIPS. So let's see what it has to say.[00:18:06] Speaker: Okay, why did the neural network attend NeurIPS? Because it heard there would be a lot of layers and fun and it wanted to train its sense of humor. So not very funny, but at least it can run on device. Yeah, so I think now we have good small models, but we also have like good frameworks and tools to use these small models.[00:18:24] On Device Models[00:18:24] Speaker: So I think we're really close to having like really on edge and on device models that are really good. And I think for a while we've had this narrative. But just training larger models is better. Of course, this is supported by science scaling laws. As you can see here, for example, when we scale the model size, the loss is lower and obviously you get a better model.[00:18:46] Speaker: But and we can see this, for example, in the GPT family of models, how we went from just a hundred million parameters to more than a trillion. parameters. And of course, we all observed the performance improvement when using the latest model. But [00:19:00] one thing that we shouldn't forget is that when we scale the model, we also scale the inference costs and time.[00:19:05] Speaker: And so the largest models were are going to cost so much more. So I think now instead of just building larger models, we should be focusing on building more efficient models. It's no longer a race for the largest models since these models are really expensive to run and they require like a really good infrastructure to do that and they cannot run on, for example, consumer hardware.[00:19:27] Speaker: And when you try to build more efficient models that match larger models, that's when you can really unlock some really interesting on device use cases. And I think a trend that we're noticing now is the trend of training smaller models longer. For example, if you compare how much, how long LLAMA was trained compared to LLAMA3, there is a huge increase in the pre training length.[00:19:50] Speaker: LLAMA was trained on 1 trillion tokens, but LLAMA3 8b was trained on 15 trillion tokens. So Meta managed to get a model that's the same size, but But it performs so much [00:20:00] better by choosing to like spend the sacrifice during training, because as we know, training is a one time cost, but inference is something that's ongoing.[00:20:08] Speaker: If we want to see what are like the small models reads in 2024, I think this mobile LLM paper by Meta is interesting. They try to study different models that are like have the less than 1 billion parameters and find which architecture makes most sense for these models. For example, they find that depth is more important than width.[00:20:29] Speaker: So it's more important to have models that have like more layers than just one. making them more wide. They also find that GQA helps, that tying the embedding helps. So I think it's a nice study overall for models that are just a few hundred million parameters. There's also the Apple intelligence tech report, which is interesting.[00:20:48] Speaker: So for Apple intelligence, they had two models, one that was like on server and another model that was on device. It had 3 billion parameters. And I think the interesting part is that they trained this model using [00:21:00] pruning. And then distillation. And for example, they have this table where they show that, like, using pruning and distillation works much better than training from scratch.[00:21:08] Speaker: And they also have some interesting insights about, like, how they specialize their models on specific tasks, like, for example, summarization and rewriting. There's also this paper by NVIDIA that was released recently. I think you've already had a talk about, like, hybrid models that was all interesting.[00:21:23] Speaker: And this model, they used, like, a hybrid architecture between state space models and transformers. And they managed to train a 1B model that's really performant without needing to train it on a lot of tokens. And regarding our work, we just recently released SmallM2, so it's a series of three models, which are the best in class in each model size.[00:21:46] Speaker: For example, our 1. 7b model outperforms Lama 1b and also Qt 2. 5. And how we managed to train this model is the following. That's where you spent a lot of time trying to curate the pre training datasets. We did a lot of [00:22:00] ablations, trying to find which datasets are good and also how to mix them. We also created some new math and code datasets that we're releasing soon.[00:22:08] Speaker: But you basically really spent a lot of time trying to find what's the best mixture that you can train these models on. And then we spent some time trying to like we also trained these models for very long. For example, small M1 was trained only on 1 trillion tokens, but this model is trained on 11 trillion tokens.[00:22:24] Speaker: And we saw that the performance kept improving. The models didn't really plateau mid training, which I think is really interesting. It shows that you can train such small models for very long and keep getting performance gains. What's interesting about SmallLM2 is that it's fully open. We also released, like the pre training code base, the fine tuning code, the datasets, and also evaluation in this repository.[00:22:45] Smol Vision Models[00:22:45] Speaker: Also there's, like, really interesting small models for text, but also for vision. For example, here you can see SmallVLM, which is a 2B model that's really efficient. It doesn't consume a lot of RAM, and it also has a good performance. There's also Moondream 0. [00:23:00] 5b, which was released recently. It's like the smallest visual language model.[00:23:04] Speaker: And as you can see, there isn't like a big trade off compared to Moondream 2b. So now I showed you that we have some really good small models. We also have the tools to use them, but why should you consider using small models and when? I think, like, small models are really interesting because of the on device feature.[00:23:23] Speaker: Because these models are small and they can run fast, you can basically run them on your laptop, but also on your mobile phone. And this means that your dataset stays locally. You don't have to send your queries to third parties. And this really enhances privacy. That was, for example, one of the big selling points for Apple Intelligence.[00:23:42] Speaker: Also, right now, we really have a lot of work to do. So many frameworks to do on device inference. For example, there's MLX, MLC, Llama, CPP, Transformers, JS. So we have a lot of options and each of them have like great features. So you have so many options for doing that. Small models are also really powerful if you choose to specialize them.[00:24:00][00:24:00] Speaker: For example, here there's a startup called Numind, which took small LM and then they fine tuned it on text extraction datasets. And they managed to get a model that's not very far from models that are much larger. So I think text extraction is like one use case where small models can be really performant and it makes sense to use them instead of just using larger models.[00:24:19] Speaker: You can also chat with these models in browser. For example, here, you can go there, you can load the model, you can even turn off your internet and just start chatting with the model locally. Speaking of text extraction, if you don't want to fine tune the models, there's a really good method of structure generation.