It is mighty hot in New York rn... but not nearly as spicy as the interviews on my podcast in June! ICYMI, here are the best bits of my on-air convos last month:
1. Two-time mega-bestselling O'Reilly author Chip Huyen on what's left for humans to do when the cost of building software is headed to $0.
2. Andrey Kurenkov, co-host of my favorite podcast ("Last Week in A.I.") and Founding A.I. Lead at Astrocade, on effective vibe-coding.
3. Lightning AI's VP of Infrastructure Frank Basso on what it's actually like inside an A.I. data center.
4. Gilbert Eijkelenboom on why 85% of data scientists can't communicate their work effectively... and the framework for fixing this.
5. In a role-reversal for landmark Episode #1001, the founder and original host of the SuperDataScience Podcast, Kirill Eremenko, interviewed me. In this clip, we discussed whether AGI would require something like consciousness to be realized.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: Podcast
People Skills for Analytical Thinkers, with Bestselling Author Gilbert Eijkelenboom
Gilbert Eijkelenboom was a pro poker player who read his opponents through data alone but in today's episode, the bestselling author explains why the people side of data science matters more than the math.
More on Gilbert:
• Wrote the bestselling book "People Skills for Analytical Thinkers".
• Run MindSpeaking, a firm that's trained over 15,000 (mostly technical) folks on "people skills".
• Folks love his invaluable content, allowing him to gather over 200k followers.
• Was previously Managing Consultant on data and digital analytics for Capgemini, as well as a professional poker player on BetVictor.
• Holds a Master's in behavioral economics from Maastricht University.
In today's episode, Gilbert covers:
• Why no matter how good your model or analysis is, it only creates value once people actually use it, which makes communication a core data skill rather than an optional extra.
• His "and, but, therefore" communications framework.
• How research suggests only around 15% of people are self-aware and his tips for closing that gap.
• How experiences in childhood install personal "algorithms" in our adult behavior like avoiding conflict or staying silent... but we can change as adults (and he also provides tips on how).
Thanks to Kate Strachnyi for suggesting Gilbert as a guest!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Recursive Self-Improvement
Recursive Self-Improvement (RSI) is suddenly a term that's everywhere. What is RSI? How concerned should be about it? And how soon can we expect it? Here's the skinny:
WHAT IS RSI?
• The idea: An A.I. gets good enough at A.I. research to build a more capable successor, which builds an even better one, in a loop that compounds every turn.
• What we have today is *not* RSI but "A.I.-assisted coding", in which humans still set the goals and judge the results (actual RSI takes the human out of the loop, as shown in the diagram).
• RSI isn't a new concept; it's been around since at least 1965 when mathematician I.J. Good described an "intelligence explosion".
WHAT'S THE CONCERN?
RSI could unleash Artificial Superintelligence (ASI) and "the singularity", a point beyond which there could be radical abundance and radically positive outcomes for humanity... but we have no idea what will happen beyond the singularity and that's also a cause for concern (e.g., human extinction risk, Terminator-style "SkyNet", etc.).
HOW CLOSE ARE WE TO RSI?
• Anthropic reports that, as of May 2026, over 80% of code merged into its production codebase was written by Claude — up from low single digits before early 2025.
• On the hardest open-ended problems, its models' success rate jumped from under 20% in late 2025 to 76% by May.
• Think-tank METR finds the length of tasks A.I. can handle solo is now doubling roughly every four months, up from the "doubling every seven months" trend of the past few years.
• Anthropic co-founder Jack Clark puts a 60% chance on an A.I. creating its own successor, with no human involved, by the end of 2028.
REASONS TO BE SKEPTIC
• Skeptics flag two bottlenecks: compute (chips are scarce) and data (success is hard to verify outside code and math, risking "recursive drift").
• Others note the gap between today's coding agents and real RSI is wider than the hype suggests.
BOTTOM LINE
The productivity gains from coding assistants are real, accelerating rapidly and already in your hand. The closer we get to systems that improve themselves, the more it pays to keep human checkpoints, monitoring and oversight firmly in place.
