Podcasts about amplify partners

In this short segment of the Revenue Builders Podcast, John McMahon, John Kaplan and Sunil Dhaliwal, founder and general partner at Amplify Partners, explore the board's perspective on sales leadership. Sunil shares critical insights on selling in the early stages of a startup, knowing when to walk away from a deal, and the importance of transparency in sales forecasting. The conversation dives into the competencies that separate exceptional sales leaders from the rest—honesty, market assessment, and adaptability. If you're leading sales at a high-growth company or thinking about joining a startup, this episode is packed with must-know strategies.KEY TAKEAWAYS[00:00:47] The Pressure of Startup Sales – Why every deal feels like life or death[00:01:27] The "Man on an Island" Feeling – The loneliness of sales leadership in startups[00:02:29] The Risk of Overpromising – How inaccurate forecasts hurt the whole company[00:03:16] The Cost of Poor Sales Leadership – When inaccurate reporting sets a company back quarters[00:04:09] The Most Critical Competency – Why great sales leaders must accurately assess the battlefield[00:05:38] Market Awareness & Adaptability – Understanding product fit vs. chasing deals[00:06:02] The Power of Asking the Right Questions – Why startups need more than just "closing" skills[00:08:00] Honest Forecasting – How sales leaders should communicate realistic expectationsQUOTES[00:00:47] "A lot of people have a hard time backing away from a deal, but sometimes the right move is to walk away."[00:01:27] "Good sales leaders in startups embrace that lonely moment and are honest about what's going on."[00:03:16] "The worst thing you can do is overpromise. You're not just hurting yourself—you're setting the company back quarters."[00:04:09] "Great sales leaders must be able to accurately orient themselves on the battlefield and communicate what's happening."[00:05:01] "At a startup, the product won't work the way you want it to. It's not us vs. them—it's about understanding what the product can and should do."[00:06:36] "Startup sales isn't just about closing—it's about figuring out whether you should even be in this deal at all."Listen to the full conversation through the link below.https://revenue-builders.simplecast.com/episodes/a-board-members-perspective-on-sales-leadership-with-sunildhaliwalEnjoying the podcast? Sign up to receive new episodes straight to your inbox:https://hubs.li/Q02R10xN0Check out John McMahon's book here:Amazon Link: https://a.co/d/1K7DDC4Check out Force Management's Ascender platform here: https://my.ascender.co/Ascender/Force Management is hiring for a Sales Director. Apply here: https://hubs.li/Q02Zb8WG0Read Force Management's eBook: https://www.forcemanagement.com/roi-of-sales-messaging

In this episode of the Revenue Builders Podcast, hosts John McMahon and John Kaplan are joined by Sunil Dhaliwal, a seasoned venture capitalist and founding partner of Amplify Partners. The discussion dives deep into the challenges and skills required for startup sales leaders, emphasizing the importance of accuracy, adaptability, and honest communication. They explore the different growth stages of a company and how the role of a sales leader evolves with them. They also touch on recruiting, retaining talent, and preparing for board presentations. Dhaliwal shares his journey from Battery Ventures to founding Amplify Partners and provides insights into emerging trends and the impact of AI on various industries.ADDITIONAL RESOURCESConnect and learn more about Sunil Dhaliwal.https://www.linkedin.com/in/sunildhaliwal/Enjoying the podcast? Sign up to receive new episodes straight to your inbox: https://hubs.li/Q02R10xN0Check out John McMahon's book, The Qualified Sales Leader: https://www.amazon.com/Qualified-Sales-Leader-Proven-Lessons/dp/0578895064/HERE ARE SOME KEY SECTIONS TO CHECK OUT[00:01:52] The Challenges of Startup Sales Leadership[00:02:49] The Importance of Accuracy in Sales Leadership[00:04:03] Finding the Right Sales Leader for Startups[00:05:47] Navigating the Maze of Early-Stage Sales[00:08:23] Communicating with the Board: Honesty and Accuracy[00:11:29] Key Competencies for Startup Sales Leaders[00:19:14] Presenting to the Board: Best Practices[00:24:26] Establishing an Effective Operating Rhythm[00:30:43] Seeking Advice from Experienced Leaders[00:31:36] Key Metrics for Young Companies[00:32:07] Importance of New Logo Growth[00:33:58] Retention and Sales Productivity[00:34:32] Challenges in Scaling Sales Teams[00:42:51] Stages of Company Growth[00:46:32] The Role of a CRO[00:50:27] Energy Management in Leadership[00:56:33] Founding Amplify Partners[01:00:03] Identifying Emerging Trends[01:02:52] The Future of AI in BusinessHIGHLIGHT QUOTES[00:03:19] "Their job is accurately conveying what's happening out there. Where are we good? Where are we bad?"[00:12:03] "At different stage companies, if the product's not working, I can complain about it in a startup company. The product is not going to work the way everybody wants it to work."[00:13:32] "Early stage companies, the mission for a CRO is more about discovery and asking questions rather than simply closing deals."[00:17:39] "Your biggest problem is somewhere between your CEO and founder, your board, there is stuff that they believe that isn't true."

Great news - Season 6 Episode 146 Pulse of AI Podcast is live!  Luma AI's founding team member Sam Sinha joins podcast host Jason Stoughton to talk about Luma AI's growth from a team of 5 to the juggernaut behind the Dream Machine video creativity tool, the essence and future of creativity in an AI powered and co-piloted world, the types of people they are looking to hire and so much more! To be notified about future conversations with the leaders of the AI revolution sign up for our newsletter at www.thepulseofai.com   

This week on Generative Now, Lightspeed Partner and host Michael Mignano talks to Lisha Li, Founder and CEO of Rosebud AI. Rosebud AI allows users to generate video with a few simple prompts by leveraging AI for every aspect of game design. Lisha shares her diverse background from academia to venture capital and acting, leading up to the inception of Rosebud. Michael and Lisha talk about lessons learned when developing consumer AI products and the different iterations of Rosebud AI and its evolution including stock photos and the viral app TokkingHeads. Lisha is the Founder and CEO of Rosebud AI. She is a former principal at Amplify Partners. Lisha completed her PhD at UC Berkeley focusing on deep learning and probability applied to the problem of clustering in graphs. While at Berkeley she also did statistical consulting, advising on methods and analysis for experimentation and interpretation, and interned as a data scientist at Pinterest and Stitch Fix. She earned her Master of Science in Mathematics at the University of Toronto, with Highest Distinction advised by Prof. Balazs Szegedy in the area of Graph Limits. Episode Chapters (00:00) Introduction to Lisha Li and Rosebud AI (00:47) Lisha Li's Career: Academia, Acting, VC, and AI (07:54) The Genesis of Rosebud AI: Creating a Creative Consumer AI Product  (12:14) Navigating Game Development with AI (17:43) Democratizing Game Creation with Rosebud (31:20) Building Rosebud's AI-Powered Platform (35:56) Looking Ahead: The Future of AI in Gaming  Stay in touch: www.lsvp.com X: https://twitter.com/lightspeedvp LinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/ Instagram: https://www.instagram.com/lightspeedventurepartners/ Subscribe on your favorite podcast app: generativenow.co Email: generativenow@lsvp.com The content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.

The visionary story of dbt Labs, formerly known as Fishtown Analytics, is a tale of remarkable innovation, growth, and adaptability. Founded by Drew Banin with a passion for data and a desire to make data teams an essential part of every organization, dbt Labs has been leading the charge in data transformation. The venture hs attracted funding from top-tier investors like Amplify Partners, Sequoia Capital, Coatue, Tiger Global, and Andreessen Horowitz.

We're joined by Sarah Catanzaro, General Partner at Amplify Partners and one of the leading investors in AI, ML, and data to talk about the startup landscape, LLMs, and more.

Renee Shah is a partner at Amplify Partners, an early stage venture capital firm. We discuss some broad industry trends: Edge, Wasm, Distributed Systems, Functional Programming, and much more! Discuss this episode: https://discord.gg/nPa76qF