[00:24:36] Speaker: We can basically force the models to follow a JSON schema that you defined. For example, here, we try to force the model to follow a schema for extracting key information from GitHub issues. So you can input free text, which is a complaint about a GitHub repository, something not working. And then you can run it there and the model can extract anything that is relevant for your GitHub issue creation.[00:24:58] Speaker: For example, the [00:25:00] priority, for example, here, priority is high, the type of the issue bug, and then a title and the estimation of how long this will take to fix. And you can just like do this in the browser, you can transform your text into a GitHub issue that's properly formatted.[00:25:14] What's Next[00:25:14] Speaker: So what's next for synthetic data and small models?[00:25:18] Speaker: I think that domain specific synthetic data is going to be, it's already important, it's going to be even more important. For example, generating synthetic data for math. I think this really would help improve the reasoning of a lot of models. And a lot of people are doing it, for example, Quint 2. 12 math, everyone's trying to reproduce a one.[00:25:37] Speaker: And so I think for synthetic data, trying to specialize it on some domains is going to be really important. And then for small models, I think specializing them through fine tuning, it's also going to be really important because I think a lot of companies are just trying to use these large models because they are better.[00:25:53] Speaker: But on some tasks, I think you can already get decent performance with small models. So you don't need to Pay like a [00:26:00] cost that's much larger just to make your model better at your task by a few percent. And this is not just for text. And I think it also applies for other modalities like vision and audio.[00:26:11] Speaker: And I think you should also watch out for on device frameworks and applications. For example, like the app I showed, or lama, all these frameworks are becoming really popular and I'm pretty sure that we're gonna get like more of them in 2025. And users really like that. Maybe for other, I should also say hot take.[00:26:28] Speaker: I think that like in AI, we just started like with fine tuning, for example, trying to make BERT work on some specific use cases, and really struggling to do that. And then we had some models that are much larger. So we just switched to like prompt engineering to get the models And I think we're going back to fine tuning where we realize these models are really costly.[00:26:47] Speaker: It's better to use just a small model or try to specialize it. So I think it's a little bit of a cycle and we're going to start to see like more fine tuning and less of just like a prompt engineering the models. So that was my talk. Thank you for following. And if you have [00:27:00] any questions, we can take them now. 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Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all our LS supporters who helped fund the venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.Since Nathan Lambert ( Interconnects ) joined us for the hit RLHF 201 episode at the start of this year, it is hard to overstate how much Open Models have exploded this past year. In 2023 only five names were playing in the top LLM ranks, Mistral, Mosaic's MPT, TII UAE's Falcon, Yi from Kai-Fu Lee's 01.ai, and of course Meta's Llama 1 and 2. This year a whole cast of new open models have burst on the scene, from Google's Gemma and Cohere's Command R, to Alibaba's Qwen and Deepseek models, to LLM 360 and DCLM and of course to the Allen Institute's OLMo, OL MOE, Pixmo, Molmo, and Olmo 2 models. We were honored to host Luca Soldaini, one of the research leads on the Olmo series of models at AI2.Pursuing Open Model research comes with a lot of challenges beyond just funding and access to GPUs and datasets, particularly the regulatory debates this year across Europe, California and the White House. We also were honored to hear from and Sophia Yang, head of devrel at Mistral, who also presented a great session at the AI Engineer World's Fair Open Models track!Full Talk on YouTubePlease like and subscribe!Timestamps* 00:00 Welcome to Latent Space Live * 00:12 Recap of 2024: Best Moments and Keynotes * 01:22 Explosive Growth of Open Models in 2024 * 02:04 Challenges in Open Model Research * 02:38 Keynote by Luca Soldani: State of Open Models * 07:23 Significance of Open Source AI Licenses * 11:31 Research Constraints and Compute Challenges * 13:46 Fully Open Models: A New Trend * 27:46 Mistral's Journey and Innovations * 32:57 Interactive Demo: Lachat Capabilities * 36:50 Closing Remarks and NetworkingTranscriptSession3Audio[00:00:00] AI Charlie: Welcome to Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. As a special treat this week, we're recapping the best of 2024 going domain by domain. We sent out a survey to the over 900 of you who told us what you wanted, and then invited the best speakers in the latent space network to cover each field.[00:00:28] AI Charlie: 200 of you joined us in person throughout the day, with over 2, 200 watching live online. Our next keynote covers the state of open models in 2024, with Luca Soldani and Nathan Lambert of the Allen Institute for AI, with a special appearance from Dr. Sophia Yang of Mistral. Our first hit episode of 2024 was with Nathan Lambert on RLHF 201 back in January.[00:00:57] AI Charlie: Where he discussed both reinforcement learning for language [00:01:00] models and the growing post training and mid training stack with hot takes on everything from constitutional AI to DPO to rejection sampling and also previewed the sea change coming to the Allen Institute. And to Interconnects, his incredible substack on the technical aspects of state of the art AI training.[00:01:18] AI Charlie: We highly recommend subscribing to get access to his Discord as well. It is hard to overstate how much open models have exploded this past year. In 2023, only five names were playing in the top LLM ranks. Mistral, Mosaics MPT, and Gatsby. TII UAE's Falcon, Yi, from Kaifu Lee's 01. ai, And of course, Meta's Lama 1 and 2.[00:01:43] AI Charlie: This year, a whole cast of new open models have burst on the scene. From Google's Jemma and Cohere's Command R, To Alibaba's Quen and DeepSeq models, to LLM360 and DCLM, and of course, to the Allen Institute's OLMO, [00:02:00] OLMOE, PIXMO, MOLMO, and OLMO2 models. Pursuing open model research comes with a lot of challenges beyond just funding and access to GPUs and datasets, particularly the regulatory debates this year across Europe.[00:02:14] AI Charlie: California and the White House. We also were honored to hear from Mistral, who also presented a great session at the AI Engineer World's Fair Open Models track. As always, don't forget to check the show notes for the YouTube link to their talk, as well as their slides. Watch out and take care.[00:02:35] Luca Intro[00:02:35] Luca Soldaini: Cool. Yeah, thanks for having me over. I'm Luca. I'm a research scientist at the Allen Institute for AI. I threw together a few slides on sort of like a recap of like interesting themes in open models for, for 2024. Have about maybe 20, 25 minutes of slides, and then we can chat if there are any questions.[00:02:57] Luca Soldaini: If I can advance to the next slide. [00:03:00] Okay, cool. So I did the quick check of like, to sort of get a sense of like, how much 2024 was different from 2023. So I went on Hugging Face and sort of get, tried to get a picture of what kind of models were released in 2023 and like, what do we get in 2024?[00:03:16] Luca Soldaini: 2023 we get, we got things like both LLAMA 1 and 2, we got Mistral, we got MPT, Falcon models, I think the YI model came in at the end. Tail end of the year. It was a pretty good year. But then I did the same for 2024. And it's actually quite stark difference. You have models that are, you know, reveling frontier level.