Podcasts about ml ai

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.// BioMike Del Balso is the CEO and co-founder of Tecton, where he's building the industry's first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google's Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.// Related LinksWebsite: www.tecton.ai~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Mike on LinkedIn: /michaeldelbalsoTimestamps:[00:00] Smarter decisions, less manual work[03:52] Data pipelines: pain and fixes[08:45] Why Tecton was born[11:30] ML use cases shift[14:14] Models for big bets[18:39] Build or buy drama[20:20] Fintech's data playbook[23:52] What really needs real-time[28:07] Speeding up ML delivery[32:09] Valuing ML is tricky[35:29] Simplifying ML toolkits[37:18] AI copilots in action[42:13] AI that fights fraud[45:07] Teaming up across coasts[46:43] Tecton + Generative AI?

In this first of a two part series of episodes on federated learning, we dive into the evolving world of federated learning and distributed AI frameworks with Patrick Foley from Intel. We explore how frameworks like OpenFL and Flower are enabling secure, collaborative model training across silos, especially in sensitive fields like healthcare. The conversation touches on real-world use cases, the challenges of distributed ML/AI experiments, and why privacy-preserving techniques may become essential for deploying AI to production.Featuring:Patrick Foley – LinkedInChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XLinks:IntelOpenFLSponsors:NordLayer is a toggle-ready network security platform built for modern businesses. It combines VPN, access control, and threat protection in one easy-to-use platform. No hardware. No complex setup. Just secure connection and full control—in less than 10 minutes. Up to 22% off NordLayer yearly plans plus 10% on top with the coupon code practically-10.

I've been quietly cooking up a limited-run podcast: AI Product Builders.This isn't another AI hype machine. I'm sitting down with designers and builders who are actually shipping real work. We're digging into:* Real talk on integrating AI into existing products and worklflows* Play-by-play stories of launching net-new experiences (minus the vaporware)* Honest takes on ethics, personalization, and what “future-of-work” really means for design teams.Meet the line-up:* Ovetta Sampson — Owner, Right AI: guiding SMBs through safe ML/AI adoption.* Pablo Stanley — Founder, Lummi.ai: a fresh library of AI-generated imagery.* Filip Skrzesinski — Co-Founder, Subframe: the UI design tool for the AI era.* Jason Demetillo — Lead Designer, Canva Sheets: helping the creative workforce closer to getting things done more efficiently and effectively.* Kyle Zantos — AI design consultant helping teams ship with emerging no-code aka "vibe-coding" tools.The series drops Monday, May 5th.You can follow on LinkedIn and subscribe to Technically Speaking on Substack to get access as soon as it's available. Get full access to Technically Speaking at technicallyspeakinghw.substack.com/subscribe

Join us for Driving Business Impact with AI: A Conversation with Maddie Daianu of Credit Karma. In this episode of the Data Science Salon Podcast, host Anna Anisin sits down with Maddie Daianu, Head of Data & AI at Credit Karma and former executive at Meta, to explore how AI, machine learning, and data-driven strategy can fuel enterprise-wide transformation. With a proven track record of driving revenue growth and innovation through AI, Maddie shares her approach to aligning ML initiatives with business outcomes, scaling high-performing teams, and influencing executive stakeholders around data-led opportunities. This conversation covers everything from monetization and predictive analytics to building a collaborative culture between technical and non-technical teams. Maddie also offers practical advice for data leaders navigating complex industries like fintech, and her perspective on the trends shaping the future of AI in business. Whether you're leading a data team or just beginning to build one, this episode offers actionable insights on harnessing the power of AI to drive meaningful impact at scale. Tune in to gain leadership insights from one of the data industry's most influential figures and learn how you can contribute to a more diverse and inclusive data science landscape. Learn more about ML/AI in Finance at DSS NYC on May 15: https://www.datascience.salon/newyork/

Sviatoslav Hnizdovskyi is CEO at OpenMinds. He is an expert at countering influence operations and disinformation. Sviatoslav is a serial tech entrepreneur and investor dedicated to helping Ukraine and democratic nations counter authoritarian influence in the global fight for free and open societies. At OpenMinds he leads a cognitive defence tech company that collaborates with over 30 governments and organizations worldwide, including Ukraine, the US, the UK, NATO members, strategic communications agencies, and leading research institutions. Their mission is to combat authoritarian influence, safeguard information integrity, develop cutting-edge AI tech for national security priorities of the democratic world.----------OpenMinds is a cognitive defence tech company countering authoritarian influence in the battle for free and open societies. They work with over 30 governments and organisations worldwide, including Ukraine, the UK, and NATO member governments, leading StratCom agencies, and research institutions. Their expertise lies in accessing restricted and high-risk environments, including conflict zones and closed platforms.We combine ML technologies with deep local expertise, particularly on Russia and Ukraine. OpenMinds team is based in Kyiv, London, Ottawa, and Washington, DC, includes behavioural scientists, ML/AI engineers, data journalists, communications experts, and regional specialists.----------LINKS:https://x.com/s_hnizdovskyihttps://www.linkedin.com/in/hnizdovskyi/https://www.openminds.ltd/https://www.atlanticcouncil.org/blogs/ukrainealert/how-strong-is-russian-public-support-for-the-invasion-of-ukraine/https://www.facebook.com/sviatoslav.hnizdovskyi/----------SILICON CURTAIN FILM FUNDRAISER - A project to make a documentary film in Ukraine, to raise awareness of Ukraine's struggle and in supporting a team running aid convoys to Ukraine's frontline towns.https://buymeacoffee.com/siliconcurtain/extras----------Easter Pysanky: Silicon Curtain - https://car4ukraine.com/campaigns/easter-pysanky-silicon-curtainCar for Ukraine has joined forces with a group of influencers, creators, and news observers during this special Easter season. In peaceful times, we might gift a basket of pysanky (hand-painted eggs), but now, we aim to deliver a basket of trucks to our warriors.This time, our main focus is on the Seraphims of the 104th Brigade and Chimera of HUR (Main Directorate of Intelligence), highly effective units that: - disrupt enemy logistics - detect and strike command centers - carry out precision operations against high-value enemy targetshttps://car4ukraine.com/campaigns/easter-pysanky-silicon-curtain----------SILICON CURTAIN FILM FUNDRAISERA project to make a documentary film in Ukraine, to raise awareness of Ukraine's struggle and in supporting a team running aid convoys to Ukraine's front-line towns.https://buymeacoffee.com/siliconcurtain/extras----------SUPPORT THE CHANNEL:https://www.buymeacoffee.com/siliconcurtainhttps://www.patreon.com/siliconcurtain----------TRUSTED CHARITIES ON THE GROUND:Save Ukrainehttps://www.saveukraineua.org/Superhumans - Hospital for war traumashttps://superhumans.com/en/UNBROKEN - Treatment. Prosthesis. Rehabilitation for Ukrainians in Ukrainehttps://unbroken.org.ua/Come Back Alivehttps://savelife.in.ua/en/Chefs For Ukraine - World Central Kitchenhttps://wck.org/relief/activation-chefs-for-ukraineUNITED24 - An initiative of President Zelenskyyhttps://u24.gov.ua/Serhiy Prytula Charity Foundationhttps://prytulafoundation.orgNGO “Herojam Slava”https://heroiamslava.org/kharpp - Reconstruction project supporting communities in Kharkiv and Przemyślhttps://kharpp.com/NOR DOG Animal Rescuehttps://www.nor-dog.org/home/----------

Why is generative AI essential now? I hosted Kevin McGrath, Co-Founder & CEO, Meibel on The Ravit Show at The AI Summit New York to discuss Generative AI for Production.Kevin shared how Meibel's Explainable AI platform is empowering product and engineering leaders to build and deploy generative AI solutions with confidence. From accelerating innovation to measuring ROI and ensuring AI accountability, Meibel's approach is a game-changer for organizations aiming to integrate AI into their product.During our conversation, we explored:-- The growing importance of generative AI in today's landscape-- How generative AI differs fundamentally from traditional ML/AI approaches-- The value of companies building their own AI solutions to stay competitive-- The typical journey customers experience when implementing generative AI-- Strategies to address challenges like expertise gaps and risk mitigationIt was an insightful discussion that highlighted the transformative potential of generative AI and practical strategies for making it work in real-world production environments.#data #ai #aisummitnewyork #meibel #theravitshow

STRACE: System Call Tracing Utility — Advanced Diagnostic AnalysisI. Introduction & Empirical Case StudyCase Study: Weta Digital Performance OptimizationDiagnostic investigation of Python execution latency (~60s initialization delay)Root cause identification: Excessive filesystem I/O operations (103-104 redundant calls)Resolution implementation: Network call interception via wrapper scriptsPerformance outcome: Significant latency reduction through filesystem access optimizationII. Technical Foundation & Architectural ImplementationEtymological & Functional ClassificationUnix/Linux diagnostic utility implementing ptrace() syscall interfacePrimary function: Interception and recording of syscalls executed by processesSecondary function: Signal receipt and processing monitoringEvolutionary development: Iterative improvement of diagnostic capabilitiesImplementation ArchitectureKernel-level integration via ptrace() syscallNon-invasive process attachment methodologyRuntime process monitoring without source code access requirementIII. Operational Parameters & Implementation MechanicsProcess Attachment MechanismDirect PID targeting via ptrace() syscall interfaceProduction-compatible diagnostic capabilities (non-destructive analysis)Long-running process compatibility (e.g., ML/AI training jobs, big data processing)Execution ModalitiesProcess hierarchy traversal (-f flag for child process tracing)Temporal analysis with microsecond precision (-t, -r, -T flags)Statistical frequency analysis (-c flag for syscall quantification)Pattern-based filtering via regex implementationOutput TaxonomyFormat specification: syscall(args) = return_value [error_designation]64-bit/32-bit differentiation via ABI handlersTemporal annotation capabilitiesIV. Advanced Analytical CapabilitiesPerformance MetricsMicrosecond-precision timing for syscall latency evaluationStatistical aggregation of call frequenciesExecution path profilingI/O & System Interaction AnalysisFile descriptor tracking and comprehensive I/O operation monitoringSignal interception analysis with complete signal delivery visualizationIPC mechanism examination (shared memory segments, semaphores, message queues)V. Methodological Limitations & ConstraintsPerformance Impact ConsiderationsExecution degradation (5-15×) from context switching overheadTemporal resolution limitations (microsecond precision)Non-deterministic elements: Race conditions & scheduling anomaliesHeisenberg uncertainty principle manifestation: Observer effect on traced processesVI. Ecosystem Position & Comparative AnalysisComplementary Diagnostic Toolsltrace: Library call tracingftrace: Kernel function tracingperf: Performance counter analysisAbstraction Level DifferentiationComplementary to GDB (implementation level vs. code level analysis)Security implications: Privileged access requirement (CAP_SYS_PTRACE capability)Platform limitations: Disabled on certain proprietary systems (e.g., Apple OS)VII. Production Application DomainsDiagnostic ApplicationsRoot cause analysis for syscall failure patternsPerformance bottleneck identificationRunning process diagnosis without termination requirementSystem AnalysisSecurity auditing (privilege escalation & resource access monitoring)Black-box behavioral analysis of proprietary/binary softwareContainerization diagnostic capabilities (namespace boundary analysis)Critical System RecoverySubprocess deadlock identification & resolutionNon-destructive diagnostic intervention for long-running processesRecovery facilitation without system restart requirements