Sign up for the next LLM in production conference here: https://go.mlops.community/LLMinprod Watch all the talks from the first conference: https://go.mlops.community/llmconfpart1 // Abstract In this panel discussion, the topic of the cost of running large language models (LLMs) is explored, along with potential solutions. The benefits of bringing LLMs in-house, such as latency optimization and greater control, are also discussed. The panelists explore methods such as structured pruning and knowledge distillation for optimizing LLMs. OctoML's platform is mentioned as a tool for the automatic deployment of custom models and for selecting the most appropriate hardware for them. Overall, the discussion provides insights into the challenges of managing LLMs and potential strategies for overcoming them. // Bio Lina Weichbrodt Lina is a pragmatic freelancer and machine learning consultant that likes to solve business problems end-to-end and make machine learning or a simple, fast heuristic work in the real world. In her spare time, Lina likes to exchange with other people on how they can implement best practices in machine learning, talk to her at the Machine Learning Ops Slack: shorturl.at/swxIN. Luis Ceze Luis Ceze is Co-Founder and CEO of OctoML, which enables businesses to seamlessly deploy ML models to production making the most out of the hardware. OctoML is backed by Tiger Global, Addition, Amplify Partners, and Madrona Venture Group. Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has taught for 15 years. Luis co-directs the Systems and Architectures for Machine Learning lab (sampl.ai), which co-authored Apache TVM, a leading open-source ML stack for performance and portability that is used in widely deployed AI applications. Luis is also co-director of the Molecular Information Systems Lab (misl.bio), which led pioneering research in the intersection of computing and biology for IT applications such as DNA data storage. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, and the Wall Street Journal. Ceze is a Venture Partner at Madrona Venture Group and leads their technical advisory board. Jared Zoneraich Co-Founder of PromptLayer, enabling data-driven prompt engineering. Compulsive builder. Jersey native, with a brief stint in California (UC Berkeley '20) and now residing in NYC. Daniel Campos Hailing from Mexico Daniel started his NLP journey with his BS in CS from RPI. He then worked at Microsoft on Ranking at Bing with LLM(back when they had 2 commas) and helped build out popular datasets like MSMARCO and TREC Deep Learning. While at Microsoft he got his MS in Computational Linguistics from the University of Washington with a focus on Curriculum Learning for Language Models. Most recently, he has been pursuing his Ph.D. at the University of Illinois Urbana Champaign focusing on efficient inference for LLMs and robust dense retrieval. During his Ph.D., he worked for companies like Neural Magic, Walmart, Qualtrics, and Mendel.AI and now works on bringing LLMs to search at Neeva. Mario Kostelac Currently building AI-powered products in Intercom in a small, highly effective team. I roam between practical research and engineering but lean more towards engineering and challenges around running reliable, safe, and predictable ML systems. You can imagine how fun it is in LLM era :). Generally interested in the intersection of product and tech, and building a differentiation by solving hard challenges (technical or non-technical). Software engineer turned into Machine Learning engineer 5 years ago.

Das Startup Magic hat einer Series A 23 Millionen US-Dollar unter der Leitung von Alphabets CapitalG und mit Beteiligung von Elad Gil, Nat Friedman und Amplify Partners erhalten. Die Plattform, die noch nicht allgemein verfügbar ist, soll Software-Ingenieuren beim Schreiben, Überprüfen und Debuggen von Code helfen und kann in natürlicher Sprache kommunizieren und mit Anwendern zusammenarbeiten. Das Ziel von Magic ist es, die Kosten und den Zeitaufwand für die Softwareentwicklung zu reduzieren. Der CEO von Magic, Eric Steinberger, sagt, dass das Tool aufgrund einer neuen neuronalen Netzwerkarchitektur sogar mehr kann als der Copilot von GitHub.

Sarah Catanzaro is a General Partner at Amplify Partners, and one of the leading investors in AI and ML. Her investments include RunwayML, OctoML, and Gantry.Sarah and Lukas discuss lessons learned from the "AI renaissance" of the mid 2010s and compare the general perception of ML back then to now. Sarah also provides insights from her perspective as an investor, from selling into tech-forward companies vs. traditional enterprises, to the current state of MLOps/developer tools, to large language models and hype bubbles.Show notes (transcript and links): http://wandb.me/gd-sarah-catanzaro---⏳ Timestamps: 0:00 Intro1:10 Lessons learned from previous AI hype cycles11:46 Maintaining technical knowledge as an investor19:05 Selling into tech-forward companies vs. traditional enterprises25:09 Building point solutions vs. end-to-end platforms36:27 LLMS, new tooling, and commoditization44:39 Failing fast and how startups can compete with large cloud vendors52:31 The gap between research and industry, and vice versa1:00:01 Advice for ML practitioners during hype bubbles1:03:17 Sarah's thoughts on Rust and bottlenecks in deployment1:11:23 The importance of aligning technology with people1:15:58 Outro---

Produced by Foundersuite (www.foundersuite.com), "How I Raised It" goes behind the scenes with startup founders and investors who have raised capital. This episode is with Jennifer Smith of Scribe (https://scribehow.com/), a platform that helps you turn any process into a step-by-step guide. In this episode, we discuss her time in management consulting (and whether consultants make good founders), how she ran a really tight, 2-week process, raising capital while pregnant, how fundraising is like dating, and much more. Scribe has raised a total of $30 million in funding including a $22M Series A and an $8M seed round. The Series A was led by Tiger Global Management. Other investors include Amplify Partners, who led the seed, along with Haystack Ventures, XYZ Ventures, AME Cloud Ventures, Morado Ventures, and SEV. Angel investors include John Thompson (former chairman of Microsoft), Scott Belsky (Chief Product Officer at Adobe), Nick Mehta (CEO of Gainsight) and Eric Wu (CEO of Opendoor). How I Raised It is produced by Foundersuite, makers of software to raise capital and manage investor relations. Foundersuite's customers have raised over $9.7 Billion since 2016. Create a free account at www.foundersuite.com.

Dan Lorenc is Founder & CEO of Chainguard, the platform to secure your software supply chain. Chainguard supports many popular open source projects such as Sigstore, SLSA, and Tekton. Chainguard has raised $55M from investors including Sequoia and Amplify Partners. In this episode, we discuss the importance of market education when creating a new category of software, assessing market timing when launching your company, some of Chainguard's unique content strategies, and more!

MLOps Coffee Sessions #121 with Luis Ceze, CEO and Co-founder of OctoML, Bringing DevOps Agility to ML co-hosted by Mihail Eric. // Abstract There's something about this idea where people see a future where you don't need to think about infrastructure. You should just be able to do what you do and infrastructure happens. People understand that there is a lot of complexity underneath the hood and most data scientists or machine learning engineers start deploying things and shouldn't have to worry about the most efficient way of doing this. // Bio Luis Ceze is Co-Founder and CEO of OctoML, which enables businesses to seamlessly deploy ML models to production making the most out of the hardware. OctoML is backed by Tiger Global, Addition, Amplify Partners, and Madrona Venture Group. Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has taught for 15 years. Luis co-directs the Systems and Architectures for Machine Learning lab (sampl.ai), which co-authored Apache TVM, a leading open-source ML stack for performance and portability that is used in widely deployed AI applications. Luis is also co-director of the Molecular Information Systems Lab (misl.bio), which led pioneering research in the intersection of computing and biology for IT applications such as DNA data storage. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, and the Wall Street Journal. Ceze is a Venture Partner at Madrona Venture Group and leads their technical advisory board. // MLOps Jobs board https://mlops.pallet.xyz/jobs MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Landing page: https://octoml.ai/ The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics by Daniel James Brown: https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/ Connect with Luis on LinkedIn: https://www.linkedin.com/in/luis-ceze-50b2314/ Timestamps: [00:00] Introduction to Luis Ceze [06:28] MLOps does not exist [10:41] Semantics argument [16:25] Parallel programming standpoint [18:09] TVM [22:51] Optimizations [24:18] TVM in the ecosystem [27:10] OctoML's further step [30:42] Value chain [33:58] Mature players [35:48] Talking to SRE's and Machine Learning Engineers [36:32] Building OctoML [40:20] My Octopus Teacher [42:15] Environmental effects of Sustainable Machine Learning [44:50] Bridging the gap from OctoML to biological mechanisms [50:02] Programmability [57:13] Academia making the impact [59:40] Rapid fire questions [1:03:39] Wrap up

Today I'm chatting with Emilie Shario, a Data Strategist in Residence at Amplify Partners. Emilie thinks data teams should operate like product teams. But what led her to that conclusion, and how has she put the idea into practice? Emilie answers those questions and more, delving into what kind of pushback and hiccups someone can expect when switching from being data-driven to product-driven and sharing advice for data scientists and analytics leaders.   Highlights / Skip to:   Answering the question “whose job is it” (5:18) Understanding and solving problems instead of just building features people ask for (9:05) Emilie explains what Amplify Partners is and talks about her work experience and how it fuels her perspectives on data teams (11:04) Emilie and I talk about the definition of data product (13:00) Emilie talks about her approach to building and training a data team (14:40) We talk about UX designers and how they fit into Emilie's data teams (18:40) Emilie talks about the book and blog “Storytelling with Data” (21:00) We discuss the push back you can expect when trying to switch a team from being data driven to being product driven (23:18) What hiccups can people expect when switching to a product driven model (30:36) Emilie's advice for data scientists and and analyst leaders (35:50) Emilie explains what Locally Optimistic is (37:34)   Quotes from Today's Episode “Our thesis is…we need to understand the problems we're solving before we start building solutions, instead of just building the things people are asking for.” — Emilie (2:23)   “I've seen this approach of flipping the ask on its head—understanding the problem you're trying to solve—work and be more successful at helping drive impact instead of just letting your data team fall into this widget builder service trap.” — Emilie (4:43)   “If your answer to any problem to me is, ‘That's not my job,' then I don't want you working for me because that's not what we're here for. Your job is whatever the problem in front of you that needs to be solved.” — Emilie (7:14)   “I don't care if you have all of the data in the world and the most talented machine learning engineers and you've got the ability to do the coolest new algorithm fancy thing. If it doesn't drive business impact, it doesn't matter.” — Emilie (7:52)   “Data is not just a thing that anyone can do. It's not just about throwing numbers in a spreadsheet anymore. It's about driving business impact. But part of how we drive business impact with data is making it accessible. And accessible isn't just giving people the numbers, it's also communicating with it effectively, and UX is a huge piece of how we do that.” — Emilie (19:57)   “There are no null choices in design. Someone is deciding what some other human—a customer, a client, an internal stakeholder—is going to use, whether it's a React app, or a Power BI dashboard, or a spreadsheet dump, or whatever it is, right? There will be an experience that is created, whether it is intentionally created or not.” — Brian (20:28)   “People will think design is just putting in colors that match together, like, or spinning the color wheel and seeing what lands. You know, there's so much more to it. And it is an expertise; it is a domain that you have to develop.” — Emilie (34:58)   Links Referenced: Blog post by Rifat Majumder storytellingwithdata.com Experiencing Data Episode 28 with Cole Nussbaumer Knaflic locallyoptimistic.com Twitter: @emilieschario