[00:03:38] Luca Soldaini: Performance of what you can get from closed models from like Quen, from DeepSeq. We got Llama3. We got all sorts of different models. I added our own Olmo at the bottom. There's this growing group of like, Fully open models that I'm going to touch on a little bit later. But you know, just looking at the slides, it feels like 2024 [00:04:00] was just smooth sailing, happy knees, much better than previous year.[00:04:04] Luca Soldaini: And you know, you can plot you can pick your favorite benchmark Or least favorite, I don't know, depending on what point you're trying to make. And plot, you know, your closed model, your open model and sort of spin it in ways that show that, oh, you know open models are much closer to where closed models are today versus to Versus last year where the gap was fairly significant.[00:04:29] Luca Soldaini: So one thing that I think I don't know if I have to convince people in this room, but usually when I give this talks about like open models, there is always like this background question in, in, in people's mind of like, why should we use open models? APIs argument, you know, it's, it's. Just an HTTP request to get output from a, from one of the best model out there.[00:04:53] Luca Soldaini: Why do I have to set up infra and use local models? And there are really like two answer. There is the more [00:05:00] researchy answer for this, which is where it might be. Background lays, which is just research. If you want to do research on language models, research thrives on, on open models, there is like large swath of research on modeling, on how these models behave on evaluation and inference on mechanistic interpretability that could not happen at all if you didn't have open models they're also for AI builders, they're also like.[00:05:30] Luca Soldaini: Good use cases for using local models. You know, you have some, this is like a very not comprehensive slides, but you have things like there are some application where local models just blow closed models out of the water. So like retrieval, it's a very clear example. We might have like constraints like Edge AI applications where it makes sense.[00:05:51] Luca Soldaini: But even just like in terms of like stability, being able to say this model is not changing under the hood. It's, there's plenty of good cases for, [00:06:00] for open models. And the community is just not models. Is I stole this slide from one of the Quent2 announcement blog posts. But it's super cool to see like how much tech exists around open models and serving them on making them efficient and hosting them.[00:06:18] Luca Soldaini: It's pretty cool. And so. It's if you think about like where the term opens come from, comes from like the open source really open models meet the core tenants of, of open, of open source specifically when it comes around collaboration, there is truly a spirit, like through these open models, you can build on top of other people.[00:06:41] Luca Soldaini: innovation. We see a lot of these even in our own work of like, you know, as we iterate in the various versions of Alma it's not just like every time we collect from scratch all the data. No, the first step is like, okay, what are the cool data sources and datasets people have put [00:07:00] together for language model for training?[00:07:01] Luca Soldaini: Or when it comes to like our post training pipeline We one of the steps is you want to do some DPO and you use a lot of outputs of other models to improve your, your preference model. So it's really having like an open sort of ecosystem benefits and accelerates the development of open models.[00:07:23] The Definition of Open Models[00:07:23] Luca Soldaini: One thing that we got in 2024, which is not a specific model, but I thought it was really significant, is we first got we got our first open source AI definition. So this is from the open source initiative they've been generally the steward of a lot of the open source licenses when it comes to software and so they embarked on this journey in trying to figure out, okay, How does a license, an open source license for a model look like?[00:07:52] Luca Soldaini: Majority of the work is very dry because licenses are dry. So I'm not going to walk through the license step by [00:08:00] step, but I'm just going to pick out one aspect that is very good and then one aspect that personally feels like it needs improvement on the good side. This this open source AI license actually.[00:08:13] Luca Soldaini: This is very intuitive. If you ever build open source software and you have some expectation around like what open source looks like for software for, for AI, sort of matches your intuition. So, the weights need to be fairly available the code must be released with an open source license and there shouldn't be like license clauses that block specific use cases.[00:08:39] Luca Soldaini: So. Under this definition, for example, LLAMA or some of the QUEN models are not open source because the license says you can't use this model for this or it says if you use this model you have to name the output this way or derivative needs to be named that way. Those clauses don't meet open source [00:09:00] definition and so they will not be covered.[00:09:02] Luca Soldaini: The LLAMA license will not be covered under the open source definition. It's not perfect. One of the thing that, um, internally, you know, in discussion with with OSI, we were sort of disappointed is around the language. For data. So you might imagine that an open source AI model means a model where the data is freely available.[00:09:26] Luca Soldaini: There were discussion around that, but at the end of the day, they decided to go with a softened stance where they say a model is open source if you provide sufficient detail information. On how to sort of replicate the data pipeline. So you have an equivalent system, sufficient, sufficiently detailed.[00:09:46] Luca Soldaini: It's very, it's very fuzzy. Don't like that. An equivalent system is also very fuzzy. And this doesn't take into account the accessibility of the process, right? It might be that you provide enough [00:10:00] information, but this process costs, I don't know, 10 million to do. Now the open source definition. Like, any open source license has never been about accessibility, so that's never a factor in open source software, how accessible software is.[00:10:14] Luca Soldaini: I can make a piece of open source, put it on my hard drive, and never access it. That software is still open source, the fact that it's not widely distributed doesn't change the license, but practically there are expectations of like, what we want good open sources to be. So, it's, It's kind of sad to see that the data component in this license is not as, as, Open as some of us would like would like it to be.[00:10:40] Challenges for Open Models[00:10:40] Luca Soldaini: and I linked a blog post that Nathan wrote on the topic that it's less rambly and easier to follow through. One thing that in general, I think it's fair to say about the state of open models in 2024 is that we know a lot more than what we knew in, [00:11:00] in 2023. Like both on the training data, like And the pre training data you curate on like how to do like all the post training, especially like on the RL side.[00:11:10] Luca Soldaini: You know, 2023 was a lot of like throwing random darts at the board. I think 2024, we have clear recipes that, okay, don't get the same results as a closed lab because there is a cost in, in actually matching what they do. But at least we have a good sense of like, okay, this is, this is the path to get state of the art language model.