Are you prepared for the hidden UX taxes that AI and LLM features might be imposing on your B2B customers—without your knowledge? Are you certain that your AI product or features are truly delivering value, or are there unseen taxes that are working against your users and your product / business? In this episode, I'm delving into some of UX challenges that I think need to be addressed when implementing LLM and AI features into B2B products.   While AI seems to offer the change for significantly enhanced productivity, it also introduces a new layer of complexity for UX design. This complexity is not limited to the challenges of designing in a probabilistic medium (i.e. ML/AI), but also in being able to define what “quality” means. When the product team does not have a shared understanding of what a measurably better UX outcome means, improved sales and user adoption are less likely to follow.    I'll also discuss aspects of designing for AI that may be invisible on the surface. How might AI-powered products change the work of B2B users? What are some of the traps I see some startup clients and founders I advise in MIT's Sandbox venture fund fall into?   If you're a product leader in B2B / enterprise software and want to make sure your AI capabilities don't end up creating more damage than value for users,  this episode will help!     Highlights/ Skip to    Improving your AI model accuracy improves outputs—but customers only care about outcomes (4:02) AI-driven productivity gains also put the customer's “next problem” into their face sooner. Are you addressing the most urgent problem they now have—or used to have? (7:35) Products that win will combine AI with tastefully designed deterministic-software—because doing everything for everyone well is impossible and most models alone aren't products (12:55) Just because your AI app or LLM feature can do ”X” doesn't mean people will want it or change their behavior (16:26) AI Agents sound great—but there is a human UX too, and it must enable trust and intervention at the right times (22:14) Not overheard from customers: “I would buy this/use this if it had AI” (26:52) Adaptive UIs sound like they'll solve everything—but to reduce friction, they need to adapt to the person, not just the format of model outputs (30:20) Introducing AI introduces more states and scenarios that your product may need to support that may not be obvious right away (37:56)   Quotes from Today's Episode Product leaders have to decide how much effort and resources you should put into model improvements versus improving a user's experience. Obviously, model quality is important in certain contexts and regulated industries, but when GenAI errors and confabulations are lower risk to the user (i.e. they create minor friction or inconveniences), the broader user experience that you facilitate might be what is actually determining the true value of your AI features or product. Model accuracy alone is not going to necessarily lead to happier users or increased adoption. ML models can be quantifiably tested for accuracy with structured tests, but because they're easier to test for quality vs. something like UX doesn't mean users value these improvements more. The product will stand a better chance of creating business value when it is clearly demonstrating it is improving your users' lives. (5:25) When designing AI agents, there is still a human UX - a beneficiary - in the loop. They have an experience, whether you designed it with intention or not. How much transparency needs to be given to users when an agent does work for them? Should users be able to intervene when the AI is doing this type of work?  Handling errors is something we do in all software, but what about retraining and learning so that the future user experiences is better? Is the system learning anything while it's going through this—and can I tell if it's learning what I want/need it to learn? What about humans in the loop who might interact with or be affected by the work the agent is doing even if they aren't the agent's owner or “user”? Who's outcomes matter here? At what cost? (22:51) Customers primarily care about things like raising or changing their status, making more money, making their job easier, saving time, etc. In fact,I believe a product marketed with GenAI may eventually signal a negative / burden on customers thanks to the inflated and unmet expectations around AI that is poorly implemented in the product UX. Don't think it's going to be bought just because it using  AI in a novel way. Customers aren't sitting around wishing for “disruption” from your product; quite the opposite. AI or not, you need to make the customer the hero. Your AI will shine when it delivers an outsized UX outcome for your users (27:49) What kind of UX are you delivering right out of the box when a customer tries out your AI product or feature? Did you design it for tire kicking, playing around, and user stress testing? Or just an idealistic happy path? GenAI features inside b2b products should surface capabilities and constraints particularly around where users can create value for themselves quickly.  Natural hints and well-designed prompt nudges in LLMs for example are important to users and to your product team: because you're setting a more realistic expectation of what's possible with customers and helping them get to an outcome sooner. You're also teaching them how to use your solution to get the most value—without asking them to go read a manual. (38:21)

פרק שני בסדרת ״מושג בקצרה עם מייק״ בה נצלול לעומקם של מושגים מעולמות הML וAI. בכל פרק נתמקד במושג אחד מרכזי – נסביר אותו בפשטות, נדון במשמעויותיו המעשיות, ונבחן את השפעתו על התחום והתעשייה. 

In this episode, Daniel Valdivia, an engineer from MinIO, discusses his participation at KubeCon and his work in Kubernetes integrations and AI initiatives. We discussed the significance of object storage standardization via the Open Platform for Enterprise AI (OPEA), emphasizing the flexibility and scalability of MinIO's offerings. Daniel highlights MinIO's contributions to open source projects like PyTorch and Spark and shares insights on new hardware technologies like PCIe Gen 5. Daniel also announces the launch of MinIO's new AI store, designed to empower enterprises to efficiently manage exascale infrastructure and AI pipelines. 00:00 Introduction 00:13 Meet Daniel Valdivia: Engineer at Minio 00:24 The Importance of Kubernetes Integrations 00:43 Intel's Open Platform for Enterprise AI 00:58 MinIO's Unique Object Storage Solutions 01:56 Community Participation and Contributions 02:18 Ensuring Compatibility with AI Hardware 03:20 The Role of OPEA in Enterprise AI 05:56 Open Source Contributions and Challenges 09:12 Future of AI and Hardware Innovations 13:23 Big Announcement 14:40 Conclusion and Final Thoughts   Guest: Daniel Valdivia is an engineer with MinIO where he focuses on Kubernetes, ML/AI and VMware. Prior to joining MinIO, Daniel was the Head of Machine Learning for Espressive. Daniel has held senior application development roles with ServiceNow, Oracle and Freescale. Daniel holds a Bachelor of Engineering from Tecnológico de Monterrey, Campus Guadalajara and Bachelor of Science in Computer Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey.

Diverse threat vectors and complex attacks - how to expand your detection and response programmesHelping your SOC analysts achieve more - finding the right combination of internal ML/AI tools and external providersPathways to accelerate your SOC development to detect and mitigate attacks earlier This episode is hosted by Thom Langford:https://www.linkedin.com/in/thomlangford/Mike Johnson, Global Cyber Threat & Incident Response Manager, Verifonehttps://www.linkedin.com/in/mike---johnson/Alan Jenkins, CISO Team lead, Saepiohttps://www.linkedin.com/in/alanjenkins/Josh Davies, Principal Technical Manager, Fortrahttps://www.linkedin.com/in/jdgwilym/

We talked about: 00:00 DataTalks.Club intro 00:00 Large Hadron Collider and Mentorship 02:35 Career overview and transition from physics to data science 07:02 Working at the Large Hadron Collider 09:19 How particles collide and the role of detectors 11:03 Data analysis challenges in particle physics and data science similarities 13:32 Team structure at the Large Hadron Collider 20:05 Explaining the connection between particle physics and data science 23:21 Software engineering practices in particle physics 26:11 Challenges during interviews for data science roles 29:30 Mentoring and offering advice to job seekers 40:03 The STAR method and its value in interviews 50:32 Paid vs unpaid mentorship and finding the right fit ​About the speaker: ​Anastasia is a particle physicist turned data scientist, with experience in large-scale experiments like those at the Large Hadron Collider. She also worked at Blue Yonder, scaling AI-driven solutions for global supply chain giants, and at Kaufland e-commerce, focusing on NLP and search. Anastasia is a mentor for Ml/AI, dedicated to helping her mentees achieve their goals. She is passionate about growing the next generation of data science elite in Germany: from Data Analysts up to ML Engineers. Join our Slack: https://datatalks .club/slack.html

Discover the ultimate resource for mastering Machine Learning and Artificial Intelligence with the "AI and Machine Learning For Dummies" app.iOs: https://apps.apple.com/ca/app/machine-learning-for-dummies/id1611593573PRO Version (No ADS): https://apps.apple.com/ca/app/machine-learning-for-dummies-p/id1610947211The "AI and Machine Learning For Dummies" app is a comprehensive learning resource for anyone interested in artificial intelligence and machine learning, regardless of their experience level. It offers over 600 quizzes covering various topics, including cloud ML operations on AWS, Azure, and GCP, machine learning fundamentals and advanced concepts, and artificial intelligence, including neural networks, generative AI, and large language models. The app also includes interactive scorecards, countdown timers, cheat sheets, interview preparation materials, and updates on the latest AI developments. Users can choose between a free version with ads and a paid version without ads, with the ability to view all answers.Whether you are a beginner or an experienced professional, this app offers a rich array of content to boost your AI and ML knowledge. Featuring over 600 quizzes covering cloud ML operations on AWS, Azure, and GCP, along with fundamental and advanced topics, it provides everything you need to elevate your expertise.Key Features:500+ questions covering AI Operations on AWS, Azure, and GCP with detailed answers and references.100+ questions on Machine Learning Basics and Advanced concepts with detailed explanations.100+ questions on Artificial Intelligence, including both fundamental and advanced concepts (Neural Networks, Generative AI, LLMs etc..), illustrated with in-depth answers and references.100+ Quizzes about Top AI Tools like ChatGPT, Gemini, Claude, Perplexity, NotebookLM, TensorFlow, PyTorch, IBM Watson, Google Cloud API, etc.Interactive scorecard and countdown timer for an engaging learning journey.AI and Machine Learning cheat sheets for quick reference.Comprehensive Machine Learning and AI interview preparation materials updated daily.Stay informed with the latest developments in the AI world.Download now and get access to the most comprehensive ML and AI resource available!Note: We are not affiliated with Microsoft, Google, or Amazon. This app is created based on publicly available materials and certification guides. We aim to assist you in your exam preparation, but passing an exam is not guaranteed.iOs: https://apps.apple.com/ca/app/machine-learning-for-dummies/id1611593573PRO Version (No ADS, See All Answers): https://apps.apple.com/ca/app/machine-learning-for-dummies-p/id1610947211