Hugo speaks with Sarah Catanzaro, General Partner at Amplify Partners, about investing in data science and machine learning tooling and where we see progress happening in the space. Sarah invests in the tools that we both wish we had earlier in our careers: tools that enable data scientists and machine learners to collect, store, manage, analyze, and model data more effectively. As you'll discover, Sarah identifies as a scientist first and an investor second and still believes that her mission is to enable companies to become data-driven and to generate ROI through machine and statistical learning. In her words, she's still that cuckoo kid who's ranting and raving about how data and AI will shift every tide. In this conversation, we talk about what scientific inquiry actually is and the elements of playfulness and seriousness it necessarily involves, and how it can be used to generate business value. We talk about Sarah's unorthodox path from a data scientist working in defense to her time at Palantir and how that led her to build out a data team and function for a venture capital firm and then to becoming a VC in the data tooling space. We then really dive into the data science and machine learning tooling space to figure out why it's so fragmented: we look to the data analytics stack and software engineering communities to find historical tethers that may be useful. We discuss the moving parts that led to the establishment of a standard, a system of record, and clearly defined roles in analytics and what we can learn from that for machine learning! We also dive into the development of tools, workflows, and division of labour as partial exercises in pattern recognition and how this can be at odds with the variance we see in the machine learning landscape, more generally! Two take-aways are that we need best practices and we need more standardization. We also discussed that, with all our focus and conversations on tools, what conversation we're missing and Sarah was adamant that we need to be focusing on questions, not solutions, and even questioning what ML is useful for and what it isn't, diving into a bunch of thoughtful and nuanced examples. I'm also grateful that Sarah let me take her down a slightly dangerous and self-critical path where we riffed on both our roles in potentially contributing to the tragedy of commons we're all experiencing in the data tooling landscape, me working in tool building, developer relations, and in marketing, and Sarah in venture capital.

This week, Sarah Catanzaro, General Partner at Amplify Partners joins Jon for an episode that dives into the venture capital side of data science. Learn how to fund your data science business idea, take note of what start-ups can do to survive or raise capital in the current economic climate, and discover how to break into the field of venture capital yourself. In this episode you will learn: • Angel vs. venture capital vs. private equity investment [7:27] • How early-stage investment is made prior to a firm having product-market fit [14:33] • How to pick winners in early-stage investments [28:08] • Tricks to accelerating from a data science idea to obtaining funding [36:21] • Observational causal inference [44:01] • How to get involved in venture capital [47:37] Additional materials: www.superdatascience.com/601

Highlights from this week's conversation include:Emilie's background and career journey (3:00)Hypergrowth at GitLab (5:23)Being close to the money in data (9:50)Big things taken from GitLab to Netlify (13:00)Defining “data organization” (17:53)The first roles you should hire for (22:06)Defining “analytics engineer” (23:44)One role to bridge different needs (27:26)Why data analysts are needed (30:51)How to avoid a kitchen sink of data (40:20)Data engineer archetype (45:48)Data roles crossing over (48:09)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Eric and Kostas preview their upcoming conversation with Emilie Schario from Amplify Partners.

In episode 12 of The Right Track, Stefania Olafsdottir speaks with Emilie Schario of Amplify Partners. Together they discuss impact-driven development, actionable tactics for prioritizing relationships within data teams, the quandary of data job titles, and insights on improving data trust.

In episode 12 of The Right Track, Stefania Olafsdottir speaks with Emilie Schario of Amplify Partners. Together they discuss impact-driven development, actionable tactics for prioritizing relationships within data teams, the quandary of data job titles, and insights on improving data trust.

In episode 12 of The Right Track, Stefania Olafsdottir speaks with Emilie Schario of Amplify Partners. Together they discuss impact-driven development, actionable tactics for prioritizing relationships within data teams, the quandary of data job titles, and insights on improving data trust.

In episode 12 of The Right Track, Stefania Olafsdottir speaks with Emilie Schario of Amplify Partners. Together they discuss impact-driven development, actionable tactics for prioritizing relationships within data teams, the quandary of data job titles, and insights on improving data trust.

This week we discuss the rise of WASM, Cloudflare's Post Mortem, Oracle Cloud news and the future of CAPTCHAs. Plus, some thoughts on buzzwords, sprinklers and dogs. Runner-up Titles Chrome fixes everything Plateau of Productivity When the curve nopes. When the parabola yeets. First class SaaS Thanks for the legwork fool The cash cow has run out of milk Sign-a-tar Tumbler is now HIPAA compliant Working has its privileges Let go of this dog now Rundown Fermyon Launches With First Cloud-Native WebAssembly PaaS (https://www.globenewswire.com/news-release/2022/06/21/2466499/0/en/Fermyon-Launches-With-First-Cloud-Native-WebAssembly-PaaS-for-Developers-Creating-Microservice-Based-Apps-Raises-6-Million-Seed-Funding-Led-by-Amplify-Partners.html) Cloudflare Cloudflare Post Mortem (https://blog.cloudflare.com/cloudflare-outage-on-june-21-2022/) Massive Cloudflare outage caused by network configuration error (https://www.bleepingcomputer.com/news/technology/massive-cloudflare-outage-caused-by-network-configuration-error/) The Hardest Working Office Design In America Encrypts Your Data–With Lava Lamps (https://www.fastcompany.com/90137157/the-hardest-working-office-design-in-america-encrypts-your-data-with-lava-lamps) Oracle Oracle Versus Amazon: Oracle Will Rip and Replace AWS at Cerner (https://accelerationeconomy.com/cloud-wars/oracle-versus-amazon-oracle-will-rip-and-replace-aws-at-cerner/) Exclusive: TikTok moves U.S. user data to Oracle servers (https://www.reuters.com/technology/exclusive-tiktok-moves-us-user-data-oracle-servers-company-2022-06-17/) DocuSign CEO Dan Springer steps down (https://www.cnbc.com/2022/06/21/docusign-ceo-dan-springer-steps-down.html) Removing people from security Password policies of 120 websites (https://passwordpolicies.cs.princeton.edu/>

Emilie Schario is the Data Strategist in Residence at Amplify Partners – and also happens to be one of those people somehow able to fit 26 hours into a 24-hour day.  In this episode of Hands On, Emilie talks with Bryan about leadership, data, community, forging your own path and finding comfort within the chaos.  She also explains why the most useful data isn't really data at all, but rather the context in which it exists.  On top of all that, Emily shares great insight on growing a proactive mindset inside a team.  Join us on this episode of Hands On to uncover some truly expert perspectives on data analytics and community building.Hands On gives listeners an authentic look at how startup leaders drive success, growth and strategy.  The conversations are relaxed and sincere, offering an invaluable glimpse of what's 'under the hood' – and what it takes to excel in spaces that tend to be uncharted.  In each episode, our esteemed guests speak freely about the challenges, breakthroughs and lessons learned that have shaped their growth both personally and professionally.  Like picking something up and examining it from all angles, with Hands On we look closely at what it takes to build companies, careers and relationships.  Episode resources:Get in touch with Emilie Schario on Twitter and LinkedinAmplify Partners - WebsiteFollow Index Ventures on Linkedin, Twitter and TikTokThank you for listening to Hands On, brought to you by Index Ventures.  Don't hesitate to follow our hosts Molly and Bryan on social media to know more about them.  If you enjoyed this show, please like, share and leave a review to help us reach new audiences! This show is produced by StudioPod Media in San Francisco.  Our Producer is Justin Berardi and Nicole Genova is the Show Coordinator.  Editing and music provided by nodalab.

DeVaris Brown & Ali Hamidi are Co-founders of Meroxa, the platform for building real-time data pipelines. The company's open-source project Conduit lets users build and run their data pipelines. Meroxa has raised almost $20M from investors including Drive Capital, Root VC, and Amplify Partners.

Emilie Schario is a Data Strategist-in-residence at Amplify Partners. Previously, she was the Director of Data at Netlify, where she led 8% of the company's headcount, and was the first data analyst at many companies, including GitLab, Doist, and Smile Direct Club.In this episode, we cover a range of topics including:Emilie's journey into the world of data science:- How she entered the world of data science- Her learnings along the way- Why Locally Optimistic is her favorite data communityCareers, Jobs, and Interviews:- How can new professionals evaluate what area they like within data science- How should a data scientist look for jobs?- How do you interview people? - What are some of the red flags during hiring?- What should a data scientist do during the first 30 days of the job?Culture:- How should data science professionals talk to customers?- What does good data science culture look like?- How should first time managers think about imparting culture?Current and future trends:- What's your favorite resource for data science? And why?- What has been the biggest positive development in ML compared to 5 years ago? - Looking forward, what aspect of ML excites you the most?