[00:11:31] Luca Soldaini: I think that one thing that it's a downside of 2024 is that I think we are more research constrained in 2023. It feels that, you know, the barrier for compute that you need to, to move innovation along as just being right rising and rising. So like, if you go back to this slide, there is now this, this cluster of models that are sort of released by the.[00:11:57] Luca Soldaini: Compute rich club. Membership is [00:12:00] hotly debated. You know, some people don't want to be. Called the rich because it comes to expectations. Some people want to be called rich, but I don't know, there's debate, but like, these are players that have, you know, 10, 000, 50, 000 GPUs at minimum. And so they can do a lot of work and a lot of exploration and improving models that it's not very accessible.[00:12:21] Luca Soldaini: To give you a sense of like how I personally think about. Research budget for each part of the, of the language model pipeline is like on the pre training side, you can maybe do something with a thousand GPUs, really you want 10, 000. And like, if you want real estate of the art, you know, your deep seek minimum is like 50, 000 and you can scale to infinity.[00:12:44] Luca Soldaini: The more you have, the better it gets. Everyone on that side still complains that they don't have enough GPUs. Post training is a super wide sort of spectrum. You can do as little with like eight GPUs as long as you're able to [00:13:00] run, you know, a good version of, say, a LLAMA model, you can do a lot of work there.[00:13:05] Luca Soldaini: You can scale a lot of the methodology, just like scales with compute, right? If you're interested in you know, your open replication of what OpenAI's O1 is you're going to be on the 10K spectrum of our GPUs. Inference, you can do a lot with very few resources. Evaluation, you can do a lot with, well, I should say at least one GPUs if you want to evaluate GPUs.[00:13:30] Luca Soldaini: Open models but in general, like if you are, if you care a lot about intervention to do on this model, which it's my prefer area of, of research, then, you know, the resources that you need are quite, quite significant. Yeah. One other trends that has emerged in 2024 is this cluster of fully open models.[00:13:54] Luca Soldaini: So Omo the model that we built at ai, two being one of them and you know, it's nice [00:14:00] that it's not just us. There's like a cluster of other mostly research efforts who are working on this. And so it's good to to give you a primer of what like fully open means. So fully open, the easy way to think about it is instead of just releasing a model checkpoint that you run, you release a full recipe so that other people working on it.[00:14:24] Luca Soldaini: Working on that space can pick and choose whatever they want from your recipe and create their own model or improve on top of your model. You're giving out the full pipeline and all the details there instead of just like the end output. So I pull up the screenshot from our recent MOE model.[00:14:43] Luca Soldaini: And like for this model, for example, we released the model itself. Data that was trained on, the code, both for training and inference all the logs that we got through the training run, as well as every intermediate checkpoint and like the fact that you release different part of the pipeline [00:15:00] allows others to do really cool things.[00:15:02] Luca Soldaini: So for example, this tweet from early this year from folks in news research they use our pre training data to do a replication of the BitNet paper in the open. So they took just a Really like the initial part of a pipeline and then the, the thing on top of it. It goes both ways.[00:15:21] Luca Soldaini: So for example, for the Olmo2 model a lot of our pre trained data for the first stage of pre training was from this DCLM initiative that was led by folks Ooh, a variety of ins a variety of institutions. It was a really nice group effort. But you know, for When it was nice to be able to say, okay, you know, the state of the art in terms of like what is done in the open has improved.[00:15:46] AI2 Models - Olmo, Molmo, Pixmo etc[00:15:46] Luca Soldaini: We don't have to like do all this work from scratch to catch up the state of the art. We can just take it directly and integrate it and do our own improvements on top of that. I'm going to spend a few minutes doing like a [00:16:00] shameless plug for some of our fully open recipes. So indulge me in this.[00:16:05] Luca Soldaini: So a few things that we released this year was, as I was mentioning, there's OMOE model which is, I think still is state of the art MOE model in its size class. And it's also. Fully open, so every component of this model is available. We released a multi modal model called Molmo. Molmo is not just a model, but it's a full recipe of how you go from a text only model to a multi modal model, and we apply this recipe on top of Quent checkpoints, on top of Olmo checkpoints, as well as on top of OlmoE.[00:16:37] Luca Soldaini: And I think there'd be a replication doing that on top of Mistral as well. The post training side we recently released 2. 0. 3. Same story. This is a recipe on how you go from a base model to A state of the art post training model. We use the Tulu recipe on top of Olmo, on top of Llama, and then there's been open replication effort [00:17:00] to do that on top of Quen as well.[00:17:02] Luca Soldaini: It's really nice to see like, you know, when your recipe sort of, it's kind of turnkey, you can apply it to different models and it kind of just works. And finally, the last thing we released this year was Olmo 2, which so far is the best state of the art. Fully open language model a Sera combines aspect from all three of these previous models.[00:17:22] Luca Soldaini: What we learn on the data side from MomoE and what we learn on like making models that are easy to adapt from the Momo project and the Tulu project. I will close with a little bit of reflection of like ways this, this ecosystem of open models like it's not all roses. It's not all happy. It feels like day to day, it's always in peril.[00:17:44] Luca Soldaini: And, you know, I talked a little bit about like the compute issues that come with it. But it's really not just compute. One thing that is on top of my mind is due to like the environment and how you know, growing feelings about like how AI is treated. [00:18:00] It's actually harder to get access to a lot of the data that was used to train a lot of the models up to last year.[00:18:06] Luca Soldaini: So this is a screenshot from really fabulous work from Shane Longpre who's, I think is in Europe about Just access of like diminishing access to data for language model pre training. So what they did is they went through every snapshot of common crawl. Common crawl is this publicly available scrape of the, of a subset of the internet.[00:18:29] Luca Soldaini: And they looked at how For any given website whether a website that was accessible in say 2017, what, whether it was accessible or not in 2024. And what they found is as a reaction to like the close like of the existence of closed models like OpenAI or Cloud GPT or Cloud a lot of content owners have blanket Blocked any type of crawling to your website.[00:18:57] Luca Soldaini: And this is something that we see also internally at [00:19:00] AI2. Like one project that we started this year is we wanted to, we wanted to understand, like, if you're a good citizen of the internet and you crawl following sort of norms and policy that have been established in the last 25 years, what can you crawl?