Dzisiaj skupimy się na wdrażaniu AI na produkcję. Omówię trzy kluczowe kwestie:1️⃣ Paradoks danych, zwykle zgadzamy się, że dane są ważne, ale często nie poświęcamy im tyle uwagi, ile potrzebują.2️⃣ Przesadna koncentracja na narzędziach, owszem narzędzia są ważne, ale nie najważniejsze.3️⃣ Cechy dobrego projektu na produkcję. Powinien być wiarygodny, kontrolowany, audytowalny i łatwy w naprawie błędów.Partnerem podcastu jest DataWorkshop - gdzie zajmują się praktycznym ML/AI.Na koniec odcinka też odpowiadam na pytania:Jakie są najczęstsze błędy firm, które próbują wdrożyć AI (główny mit)?Jakie są największe wyzwania związane z modelami LLM przy wdrażaniu je na produkcję?Jakie praktyczne wskazówki mam dla Ciebie, aby wdrożyć AI w swojej firmie? Najważniejszym elementem udanego wdrożenia AI jest odpowiednie przygotowanie danych. To właśnie na poziomie danych wykonuje się 50-80% całej pracy. Kluczowe jest zadbanie o:Jakość danychOdpowiednią strukturyzację (np. w bazie danych lub systemie plików)Łatwość wyszukiwania potrzebnych informacjiMożliwość aktualizacji danychZarządzanie dostępami i uprawnieniamiPowiem Ci trzy historie (projekty LLM), co najmniej trzy, będzie pewnie ich więcej, ale takie trzy przypadki użycia, w których wprost jako DataWorkshop jesteśmy teraz zaangażowani. Myślę, że to pobudzi Twoją wyobraźnię i lepiej zrozumiesz, co jest ważniejsze. Bo pamiętaj, że w większości przypadków są różne szacunki, 80%, 90%, nawet jeśli 50%, zwykle ML nie działa. Historia pierwsza - "Mentor"Organizacja zajmuje się mentoringiem w obszarze IT, skupiając się na wiedzy organizacyjnej, menedżerskiej i liderskiej. Obecnie zapraszani są eksperci, którzy prowadzą warsztaty. Są pewne wyzwania: ciężko jest to uspójnić, bo różni eksperci prezentują wiedzę w inny sposób i co jeszcze jest Trudności ze znalezieniem praktyków, bo znalezienie i zaangażowanie zapracowanych ekspertów jest trudne.Pojawił się pomysł, aby ocyfrować wiedzę i częściowo zautomatyzować mentoring przy pomocy AI. Czy to w ogóle możliwe?Historia druga - "Egzamin"Drugi projekt nazwijmy "Egzamin". W szkole zawodowej uczniowie zdają egzaminy, aby zdobyć kwalifikacje. Celem projektu jest stworzenie asystenta AI, który zdałby ten egzamin. Dlaczego to istotne? Zdając egzamin, asystent udowodniłby, że rozumie daną branżę. Można go by potem rozwijać, aby podpowiadał i prognozował. Klasyczne uczenie maszynowe i LLM mogą tu współdziałać. LLM może posiadać ogólną wiedzę zdobytą w procesie uczenia, a klasyczne algorytmy ML mogą prognozować wartości, np. popyt.Historia trzecia - "Helpdesk"Trzecia projekt nazwijmy "Helpdesk", projekt, w którym zachowanie poufności jest kluczowe. Nie mogę zdradzać szczegółów branży. W skrócie, chodzi o wykorzystanie LLM do stworzenia chatbota obsługującego bazę wiedzy i odpowiadającego na pytania użytkowników.Co znajdziesz w tym odcinku?1️⃣ Paradoks danych – mówimy o ich znaczeniu, ale często zaniedbujemy realne działania na rzecz ich jakości.2️⃣ Dlaczego 80-90% projektów ML nie trafia na produkcję? Poznaj najczęstsze błędy.3️⃣ Trzy inspirujące przykłady z życia – mentoring z AI, egzamin z udziałem LLM oraz obsługa klienta wspomagana przez AI.4️⃣ Kontrola i audytowalność – jak stworzyć projekt, który będzie skalowalny, zaufany i gotowy do poprawy błędów.5️⃣ LLM i klasyczne ML – współpraca, a nie konkurencja.6️⃣ Zadbaj o to, co naprawdę ważne! 7️⃣ Jeśli chcesz lepiej zrozumieć, jak skutecznie wdrażać modele ML w Twojej organizacji, nie przegap tego odcinka!

Jason Benn is an ML engineer and truly the epitome of a lifelong learner (Cal Newport even wrote about Jason in one of his books on learning!). Oz and Charlie catch up with Jason on his current self-directed ML sabbatical - which he's corralled into a co-working cohort called mleclub.com (similar to Recurse Center but with an ML / AI focus). We discuss the tactical, strategic, and emotional side to effective self-directed learning, and close out with a new segment tentatively called "Would you read the top article on Hacker News right now?".ShownotesMinerva University [book] So Good They Can't Ignore You - Cal Newport[book] Why Greatness Cannot Be Planned - Kenneth O. O. Stanley, Joel LehmanJason Benn's website MLE Club

I've spent the last three weeks visiting the UK, Australia, and New Zealand. Here are my observations and anecdotes about the data and ML/AI industry from countless chats with executives, practitioners, and pundits.

This week on Data in Biotech, we're joined by Martin Permin, the co-founder of Invert, a company that builds software that automates bioprocessing. Martin talks us through his own unique journey into biotech - starting from a role at Airbnb - through to co-founding Invert. Invert helps users grab data from their instruments, map out their individual processes, clean up the data for analysis, and look for ways to speed up the “mundane” data cleaning tasks that often take up the majority of one's time.  With our host, Ross Katz, Martin tells us the statistical problems Invert works to solve for their different types of clients: biologic development labs, full-scale manufacturers, and CDMOs. While they all approach data cleaning and analysis from different directions, Invert can see how clients use the system and look for ways to automate repeated processes to help them save time. They discuss implementing Invert into the Design, Build, Test, Learn Loop and why Invert is invested in reducing how many times one has to go around that loop. Martin explains how his company looks to reduce the risk in tech transfer in both directions, in terms of time and labor.  Then, the conversation moves to ML/AI, where Martin tells us how a lot of his customers are finding that the bottlenecks in their processes aren't where they thought they were, thanks to using Invert for process automation.  Finally, Martin gives us his opinions on the future trends around the corner for the biotech industry - and how Invert is preparing themselves and their customers.  Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:29] Introduction to Martin and his journey into biotech [4:10] Introduction to Invert - the what and why [6:47] How Invert is implemented into a customer's workflow [11:36] The problems Invert can solve [16:16] Design > build > test > learn… and how Invert facilitates that [20:00] CDMOs and contractors - how Invert works with their different customers [22:15] The use of ML/AI in bio-processing [33:40] Trends in Biotech that will influence Invert over the long-term

In this episode of Space Cafe Radio, host Torsten Kriening interviews Kevin Pomfret, partner at Williams Mullen Law Firm and founder of the Center for Spatial Law and Policy. Kevin leverages his 30 years of experience in corporate and transactional law to help companies in the space and spatial sectors grow and innovate. He works with a wide range of businesses that use technologies such as small sats, cloud computing, 5G, ML/AI, 3D and UAVs to develop commercial, government and societal applications.They discuss the critical gap between the space and geospatial sectors, particularly focusing on the practical applications and challenges in disaster response, agriculture, and other areas in the Global South. Kevin highlights the importance of legal and policy frameworks for successful integration and use of space and geospatial data. The discussion includes examples from Mexico and Chile, the role of various stakeholders, and lessons that emerging space ecosystems can learn from more established programs.Useful links:Laura Delgado Lopez - Co-authorOrbital Dynamics: The Domestic and Foreign Policy Forces Shaping Latin American Engagement in SpaceBook: Geospatial Law, Policy and Ethics: Where Geospatial Technology is Taking the LawSpace Café Radio brings you talks, interviews, and reports from the team of SpaceWatchers while out on the road. Each episode has a specific topic, unique content, and a personal touch. Enjoy the show, and let us know your thoughts at radio@spacewatch.globalWe love to hear from you. Send us your thought, comments, suggestions, love lettersYou can find us on: Spotify and Apple Podcast!Please visit us at SpaceWatch.Global, subscribe to our newsletters. Follow us on LinkedIn and Twitter!

Today, Host Sandy Vance is talking with Robert Dwyer, PhD EVP, Chief Data Scientist at Certilytics about AI and healthcare. Dr. Dwyer is here to help demystify and readiness  of generative AI. They delve into the transformative impact of generative AI on healthcare data and its revolutionary potential for the industry. Join us as we explore Certilytics, a company dedicated to creating a user-friendly interface for interacting with healthcare data, and their role in empowering healthcare organizations to enhance their efficiency with AI.AI's role in healthcare is now presenting new opportunities for healthcare data and analytics to go beyond just Large Language Models (LLMs) and deliver business intelligence that can impact healthcare administration strategies and help accelerate improved outcomes & efficiency in healthcare. In this episode, they talk about:Dr. Dwyer's background in generative AI and his journey to CertilyticsHow generative AI is transforming the insights that healthcare leaders and organizations can derive from their dataThe impact of data availability on the use and effectiveness of AI in healthcareCertilytics' goal to develop a user-friendly interface that enhances how users interact with healthcare dataThe current surge in AI technology and its implications for the industryThe latest advancements and innovations in large language modelsThe influence of federal regulations on the adoption and application of AI in healthcareHow healthcare organizations are leveraging AI-enriched data to improve outcomes and efficiency A Little About Dr. Robert Dwyer:Dr. Dwyer joined Certilytics in 2014, bringing over a decade of mathematical modeling and machine learning experience in both the private sector and academia, where he worked on problems ranging from the quantification of medical and financial risk to predictive genomics. He is responsible for overseeing the design and pathing of new ML/AI products. Dr. Dwyer graduated from the University of Virginia in 2009 with a B.S. in Biology. He then obtained his M.S. and Ph.D. in computational biology from Princeton University, where he developed variations of maximum entropy algorithms to predict three-dimensional protein folding patterns by mining genomic sequence data. Prior to Certilytics, he worked with a number of startups and think tanks to develop algorithms to predict student loan repayment rates and to track Defense Department allocations.Do you have any questions for Robert Dwyer? Reach out by emailing him at robert.dwyer@certilytics.com. 