Show Notes(01:51) Nick shared his formative experiences of her childhood — moving between different schools, becoming interested in Math, and graduating from UCLA at the age of 19.(05:45) Nick recalled working as a quant analyst focused on emerging market debt at BlackRock.(09:57) Nick went over his decision to join Airbnb as a data scientist on their growth team in 2014.(12:17) Nick discussed how data science could be used to drive community growth on the Airbnb platform.(16:35) Nick led the data architecture design and experimentation platform for Airbnb Trips, one of Airbnb's biggest product launches in 2016.(20:40) Nick provided insights on attributes of exceptional data science talent, given his time interviewing hundreds of candidates to build a data science team from 20 to 85+.(23:50) Nick went over his process of leveling up his product management skillset — leading Airbnb's Machine Learning teams and growing the data organization significantly.(26:56) Nick emphasized the importance of flexibility in his work routine.(29:27) Nick unpacked the technical and organizational challenges of designing and fostering the adoption of Bighead, Airbnb's internal framework-agnostic, end-to-end platform for machine learning.(34:54) Nick recalled his decision to leave Airbnb and become the Head of Data at Branch, which delivers world-class financial services to the mobile generation.(37:24) Nick unpacked key takeaways from his Bay Area AI meetup in 2019 called “ML Infrastructure at an Early Stage Startup” related to his work at Branch.(40:55) Nick discussed his decision to pursue a startup idea in the analytics space rather than the ML space.(43:36) Nick shared the founding story of Transform, whose mission is to make data accessible by way of a metrics store.(49:54) Nick walked through the four key capabilities of a metrics store: semantics, performance, governance, and interfaces + introduced Metrics Framework (Transform's capability to create company-wide alignment around key metrics that scale with an organization through a unified framework).(55:58) Nick unpacked Metrics Catalog — Transform's capability to eliminate repetitive tasks by giving everyone a single place to collaborate, annotate data charts, and view personalized data feeds.(59:57) Nick dissected Metrics API — Transform's capability to generate a set of APIs to integrate metrics into any other enterprise tools for enriched data, dimensional modeling, and increased flexibility.(01:02:41) Nick explained how metrics store fit into a modern data analytics stack(01:05:57) Nick shared valuable hiring lessons finding talents who fit with Transform's cultural values.(01:12:27) Nick shared the hurdles his team has to go through while finding early design partners for Transform.(01:15:38) Nick shared upcoming go-to-market initiatives that he's most excited about for Transform.(01:17:46) Nick shared fundraising advice for founders currently seeking the right investors for their startups.(01:20:45) Closing segment.Nick's Contact InfoLinkedInTwitterMediumTransform's ResourcesWebsiteBlogLinkedIn | TwitterMentioned ContentArticles + Talks“ML Infrastructure at an Early Stage” (March 2019)“Why We Founded Transform” (June 2021)“My Experience with Airbnb's Early Metrics Store” (June 2021)“The 4 Pillars of Our Workplace Culture” (Aug 2021)PeopleAirbnb's Metrics Repo Team (Paul Yang, James Mayfield, Will Moss, Jonathan Parks, and Aaron Keys)Maxime Beauchemin (Founder and CEO of Preset, Creator of Apache Airflow and Apache Superset)Emilie Schario (Data Strategist In Residence at Amplify Partners, Previously Head of Data at Netlify)Book“High-Output Management” (by Andy Grove)NotesMy conversation with Nick was recorded back in July 2021. Since then, many things have happened at Transform. I'd recommend:Registering for the Metrics Store Summit that will happen at the end of April 2022Reviewing the piece about 4 Pillars of Transform's Workplace CultureReading Nick's post on the brief history of the metrics storeExploring Transform's integrations with Mode, Hex, and Google SheetsAbout the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

How does data relate to pirates? Sarah Catanzaro, a partner at Amplify Partners, talks to Data Wranglers Joe Hellerstein and Jeffrey Heer about what's new and exciting in the modern data stack, machine learning and observable data. Her work has focused on startups that bring technological advances in machine intelligence and enterprise infrastructure to solve real-world problems. Having previously worked with the U.S. Secret Service, the Center for Advanced Defense Studies, and Palantir, Sarah learned how data can be used to understand and disrupt Somali pirate networks and other insurgent groups: like startups. As data practitioner-turned-venture capitalist, Sarah also offers insights about her career as a woman investor. #TheDataWranglers

The new year means it's time for another trek around the sun. It's also time to make a few predictions about what might change in data & analytics. Is it time for data practitioners to participate in boardroom conversations? Will we see data teams embedded in business units? What new technologies will become part of the modern data stack? Join Tim, Juan and Sarah Catanzaro from Amplify Partners,, to discuss what's in store in 2022. This episode will feature: Suggestions on organizational design that keeps data closer to the business teams. Ways to address data gaps in strategic decision making What was the best/worst prediction you made at the start of 2021?

Juan and Tim sit down for the Catalog & Cocktails Season Three Premier with Sarah Catanzaro from Amplify Partners to discuss what's in store in 2022. This Takeaway episode will feature the summary of the upcoming episode.

Your company has one definition for revenue across the organization, one definition of the customer, and one definition of sign-up. For people whose jobs are so defined by ensuring we're aligned, we can't seem to standardize on one definition for the Data Scientist. In this talk, Emilie Schario (Data Strategist-in-Residence at Amplify Partners and longtime dbt community member) proposes we lobby against the title Data Scientist, instead choosing some variation of the Core Four Data Roles: Data Analyst, Analytics Engineer, Data Engineer, and Machine Learning Engineer. Register to catch the rest of Coalesce, the Analytics Engineering Conference, at https://coalesce.getdbt.com. The Analytics Engineering Podcast is brought to you by dbt Labs.

Joining us on this installment of Talk Talent To Me is the Head of Talent at Amplify, Natasha Katoni. Prior to Amplify, Natasha was one of the very first technical recruiters at Segment, where she went on to become Technical Recruiting Manager. In today's episode, she shares her somewhat unexpected career journey and what she has learned about talent, scalable processes, and interesting problems along the way. We discuss the importance of really listening to candidates without bringing your own biases into the mix, why building strong relationships with external agencies and platforms is an essential part of the recruitment process, and why there is no one pathway in talent. Natasha speaks candidly about some of her personal motivations, successes, and shortcomings as she has progressed through her career. She even turns her own processes back on herself as a candidate and shares her advice for those currently on the job hunt: be thoughtful and process oriented, but don't forget to trust your gut! Key Points From This Episode: Natasha shares her journey at Segment, where she was one of their early recruiters. Some of the scalable process that were implemented as Segment's talent operations grew. Learn more about Segment's focus on pre-selling, candidate motivations, timing, and tools. How Natasha decided whether or not a candidate was worth the time spent on them. Questions to ask technical talent that is in high demand, like what is motivating their search. What Natasha has noticed about how candidates express their motivations or priorities. The importance of listening to candidates without bringing your own biases to the table. The value of taking a step back and reassessing your systems and potential problems. Three golden metrics: onsite to offer, offer to close, and top of funnel interested rates. Where these processes can break down and the value of building strong relationships with external agencies and platforms. Natasha shares what she loves about working at Amplify; how her personal interests align. Hear about her transition from an internal operator role at Segment to VC with Amplify. Why there is no one pathway in talent; more on Natasha's less than traditional journey. Natasha weighs in on how talent partners can be most useful to early-stage companies. Her personal motivation for accepting her current position at Amplify: interesting problems.  Natasha emphasizes the importance of trusting your gut while also being thoughtful and process oriented when looking for a job.   Tweetables:   “One of the mistakes that companies constantly make is they talk about moving as quickly as possible. That's not always helpful. You don't always want to move as fast as possible. What you want to do is capitalize on the recency bias.” — Natasha Katoni [0:07:18]   “When you want to experiment, when you want to build, when you want to fix a system that isn't working how you want it to, I would try to speak the language of the person that you're trying to convince.” — Natasha Katoni [0:16:14]   “Spending time building those relationships with external people to be able to support and add to your internal recruiting team when you need it – but also not bloat your recruiting team so that it's so big that you have to lay people off – that's the key.” — Natasha Katoni [0:22:07]   “You should be very thoughtful and process oriented when looking for a job and use the checklist process on yourself as a candidate as well.” — Natasha Katoni [0:36:25]   Links Mentioned in Today's Episode: Natasha Katoni on LinkedIn Amplify Hired Triplebyte Connery Consulting Talk Talent to Me