Listen to the most recent episode of my podcast (Episode #1004) to hear more on all of the above, including what you can do personally to mitigate the risks of RSI if that's a way you might like to make an impact!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building an AI Data Center End to End, with Lightning AI’s Frank Basso
We've done over 1,000 episodes of this show on every layer of the A.I. stack... except the one that physically runs all of it: the A.I. data center. Today we fix that in a fascinating episode with Lightning AI's Frank Basso.
Frank is VP of Infrastructure at Lightning AI, a New York-based company that has over 35,000 modern GPUs, over $500m in ARR, and that makes it easy to go from A.I. idea to product, "lightning fast" (I hold a fellowship at Lightning so am not an unbiased source on the business, btw). Frank himself is based in Los Angeles and, prior to Lightning, he spent decades directing the development of data centers in California.
In this exceptionally informative episode, Frank explains:
• How Lightning provisions its 35,000+ GPUs through hyperscale co-location.
• Why everything new is liquid-to-chip cooled.
• How GPUs talk to each other over ultra-fast east-west networks.
• What it’s actually like to stand inside a 110-decibel A.I. data hall.
• The most persistent myths about data-center water and electricity use.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Fable 5: The Full Story from Capabilities to Drama
The dust has settled, allowing me to provide you with all the key context you need to know on Fable 5, the most capable A.I. model ever offered to the public, and the US government forcing it off shelves three days later:
A NEW CLASS OF MODEL
• Anthropic stacks its models in tiers: Haiku (small and fast), Sonnet (the capable middle) and Opus (the powerful top). Sitting above all of them now is a "Mythos-class" tier.
• Fable 5 and its locked-down sibling Mythos 5 are the same underlying model... the only difference is the safeguards.
• Mythos 5 goes to trusted cyberdefenders with guardrails largely lifted; Fable 5 went to the public with them switched on.
WHAT IT COULD DO
• State-of-the-art on nearly every benchmark Anthropic tested... and the lead grows the longer and more complex the task (see chart).
• Stripe ran a codebase-wide migration on 50M lines of Ruby in a single day; work estimated at 2+ months for a full engineering team.
• Beat video "Pokémon FireRed" from raw screenshots alone, and got a 3x bigger memory boost than Opus on "Slay the Spire".
• Priced at $10/$50 per million input/output tokens: roughly 2x Opus 4.8, but under half the original Mythos Preview.
SAFETY BY DESIGN
• Classifiers watch three sensitive areas: cybersecurity, biology/chemistry and distillation (extracting a model to train a rival).
• Flagged requests quietly fall back to Opus 4.8 and the user is told.
• Triggers fire in under 5% of sessions. Anthropic admits it tuned conservatively, so some harmless prompts get bounced too.
THE THREE-DAY SHUTDOWN
• On Friday evening the federal government ordered Anthropic to switch off both Fable 5 and Mythos 5 worldwide, citing national security.
• The mechanism was an export-control action covering foreign nationals everywhere (including even, say, Canadian Anthropic employees living in the US!)... so broad that Anthropic pulled the model for absolutely everyone.
• The trigger was a reported jailbreak of the cyber safeguards by Amazon. Anthropic disputes its severity, calling it narrow and non-universal.
BOTTOM LINE
A premium-tier model, wrapped in deliberately cautious safeguards, pulled by its own government not long before Anthropic's reported IPO and the latest in a public battle between the firm and the federal government. Sessions now fall back to Opus 4.8. Whether Fable returns (and on what terms) depends on a fight that's far from over.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How AI Erased My Career Moat, an Episode #1001 Special: Jon Krohn interviewed by Kirill Eremenko
To mark cresting over 1000 episodes, today’s features a role reversal: Kirill Eremenko (who founded the podcast a decade ago) returns to host and welcomes *me* as the guest. Kirill's still got it, enjoy!