В этом выпуске моим гостем стал Савва Вяткин, ML engineer и data scientist, выпускник университета Беркли в Калифорнии. Мы обсудили опыт Саввы в поиске работы в сфере машинного обучения и анализа данных в США. Он поделился своими мыслями о том, почему даже с дипломом престижного университета, гражданством и отличным английским найти работу в Калифорнии может быть непросто. Савва рассказал о своем опыте работы с компьютерным зрением, о процессе поиска работы во время экономического спада, о важности нетворкинга и постоянного обучения новым технологиям. Он также поделился советами для тех, кто ищет работу в сфере ML/AI, и рассказал о своих планах на будущее в этой быстро развивающейся области. Савва Вяткин (Savva Vyatkin) Machine Learning Engineer & Data Scientist, выпускник университета Berkeley в Калифорнии. LinkedIn: https://www.linkedin.com/in/savva-v-a8a86a109/ Ссылки, упомянутые в видео: https://situational-awareness.ai/ Эйджизм в США. Как найти работу в IT после 60? Интервью с разработчиком Сергеем Вяткиным https://youtu.be/cSWMzqT-TcE *** Записывайтесь на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США): https://annanaumova.com Онлайн курс "Идеальное резюме и поиск работы в США": https://go.mbastrategy.com/resumecoursemain Гайд "Идеальное американское резюме": https://go.mbastrategy.com/usresume Гайд "Как оформить профиль в LinkedIn, чтобы рекрутеры не смогли пройти мимо" (предзаказ): https://link.coursecreator360.com/widget/form/ObfVCQ2clIWTdNcQBAkf Мой Telegram-канал: https://t.me/prodcastUSA Мой Instagram: https://www.instagram.com/prodcast.us/ ⏰ Timecodes ⏰ 0:00 Начало 5:54 Расскажи про свой бэкграунд 17:00 Как искал свою первую работу после выпуска из Berkley? 18:09 Почему начал искать работу? С чего начал? 23:26 Разница между ML Engineer & Data Scientist? 24:54 Где искал вакансии? Как откликался? Менял ли резюме? 37:53 Как проходили собеседования? Почему отказывали? 51:05 Что послужило успехом, что ты смог получить оффер? 52:55 Какие зарплаты у специалистов ИИ в США? 54:08 Про планы на будущее 59:35 Что послужило успехом, что ты смог получить оффер?

Lexi Pasi and I chat about symbolic logic in AI, building and managing data science teams, math, and the shapes of ML/AI problems. Lexi is one of my favorites to talk to because she's so left-field yet so effectively reasonable and logical (she does have a PhD in logic...). LinkedIn: https://www.linkedin.com/in/alexandrapasi/

SummaryArtificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human"InterviewIntroductionHow did you get involved in machine learning?Can you start by unpacking the idea of "human-like" AI?How does that contrast with the conception of "AGI"?The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment?The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models?As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability?What are the practical/architectural methods necessary to build more cognitive AI systems?How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications?What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied?What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems?When is cognitive AI the wrong choice?What do you have planned for the future of cognitive AI applications at Aigo?Contact InfoLinkedInWebsiteParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksAigo.aiArtificial General IntelligenceCognitive AIKnowledge GraphCausal ModelingBayesian StatisticsThinking Fast & Slow by Daniel Kahneman (affiliate link)Agent-Based ModelingReinforcement LearningDARPA 3 Waves of AI presentationWhy Don't We Have AGI Yet? whitepaperConcepts Is All You Need WhitepaperHellen KellerStephen HawkingThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Hugo speaks with Shreya Shankar, a researcher at UC Berkeley focusing on data management systems with a human-centered approach. Shreya's work is at the cutting edge of human-computer interaction (HCI) and AI, particularly in the realm of large language models (LLMs). Her impressive background includes being the first ML engineer at Viaduct, doing research engineering at Google Brain, and software engineering at Facebook. In this episode, we dive deep into the world of LLMs and the critical challenges of building reliable AI pipelines. We'll explore: The fascinating journey from classic machine learning to the current LLM revolution Why Shreya believes most ML problems are actually data management issues The concept of "data flywheels" for LLM applications and how to implement them The intriguing world of evaluating AI systems - who validates the validators? Shreya's work on SPADE and EvalGen, innovative tools for synthesizing data quality assertions and aligning LLM evaluations with human preferences The importance of human-in-the-loop processes in AI development The future of low-code and no-code tools in the AI landscape We'll also touch on the potential pitfalls of over-relying on LLMs, the concept of "Habsburg AI," and how to avoid disappearing up our own proverbial arseholes in the world of recursive AI processes. Whether you're a seasoned AI practitioner, a curious data scientist, or someone interested in the human side of AI development, this conversation offers valuable insights into building more robust, reliable, and human-centered AI systems. LINKS The livestream on YouTube (https://youtube.com/live/hKV6xSJZkB0?feature=share) Shreya's website (https://www.sh-reya.com/) Shreya on Twitter (https://x.com/sh_reya) Data Flywheels for LLM Applications (https://www.sh-reya.com/blog/ai-engineering-flywheel/) SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines (https://arxiv.org/abs/2401.03038) What We've Learned From A Year of Building with LLMs (https://applied-llms.org/) Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (https://arxiv.org/abs/2404.12272) Operationalizing Machine Learning: An Interview Study (https://arxiv.org/abs/2209.09125) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) In the podcast, Hugo also mentioned that this was the 5th time he and Shreya chatted publicly. which is wild! If you want to dive deep into Shreya's work and related topics through their chats, you can check them all out here: Outerbounds' Fireside Chat: Operationalizing ML -- Patterns and Pain Points from MLOps Practitioners (https://www.youtube.com/watch?v=7zB6ESFto_U) The Past, Present, and Future of Generative AI (https://youtu.be/q0A9CdGWXqc?si=XmaUnQmZiXL2eagS) LLMs, OpenAI Dev Day, and the Existential Crisis for Machine Learning Engineering (https://www.youtube.com/live/MTJHvgJtynU?si=Ncjqn5YuFBemvOJ0) Lessons from a Year of Building with LLMs (https://youtube.com/live/c0gcsprsFig?feature=share) Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

Modeli LLM to aktualnie gorący temat. Aby efektywnie wdrożyć te modele w swojej firmie, konieczne jest stworzenie własnego leaderboardu, dopasowanego do konkretnych potrzeb biznesowych. Podpowiadam jak to zrobić, czyli od czego można zacząć (3 proste kroki). Choć istnieją zewnętrzne rozwiązania, to nie dają one pełnej kontroli i gwarancji trafności oceny. Warto czerpać z doświadczeń tradycyjnego ML, pamiętając jednak o zwiększonej złożoności procesu oceny LLM.✅ Dlaczego wszystkie modele AI są błędne i jak to wpływa na biznes?

In the realm of modern leadership, Doug Hebenthal, Chief Technology Officer at RealtyMogul, champions a philosophy that resonates deeply: there's no singular smartest person in any room; rather, the collective intelligence of a team drives true success. Doug exemplifies this belief through his role at RealtyMogul, where he not only oversees technological innovation but also fosters a culture of collaboration and shared knowledge. His approach underscores the transformative power of embracing diverse perspectives and pooling expertise, essential in navigating the complexities of real estate investment technology. By enabling a culture where every voice matters and every idea contributes, Doug Hebenthal embodies how collective intelligence propels organizations like RealtyMogul towards continual growth and achievement.Here's more about Doug HebenthalI am a 30+ year veteran in the business and technology world, former CTO of Realty Mogul, CTO of International Sports Sciences Association and CTO of Knowable. At Knowable we built a Contract Intelligence platform where I gained a wealth of knowledge in Machine Learning, NLP, and what it means to unlock the data assets that are hidden in contracts.Previous to Knowable I was at Axiom as CTO for 15 months, 21+ years at Microsoft, 2 years at Amazon, 3 years at Change Healthcare. Founding member of the Xbox team at Microsoft, I was one of the earliest working on the Internet, Project leader on some of the largest consumer projects ever, Engineering Manager of very large consumer projects and one of the largest payment platforms in the world.My super powers are clearly identifying, understanding and driving very difficult, complicated and high impact projects. I am very good at simplifying complex technology into solutions that ultimately drive customer value. My leadership has had a direct impact on extreme scale projects that impacted millions of people. I am very good at building highly functional teams, and finding the perfect alchemy of talent, focus and teamwork to drive for results. My experience at Microsoft, ranging from Technical Sales, Marketing, Engineering, Project Management and Leadership has uniquely prepared me to have a significant impact in any number of different industries or companies.At Change Healthcare I helped lead the drive to substantial change in Healthcare and in particular leapfrogging the technology gap that exists by driving cloud adoption as an accelerator for solution modernization. Work I started there is still going strong and will forever change US Healthcare. I was a 2.5 year member of the Amazon Web Services CIO Advisory Board while driving the Cloud Platform at Change.Specialties: Leadership, Management, Technology, ML/AI, Cloud, Consumer Hardware, Consumer Software, Gaming, Healthcare, Mentoring, Commerce, Payments, Legal Technology and Team Building.

In this episode of the Data Science Salon Podcast, host Anna Anisin sits down with two leading experts in the ML/AI healthcare industry. First, Sumayah Rahman, Director of Data Science - Machine Learning and Infrastructure at Cedar, discusses optimizing the patient experience to make healthcare more affordable and accessible. She explains how ML-powered discounts can benefit both patients and providers, sharing practical examples of using data to enhance patient experiences and highlighting the transformative impact of AI/ML in healthcare. Next, Vaibhav Verdhan, Analytics Leader at AstraZeneca, dives into the role of computer vision in healthcare and his favorite technologies in the healthcare analytics space. He discusses how advanced analytics are driving innovation at AstraZeneca by developing, deploying, and maintaining decision support capabilities. Both guests provide valuable insights into how AI and ML are revolutionizing healthcare, offering listeners practical knowledge and inspiration.