[00:19:17] Luca Soldaini: And we found that there's a lot of website where. The norms of how you express preference of whether to crawl your data or not are broken. A lot of people would block a lot of crawling, but do not advertise that in RobustDXT. You can only tell that they're crawling, that they're blocking you in crawling when you try doing it.[00:19:37] Luca Soldaini: Sometimes you can't even crawl the robots. txt to, to check whether you're allowed or not. And then a lot of websites there's, there's like all these technologies that historically have been, have existed to make websites serving easier such as Cloudflare or DNS. They're now being repurposed for blocking AI or any type of crawling [00:20:00] in a way that is Very opaque to the content owners themselves.[00:20:04] Luca Soldaini: So, you know, you go to these websites, you try to access them and they're not available and you get a feeling it's like, Oh, someone changed, something changed on the, on the DNS side that it's blocking this and likely the content owner has no idea. They're just using a Cloudflare for better, you know, load balancing.[00:20:25] Luca Soldaini: And this is something that was sort of sprung on them with very little notice. And I think the problem is this, this blocking or ideas really, it impacts people in different ways. It disproportionately helps companies that have a headstart, which are usually the closed labs and it hurts incoming newcomer players where either have now to do things in a sketchy way or you're never going to get that content that the closed lab might have.[00:20:54] Luca Soldaini: So there's a lot, it was a lot of coverage. I'm going to plug Nathan's blog post again. That is, [00:21:00] that I think the title of this one is very succinct which is like, we're actually not, You know, before thinking about running out of training data, we're actually running out of open training data. And so if we want better open models they should be on top of our mind.[00:21:13] Regulation and Lobbying[00:21:13] Luca Soldaini: The other thing that has emerged is that there is strong lobbying efforts on trying to define any kind of, AI as like a new extremely risky and I want to be precise here. Like the problem is now, um, like the problem is not not considering the risk of this technology. Every technology has risks that, that should always be considered.[00:21:37] Luca Soldaini: The thing that it's like to me is sorry, is ingenious is like just putting this AI on a pedestal and calling it like, An unknown alien technology that has like new and undiscovered potentials to destroy humanity. When in reality, all the dangers I think are rooted in [00:22:00] dangers that we know from existing software industry or existing issues that come with when using software on on a lot of sensitive domains, like medical areas.[00:22:13] Luca Soldaini: And I also noticed a lot of efforts that have actually been going on and trying to make this open model safe. I pasted one here from AI2, but there's actually like a lot of work that has been going on on like, okay, how do you make, if you're distributing this model, Openly, how do you make it safe?[00:22:31] Luca Soldaini: How, what's the right balance between accessibility on open models and safety? And then also there's annoying brushing of sort of concerns that are then proved to be unfounded under the rug. You know, if you remember the beginning of this year, it was all about bio risk of these open models.[00:22:48] Luca Soldaini: The whole thing fizzled because as being Finally, there's been like rigorous research, not just this paper from Cohere folks, but it's been rigorous research showing [00:23:00] that this is really not a concern that we should be worried about. Again, there is a lot of dangerous use of AI applications, but this one was just like, A lobbying ploy to just make things sound scarier than they actually are.[00:23:15] Luca Soldaini: So I got to preface this part. It says, this is my personal opinion. It's not my employer, but I look at things like the SP 1047 from, from California. And I think we kind of dodged a bullet on, on this legislation. We, you know, the open source community, a lot of the community came together at the last, sort of the last minute and did a very good effort trying to explain all the negative impact of this bill.[00:23:43] Luca Soldaini: But There's like, I feel like there's a lot of excitement on building these open models or like researching on these open models. And lobbying is not sexy it's kind of boring but it's sort of necessary to make sure that this ecosystem can, can really [00:24:00] thrive. This end of presentation, I have Some links, emails, sort of standard thing in case anyone wants to reach out and if folks have questions or anything they wanted to discuss.[00:24:13] Luca Soldaini: Is there an open floor? I think we have Sophia[00:24:16] swyx: who wants to who one, one very important open model that we haven't covered is Mistral. Ask her on this slide. Yeah, yeah. Well, well, it's nice to have the Mistral person talk recap the year in Mistral. But while Sophia gets set up, does anyone have like, just thoughts or questions about the progress in this space?[00:24:32] Questions - Incentive Alignment[00:24:32] swyx: Do you always have questions?[00:24:34] Quesiton: I'm very curious how we should build incentives to build open models, things like Francois Chollet's ArcPrize, and other initiatives like that. What is your opinion on how we should better align incentives in the community so that open models stay open?[00:24:49] Luca Soldaini: The incentive bit is, like, really hard.[00:24:51] Luca Soldaini: Like, even It's something that I actually, even we think a lot about it internally because like building open models is risky. [00:25:00] It's very expensive. And so people don't want to take risky bets. I think the, definitely like the challenges like our challenge, I think those are like very valid approaches for it.[00:25:13] Luca Soldaini: And then I think in general, promoting, building, so, any kind of effort to participate in this challenge, in those challenges, if we can promote doing that on top of open models and sort of really lean into like this multiplier effect, I think that is a good way to go. If there were more money for that.[00:25:35] Luca Soldaini: For efforts like research efforts around open models. There's a lot of, I think there's a lot of investments in companies that at the moment are releasing their model in the open, which is really cool. But it's usually more because of commercial interest and not wanting to support this, this like open models in the longterm, it's a really hard problem because I think everyone is operating sort of [00:26:00] in what.[00:26:01] Luca Soldaini: Everyone is at their local maximum, right? In ways that really optimize their position on the market. Global maximum is harder to achieve.[00:26:11] Question2: Can I ask one question? No.[00:26:12] Luca Soldaini: Yeah.[00:26:13] Question2: So I think one of the gap between the closed and open source models is the mutability. So the closed source models like chat GPT works pretty good on the low resource languages, which is not the same on the open, open source models, right?[00:26:27] Question2: So is it in your plan to improve on that?[00:26:32] Luca Soldaini: I think in general,[00:26:32] Luca Soldaini: yes, is I think it's. I think we'll see a lot of improvements there in, like, 2025. Like, there's groups like, Procurement English on the smaller side that are already working on, like, better crawl support, multilingual support. I think what I'm trying to say here is you really want to be experts.