Kirill hosted the first 431 episodes of the SuperDataScience Podcast before handing me the reins five years ago. In today's role-reversal episode, we discuss:
• A.I. rapidly usurping our technical skills
• Whether we’re in an A.I. bubble
• The one key reason why I’ve seen A.I. projects fail
• Relationships between A.I. and biological neuroscience.
... so, as usual, lots of A.I. in this episode, but unusually, I’m the one answering the questions instead of asking them!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Ten Years of the Super Data Science Podcast, with Jon, Kirill and Special Guests
Today, we published Episode #1000 of the SuperDataScience Podcast! To celebrate, the show's original host Kirill Eremenko joined me and dozens of regular listeners on air to predict what the next 10 years of A.I. will bring.
In a bit more detail:
• We publish 104 episodes per year so Episode #1000 coincides with the show being about ten years old.
• The show was founded by Kirill Eremenko in 2016, who hosted over 400 episodes before handing me the reins in 2021.
• In a first for the show, Episode #1000 was streamed live online with our audience invited to join on air.
• Most folks interacted via chat functionality but a number of surprise guests came right onto the recording including Natalie Ziajski and Mario Pombo from the podcast team, rockstar A.I. entrepreneur Jepson Taylor, my 96-year-old grandmother and my very own pa, William Krohn.
• Kirill and I looked back on a decade of the podcast and fielded listener questions on topics such as A.I.’s biggest opportunities, the build-versus-buy dilemma, how to break into the field today, and how to stay grounded amid the relentless pace of A.I.
Thank you for support and listenership over all these years — we make this show for you and couldn't do it without you! We're excited to see what the next decade brings :)
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
What’s Left to Build When Software Is Free, with Chip Huyen
For today's landmark episode (#999!), I asked rockstar Chip Huyen to be my guest and she said "yes"! We discuss her book "A.I. Engineering" (the most popular O'Reilly book in 2025) and how the A.I. job landscape is shifting.
In case you haven't heard of her, more on Chip:
• Her most recent book is "AI Engineering", which was the most popular book in the O'Reilly platform last year.
• Previously wrote “Designing Machine Learning Systems”, which was also an O'Reilly mega-bestseller and was based on the Stanford University course she created and taught on the same topic.
• Is currently building a new stealth startup.
• Previously worked as VP of AI at Voltron Data, co-founder of Claypot AI, ML Engineer at Snorkel AI and Sr Deep Learning Engineer at NVIDIA.
• Holds a Master's in Computer Science from Stanford.
• Her invaluable posts have earned her over 300k followers on LinkedIn.
In this episode, Chip breaks down:
• What separates AI engineering from machine learning engineering.
• The case for a "start simple" workflow.
• The real costs of running LLMs in production.
• Physical AI.
• Robotics.
• World models.
• Why the durable problems worth solving are increasingly human ones.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in May 2026
Well, I certainly learned a lot from the outstanding guests we had on my podcast in May. ICYMI, today's episode features the best parts of my conversations with them:
1. Rubrik's Anneka Gupta and Cal Al-Dhubaib on how, in the Mythos era, the old cybersecurity playbook of prevention and detection is no longer enough, and how A.I. agents themselves are becoming a new source of data exposure inside organizations.
2. marimo's Dr. Trevor Manz on why code notebooks have become the natural working memory for A.I. coding agents. Trevor walks me through the Marimo Pair skill, which lets you drive a notebook from your agent, collaborating with Claude Code or Codex in real time as you load, explore, and visualize your data.
3. Jazmia Henry of collide. walks me through her work as a "full-stack" foundation model builder. We cover all four stages of the process: the often unglamorous slog of data curation, building bespoke tokenizers and embeddings, model training and reinforcement learning, and the inference layer that serves it all to end users.
4. Jacob Miller and Jeremy Mumford of Pattern (and authors of the great, brand-new book "Architected Intelligence") argue that the most expensive AI mistake an organization can make is failing slowly and sticking with prototypes long past their sell-by date because the traditional software mindset says you have to. We, of course, also discuss a solution.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How This Text-to-Video-Game AI Startup Hit 20M Users
Imagine being able to vibe-code full-blown video games... for free! My returning guest, Dr. Andrey Kurenkov, helped engineer Astrocade to do just that... and already 20 million people have played games through their platform.