Show Notes(01:48) Sarah talked about the formative experiences of her upbringing: growing up interested in the natural sciences and switching focus on terrorism analysis after experiencing the 9/11 tragedy with her own eyes.(04:07) Sarah discussed her experience studying International Security Studies at Stanford and working at the Center for International Security and Cooperation.(07:15) Sarah recalled her first job out of college as a Program Director at the Center for Advanced Defense Studies — collaborating with academic researchers to develop computational approaches that counter terrorism and piracy.(09:48) Sarah went over her time as a cyber-intelligence analyst at Cyveillance, which provided threat intelligence services to enterprises worldwide.(12:22) Sarah walked over her time at Palantir as an embedded analyst, where she observed the struggles that many agencies had with data integration and modeling challenges.(15:26) Sarah unpacked the challenges of building out the data team and applying the data work at Mattermark.(20:15) Sarah shared her opinion on the career trajectory for data analysts and data scientists, given her experience as a manager for these roles.(23:43) Sarah shared the power of having a peer group and building a team culture that she was proud of at Mattermark.(26:41) Sarah joined Canvas Ventures as a Data Partner in 2016 and shared her motivation for getting into venture capital.(29:47) Sarah revealed the secret sauce to succeed in venture — stamina.(32:00) Sarah has been an investor at Amplify Partners since 2017 and shared what attracted her about the firm's investment thesis and the team.(35:28) Sarah walked through the framework she used to prove her value upfront as the new investor at Amplify.(38:35) Sarah shared the details behind her investment on the Series A round for OctoML, a Seattle-based startup that leverages Apache TVM to enable their clients to simply, securely, and efficiently deploy any model on any hardware backend.(44:39) Sarah dissected her investment on the seed round for Einblick, a Boston-based startup that builds a visual computing platform for BI and analytics use cases.(48:45) Sarah mentioned the key factors inspiring her investment in the seed round for Metaphor Data, a meta-data platform that grew out of the DataHub open-source project developed at LinkedIn.(53:57) Sarah discussed what triggered her investment in the Series A round for Runway, a New York-based team building the next-generation creative toolkit powered by machine learning.(58:36) Sarah unpacked the advice she has been giving her portfolio companies in hiring decisions and expanding their founding team (and advice they should ignore).(01:01:29) Sarah went over the process of curating her weekly newsletter called Projects To Know (active since 2019).(01:05:00) Sarah predicted the 3 trends in the data ecosystem that will have a disproportionately huge impact in the future.(01:11:15) Closing segment.Sarah's Contact InfoAmplify PageTwitterLinkedInMediumAmplify Partners' ResourcesWebsiteTeamPortfolioBlogMentioned ContentBlog PostsOur Investment in OctoMLAnnouncing Our Investment in EinblickOur Investment in Metaphor DataOur Series A Investment in RunwayPeopleSunil Dhaliwal (General Partner at Amplify Partners)Mike Dauber (General Partner at Amplify Partners)Lenny Pruss (General Partner at Amplify Partners)Mike Volpi (Co-Founder and Partner at Index Ventures)Gary Little (Co-Founder and General Partner at Canvas Ventures)Book“Zen and the Art of Motorcycle Maintenance” (by Robert Pirsig)New UpdatesSince the podcast was recorded, Sarah has been keeping her stamina high!Her investments in Hex (data workspace for teams) and Meroxa (real-time data platform) have been made public.She has also spoken at various panels, including SIGMOD, REWORK, University of Chicago, and Utah Nerd Nights.Be sure to follow @sarahcat21 on Twitter to subscribe to her brain on the intersection of data, VC, and startups!

Show Notes(01:48) Sarah talked about the formative experiences of her upbringing: growing up interested in the natural sciences and switching focus on terrorism analysis after experiencing the 9/11 tragedy with her own eyes.(04:07) Sarah discussed her experience studying International Security Studies at Stanford and working at the Center for International Security and Cooperation.(07:15) Sarah recalled her first job out of college as a Program Director at the Center for Advanced Defense Studies — collaborating with academic researchers to develop computational approaches that counter terrorism and piracy.(09:48) Sarah went over her time as a cyber-intelligence analyst at Cyveillance, which provided threat intelligence services to enterprises worldwide.(12:22) Sarah walked over her time at Palantir as an embedded analyst, where she observed the struggles that many agencies had with data integration and modeling challenges.(15:26) Sarah unpacked the challenges of building out the data team and applying the data work at Mattermark.(20:15) Sarah shared her opinion on the career trajectory for data analysts and data scientists, given her experience as a manager for these roles.(23:43) Sarah shared the power of having a peer group and building a team culture that she was proud of at Mattermark.(26:41) Sarah joined Canvas Ventures as a Data Partner in 2016 and shared her motivation for getting into venture capital.(29:47) Sarah revealed the secret sauce to succeed in venture — stamina.(32:00) Sarah has been an investor at Amplify Partners since 2017 and shared what attracted her about the firm's investment thesis and the team.(35:28) Sarah walked through the framework she used to prove her value upfront as the new investor at Amplify.(38:35) Sarah shared the details behind her investment on the Series A round for OctoML, a Seattle-based startup that leverages Apache TVM to enable their clients to simply, securely, and efficiently deploy any model on any hardware backend.(44:39) Sarah dissected her investment on the seed round for Einblick, a Boston-based startup that builds a visual computing platform for BI and analytics use cases.(48:45) Sarah mentioned the key factors inspiring her investment in the seed round for Metaphor Data, a meta-data platform that grew out of the DataHub open-source project developed at LinkedIn.(53:57) Sarah discussed what triggered her investment in the Series A round for Runway, a New York-based team building the next-generation creative toolkit powered by machine learning.(58:36) Sarah unpacked the advice she has been giving her portfolio companies in hiring decisions and expanding their founding team (and advice they should ignore).(01:01:29) Sarah went over the process of curating her weekly newsletter called Projects To Know (active since 2019).(01:05:00) Sarah predicted the 3 trends in the data ecosystem that will have a disproportionately huge impact in the future.(01:11:15) Closing segment.Sarah's Contact InfoAmplify PageTwitterLinkedInMediumAmplify Partners' ResourcesWebsiteTeamPortfolioBlogMentioned ContentBlog PostsOur Investment in OctoMLAnnouncing Our Investment in EinblickOur Investment in Metaphor DataOur Series A Investment in RunwayPeopleSunil Dhaliwal (General Partner at Amplify Partners)Mike Dauber (General Partner at Amplify Partners)Lenny Pruss (General Partner at Amplify Partners)Mike Volpi (Co-Founder and Partner at Index Ventures)Gary Little (Co-Founder and General Partner at Canvas Ventures)Book“Zen and the Art of Motorcycle Maintenance” (by Robert Pirsig)New UpdatesSince the podcast was recorded, Sarah has been keeping her stamina high!Her investments in Hex (data workspace for teams) and Meroxa (real-time data platform) have been made public.She has also spoken at various panels, including SIGMOD, REWORK, University of Chicago, and Utah Nerd Nights.Be sure to follow @sarahcat21 on Twitter to subscribe to her brain on the intersection of data, VC, and startups!

Listen now (46 min) | Amplify Partners has quickly become one of the true breakouts from the early emerging manager movement. Led by Sunil Dhaliwal, who started Amplify nearly a decade ago after a 14 year tenure at Battery Ventures, the firm has over $750MM in AUM and has invested in companies such as Datadog and Fastly. Get on the email list at ventureunlocked.substack.com

Coffee Sessions #33 with Sarah Catanzaro of Amplify Partners, MLOps Investments. //Bio Sarah Catanzaro is a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems. Her investments at Amplify include startups like RunwayML, Maze Design, OctoML, and Metaphor Data among others. Sarah also has several years of experience defining data strategy and leading data science teams at startups and in the defense/intelligence sector including through roles at Mattermark, Palantir, Cyveillance, and the Center for Advanced Defense Studies. //We had a wide-ranging discussion with Sarah, three takeaways stood out: The relationship between unstructured data and structured data is due for change. In most settings, you have some form of structured data (i.e. a metadata table) and unstructured data (i.e. images, text, etc.) Managing the relationship between these forms of data can constitute the bulk of MLOps. Because of this difficulty, Sarah forecasted new tooling arising to make data management easier. Academic benchmarks suffer from a lack of transparency on production/industry use cases. In conversation with Andrew Ng, Sarah shared her lesson that despite all the blame industry professionals place on academics for narrowly optimizing to benchmarks with little practical meaning, they also share the blame for making it difficult to create meaningful benchmarks. Companies are loath to share realistic data and the true context in which ML has to operate. MLOps is due for consolidation, especially as companies adopt platform-driven strategies. As many of you all know, there are tons and tons of MLOps tools out there. As more companies address these challenges, Sarah predicted that many of the point solutions would start to be consolidated into larger platforms. // Other Links https://amplifypartners.com/team/sarah/ https://projectstoknow.amplifypartners.com/ml-and-data https://twitter.com/sarahcat21/status/1360105479620284419 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Sarah on LinkedIn: https://www.linkedin.com/in/sarah-catanzaro-9770b98/

Talk about a rocketship… Fishtown Analytics, named for Philly’s hip namesake neighborhood, started this year with 16 employees. Next year, it’ll have over 100. I wanted to find out how CEO Tristan Handy is doing it. He Co-Founded Fishtown, which created the open-source data engineering platform named dbt (“data build tool”) that’s used by thousands of data analysts & engineers. dbt enables the modern analytics workflow by organizing, cleansing, and aggregating data so that it’s ready for analysis. It’s a big vision. So, it’s backed by tens of millions in Venture Capital by Sequoia Capital, Andreessen Horowitz, and Amplify Partners. In this 20-minute conversation, Tristan reveals how he’s scaling the team 6x this year. And how you can do the same.

Where is Venture Capital today? And how do you hack it? SaaStr CEO and Founder, Jason Lemkin, sits down with Sunil Dhaliwal, General Partner at Amplify Partners to discuss.