[00:26:54] Luca Soldaini: who are actually in those countries that teach those languages to [00:27:00] participate in the international community. To give you, like, a very easy example I'm originally from Italy. I think I'm terribly equipped to build a model that works well in Italian. Because one of the things you need to be able to do is having that knowledge of, like, okay, how do I access, you know, how Libraries, or content that is from this region that covers this language.[00:27:23] Luca Soldaini: I've been in the US long enough that I no longer know. So, I think that's the efforts that folks in Central Europe, for example, are doing. Around like, okay, let's tap into regional communities. To get access you know, to bring in collaborators from those areas. I think it's going to be, like, very crucial for getting products there.[00:27:46] Mistral intro[00:27:46] Sophia Yang: Hi everyone. Yeah, I'm super excited to be here to talk to you guys about Mistral. A really short and quick recap of what we have done, what kind of models and products we have released in the [00:28:00] past year and a half. So most of you We have already known that we are a small startup funded about a year and a half ago in Paris in May, 2003, it was funded by three of our co founders, and in September, 2003, we released our first open source model, Mistral 7b yeah, how, how many of you have used or heard about Mistral 7b?[00:28:24] Sophia Yang: Hey, pretty much everyone. Thank you. Yeah, it's our Pretty popular and community. Our committee really loved this model, and in December 23, we, we released another popular model with the MLE architecture Mr. A X seven B and oh. Going into this year, you can see we have released a lot of things this year.[00:28:46] Sophia Yang: First of all, in February 2004, we released MrSmall, MrLarge, LeChat, which is our chat interface, I will show you in a little bit. We released an embedding model for, you [00:29:00] know, converting your text into embedding vectors, and all of our models are available. The, the big cloud resources. So you can use our model on Google cloud, AWS, Azure Snowflake, IBM.[00:29:16] Sophia Yang: So very useful for enterprise who wants to use our model through cloud. And in April and May this year, we released another powerful open source MOE model, AX22B. And we also released our first code. Code Model Coastal, which is amazing at 80 plus languages. And then we provided another fine tuning service for customization.[00:29:41] Sophia Yang: So because we know the community love to fine tune our models, so we provide you a very nice and easy option for you to fine tune our model on our platform. And also we released our fine tuning code base called Menstrual finetune. It's open source, so feel free to take it. Take a look and.[00:29:58] Sophia Yang: More models. [00:30:00] On July 2, November this year, we released many, many other models. First of all is the two new small, best small models. We have Minestra 3B great for Deploying on edge devices we have Minstrel 8B if you used to use Minstrel 7B, Minstrel 8B is a great replacement with much stronger performance than Minstrel 7B.[00:30:25] Sophia Yang: We also collaborated with NVIDIA and open sourced another model, Nemo 12B another great model. And Just a few weeks ago, we updated Mistral Large with the version 2 with the updated, updated state of the art features and really great function calling capabilities. It's supporting function calling in LatentNate.[00:30:45] Sophia Yang: And we released two multimodal models Pixtral 12b. It's this open source and Pixtral Large just amazing model for, models for not understanding images, but also great at text understanding. So. Yeah, a [00:31:00] lot of the image models are not so good at textual understanding, but pixel large and pixel 12b are good at both image understanding and textual understanding.[00:31:09] Sophia Yang: And of course, we have models for research. Coastal Mamba is built on Mamba architecture and MathRoll, great with working with math problems. So yeah, that's another model.[00:31:29] Sophia Yang: Here's another view of our model reference. We have several premier models, which means these models are mostly available through our API. I mean, all of the models are available throughout our API, except for Ministry 3B. But for the premier model, they have a special license. Minstrel research license, you can use it for free for exploration, but if you want to use it for enterprise for production use, you will need to purchase a license [00:32:00] from us.[00:32:00] Sophia Yang: So on the top row here, we have Minstrel 3b and 8b as our premier model. Minstrel small for best, best low latency use cases, MrLarge is great for your most sophisticated use cases. PixelLarge is the frontier class multimodal model. And, and we have Coastral for great for coding and then again, MrEmbedding model.[00:32:22] Sophia Yang: And The bottom, the bottom of the slides here, we have several Apache 2. 0 licensed open way models. Free for the community to use, and also if you want to fine tune it, use it for customization, production, feel free to do so. The latest, we have Pixtros 3 12b. We also have Mr. Nemo mum, Coastal Mamba and Mastro, as I mentioned, and we have three legacy models that we don't update anymore.[00:32:49] Sophia Yang: So we recommend you to move to our newer models if you are still using them. And then, just a few weeks ago, [00:33:00] we did a lot of, uh, improvements to our code interface, Lachette. How many of you have used Lachette? Oh, no. Only a few. Okay. I highly recommend Lachette. It's chat. mistral. ai. It's free to use.[00:33:16] Sophia Yang: It has all the amazing capabilities I'm going to show you right now. But before that, Lachette in French means cat. So this is actually a cat logo. If you You can tell this is the cat eyes. Yeah. So first of all, I want to show you something Maybe let's, let's take a look at image understanding.[00:33:36] Sophia Yang: So here I have a receipts and I want to ask, just going to get the prompts. Cool. So basically I have a receipt and I said I ordered I don't know. Coffee and the sausage. How much do I owe? Add a 18 percent tip. So hopefully it was able to get the cost of the coffee and the [00:34:00] sausage and ignore the other things.[00:34:03] Sophia Yang: And yeah, I don't really understand this, but I think this is coffee. It's yeah. Nine, eight. And then cost of the sausage, we have 22 here. And then it was able to add the cost, calculate the tip, and all that. Great. So, it's great at image understanding, it's great at OCR tasks. So, if you have OCR tasks, please use it.[00:34:28] Sophia Yang: It's free on the chat. It's also available through our API. And also I want to show you a Canvas example. A lot of you may have used Canvas with other tools before. But, With Lachat, it's completely free again. Here, I'm asking it to create a canvas that's used PyScript to execute Python in my browser.[00:34:51] Sophia Yang: Let's see if it works. Import this. Okay, so, yeah, so basically it's executing [00:35:00] Python here. Exactly what we wanted. And the other day, I was trying to ask Lachat to create a game for me. Let's see if we can make it work. Yeah, the Tetris game. Yep. Let's just get one row. Maybe. Oh no. Okay. All right. You get the idea. I failed my mission. Okay. Here we go. Yay! Cool. Yeah. So as you can see, Lachet can write, like, a code about a simple game pretty easily. And you can ask Lachet to explain the code. Make updates however you like. Another example. There is a bar here I want to move.[00:35:48] Sophia Yang: Okay, great, okay. And let's go back to another one. Yeah, we also have web search capabilities. Like, you can [00:36:00] ask what's the latest AI news. Image generation is pretty cool. Generate an image about researchers. Okay. In Vancouver? Yeah, it's Black Forest Labs flux Pro. Again, this is free, so Oh, cool.[00:36:19] Sophia Yang: I guess researchers here are mostly from University of British Columbia. That's smart. Yeah. So this is Laia ira. Please feel free to use it. And let me know if you have any feedback. We're always looking for improvement and we're gonna release a lot more powerful features in the coming years.[00:36:37] Sophia Yang: Thank you. Get full access to Latent Space at www.latent.space/subscribe