More on Andrey:
• Founding A.I. Lead at Astrocade, a Bay Area-based startup that has raised $68m in venture capital to create the TikTok of video games, where creators create games for free and you play them for free.
• Co-host (alongside Jeremie Harris) of my favorite podcast, "Last Week in A.I.".
• Holds a PhD from Stanford University, where his research focused on machine vision and robotics.
In this episode, we discuss:
• The fascinating Astrocade journey, of course.
• The surprising pace of humanoid robotics.
• Why he's a skeptic on Artificial Super Intelligence.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
TrueFoundry’s Nikunj Bajaj on How to Get $100M Returns on AI Agent Deployments
Imagine being able to deploy an AI agent and getting a return of over $100m from that single deployment. My guest today, Nikunj Bajaj, has facilitated that multiple times! Lots to learn from him, enjoy!
Nikunj:
• CEO and co-founder of TrueFoundry, a Bay Area-based startup that has raised over $20m to solve the thorniest problems that enterprises face when deploying agents.
• His clients include demanding organizations like NVIDIA and Siemens.
• Was previously ML tech lead at Facebook.
• Holds a master's in computer science from University of California, Berkeley.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Episode #1000: Join Us Live for the First-Ever Interactive SDS Podcast!
Ten years ago, Kirill Eremenko founded The SuperDataScience Podcast. To celebrate the upcoming Episode #1000, we are inviting you to join us both in a format we've never tried before:
It will be the first-ever interactive episode where you can join in online as we record, and ask your questions... or I suppose just make comments! You'll be able to ask share your thoughts in the chat or come right onto the show via video.
Kirill (founder, original host) and I (current host) will both be there, so you can ask us anything, e.g.:
• Did Kirill think the show would last ten years and 1000 episodes?
• How has data science transformed over the past decade?
• Did Jon have hair on his head ten years ago?
Date: Next Thursday, June 4th
Time: 5pm Eastern Time / 2pm Pacific Time
To get a calendar invite that includes the URL to join us live, check out the Luma link below ⬇️
luma.com/7vl7mdos
The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Whether you join us or not for the interactive recording, Episode #1000 will be published on Friday June 12th!
End-to-End Foundation Models for the Energy Industry, with Jazmia Henry
What does it take to build foundation LLMs from scratch today? Deeply impressive Jazmia Henry breaks down the four stages in today's episode, enjoy!
Jazmia:
• Holds degrees from Tulane University and Columbia University... and is partway through a PhD at the University of Oxford.
• Held a technical fellowship at Stanford University.
• Previously worked as a data strategist at Morgan Stanley, head of ML at The Motley Fool and a Lead Applied AI engineer at Microsoft.
• Published a top paper at NeurIPS, the world's most prestigious academic AI conference.
• Currently works as "Member of Technical Staff for AI/ML" at collide., a Texas-based startup that’s building AI infrastructure (including all aspects of specialized foundation models) for the energy industry.
Key topics covered in this episode include:
• What foundation models are.
• Her "full-stack" foundation-model building's four distinct stages.
• How reinforcement learning (RL) models are "bursty" because they idle the GPU during reward calculation and then dump enormous loads on it all at once.
• Reward hacking by RL models.
Thanks to Mark Freeman II for recommending Jazmia as a guest.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI’s Putting Recent Grads Out of Work; Here’s How to Get Hired Anyway!
Computer science/engineering grads had an employment advantage (see chart) that, since ChatGPT's release, has disappeared. Is A.I. to blame? Here's what the data say and what new grads (or anyone!) can do about it:
THE EMPLOYMENT LANDSCAPE
• NY Fed: unemployment for recent computer-science grads (22-27) sits at 7.0%, and computer engineering at 7.8% (roughly on par with fine arts and anthropology grads!)