Bay Area-based Iron Ox today announced a $20 million Series B. The funding, led by Pathbreaker Venture and family office firms, brings the robotics company's total funding up to $45 million to date. A number of other investors also took part in the round, including Crosslink Capital, Amplify Partners, ENIAC Ventures, R7 Partners, Tuesday Ventures, […]

In this episode, Erasmus Elsner is talking to Sarah Catanzaro from Amplify Partners. As a partner at Amplify, Sarah focuses on startups that apply technological advances in machine intelligence and enterprise infrastructure to solve real-world problems. In this session we discuss her journey from data scientist to venture capital, her take on recent data sciences trends and her most recent investments, including OctoML, InterVenn Biosciences, Maze and Bayes.

Gremlin is a fast growing company in the chaos engineering space founded in 2016. The company has pioneered the space by offering failure-as-a-service. So far, Gremlin has raised almost $27m from the likes of Amplify Partners, Index Ventures and Redpoint. In this episode, Erasmus Elsner is joined by co-founder and CTO Matthew Fornaciari to talk about their founder, product and financing journey. Check out the Youtube version on: https://channel.sandhillroad.io

SHOW: 419DESCRIPTION: Brian talks with Sarah Catanzaro (@sarahcat21, Principal at @AmplifyPartners) about approaches to evaluate complex markets and business models. SHOW SPONSOR LINKS:PricingWire: Monetization & Pricing Strategy for Software & Technology InnovatorsPricingWire - Pricing Metric Decision GuideDatadog Homepage - Modern Monitoring and AnalyticsTry Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt[FREE] Try an IT Pro ChallengeGet 20% off VelocityConf passes using discount code CLOUDCLOUD NEWS OF THE WEEK:Australia, US negotiate CLOUD Act data swap pactSHOW INTERVIEW LINKS:Sarah’s Homepage at Amplify PartnersSHOW NOTES:Topic 1 - Welcome to the show. Tell about your background prior to joining Amplify Partners. Topic 2 - There’s an interesting thread that weaves its way through aspects of your career - being able to build intelligent models to understand and predict somewhat unstable environments. Can you walk us through the process of how those models and the analysis comes together? Topic 3 - There’s a level of responsibility through the supply-chain of our industry. I feel like VCs have a responsibility to not only spot trends, but also spot frauds. How does your background and experience help you determine good investments, or strong potential trends? Topic 4 - One of your focus areas is machine intelligence. What is the state of machine intelligence today, and where are some of the areas that encourage you that it can become more widely used? FEEDBACK?Email: show at thecloudcast dot netTwitter: @thecloudcastnet and @ServerlessCast

This week, Jon Foust and Michelle Casbon bring you another fascinating interview from our time at Next! Michelle and special guest Amanda were able to catch up with Paco Nathan of Derwen AI to talk about his experience at Next and learn what Derwen is doing to advance AI. Paco and Derwen have been working extensively on ways developer relations can be enhanced by machine learning. Along with O’Reilly Media, Derwen just completed three surveys, called ABC (AI, Big Data, and Cloud), to look at the adoption of AI and the cloud around the world. The particular interest in these studies is a comparison between countries who have been using AI, Big Data, and Cloud for years and countries who are just beginning to get involved. One of the most interesting things they learned is how much budget companies are allocating to machine learning projects. They also noticed that more and more large enterprises are moving, at least partially, to the cloud. One of the challenges Paco noticed was the difference between machine learning projects in testing versus how they act once they go live. Here, developers come across bias, ethical, and safety issues. Good data governance polices can help minimize these problems. Developing good data governance policies is complex, especially with security issues, but it’s an important conversation to have. In the process of computing the survey data, Paco discovered many big companies spend a lot of time with this issue and even employ checklists of requirements before projects can be made live. In his research, Paco also discovered that about 54% of companies are non-starters. Usually, their problems stem from tech debt and issues with company personnel who do not recognize the need for machine learning. The companies working toward integrating machine learning tend to have issues finding good staff. Berkeley is working to solve this problem by requiring data science classes of all students. But as Paco says, data science is a team sport that works well with a team of people from different disciplines. Paco is an advocate of mentoring, to help the next generation of data scientists learn and grow, and of unbundling corporate decision making to help advance AI. Amanda, Michelle, and Paco wrap up their discussion with a look toward how to change ML biases. People tend to blame ML for bias outcomes, but models are subject to data we feed in. Humans have to make decisions to work around that by looking at things from a different perspective and taking steps to avoid as much bias as we can. ML and humans can work together to find these biases and help remove them. Paco Nathan Paco Nathan is the Managing Parter at Derwen. He has 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Paco is also the Co-chair Rev. Advisor for Amplify Partners, Recognai, Primer AI, and Data Spartan. He was formerly the Director of Community Evangelism for Databricks and Apache Spark. Cool things of the week CERN recreated the Higgs discovery on GCP video To discover the Higgs yourself, check out the CERN open data portal site Fun facts from Michelle’s visit: Seven total, four main experiments ATLAS (largest, general-purpose) site CMS (prettiest, general-purpose) site ALICE (heavy-ion) site LHCb (interactions of b-hadrons, matter/antimatter asymmetry) site The French/Swiss border runs across the CERN property Streetview of CERN control center site CERN is the birthplace of the web Where the protons come from site Watch Particle Fever movie Interview Derwen, Inc. site Derwen, Inc. Blog blog Cloud Programming Simplified: A Berkeley View on Serverless Computing paper Apache Spark site Google Cloud Storage site Datastore site Kubeflow site Quicksilver site O’Reilly Media site Google Knowledge Graph site Jupyter site JupyterCon site The Economics of Artificial Intelligence site “Why Do Businesses Fail At Machine Learning?” by Cassie Kozyrkov video The Gutenberg Galaxy site Programmed Inequality site Question of the week Stadia Connect occurred last Thursday. What are some of the biggest announcements that came out of it? Where can you find us next? Jon is in New York for Games for Change. Michelle and Mark Mirchandani are back in San Francisco. Brian & Aja are at home in Seattle. Gabi is in Brazil. Sound Effect Attribution “Crowd laugh.wav” by tom_woysky of Freesound.org

Sara Catanzaro is a principal at Amplify Partners, a Venture Capital firm that specializes in early-stage companies innovating with Machine Learning and AI. Sarah helps guide founders and innovators because of her incredible expertise built over years using data science to innovate for both the private sector and protecting US National Security.

In this episode of the ARCHITECHT Show, Amplify Partners' Sunil Dhaliwal discusses a wide range of issues, starting with his firm's new $200 million fund focused on investing in technical founders. In addition to highlighting the opportunities and risks of that strategy, Dhaliwal also tackles a wide range of issues, including: the state of the database market; identifying good artificial intelligence startups; how big data became went from ideas to infrastructure; and the ongoing evolution of open source business models.

Mabl, a Boston-based startup from the folks who brought you Stackdriver, wants to change software testing using machine learning, and today it announced a $20 million Series B investment led by GV (formerly Google Ventures). Existing investors CRV and Amplify Partners also participated. As part of the deal, Karim Faris, general partner at GV will be joining the Mabl board. Today's investment comes on top of a $10 million Series A announced in February.

Produced by Foundersuite.com, "How I Raised It" goes behind the scenes with startup founders who have raised capital. This episode is with Jessica Chang, CEO of WeeCare, a developer of a SaaS based platform to start an in-home daycare. The company raised $4.2 million of Seed funding in a deal led by Social Capital. Wavemaker Partners, Fika Ventures, Amplify Partners, and Fuel Ventures also participated in the round. In this episode, Jessica talks about the emerging SoCal startup scene, aka "Silicon Beach"; picking solid pre-seed investors, what to do when a competitor is raising capital at the same time, fundraising tips from her investment banker days, and much more.

Aaron and Brian talk with Lenny Pruss (@lennypruss, Partner at Amplify Partners) about the evolution of application and infrastructure architectures, how AI/ML are radically changing how applications are designed, the new inputs to application systems, and how VCs are investing in companies that can augment new cloud services. Show Links: Lenny Pruss @ Amplify Partners Infrastructure 3.0: Building blocks for the AI revolution Software 2.0 Infrastructure 2.0 CNCF Cloud-Native Landscape Google AutoML AWS & Microsoft announce “Gluon” AWS DeepLens [PODCAST] @PodCTL - Containers | Kubernetes - RSS Feed, iTunes, Google Play, Stitcher, TuneIn and all your favorite podcast players [A CLOUD GURU] Get The Cloudcast Alexa Skill [A CLOUD GURU] A Cloud Guru Membership - Start your free trial. Unlimited access to the best cloud training and new series to keep you up-to-date on all things AWS. [A CLOUD GURU] FREE access to AWS Certification Exam Prep Guide - At A Cloud Guru, the #1 question received from students is "I want to pass the AWS cert exam, so where do I start?" This course is your answer. [FREE] eBook from O'Reilly Show Notes Topic 1 - Welcome to the show. Tell us about your background and how you eventually got into the VC side of the business. Topic 1a - Infrastructure and applications evolve in 'lock-step', so where are we today in terms of this evolution and where are we going?" Topic 2 - You mention this concept of “connectivity vs. cognition” is what makes ML/AI radically different from software of previous generations. Let’s discuss what that means. Topic 3 - “The result is an application that is “smart,” but exceptionally data-intensive and computationally expensive”. What are the problems this creates on today’s infrastructure? Topic 4 - “For ML/AI to reach its full potential, it must graduate from the academic discipline it is today into an engineering one. What that means in practice is that there needs to be new abstractions, interfaces, systems, and tooling to make developing and deploying intelligent applications easy for developers.” - Give us some of the first steps that will make this happen. Topic 5 - As a VC, you’re in the business of finding the companies that might turn this into a reality. Who are some of those companies that are on your radar? Feedback? Email: show at thecloudcast dot net Twitter: @thecloudcastnet and