In this episode of The Cognitive Revolution, Nathan explores METR's groundbreaking REBench evaluation framework with Neev Parikh. We dive deep into how this new benchmark assesses AI systems' ability to perform real machine learning research tasks, from optimizing GPU kernels to fine-tuning language models. Join us for a fascinating discussion about the current capabilities of AI models like Claude 3.5 and GPT-4, and what their performance tells us about the trajectory of artificial intelligence development. Check out METR's work: blog post: https://metr.org/blog/2024-11-22-evaluating-r-d-capabilities-of-llms/ paper: https://metr.org/AI_R_D_Evaluation_Report.pdf jobs: https://hiring.metr.org/ The Cognitive Revolution Ask Me Anything and Listener Survey: https://docs.google.com/forms/d/1aYv2XLID7RqGxj2_Y4_6x9mo_aqXcGCeLw1EQhy4IpY/edit Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: GiveWell: GiveWell has spent over 17 years researching global health and philanthropy to identify the highest-impact giving opportunities. Over 125,000 donors have contributed more than $2 billion, saving over 200,000 lives through evidence-backed recommendations. First-time donors can have their contributions matched up to $100 before year-end. Visit https://GiveWell.org, select podcast, and enter Cognitive Revolution at checkout to make a difference today. SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: (00:00:00) Teaser (00:01:04) About the Episode (00:05:14) Introducing METR (00:07:36) Specialization of AI Risk (00:09:52) AI R&D vs. Autonomy (00:12:41) Benchmark Design Choices (00:16:04) Benchmark Design Principles (Part 1) (00:18:54) Sponsors: GiveWell | SelectQuote (00:21:44) Benchmark Design Principles (Part 2) (00:22:35) AI vs. Human Evaluation (00:26:55) Optimizing Runtimes (00:36:02) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:38:20) AI Myopia (00:43:37) Optimizing Loss (00:47:59) Optimizing Win Rate (00:50:24) Best of K Analysis (01:02:26) Best of K Limitations (01:09:04) Agent Interaction Modalities (01:12:34) Analyzing Benchmark Results (01:17:16) Model Performance Differences (01:22:49) Elicitation and Scaffolding (01:27:08) Context Window & Best of K (01:35:17) Reward Hacking & Bad Behavior (01:43:47) Future Directions & Hiring (01:46:20) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/

This week, the Equity pod gang — which included newcomer Max Zeff, Margaux MacColl, and Kirsten Korosec — noticed an emerging trend: the worlds of AI and defense tech are colliding. Listen to the full episode to hear about: A new fund is in town. And surprise, surprise, Humba Ventures' $40 million fund is focused on deep tech and defense.  How enterprise AI startup Cohere is unlike all the other AI startups out there, and why they've been so quiet. Particularly in this new deal with Palantir. Dig into the great philosophical question of 2024: is it dumb to IPO in an election year? And, perhaps more importantly, will this IPO dry spell continue in 2025? Should founders be cautious of investors with foreign backing? Equity is TechCrunch's flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday. Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. Bryce Durbin is our Illustrator. We'd also like to thank the audience development team and Henry Pickavet, who manages TechCrunch audio products.

“We are about to see a bunch of people who are powerful, successful, and feel like they know the best way to do things bring this approach into government.” The BetaKit Podcast reviews the biggest tech stories of 2024 before doling out annual letter grades for Shopify, Wealthsimple, Cohere, and many more. A podcast so good we had to record it twice. Have a different take on 2024? Let us know: podcast@betakit.com. The BetaKit Podcast is presented by ‘Devious Web,' a Shelley Grandy mystery novel available in digital or paperback formats. A successful Canadian entrepreneur is offered millions from Silicon Valley for his data analytics business. As CEO Tom Oliver considers the deal, he is targeted by an unknown perpetrator, and his friend, homicide detective Jason Liu, must strive to keep him safe. If you enjoyed ‘Suits' and ‘Succession,' this book is for you. Order your copy today.

This episode is sponsored by Oracle. Harness the power of AI without overspending with Oracle Cloud Infrastructure (OCI). Ideal for AI model training, OCI offers 4-8x more bandwidth than competitors at half the cost.Transform your business like Uber and Cohere with OCI.Try it for free at https://oracle.com/gameplanWe're diving into a classic Q&A session today!!I'm answering all your questions about fitness, training, nutrition, motivation, and more, straight from my Instagram. From my go-to workouts when energy is low, to my favorite supplements, cheat meals, and top fitness advice—this video covers it all. Smash that like like button and subscribe for more!Try Whoop for free: http://join.whoop.com/LipsettJoin my mentorship program: https://www.bygameplan.com/mentorshipAlphalete Athletics: https://alphaleteathletics.comCode: LIPSETT for 10% offGHOST Supplements: https://www.ghostlifestyle.comDiscount Code: LIPSETT GHOST Supplements UK: https://uk.ghostlifestyle.com​​​​​​​ Discount Code: LIPSETT

Nathan welcomes back computational biochemist Amelie Schreiber for a fascinating update on AI's revolutionary impact in biology. In this episode of The Cognitive Revolution, we explore recent breakthroughs including AlphaFold3, ESM3, and new diffusion models transforming protein engineering and drug discovery. Join us for an insightful discussion about how AI is reshaping our understanding of molecular biology and making complex protein engineering tasks more accessible than ever before. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Shopify: Shopify is the world's leading e-commerce platform, offering a market-leading checkout system and exclusive AI apps like Quikly. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: (00:00:00) Teaser (00:00:46) About the Episode (00:04:30) AI for Biology (00:07:14) David Baker's Impact (00:11:49) AlphaFold 3 & ESM3 (00:16:40) Protein Interaction Prediction (Part 1) (00:16:44) Sponsors: Shopify | SelectQuote (00:19:18) Protein Interaction Prediction (Part 2) (00:31:12) MSAs & Embeddings (Part 1) (00:32:32) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:34:49) MSAs & Embeddings (Part 2) (00:35:57) Beyond Structure Prediction (00:51:13) Dynamics vs. Statics (00:57:24) In-Painting & Use Cases (00:59:48) Workflow & Platforms (01:06:45) Design Process & Success Rates (01:13:23) Ambition & Task Definition (01:19:25) New Models: PepFlow & GeoAB (01:28:23) Flow Matching vs. Diffusion (01:30:42) ESM3 & Multimodality (01:37:10) Summary & Future Directions (01:45:34) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