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ MLOps Coffee Sessions Special episode with LatticeFlow, How to Build Production-Ready AI Models for Manufacturing, fueled by our Premium Brand Partner, LatticeFlow. Deploying AI models in manufacturing involves navigating several technical challenges such as costly data acquisition, class imbalances, data shifts, leakage, and model degradation over time. How can you uncover the causes of model failures and prevent them effectively? This discussion covers practical solutions and advanced techniques to build resilient, safe, and high-performing AI systems in the manufacturing industry. // Bio Pavol Bielik Pavol earned his PhD at ETH Zurich, specializing in machine learning, symbolic AI, synthesis, and programming languages. His groundbreaking research earned him the prestigious Facebook Fellowship in 2017, representing the sole European recipient, along with the Romberg Grant in 2016. Following his doctorate, Pavol's passion for ensuring the safety and reliability of deep learning models led to the founding of LatticeFlow. Building on a more than a decade of research, Pavol and a dynamic team of researchers at LatticeFlow developed a platform that equips companies with the tools to deliver robust and high-performance AI models, utilizing automatic diagnosis and improvement of data and models. Aniket Singh Vision Systems Engineer AI Researcher Mohan Mahadevan Mohan Mahadevan is a seasoned technology leader with 25 years of experience in building computer vision (CV) and machine learning (ML) based products. Mohan has led teams to successfully deliver real world solutions spanning hardware, software, and AI based solutions in over 20 product families across a diverse range of domains, including Semiconductors, Robotics, Fintech, and Insuretech. Mohan Mahadevan has led global teams in the development of cutting-edge technologies across a range of disciplines including computer vision, machine learning, optical and hardware architectures, system design, computational optimization and more. Jürgen Weichenberger 20+ years of advanced analytics, data science, database design, architecture, and implementation on various platforms to solve Complex Industry Problems. Industrial Analytics is the fusion of manufacturing, production, reliability, integrity, quality, sales- and market-analytics and covering 10 Industries. By combining skills and experience, we are creating the next-generation AI & ML Solutions for our clients. Leveraging a unique formula which allows us to model some of the most challenging manufacturing problems while building, scaling, and enabling the end-user to leverage the next generation data products. The Strategy & Innoation Team at Schneider is specialising on Industrial-Grade Challenges where we are applying ML & AI methods to achieve state of the art results. Personally, I am driving my team and my own education to extend the limits of AI & ML beyond the current possible. I hold more than 15 patents and I am working on new innovations. I am working with our partner eco-system to enrich our accelerators with modern ML/AI techniques and integrating robotic equipment allows me to create next generation solutions. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://latticeflow.ai/ --------------- ✌️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/ Timestamps: [00:00] Demetrios' Intro [00:48] Announcements [01:57] Join us at our first in-person conference on June 25 all about AI Quality! [03:39] Speakers' intros [06:00] AI ML uncommon use cases [10:14] Challenges in Implementing AI and ML in Heavy Industries [11:41] Optimizing AI use cases [18:07] Moving from PoC to Production [20:53] Hybrid AI Integration for Safety [28:28] Training AI for Defect Variability [33:18] Challenges in AI Integration [35:39] Metrics for Evaluating Success [37:27] Challenges in AI Integration [44:39] Usage of LLMs [50:34] Fine-tuning AI Models [53:20] Trust Dynamics: TML vs LLM [55:23] Wrap up

In this episode, Anna sits down with two leaders in the finance industry, exploring the forefront of AI, ML, and ESG innovations. First, let's welcome Laura Gabrysiak, Data Science Leader at Visa. Laura develops statistical models and decision analytics tools that enable Visa clients to transform massive amounts of data into actionable ML models and AI implementations. She's also passionate about fostering the local data science community in Miami as the Founder of R-Ladies Miami. In this conversation, they dive into the future of ML/AI in financial services and the impactful work being done with Code Art to promote diversity in tech. Next, we have Rochelle March, former Head of ESG Product at Dun & Bradstreet. Rochelle specializes in impact analysis related to carbon, water, and the Sustainable Development Goals, and applies machine learning to ESG products. She also teaches data and analytics at Bard College's MBA program, sits on the advisory board for USL Technology, Inc., and mentors fellows in the Environmental Defense Fund's Climate Corps program. Since recording this episode, Rochelle has started her own company, People Places Words Actions. In our discussion, we explore her journey in ESG innovation and analytics, why ESG data is crucial for responsible investment decisions, and how it drives sustainable business practices. Tune in to learn from these industry thought leaders and gain insights into the cutting-edge applications of AI and ESG data in the finance sector.

In this episode, Anna sits down with two distinguished leaders in the ML/AI finance industry. First, we have Harry Mendell, Technology Group Data Architect at the Federal Reserve Bank of New York, who brings over 30 years of expertise in FinTech. Harry shares compelling stories and discusses emerging trends in the finance sector. Following Harry, Supreet Kaur, AVP at Morgan Stanley and product owner for various AI products, joins the conversation. Supreet provides insights into the use of synthetic data to protect customer privacy in FinTech, ensuring informed decision-making. This deep dive into synthetic data highlights its growing importance in the industry.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing Human-aligned AI Summer School, published by Jan Kulveit on May 22, 2024 on The AI Alignment Forum. The fourth Human-aligned AI Summer School will be held in Prague from 17th to 20th July 2024. We will meet for four intensive days of talks, workshops, and discussions covering latest trends in AI alignment research and broader framings of AI alignment research. Apply now , applications are evaluated on a rolling basis. The intended audience of the school are people interested in learning more about the AI alignment topics, PhD students, researchers working in ML/AI outside academia, and talented students. Format of the school The school is focused on teaching and exploring approaches and frameworks, less on presentation of the latest research results. The content of the school is mostly technical - it is assumed the attendees understand current ML approaches and some of the underlying theoretical frameworks. This year, the school will cover these main topics: Overview of the alignment problem and current approaches. Alignment of large language models: RLHF, DPO and beyond. Methods used to align current large language models and their shortcomings. Evaluating and measuring AI systems: How to understand and oversee current AI systems on the behavioral level. Interpretability and the science of deep learning: What's going on inside of the models? AI alignment theory: While 'prosaic' approaches to alignment focus on current systems, theory aims for deeper understanding and better generalizability. Alignment in the context of complex systems and multi-agent settings: What should the AI be aligned to? In most realistic settings, we can expect there are multiple stakeholders and many interacting AI systems; any solutions to alignment problem need to solve multi-agent settings. The school consists of lectures and topical series, focused smaller-group workshops and discussions, expert panels, and opportunities for networking, project brainstorming and informal discussions. Detailed program of the school will be announced shortly before the event. See below for a program outline and e.g. the program of the previous school for an illustration of the program content and structure. Confirmed speakers Stephen Casper - Algorithmic Alignment Group, MIT. Stanislav Fort - Google DeepMind. Jesse Hoogland - Timaeus. Jan Kulveit - Alignment of Complex Systems, Charles University. Mary Phuong - Google DeepMind. Deger Turan - AI Objectives Institute and Metaculus. Vikrant Varma - Google DeepMind. Neel Nanda - Google DeepMind. (more to be announced later) Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

In this episode of Deciphered, Adam Davis, expert associate partner at Bain & Company, is joined by Wendy Redshaw, Chief Digital Information Officer at NatWest, and Jason Maude, chief technology advocate of Starling Bank, to discuss if there is still a technology gap between incumbents vs challengers.Timestamps:04:15 Incumbents vs Challengers – is there still a technology gap?05:24 How would you explain the technology gap previously between large tier 1s and neobanks?09:57 What are the key differences between a challenger stack and an incumbent stack?12:31 How have Starling maintained a simple / flexible architecture through the launch of new products and the scaling of your customer base?15:24 Occasional vs repeatable releases16:02 Centralisation efforts that have happened with NatWest's tech estate over recent times19:36 Is the money banks spend on IT transformation programmes worth it?21:33 Do you believe the combination of ML / AI and Generative AI, alongside exclusive Cloud deployment, will level the playing field?25:15 What underpins the culture you want to see at your organizations, and how do you encourage it?29:57 How do you keep up with Futurology and trends, and does any of that thinking feature in your tech roadmaps?34:30 What is your one prediction for tech trends into 2024?Please subscribe to the show so you never miss an episode, and leave us a review if you enjoy the show!You can find Adam Davis hereYou can find Wendy Redshaw hereYou can find Jason Maude hereFor more insights from the Deciphered podcast, visit the page on Bain's website

Join Nate Matherson as he sits down with Ray Grieselhuber for the forty-ninth episode of the Optimize podcast. Ray is the CEO of DemandSphere, a Y Combinator-backed data and analytics platform built for e-commerce and programmatic SEO teams, both in-house and agency. With over 17 years of experience in building and scaling SEO solutions, he leads a team of experts in ML, AI, data engineering, and organic marketing In our episode today, Ray shares his insights on the evolving landscape of SERP features and their impact on user behavior, the challenges of Google's focus on bigger brands in recent algorithm updates, and why he emphasizes the importance of holistic monitoring of the SERP for effective SEO strategies. Ray highlights the importance of SERP analytics in understanding both past and future trends in search. He also touches on the significance of user behavior metrics in Google's rankings and shares his thoughts on Google's evolving algorithm. Closing the episode is our popular lightning round of questions!   Learn More About Ray Grieselhuber Ray Grieselhuber is the CEO of DemandSphere, a Y Combinator-backed data and analytics platform built for ecommerce and programmatic SEO teams, both in-house and agency. With over 17 years experience in building and scaling SEO solutions, he leads a team of experts in ML / AI, data engineering, and organic marketing.Links:https://www.demandsphere.comTwitter: https://twitter.com/raygrieselhuberLinkedIn: https://www.linkedin.com/in/raygrieselhuber/

Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human" Interview Introduction How did you get involved in machine learning? Can you start by unpacking the idea of "human-like" AI? How does that contrast with the conception of "AGI"? The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment? The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models? As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems? How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications? What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems? When is cognitive AI the wrong choice? What do you have planned for the future of cognitive AI applications at Aigo? Contact Info LinkedIn (https://www.linkedin.com/in/vosspeter/) Website (http://optimal.org/voss.html) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Aigo.ai (https://aigo.ai/) Artificial General Intelligence (https://aigo.ai/what-is-real-agi/) Cognitive AI (https://aigo.ai/cognitive-ai/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Causal Modeling (https://en.wikipedia.org/wiki/Causal_model) Bayesian Statistics (https://en.wikipedia.org/wiki/Bayesian_statistics) Thinking Fast & Slow (https://amzn.to/3UJKsmK) by Daniel Kahneman (affiliate link) Agent-Based Modeling (https://en.wikipedia.org/wiki/Agent-based_model) Reinforcement Learning (https://en.wikipedia.org/wiki/Reinforcement_learning) DARPA 3 Waves of AI (https://www.darpa.mil/about-us/darpa-perspective-on-ai) presentation Why Don't We Have AGI Yet? (https://arxiv.org/abs/2308.03598) whitepaper Concepts Is All You Need (https://arxiv.org/abs/2309.01622) Whitepaper Hellen Keller (https://en.wikipedia.org/wiki/Helen_Keller) Stephen Hawking (https://en.wikipedia.org/wiki/Stephen_Hawking) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