Mike Dauber is a General Partner @ Amplify Partners, the fund that backs technical founders, building technical products for technical buyers. Their portfolio consists of the likes of DataDog, Fastly, Engagio and many more incredible companies. As for Mike, prior to joining Amplify he spent more than six years at Battery Ventures, where he lead early-stage enterprise investments on the West Coast. While at Battery, he was on the Boards of Cask, Duetto, Interana, and Platfora (acquired WDAY). Mike also lead Battery’s investment in Vera, which is also in Amplify’s portfolio. He also previously invested in Splunk (SPLK) and RelateIQ (acquired CRM). As a result of this success, Mike was named to Forbes’ Midas Brink List in 2014. In Today’s Episode You Will Learn: How Mike made his way into the world of early stage enterprise investing with Battery and came to be a GP with Amplify? What does Mike mean when he says he looks for “practitioner founders”? What are the benefits of these types of founders? Why do they find product market fit faster? Does this tunnel vision not sometimes mean a lack of naivete, which can be good? Why does Mike believe that hiring sales people is like being thirsty? How can founders discover the optimal cadence for expanding the sales team? Why must founders differentiate between customers and money? Why does Mike believe that everyone needs to find their Hobbesian advisor? What characteristics should this person have? How can you find this advisor? What should their incentives be?   Why does Mike believe that founders need to set the hook for VCs in the first meeting? How does this compare to how founders traditionally pitch? What should they look for in those early VC meetings? 60 Second SaaStr Why does Mike disagree with deal attribution in VC? Cyber investing: Should you invest if not a domain expert? Is enterprise investing spreadsheet investing? If you would like to find out more about the show and the guests presented, you can follow us on Twitter here: Jason Lemkin Harry Stebbings SaaStr Mike Dauber

O'Reilly Radar Podcast: David Beyer on AI adoption challenges, the complexities of getting an AI ROI, and the dangers of hype.This week, I sit down with David Beyer, an investor with Amplify Partners. We talk about machine learning and artificial intelligence, the challenges he’s seeing in AI adoption, and what he thinks is missing from the AI conversation.Here are a few highlights: Complexities of AI adoption AI adoption is actually a multifaceted question. It's something that touches on policy at the government level. It touches on labor markets and questions around equity and fairness. It touches on broad commercial questions around industries and how they evolve over time. There's many, many ways to address this. I think a good way to think about AI adoption at the broader, more abstract level of sectors or categories is to actually zoom down a bit and look at what it is actually replacing. The way to do that is to think at the atomic level of jobs and work. What is work? People have been talking about questions of productivity and efficiency for quite some time, but a good way to think of it from the lens of the computer or machine learning is to divide work into four categories. It's a two-by-two matrix of cognitive and manual, cognitive versus manual work, and routine versus non-routine work. The 90s internet and computer revolution, for the most part, tackled the routine work—Spreadsheets and word processing, things that could be specified by an explicit set of instructions. The more interesting stuff that's happening now, and that should be happening over the next decade, is how does software start to impact non-routine, both cognitive and manual, work? Cognitive work is tricky. It can be divided into two categories: things that are analytical (so, math and science and the like) and things that are more interpersonal and social—sales, being a good example. Then with non-routine work, the first instinct is to think about whether the job seems simple to us as people—so, cleaning a room for us, at first blush, seems like something pretty much anyone who's able could do; it's actually incredibly difficult. There's this bizarre, unexpected result that the hard problems are easier to automate, things like logic. The easier problems are incredibly hard to automate—things that require visuospatial orientation, navigating complex and potentially changing terrain. Things that we have basically been programmed over millennia in our brains to accomplish are actually very difficult to do from the perspective of coding a set of instructions into a computer. AI ROI The question I have in my mind is: in the 90s and 2000s, was simply applying computers to business and communication its own revolution? Does machine learning and AI constitute a new category or is machine learning the final complement to extract the productivity out of that initial Silicon revolution, so to speak? There's this economic historian Paul David, out of Oxford, who wrote an interesting thing looking at American factories and how they adapted to electrification because, previously, a lot of them were steam powered. The initial adoption was really with a lack of imagination: they used motors where steam used to be and hadn't really redesigned anything. They didn't really get much of any productivity. It was only when that crop of old managers was replaced with new managers that people fully redesigned the factory to what we now recognize as the modern factory. The question is the technology itself: from our perspective as investors, it's insufficient. You need business process and workplace rethinking. An area of research, as it relates to this model of AI adoption, is how reconstructible is it—is there an index to describe how particular industries or particular workflows or businesses can be remodeled to use machine learning with more leverage? I think that speaks to how those managers in those instances are going to look at ROI. If the payback period for a particular investment is uncertain or really long, we're less likely to adopt it, which is why you're seeing a lot of pickup of robots in factories. You can specify and drive the ROI; the payback period for that is coming down because it's incredibly clear, well-defined. Another industry is, for example, using machine learning in a legal setting for a law firm. There are parts of it—for example, technology assisted review—where the ROI's pretty clear. You can measure it in time saved. Other technologies that help assist in prediction or judgment for, say, higher-level thinking, the return on that is pretty unclear. A lot of the interesting technologies coming out these days—from, in particular, deep learning—enable things that operate at a higher level than we're used to. At the same time, though, they're building products around that that do relatively high-level things that are hard to quantify. The productivity gains from that are not necessarily clear. The dangers of AI hype One thing I'd say, rather than missing from the AI conversation, is something that there's too much of: I think hype is one of them. Too many businesses now are pitching AI almost as though it's batteries included. That's dangerous because it's going to potentially lead to over-investment in things that overpromise. Then, when they under-deliver, it has a deflationary effect on people's attitudes toward the space. It almost belittles the problem itself. Not everything requires the latest whiz-bang technology. In fact, the dirty secret of machine learning—and, in a way, venture capital—is so many problems could be solved by just applying simple regression analysis. Yet, very few people, very few industries do the bare minimum.

O'Reilly Radar Podcast: David Beyer on AI adoption challenges, the complexities of getting an AI ROI, and the dangers of hype.This week, I sit down with David Beyer, an investor with Amplify Partners. We talk about machine learning and artificial intelligence, the challenges he’s seeing in AI adoption, and what he thinks is missing from the AI conversation.Here are a few highlights: Complexities of AI adoption AI adoption is actually a multifaceted question. It's something that touches on policy at the government level. It touches on labor markets and questions around equity and fairness. It touches on broad commercial questions around industries and how they evolve over time. There's many, many ways to address this. I think a good way to think about AI adoption at the broader, more abstract level of sectors or categories is to actually zoom down a bit and look at what it is actually replacing. The way to do that is to think at the atomic level of jobs and work. What is work? People have been talking about questions of productivity and efficiency for quite some time, but a good way to think of it from the lens of the computer or machine learning is to divide work into four categories. It's a two-by-two matrix of cognitive and manual, cognitive versus manual work, and routine versus non-routine work. The 90s internet and computer revolution, for the most part, tackled the routine work—Spreadsheets and word processing, things that could be specified by an explicit set of instructions. The more interesting stuff that's happening now, and that should be happening over the next decade, is how does software start to impact non-routine, both cognitive and manual, work? Cognitive work is tricky. It can be divided into two categories: things that are analytical (so, math and science and the like) and things that are more interpersonal and social—sales, being a good example. Then with non-routine work, the first instinct is to think about whether the job seems simple to us as people—so, cleaning a room for us, at first blush, seems like something pretty much anyone who's able could do; it's actually incredibly difficult. There's this bizarre, unexpected result that the hard problems are easier to automate, things like logic. The easier problems are incredibly hard to automate—things that require visuospatial orientation, navigating complex and potentially changing terrain. Things that we have basically been programmed over millennia in our brains to accomplish are actually very difficult to do from the perspective of coding a set of instructions into a computer. AI ROI The question I have in my mind is: in the 90s and 2000s, was simply applying computers to business and communication its own revolution? Does machine learning and AI constitute a new category or is machine learning the final complement to extract the productivity out of that initial Silicon revolution, so to speak? There's this economic historian Paul David, out of Oxford, who wrote an interesting thing looking at American factories and how they adapted to electrification because, previously, a lot of them were steam powered. The initial adoption was really with a lack of imagination: they used motors where steam used to be and hadn't really redesigned anything. They didn't really get much of any productivity. It was only when that crop of old managers was replaced with new managers that people fully redesigned the factory to what we now recognize as the modern factory. The question is the technology itself: from our perspective as investors, it's insufficient. You need business process and workplace rethinking. An area of research, as it relates to this model of AI adoption, is how reconstructible is it—is there an index to describe how particular industries or particular workflows or businesses can be remodeled to use machine learning with more leverage? I think that speaks to how those managers in those instances are going to look at ROI. If the payback period for a particular investment is uncertain or really long, we're less likely to adopt it, which is why you're seeing a lot of pickup of robots in factories. You can specify and drive the ROI; the payback period for that is coming down because it's incredibly clear, well-defined. Another industry is, for example, using machine learning in a legal setting for a law firm. There are parts of it—for example, technology assisted review—where the ROI's pretty clear. You can measure it in time saved. Other technologies that help assist in prediction or judgment for, say, higher-level thinking, the return on that is pretty unclear. A lot of the interesting technologies coming out these days—from, in particular, deep learning—enable things that operate at a higher level than we're used to. At the same time, though, they're building products around that that do relatively high-level things that are hard to quantify. The productivity gains from that are not necessarily clear. The dangers of AI hype One thing I'd say, rather than missing from the AI conversation, is something that there's too much of: I think hype is one of them. Too many businesses now are pitching AI almost as though it's batteries included. That's dangerous because it's going to potentially lead to over-investment in things that overpromise. Then, when they under-deliver, it has a deflationary effect on people's attitudes toward the space. It almost belittles the problem itself. Not everything requires the latest whiz-bang technology. In fact, the dirty secret of machine learning—and, in a way, venture capital—is so many problems could be solved by just applying simple regression analysis. Yet, very few people, very few industries do the bare minimum.