In this episode of The Cognitive Revolution, Nathan welcomes back Div Garg, Co-Founder and CEO of MultiOn, for his third appearance to discuss the evolving landscape of AI agents. We explore how agent development has shifted from open-ended frameworks to intelligent workflows, MultiOn's unique approach to agent development, and their journey toward achieving human-level performance. Dive into fascinating insights about data collection strategies, model fine-tuning techniques, and the future of agent authentication. Join us for an in-depth conversation about why 2025 might be the breakthrough year for AI agents. Check out MultiOn: https://www.multion.ai/ Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. RECOMMENDED PODCAST: Unpack Pricing - Dive into the dark arts of SaaS pricing with Metronome CEO Scott Woody and tech leaders. Learn how strategic pricing drives explosive revenue growth in today's biggest companies like Snowflake, Cockroach Labs, Dropbox and more. Apple: https://podcasts.apple.com/us/podcast/id1765716600 Spotify: https://open.spotify.com/show/38DK3W1Fq1xxQalhDSueFg CHAPTERS: (00:00:00) Teaser (00:00:40) About the Episode (00:04:10) The Rise of AI Agents (00:06:33) Open-Ended vs On-Rails (00:10:00) Agent Architecture (00:12:01) AI Learning & Feedback (00:14:01) Data Collection (Part 1) (00:18:27) Sponsors: Oracle Cloud Infrastructure (OCI) | SelectQuote (00:20:51) Data Collection (Part 2) (00:22:25) Self-Play & Rewards (00:25:04) Model Strategy & Agent Q (00:33:28) Sponsors: Weights & Biases RAG++ (00:34:39) Understanding Agent Q (00:43:16) Search & Learning (00:45:39) Benchmarks vs Reality (00:50:18) Positive Transfer & Scale (00:51:47) Fine-Tuning Strategies (00:55:16) Vision Strategy (01:00:16) Authentication & Security (01:03:48) Future of AI Agents (01:16:14) Cost, Latency, Reliability (01:19:30) Avoiding the Bitter Lesson (01:25:58) Agent-Assisted Future (01:27:11) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

In this episode of The Cognitive Revolution, Nathan explores groundbreaking perspectives on AI alignment with MIT PhD student Tan Zhi Xuan. We dive deep into Xuan's critique of preference-based AI alignment and their innovative proposal for role-based AI systems guided by social consensus. The conversation extends into their fascinating work on how AI agents can learn social norms through Bayesian rule induction. Join us for an intellectually stimulating discussion that bridges philosophical theory with practical implementation in AI development. Check out: "Beyond Preferences in AI Alignment" paper: https://arxiv.org/pdf/2408.16984 "Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games" paper: https://arxiv.org/pdf/2402.13399 Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Notion: Notion offers powerful workflow and automation templates, perfect for streamlining processes and laying the groundwork for AI-driven automation. With Notion AI, you can search across thousands of documents from various platforms, generating highly relevant analysis and content tailored just for you - try it for free at https://notion.com/cognitiverevolution Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive RECOMMENDED PODCAST: Unpack Pricing - Dive into the dark arts of SaaS pricing with Metronome CEO Scott Woody and tech leaders. Learn how strategic pricing drives explosive revenue growth in today's biggest companies like Snowflake, Cockroach Labs, Dropbox and more. Apple: https://podcasts.apple.com/us/podcast/id1765716600 Spotify: https://open.spotify.com/show/38DK3W1Fq1xxQalhDSueFg CHAPTERS: (00:00:00) Teaser (00:01:09) About the Episode (00:04:25) Guest Intro (00:06:25) Xuan's Background (00:12:03) AI Near-Term Outlook (00:17:32) Sponsors: Notion | Weights & Biases RAG++ (00:20:18) Alignment Approaches (00:26:11) Critiques of RLHF (00:34:40) Sponsors: Oracle Cloud Infrastructure (OCI) (00:35:50) Beyond Preferences (00:40:27) Roles and AI Systems (00:45:19) What AI Owes Us (00:51:52) Drexler's AI Services (01:01:08) Constitutional AI (01:09:43) Technical Approach (01:22:01) Norms and Deviations (01:32:31) Norm Decay (01:38:06) Self-Other Overlap (01:44:05) Closing Thoughts (01:54:23) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

In this episode of No Priors, Sarah is joined by Aidan Gomez, cofounder and CEO of Cohere. Aidan reflects on his journey to co-authoring the groundbreaking 2017 paper, “Attention is All You Need,” during his internship, and shares his motivations for building Cohere, which delivers AI-powered language models and solutions for businesses. The discussion explores the current state of enterprise AI adoption and Aidan's advice for companies navigating the build vs. buy decision for AI tools. They also examine the drivers behind the flattening of model improvements and discuss where large language models (LLMs) fall short for predictive tasks. The conversation explores what the market has yet to account for in the rapidly evolving AI ecosystem, as well as Aidan's personal perspectives on AGI—what it might look like and when it could arrive. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AidanGomez Show Notes: 0:00 Introduction 0:36 Co-authoring “Attention is all you need” 2:27 Leaving Google and founding Cohere 4:04 Cohere's mission and models 6:15 Pitfalls of current AI  8:14 How enterprises are deploying AI today 10:58 Build vs. buy strategy for AI tools 14:37 Barriers to enterprise adoption  20:04 Which types of companies should pretrain models? 24:25 Addressing flaws in open-source models 25:12 Current and expected progress in scaling laws 29:54 Advances in multi-step problem solving and reasoning 32:29  Key drivers behind the flattening curve of model improvements  36:25 Exploring AGI 39:59 Limitations of LLMs 42:10 What the market has mispriced

Aidan Gomez is the co-author of the "Attention Is All You Need" paper that launched the AI revolution and CEO of Cohere, an enterprise AI company. Gomez joins Big Technology to discuss the myths, facts, and realities of today's AI landscape. Tune in to hear why the real value of AI isn't in flashy consumer apps but in automating crucial back-office processes that could save businesses billions. We also cover the truth about AI capabilities, the likelihood of AGI, synthetic data training, and whether an intelligence explosion is possible. Hit play for a refreshingly grounded discussion about where AI is actually making an impact, from one of the field's pioneering voices. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here's 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Artificial intelligence may not be as smart as humans — at least not yet — but the technology is progressing faster than Aidan Gomez ever imagined. Now, the Cohere CEO says the trick is convincing people and companies to embrace it. On POLITICO Tech, Gomez sits down with host Steven Overly to talk about what that will take and how fast it can happen. Learn more about your ad choices. Visit megaphone.fm/adchoices

A bunch of Apple stories today. FDA approval for sleep apnea detection for the watch. Signs of poor pre-order sales for the phone. And a quick review of the new Airpods. Also, how did Intel lose out on making the chips for the next gen Playstation. And are dating apps responsible for income inequality?Sponsors:ArcticWolf.com/registerLinks:Apple Watch sleep apnea detection gets FDA approval (TechCrunch)iPhone 16 first weekend pre-order analysis: estimated total sales of about 37 million units; Pro series demand lower than expected (Ming-Chi Kuo)France picks Sejourne as nominee for EU Commission after Breton clash (Reuters)Slack now lets users add AI agents from Asana, Cohere, Adobe, Workday and more (VentureBeat)Exclusive: How Intel lost the Sony PlayStation business (Reuters)Apple AirPods 4 review: defying expectations (The Verge)Online Dating Caused a Rise in US Income Inequality, Research Paper Shows (Bloomberg)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.