The Joint Readiness Training Center is pleased to present the fifty-third episode to air on ‘The Crucible - The JRTC Experience.' Hosted by the Commander of Ops Group (COG), COL Matthew Hardman. Today's guest is Director of Operations Research Center at the U.S. Military Academy at West Point, LTC David Beskow, PhD. He has a PhD in Societal Computing from Carnegie Mellon University School of Computer Science and serves in the Department of Systems Engineering.   The Operations Research Center (ORCEN) provides a dedicated analytical capability that engages problems of national significance for the purpose of enriching cadet education, enhancing the professional development of Operations Research Systems Analysis Officer Faculty, integrating emerging technologies and analytical tools into the Academic Program, and sustaining ties between the Academy, the Army, and the Department of Defense (DoD). The United States Military Academy (USMA) is a United States service academy in West Point, New York. It was originally established as a fort during the Revolutionary War, as it sits on strategic high ground overlooking the Hudson River 50 miles (80 km) north of New York City. It is the oldest of the five American service academies and educates cadets for commissioning into the United States Army.   In this episode we continue to discuss warfighting on the modern battlefield, the incorporation of technology as a combat multiplier, and preparing the force for AI centric warfare of the future. Specifically, we discuss using data to feed intelligence and the operations process as well as how the Army is planning to incorporate emerging technologies into its formations on the modern battlefield. We also look at the application of machine learning to sift through massive amounts of data to find the nuggets of key information, classify it, and then start to do predictive analysis. LTC Beskow's department has been tasked to look at: How do we become more data enabled as a fighting formation? How can we better utilize technology, especially ML/AI? Do we have the right systems in place to collect the data to feed ML/AI? If not, what methodology would you recommend? For the CTCs like JRTC, his team is looking at: What data do the CTCs produce that the Army could leverage? What changes to the collection requirements would you recommend that would be least impactful from a collection process but be massively impactful to the Army at large? How can we better use the data? Understanding human performance, streamlining our acquisitions, better utilization of ML/AI, etc.   Part of S01 “The Leader's Laboratory” series.   For additional information and insights from this episode, please check-out our Instagram page @the_jrtc_crucible_podcast   Be sure to follow us on social media to keep up with the latest warfighting TTPs learned through the crucible that is the Joint Readiness Training Center.   Follow us by going to: https://linktr.ee/jrtc and then selecting your preferred podcast format.   Again, we'd like to thank our guests for participating. Don't forget to like, subscribe, and review us wherever you listen or watch your podcasts — and be sure to stay tuned for more in the near future.   “The Crucible – The JRTC Experience” is a product of the Joint Readiness Training Center.

Shayna Solis is a creative soul applying her vision and dreams to shake up technology applications through her company Navteca. Focusing on emerging tech and IT innovation, like cloud, ML/AI, and virtual reality, Navteca develops technical solutions for government clients like NASA, NOAA, and the Spain Ministry of Tourism. Shayna's vision for Navteca extends beyond business success, aiming to leverage technology, particularly AI and data visualization, to address pressing global challenges like climate change. Here are the highlights of our talk: - Voice Atlas Revolutionizes Access to Knowledge: Navteca's Voice Atlas is a product born out of their exploration of natural language processing and AI. Initially piloted with NASA, Voice Atlas enables users to access designated knowledge bases securely and seamlessly through voice-enabled devices, revolutionizing information accessibility and usability. - A Twist on the Path to Being a Founder: Shayna's journey to becoming a tech entrepreneur wasn't linear. With a background in fine arts and a childhood dream of becoming a NASA astronaut, she emphasizes the value of multidisciplinary experiences in bringing fresh perspectives to the tech industry. Her eclectic inspirations from music, literature, and current affairs underscore her multifaceted approach to innovation and leadership. - Global Teams Cultivates Creativity: Despite being headquartered in Washington, DC, Navteca boasts a global team spanning across continents. Shayna underscores the importance of diversity and creativity within their team, fostering an environment where innovative ideas flourish. - Lessons in Entrepreneurship: Shayna shares candid insights into the challenges of entrepreneurship, from facing fears head-on to the importance of building and maintaining relationships, especially during tough times such as the COVID-19 pandemic. - Tech Ecosystem in Washington, DC: Contrary to misconceptions, Washington, DC, hosts a vibrant tech ecosystem, particularly in collaboration with government agencies. Shayna highlights the opportunities and benefits of being located in DC for tech entrepreneurs aiming to work with governmental and international organizations. Shayna Solis is the co-founder, CEO, and creative mastermind behind Navteca, a technology company with a focus on emerging technologies and IT innovation. For more than a decade, Navteca has created practical applications based on new and emerging technology to help fulfill customers' needs, solve real-world problems, and use technology for good through artificial intelligence and machine learning, cloud technology, high-performance computing, platform engineering, virtual and augmented reality, and GIS data visualization. Solis is also the visionary behind Voice Atlas, a safe and secure conversational AI product tailored for government and enterprise use. Currently employed by entities such as the D.C. Government, NASA, and the Johns Hopkins University Applied Physics Lab, Voice Atlas facilitates insightful answers and knowledge across custom, curated databases. She is a graduate of Towson University, where she earned her Bachelor's degree with a dual concentration in English and Spanish. Connect with Shayna: Website: https://navteca.com/index.html LinkedIn: https://www.linkedin.com/in/shaynasolis/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web and it is ranked the number 10 CEO podcast to listen to in 2024! https://podcasts.feedspot.com/ceo_podcasts/ LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ Twitter: @DisruptiveCEO #digitalmarketing #branding #socialgood #CEO #startup #startupstory #founder #business #businesspodcast #podcast #aerospace Learn more about your ad choices. Visit megaphone.fm/adchoices

Patrick Beukema has a Ph.D. in neuroscience and has worked on AI models for brain decoding, which analyzes the brain's activity to decipher what people are seeing and thinking. Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to LatticeFlow for sponsoring this episode. LatticeFlow - https://latticeflow.ai/ MLOps podcast #225 with Patrick Beukema, Head / Technical Lead of the Environmental AI, Applied Science Organization at AI2, Beyond AGI, Can AI Help Save the Planet? // Abstract AI will play a central role in solving some of our greatest environmental challenges. The technology that we need to solve these problems is in a nascent stage -- we are just getting started. For example, the combination of remote sensing (satellites) and high-performance AI operating at a global scale in real-time unlocks unprecedented avenues to new intelligence. MLOPs is often overlooked on AI teams, and typically there is a lot of friction in integrating software engineering best practices into the ML/AI workflow. However, performance ML/AI depends on extremely tight feedback loops from the user back to the model that enables high iteration velocity and ultimately continual improvement. We are making progress but environmental causes need your help. Join us fight for sustainability and conservation. // Bio Patrick is a machine learning engineer and scientist with a deep passion for leveraging artificial intelligence for social good. He currently leads the environmental AI team at the Allen Institute for Artificial Intelligence (AI2). His professional interests extend to enhancing scientific rigor in academia, where he is a strong advocate for the integration of professional software engineering practices to ensure reliability and reproducibility in academic research. Patrick holds a Ph.D. from the Center for Neuroscience at the University of Pittsburgh and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where his research focused on neural plasticity and accelerated learning. He applied this expertise to develop state-of-the-art deep learning models for brain decoding of patient populations at a startup, later acquired by BlackRock. His earlier academic work spanned research on recurrent neural networks, causal inference, and ecology and biodiversity. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Variety of relevant papers/talks/links on Patrick's website: https://pbeukema.github.io/ --------------- ✌️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 Patrick on LinkedIn: https://www.linkedin.com/in/plbeukema/ Timestamps: [00:00] AI Quality Conference [01:29] Patrick's preferred coffee [02:00] Takeaways [04:14] Learning how to learn journey [07:04] Patrick's day to day [08:39] Environmental AI [11:07] Environmental AI models [14:35] Nature Inspires Scientific Advances [18:11] R&D [24:58] Iterative Feedback-Driven Development [26:37 - 28:07] LatticeFlow Ad [33:58] Balancing Metrics for Success [38:16] Model Retraining Pipeline [44:11] Series Models: Versatility [45:57] Edge Models Enhance Output [50:22] Custom Models for Specific Data [53:53] Wrap up

As AR and AI continue to sweep the construction industry, what are some of the best and most proven ways to leverage this enigmatic technology? Could it, in fact, help us address some of the largest and most persistent pain points of the industry at large? Join Todd and guest NK Chaitanya as they discuss the connection between data and AI, NK's company ConstructN.ai, how AR can be leveraged to increase onsite safety, and the burgeoning benefits of artificial intelligence. Chaitanya Naredla Kirshan, widely known as NK, stands at the forefront of the rapidly evolving technology industry. As the Co-Founder and CEO of Constructn.ai, NK is pioneering the future of construction monitoring, bringing simplicity and automation to complex processes across the build timeline. His rich experience weaves together advanced domains such as robotics, computer vision, extended reality (XR), machine learning/artificial intelligence (ML/AI), and the Internet of Things technologies.

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, “Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications.” Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