Charles Fracchia is the founder and CEO of BioBright, a company building the smart lab to improve reproducibility in bio-medical research. He is interested in how artificial intelligence, automation and human-computer interfaces can improve the human ability to do research. He completed his bachelors in biology at Imperial College, his masters between the MIT Media Lab and Harvard Medical School. In 2016, Charles was named one of 35 innovators under 35 by the MIT Technology Review. He is the recipient of several awards including IBM PhD fellowships, an Extraordinary Minds fellowship, one of the first Awesome Foundation fellowships and an Amplify Partners fellowship. He is the author of several patents and is actively authoring more in the field of future laboratory tools. Charles has also been involved in obtaining numerous grants and contracts from DARPA, NSF, Google X, Knight Foundation and the Shanghai High Tech Incubator totaling several millions since 2012.

The O'Reilly Radar Podcast: Evolutionary computation, its applications in deep learning, and how it's inspired by biology.In this week’s episode, David Beyer, principal at Amplify Partners, co-founder of Chart.io, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. Also note, David Beyer's new free report "The Future of Machine Intelligence" is now available for download.Here are some highlights from their conversation: Finding optimal solutions We talk about evolutionary computation as a way of solving problems, discovering solutions that are optimal or as good as possible. In these complex domains like, maybe, simulated multi-legged robots that are walking in challenging conditions—a slippery slope or a field with obstacles—there are probably many different solutions that will work. If you run the evolution multiple times, you probably will discover some different solutions. There are many paths of constructing that same solution. You have a population and you have some solution components discovered here and there, so there are many different ways for evolution to run and discover roughly the same kind of a walk, where you may be using three legs to move forward and one to push you up the slope if it's a slippery slope. You do (relatively) reliably discover the same solutions, but also, if you run it multiple times, you will discover others. This is also a new direction or recent direction in evolutionary computation—that the standard formulation is that you are running a single run of evolution and you try to, in the end, get the optimum. Everything in the population supports finding that optimum. Biological inspiration Some machine learning is simply statistics. It's not simple, obviously, but it is really based on statistics and it's mathematics-based, but some of the inspiration in evolutionary computation and neural networks and reinforcement learning really comes from biology. It doesn't mean that we are trying to systematically replicate what we see in biology. We take the components we understand, or maybe even misunderstand, but we take the components that make sense and put them together into a computational structure. That's what's happening in evolution, too. Some of the core ideas at the very high level of instruction are the same. In particular, there's selection acting on variation. That's the main principle of evolution in biology, and it's also in computation. If you take a little bit more detailed view, we have a population, and everyone is evaluated, and then we select the best ones, and those are the ones that reproduce the most, and we get a new population that's more likely to be better than the previous population. Modeling biology? Not quite yet. There's also developmental processes that most biological systems adapt and learn during their lifetime as well. In humans, the genes specify, really, a very weak starting point. When a baby is born, there's very little behavior that they can perform, but over time, they interact with the environment and that neural network gets set into a system that actually deals with the world. Yes, there's actually some work in trying to incorporate some of these ideas, but that is very difficult. We are very far from actually saying that we really model biology. OSCAR-6 innovates What got us really hooked in this area was that there are these demonstrations where evolution not only optimizes something that you know pretty well, but also comes up with something that's truly novel, something that you don't anticipate. For us, it was this one application where we were evolving a controller for a robot arm, OSCAR-6. It was six degrees of freedom, but you only needed three to really control it. One of the dimensions is that the robot can turn around its vertical axis, the main axis. The goal is to get the fingers of the robot to a particular location in 3D space that's reachable. It's pretty easy to do. We were working on putting obstacles in the way and accidentally disabled the main motor, the one that turns the robot around its main axis. We didn't know it. We ran evolution anyway, and evolution learned and evolved, found a solution that would get the fingers in the goal, but it took five times longer. We only understood what was going on when we put it on screen and looked at the visualization. What the robot was able to do was that when the target was, say, all the way to the left and it needed to turn around the main axis to get the arm close to it, it couldn't do it because it couldn't turn. Instead, it turned the arm from the elbow or shoulder, the other direction, away from the goal, then swung it back real hard; because of inertia, the whole robot would turn around its main axis, even when there was no motor. This was a big surprise. We caused big problems to the robot. We disabled a big, important component of it, but it still found a solution of dealing with it: utilizing inertia, utilizing the physical simulation to get where it needed to go. This is exactly what you would like in a machine learning system. It innovates. It finds things that you did not think about. If you have a robot stuck in a rock in Mars or it loses a wheel, you'd still like it to complete its mission. Using these techniques, we can figure out ways for it to do so.

The O'Reilly Radar Podcast: Evolutionary computation, its applications in deep learning, and how it's inspired by biology.In this week’s episode, David Beyer, principal at Amplify Partners, co-founder of Chart.io, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. Also note, David Beyer's new free report "The Future of Machine Intelligence" is now available for download.Here are some highlights from their conversation: Finding optimal solutions We talk about evolutionary computation as a way of solving problems, discovering solutions that are optimal or as good as possible. In these complex domains like, maybe, simulated multi-legged robots that are walking in challenging conditions—a slippery slope or a field with obstacles—there are probably many different solutions that will work. If you run the evolution multiple times, you probably will discover some different solutions. There are many paths of constructing that same solution. You have a population and you have some solution components discovered here and there, so there are many different ways for evolution to run and discover roughly the same kind of a walk, where you may be using three legs to move forward and one to push you up the slope if it's a slippery slope. You do (relatively) reliably discover the same solutions, but also, if you run it multiple times, you will discover others. This is also a new direction or recent direction in evolutionary computation—that the standard formulation is that you are running a single run of evolution and you try to, in the end, get the optimum. Everything in the population supports finding that optimum. Biological inspiration Some machine learning is simply statistics. It's not simple, obviously, but it is really based on statistics and it's mathematics-based, but some of the inspiration in evolutionary computation and neural networks and reinforcement learning really comes from biology. It doesn't mean that we are trying to systematically replicate what we see in biology. We take the components we understand, or maybe even misunderstand, but we take the components that make sense and put them together into a computational structure. That's what's happening in evolution, too. Some of the core ideas at the very high level of instruction are the same. In particular, there's selection acting on variation. That's the main principle of evolution in biology, and it's also in computation. If you take a little bit more detailed view, we have a population, and everyone is evaluated, and then we select the best ones, and those are the ones that reproduce the most, and we get a new population that's more likely to be better than the previous population. Modeling biology? Not quite yet. There's also developmental processes that most biological systems adapt and learn during their lifetime as well. In humans, the genes specify, really, a very weak starting point. When a baby is born, there's very little behavior that they can perform, but over time, they interact with the environment and that neural network gets set into a system that actually deals with the world. Yes, there's actually some work in trying to incorporate some of these ideas, but that is very difficult. We are very far from actually saying that we really model biology. OSCAR-6 innovates What got us really hooked in this area was that there are these demonstrations where evolution not only optimizes something that you know pretty well, but also comes up with something that's truly novel, something that you don't anticipate. For us, it was this one application where we were evolving a controller for a robot arm, OSCAR-6. It was six degrees of freedom, but you only needed three to really control it. One of the dimensions is that the robot can turn around its vertical axis, the main axis. The goal is to get the fingers of the robot to a particular location in 3D space that's reachable. It's pretty easy to do. We were working on putting obstacles in the way and accidentally disabled the main motor, the one that turns the robot around its main axis. We didn't know it. We ran evolution anyway, and evolution learned and evolved, found a solution that would get the fingers in the goal, but it took five times longer. We only understood what was going on when we put it on screen and looked at the visualization. What the robot was able to do was that when the target was, say, all the way to the left and it needed to turn around the main axis to get the arm close to it, it couldn't do it because it couldn't turn. Instead, it turned the arm from the elbow or shoulder, the other direction, away from the goal, then swung it back real hard; because of inertia, the whole robot would turn around its main axis, even when there was no motor. This was a big surprise. We caused big problems to the robot. We disabled a big, important component of it, but it still found a solution of dealing with it: utilizing inertia, utilizing the physical simulation to get where it needed to go. This is exactly what you would like in a machine learning system. It innovates. It finds things that you did not think about. If you have a robot stuck in a rock in Mars or it loses a wheel, you'd still like it to complete its mission. Using these techniques, we can figure out ways for it to do so.