According to a study conducted by Zippia, organizations with a comprehensive training program see 24% higher profit margins. So how can you improve rep readiness with a unified platform?Shawnna Sumaoang: Hi, and welcome to the Win Win Podcast. I am your host, Shawnna Sumaoang. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Marc Losito, the chief of staff at FoodChain ID. Thank you for joining us, Marc. I’d love for you to tell us about yourself, your background, and your role. Marc Losito: It’s a pleasure to be here and, currently, I serve as the Chief of Staff at FoodChain ID and the Senior Director of Strategic Initiatives. I just finished a 23-year career in the military where I finished up in strategy and operations. And so transitioning into a strategic initiatives role or an operations-based role is exciting, and fluid for me. I’ve been at FoodChain ID for over a year now, and we’ve been employing Highspot as our primary sales enablement tool for about eight months. SS: We’re excited to have you here with us today. Now I know when you first started at FoodChain ID, one of your first tasks was to implement an enablement platform. Tell us about that journey. Why was it a strategic priority for the business to invest in an enablement solution? ML: Yeah, that’s a great way to phrase it, it was a journey. So, about this time last year, our executive leadership team gathered together and began to evaluate our strategic growth options, and sales enablement kept rising to the top. Brandon Taylor, our Chief Revenue Officer our champion of Highspot really put it on the front of our growth initiatives and really championed our adoption of Highspot. It wasn’t too much longer after that, and it was about, May of last year that we began to adopt Highspot and we rolled it out in August. We have been rolling ever since. One of the key differences that we’ve seen, and it’s really just the realization of this, this growth narrative that Highspot brings to sales enablement is we were able to cut down our seller ramp time from nine months to six months immediately with the training and coaching features on Highspot and, professionalizing our onboarding, putting it all into one spot and having sellers singing off the same choir sheet, as it were. SS: That is amazing to go from nine months to six months, and I definitely want to circle back to that. I do want to get a better sense because you were the executive sponsor for the evaluation and I know that you partnered closely with other key stakeholders in the process. How did you partner with your CRO and RevOps to find the right solution for the business and ultimately gain buy-in? ML: That’s a great question. Our CRO, Brandon Taylor, was the champion of this growth initiative, and Ryan Wing, our Director of Revenue Operations, was crucial to making sure that we were all aligned on a collective vision, making sure that our strategic goals were synchronized, and to make sure that everything from, sales enablement content to the way we wanted to orchestrate our plays and the KPIs that we had set out, the first being to decrease that ramp time. We thought that was the closest crocodile to the canoe if you will. We’re continuing to chase, some other goals, and our CRO has really put a high bar on what we want to achieve with this. With Highspot in, shortening our sales cycle times, increasing our win rates, increasing our ACVs, and increasing our opportunity creation, but ultimately getting their buy-in was the first step and making sure that we were all aligned on what the opportunity was and what the return on investment could be if we unanimously supported the adoption. SS: Absolutely. And I know that having one unified solution for enablement at FoodChain ID was really important rather than separate tools to equip, train, and coach your teams. In your opinion, what has been the impact of that unified experience on your sellers and their productivity? ML: The essence here is, bringing fragmented tools from across our enterprise and bringing them into one centralized location. It’s like switching from a vehicle that has manual steering, where you’re trying to struggle to shift and pivot with market trends, market changes, and competitor dynamics. But bringing Highspot into FoodChain ID is like switching to autopilot. You’re able to cue seller behaviors so quickly. You’re able to pivot and adapt to key changes that you’re seeing in the marketplace and it allows for a seamless inflow of information. As a result, our sellers become more agile, more informed, they execute it. Seller behaviors and plays are better, and they’re significantly more effective. And we’re starting to see the impacts of that. SS: Now, we alluded to one of the big wins earlier, but I know since implementation, you guys have shortened your onboarding time from nine months to six months, and you’ve also reduced ramp time by 30%. Can you walk us through how you optimized sales onboarding and ultimately drove these very impressive results? ML: I tell you, reducing onboarding time was a challenge but it’s one that Highspot is tailor-made to go after. And so we focused on three primary areas with Highspot when it comes to onboarding, which is customized learning paths. Integrating real-world scenarios into the training process and then leveraging the enormous amount of data and feedback that you get to continuously refine your onboarding approach. And so that triad not only expedited our onboarding process, but it ensured that new team members were sales-ready in a shorter amount of time. SS: Fantastic. I love to hear that. In addition to onboarding, your team also focused on improving sales coaching. In your opinion, what is the value of real-world coaching for sales reps? ML: There’s nothing that can replace real-world coaching. You’re not going to be able to automate or AI your way out of real-world, human touch. And the crucible where theory meets practice. When we’re onboarding reps, it’s not only important for them to learn in a classroom, but it’s also important to have a setting to apply, iterate, and refine their approaches in real scenarios with a feedback loop. That accelerates their learning and adaptability. And ultimately, the adage is true that practice makes perfect. And in today’s dynamic market, that’s especially critical. SS: You did mention AI, so I’m curious. How do you plan to utilize innovation in the enablement space like AI to help your team deliver effective coaching? ML: I actually have a bit of a background in AI, from graduate school and from my time in the military. I just believe that AI opens up a whole new frontier that revolutionizes sales coaching, by using AI features like meeting intelligence, we can personalize learning you can personalize it at scale you can provide real-time feedback, and more importantly is that you can identify patterns that would be impossible for humans to detect. And it’s not about replacing the human element. Like I said, nothing is going to replace the human touch in training and coaching, but augmenting it, and learning how to use AI with that human touch is going to make coaching more impactful and insights-driven. SS: Absolutely. And I did not know that about your background, that you have a background in AI. So I’d love to get your opinion: how can AI help the business scale sales productivity more broadly? ML: I’ve seen firsthand from my time in the military, how AI can have a transformative power globally and on the battlefield. And if you think of sales as a battlefield it scales productivity by automating routine tasks that otherwise take sellers away from engaging the customers, it delivers insights that would take us ages to analyze, with torrents of data, stacks upon stacks, and personalize the customer experience as well at scale. It’s a game changer, and it turns data into a strategic asset for every organization.  SS: How do you think AI will continue to drive business innovation in the near future, especially when it comes to enablement? ML: AI is bound to expand, especially in enablement and business innovation. I think we’re going to see AI become more integrated into daily operations. Highspot is already at the forefront here with meeting intelligence, being part of every sales engagement, and providing analytics.I think those analytics will become more accurate over time as we, train new models and reach new heights. But at the end of the day, the potential is it’s pretty vast: from automating administrative tasks to delivering those strategic insights and shaping seller behavior and future directions to make our customer experience more delightful. SS: Mark, last question for you. As you look ahead, how do you plan to leverage Highspot to help you achieve some of the innovation that you’re aiming to drive this year? ML: That’s a great question. So where do we go after our tremendous start? And I think what our CRO would tell you, and our director of revenue ops would tell you is that Highspot is poised to be a cornerstone of our strategy to drive innovation.Internally to FoodChain ID, we refer to Highspot as the sales accelerator. It is what propels our sales cycles forward. So we plan to leverage its capabilities to personalize learning scale development. We’re currently going through an environment optimization where we are using the motto of all the sales enablement you need when you need it, and none of it when you don’t.So the key there. Is that a sales rep doesn’t have to wade through this swamp of sales enablement that they don’t particularly need at that time, and Highspot is tailor-made – with its filtering, its lists, and its search features – to provide the right sales enablement at the right time and none that you don’t SS: I love that. I might have to steal some of those taglines for our marketing efforts. Mark, thank you so much for joining us today. I really appreciate it. Absolutely. ML: Thank you. It’s a pleasure to be here. SS: To our audience. Thank you for listening to this episode of the Win Win podcast. Be sure to tune in next time for more insights on how you can maximize enablement success with Highspot.

Summary Artificial intelligence technologies promise to revolutionize business and produce new sources of value. In order to make those promises a reality there is a substantial amount of strategy and investment required. Colleen Tartow has worked across all stages of the data lifecycle, and in this episode she shares her hard-earned wisdom about how to conduct an AI program for your organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council (https://www.dataengineeringpodcast.com/data-council) and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Colleen Tartow about the questions to answer before and during the development of an AI program Interview Introduction How did you get involved in the area of data management? When you say "AI Program", what are the organizational, technical, and strategic elements that it encompasses? How does the idea of an "AI Program" differ from an "AI Product"? What are some of the signals to watch for that indicate an objective for which AI is not a reasonable solution? Who needs to be involved in the process of defining and developing that program? What are the skills and systems that need to be in place to effectively execute on an AI program? "AI" has grown to be an even more overloaded term than it already was. What are some of the useful clarifying/scoping questions to address when deciding the path to deployment for different definitions of "AI"? Organizations can easily fall into the trap of green-lighting an AI project before they have done the work of ensuring they have the necessary data and the ability to process it. What are the steps to take to build confidence in the availability of the data? Even if you are sure that you can get the data, what are the implementation pitfalls that teams should be wary of while building out the data flows for powering the AI system? What are the key considerations for powering AI applications that are substantially different from analytical applications? The ecosystem for ML/AI is a rapidly moving target. What are the foundational/fundamental principles that you need to design around to allow for future flexibility? What are the most interesting, innovative, or unexpected ways that you have seen AI programs implemented? What are the most interesting, unexpected, or challenging lessons that you have learned while working on powering AI systems? When is AI the wrong choice? What do you have planned for the future of your work at VAST Data? Contact Info LinkedIn (https://www.linkedin.com/in/colleen-tartow-phd/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links VAST Data (https://vastdata.com/) Colleen's Previous Appearance (https://www.dataengineeringpodcast.com/starburst-lakehouse-modern-data-architecture-episode-304) Linear Regression (https://en.wikipedia.org/wiki/Linear_regression) CoreWeave (https://www.coreweave.com/) Lambda Labs (https://lambdalabs.com/) MAD Landscape (https://mattturck.com/mad2023/) Podcast Episode (https://www.dataengineeringpodcast.com/mad-landscape-2023-data-infrastructure-episode-369) ML Episode (https://www.themachinelearningpodcast.com/mad-landscape-2023-ml-ai-episode-21) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Go to www.thejasoncavnessexperience.com for the full episode and other episodes of The Jason Cavness Experience on your favorite platforms.   Sponsor   CavnessHR delivers HR companies with 49 or fewer people with our HR platform and by providing you access to your own HRBP.    www.CavnessHR.com   Sean's Bio   I am a scientist and entrepreneur leading ML/AI teams and research in the Seattle area. I earned my Ph.D. in computational astrophysics from the University of Washington, developing source characterization algorithms for a NASA/DOE Gamma Ray Telescope. I then became involved in numerical simulation, modeling, Monte-Carlo particle transport, machine learning and anomaly detection, as well as in the direction of international nuclear policy.   I served as researcher and science lead for various projects in computational physics and was involved in development of computer vision algorithms, spatial object localization, deep learning for anomaly detection and NLP. A passion for entrepreneurialism and developing algorithms to empower business products led me to the startup world, where I have led ML/AI concepts and teams across a broad spectrum in an R&D environment, and applied descriptive and inferential statistics to marketing, and process optimization for business.   I have been active in the areas of Image Generation Models (e.g. StableDiffusion) and Large Language Models (LLM) (e.g. the GPT series) for many years and am excited for the future of tech in these areas.   I also produce the occasional academic paper about my research and continue to operate at the nexus of tech development, business and management. In my free time, you can often find me bicycling, participating in or helping to organize LARP events, or speaking at conventions about the nature of science and technology in pop culture and the future of the modern world.    We talked about the following and other items   Rock climbing and its mental benefits    Universal basic income and its effectiveness   Developing detection algorithms for nuclear security    AI research and entrepreneurship.   Startup ideas and investment criteria   Startup funding and validation process   Startup success factors Particle simulation in physics   Particle physics and detector simulations   Dark matter and the search for alternative theories   Consciousness through AI development   Ethical considerations in AI development   AI decision-making and validation   AI's impact on various industries and startups.   AI technology and its limitations   AI technology and its global implications    Sean's Social Media    Sean's LinkedIn: https://www.linkedin.com/in/sean-robinson-phd/   Sean's FB: https://www.facebook.com/sean.robinson.12532   Sean's Advice   We have a really good shot here. My whole career has been about taking this shot. How do we get beyond scarcity? This is such a big dream and one of the first times we've really had a shot at it. So if we all work hard, we probably will get there.   If you're the sort of person who's good at talking, try and convince people that this is going to be good. We are gonna get to the good side of this future. I really want it to be the good side. Please keep working towards that.

Stuart McClure is one of the foremost cybersecurity experts in the world and a serial entrepreneur with successful exits. He has built successful products leveraging ML & AI for cybersecurity. Stu is also a student of psychology and his new company, Wethos AI, leverages AI to codify traits of high performing teams.

Sheetal Rajpal, an amazing product leader with experience at Amazon, LendingTree, and PepsiCo joins the podcast to discuss the future of B2C subscription products (and all digital products, for that matter) and how ML, AI, data analysis, and more will be shaping that future. Learn more at ProductVoices.com.

Returning guest Melvin Newman tells us what's been going on with Patabid since we last saw him. There's a new partnership with City Electric Supply, improved estimating software and AI, and even more time savings for contractors. Melvin is the President/CTO of PataBid. Prior to co-founding PataBid in 2018, Melvin worked in the construction industry for 15+ years as an estimator and project manager. As an entrepreneur, Melvin co-founded PataBid and is the full stack developer on the platform, specializing in the development of the internal ML/AI systems. In addition to developing the platform, Melvin is responsible for building the deployment systems and developing key business partnerships.

Jake Watson is the writer of thedataplatform.substack.com⁠ and Principal Data Engineer at The Oakland Group. MLOps podcast #207 with Jake Watson, Principal Data Engineer at The Oakland Group, How Data Platforms Affect ML & AI. // Abstract I've always told my clients and colleagues that traditional rule-based software is difficult, but software containing Artificial Intelligence (AI) and/or Machine Learning (ML)* is even more difficult, sometimes impossible. Why is this the case? Well, software is difficult because it's like flying a plane while building it at the same time, but because AI and ML make rules on the fly based on various factors like training data, it's like trying to build a plane in flight, but some parts of the plane will be designed by a machine, and you have little idea what that is going to look like till the machine finishes. This double goes for more cutting-edge AI models like GPT, where only the creators of the software have a vague idea of what it will output. This makes software with AI / ML more of a scientific experiment than engineering, which is going to make your project manager lose their mind when you have little idea how long a task is going to take. But what will make everyone's lives easier is having solid data foundations to work from. Learn to walk before running. // Bio Jake has been working in data as an Analyst, Engineer, and/or Architect for over 10 years. Started as an analyst in the UK National Health Service converting spreadsheets to databases tracking surgical instruments. Then continued as an analyst at a consultancy (Capita) reporting on employee engagement in the NHS and dozens of UK Universities. There Jake moved reporting from Excel and Access to SQL Server, Python with frontend websites in d3.js. At Oakland Group, a data consultancy, Jake worked as a Cloud Engineer, Data Engineer, Tech Lead, and Architect depending on the project for dozens of clients both big and small (mostly big). Jake has also developed and productionised ML solutions as well in the NLP and classification space. Jake has experience in building Data Platforms in Azure, AWS, and GCP (though mostly in Azure and AWS) using Infrastructure as Code and DevOps/DataOps/MLOps. In the last year, Jake has been writing articles and newsletters for my blog, including a guide on how to build a data platform: https://thedataplatform.substack.com/p/how-to-build-a-data-platform // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://thedataplatform.substack.com/ How Data Platform Foundations Impact AI and ML Applications blog: https://thedataplatform.substack.com/p/issue-29-how-data-platform-foundations AI in Production Conference: https://home.mlops.community/public/events/ai-in-production-2024-02-15 How to Build a Data Platform blog: https://thedataplatform.substack.com/p/how-to-build-a-data-platform --------------- ✌️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 Jake on LinkedIn: https://www.linkedin.com/in/jake-watson-data/ Timestamps: [00:00] Jake's preferred coffee [00:26] AI in Production Conference teaser [02:38] Takeaways [04:00] Please like, share, and subscribe to our MLOps channels! [04:17] Data Engineer's Crucial Role [05:44] Jake's background [06:44] Data Platform Foundations blog [10:34] Data mesh organizational side of things [17:58] Importance of data modeling [20:13] Dealing with the sprawl [22:03] Data quality [23:59] Data hierarchy on building a platform [29:34] ML Platform Team Structure [31:47] Don't reinvent the wheel [34:04] Data pipelines synergy [37:31] Wrap up

Diving into the transformative trends reshaping the RevOps landscape in 2024, this episode sheds light on the escalating significance of RevOps as an essential element in orchestrating intricate customer journeys. It centers around fostering team synergy, transitioning towards real-time metrics for dynamic decision-making, and leveraging the burgeoning capabilities of ML & AI to streamline data processes. As we navigate these pivotal shifts, a critical checklist is presented, pinpointing the strategic role of RevOps, the adoption of real-time metrics, and the enhanced utilization of ML & AI tools as the key drivers poised to redefine organizational dynamics and operational efficiency.Hit the subscribe button on your favorite podcast player so you don't miss the next episode. ______________________________________________Pre-order Jake's book, "The Innovative Seller" and get access to over $300 in free bonuses! https://www.jakedunlap.com/the-innovative-seller Get instant access to AI Sales Prompt Pro, our new premium Notion database with the best ready-to-use prompts for chatGPT. Grab it here and join our community for only $50/year: https://skaled.com/insights/ai-sales-prompt-pro Registrations are OPEN for ‘Sales AI Unleashed', a webinar series w/ Kevin Dorsey. Join 1000+ sales leaders who are mastering AI and chatGPT during our free live sessions. Sign up here: https://bit.ly/sales-ai-unleashed-series If you're a leader and want to integrate AI into your org - book some time with me to talk about how we can build a custom system for your needs:https://savvycal.com/Jake-Dunlap/modern-leader Sign up for the Modern Leader newsletter for more tips and talks from Jake:Email: https://skaled.com/modern-leader-sign-upLinkedIn: https://bit.ly/modern-leader-newsletter Follow Jake:LinkedIn: https://linkedin.com/in/jakedunlapInstagram: https://instagram.com/jake_dunlap_Twitter: https://twitter.com/jaketdunlapWebsite: https://jakedunlap.com

“AI is likely to be either the best or worst thing to happen to humanity.” ~Stephen Hawking Today's episode discusses machine learning (ML) & artificial intelligence (AI) which has become increasingly prominent in Communications Centers. Despite some pushbacks, it has already become an essential part of our daily lives. We are right, however, to slow down and wonder why it is advancing so fast, who has a stake in it, and what we can do to correct its course if necessary. Most importantly, what happens when AI begins to think on its own and becomes self-aware? Some leaders speak of the potential of machine learning (ML) & artificial intelligence (AI), and how it's nothing short of miraculous, but could the blessing be a curse? But as the saying goes, with great power comes great responsibility. What about you, what do you think of ML/AI in your Communications Center. wespeakdispatch@gmail.com With our podcast plays are passing 26,000 in over 45 countries we're not sure what you're listening to, but maybe you should be tuning into ours!! Most episodes are about 15 minutes which is perfect for the drive to/from work, or on a break. We have so many amazing guests, and you could be too - send us an email or DM and we can make that happen. Watch or listen to us on your favorite podcast platform or YouTube, Facebook, Instagram & even Tiktok www.linktr.ee/WeSpeakDispatch Thanks to our sponsor Watson Consoles – please visit them at: www.watsonconsoles.com #IAM911#WSD2023#911DISPATCHERS#watsonconsoles

In this episode, I talk with Jon Bennion, a seasoned ML | AI Engineer at LLM Ops, about the fascinating world of AI in Forecasting. Jon shares his expertise in rapid prototyping of AI/ML models and how they are deployed in real-world applications, emphasizing the importance of goal metric orientation for measuring ROI. Join us as we explore the LangChain-centric approach and delve into topics such as Deep Learning, Machine Learning, and Clustering in the context of Forecasting with AI. Investor Email: jaeden@aibox.ai Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Facebook Community: ⁠https://www.facebook.com/groups/739308654562189 Follow me on X: ⁠⁠https://twitter.com/jaeden_ai⁠⁠

Value: After Hours is a podcast about value investing, Fintwit, and all things finance and investment by investors Tobias Carlisle, and Jake Taylor. See our latest episodes at https://acquirersmultiple.com/podcast We are live every Tuesday at 1.30pm E / 10.30am P. About Jake: Jake is a partner at Farnam Street. Jake's website: http://farnam-street.com/vah Jake's podcast: https://twitter.com/5_GQs Jake's Twitter: https://twitter.com/farnamjake1 Jake's book: The Rebel Allocator https://amzn.to/2sgip3l ABOUT THE PODCAST Hi, I'm Tobias Carlisle. I launched The Acquirers Podcast to discuss the process of finding undervalued stocks, deep value investing, hedge funds, activism, buyouts, and special situations. We uncover the tactics and strategies for finding good investments, managing risk, dealing with bad luck, and maximizing success. SEE LATEST EPISODES https://acquirersmultiple.com/podcast/ SEE OUR FREE DEEP VALUE STOCK SCREENER https://acquirersmultiple.com/screener/ FOLLOW TOBIAS Website: https://acquirersmultiple.com/ Firm: https://acquirersfunds.com/ Twitter: https://twitter.com/Greenbackd LinkedIn: https://www.linkedin.com/in/tobycarlisle Facebook: https://www.facebook.com/tobiascarlisle Instagram: https://www.instagram.com/tobias_carlisle ABOUT TOBIAS CARLISLE Tobias Carlisle is the founder of The Acquirer's Multiple®, and Acquirers Funds®. He is best known as the author of the #1 new release in Amazon's Business and Finance The Acquirer's Multiple: How the Billionaire Contrarians of Deep Value Beat the Market, the Amazon best-sellers Deep Value: Why Activists Investors and Other Contrarians Battle for Control of Losing Corporations (2014) (https://amzn.to/2VwvAGF), Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors (2012) (https://amzn.to/2SDDxrN), and Concentrated Investing: Strategies of the World's Greatest Concentrated Value Investors (2016) (https://amzn.to/2SEEjVn). He has extensive experience in investment management, business valuation, public company corporate governance, and corporate law. Prior to founding the forerunner to Acquirers Funds in 2010, Tobias was an analyst at an activist hedge fund, general counsel of a company listed on the Australian Stock Exchange, and a corporate advisory lawyer. As a lawyer specializing in mergers and acquisitions he has advised on transactions across a variety of industries in the United States, the United Kingdom, China, Australia, Singapore, Bermuda, Papua New Guinea, New Zealand, and Guam. He is a graduate of the University of Queensland in Australia with degrees in Law (2001) and Business (Management) (1999).