• Compare that to ~5.8% for recent grads overall and ~4% for the whole US workforce.
• Eighteen-year-olds are voting with their feet: US undergrad CS enrolment fell 11% in 2025; computer programming fell a stunning 26%.
• Demand is shrinking too: Handshake postings are down ~50% from their 2022 peak, and Revelio Labs data suggest entry-level software and data-analysis postings have dropped as much as 67%.
IS A.I. TO BLAME?
• "Yes" camp: A 2025 Stanford University study found employment for 22-25-year-olds in A.I.-exposed jobs dropped 13% since 2022, while older workers held steady. The Dallas Fed replicated it... and the decline comes from juniors never being hired, not layoffs.
• "Not so fast" camp: Google economists found posting declines were just as steep for senior workers and predate ChatGPT. A Fed study of 1M+ firms found "null effects." Their take: high interest rates and a post-pandemic hangover, with A.I. as a convenient scapegoat.
WHAT YOU CAN DO:
1. Stop competing on raw code. The human edge is now system design, architecture and deciding what to build in the first place.
2. Pick a domain. "A.I. engineer" is a common résumé; "A.I. engineer who worked alongside a hospital team for two summer internships" is a short list.
3. Build a public portfolio. Substantive GitHub repos and a Kaggle project beat CVs sent into the void.
4. Get fluent with agentic tooling, e.g., RAG, model evaluation, multi-agent orchestration. PwC found A.I.-skilled workers earn a 56% wage premium (!!!)
5. Lean on your network. Referrals and warm intros are crushing mass (often GenAI-produced) applications in this market.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Build AI-First Organizations, with Jacob Miller and Jeremy Mumford
After today's fun episode with Jacob and Jeremy — authors of the brand-new book "Architected Intelligence" — you’ll have all the key info to build successful AI features, AI products and AI-first companies. Enjoy!
Jeremy Mumford and Jacob Miller serve as Lead AI Engineer and Vice President of Platform Intelligence, respectively, at Pattern, a giant Utah-based tech company that IPO’ed on the Nasdaq exchange about six months ago.
Jacob and Jeremy's brand-new "Architected Intelligence" book was published by Wiley and this episode focuses almost exclusively on this invaluable book.
Episode highlights include:
• The "User Agnosticism Tenet", which means designing products and processes so they can be executed equally well by a human, an AI agent, or any hybrid combo.
• The shift in the "define-build-feedback" loop today where "building" is no longer the bottleneck, which means "definition" and "feedback" are where teams win or lose.
• Why workflows are deterministic, predictable, and cheaper than agents, and why the natural progression is skills first, then workflows, and only then agents.
• Why data engineering is the bedrock of AI engineering.
• Why velocity is the only durable moat in a world where everyone has access to the same frontier models.
Thanks to podcast superfan Jonathan Bown for recommending Jeremy and Jacob as guests!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Tokenmaxxing vs AI Hardware Bottlenecks
Humans (like Reinforcement Learning algos) can "reward hack": "Tokenmaxxing" being a perfect example, after employers started using "number of tokens" consumed as a proxy for developers' productivity.
Even if humans weren't engaging in this pointless time-, money- and energy-consuming behavior, however, demand for A.I. compute is so vast that everyone's scrambling to to make more available. Alas, four tricky hardware bottlenecks face us:
1. GPUs:
• NVIDIA data-center GPU lead times now run 36–52 weeks, with Blackwell chips sold out through mid-2026.
• The real choke point isn't fabrication: It's TSMC's "CoWoS" advanced packaging, which is sold out through 2026. Nvidia alone has locked up ~60% of CoWoS capacity through 2027.
2. High-Bandwidth Memory (HBM):
• Demand has quintupled since 2023, and only three companies (SK hynix, Samsung and Micron) make it.
• All three are sold out well into 2026 and new HBM factories take 18–24 months to come online.
3. CPUs:
• As workloads shift toward agentic AI, the CPU:GPU ratio jumps from ~1:12 (for GenAI-only chatbots) to 1:1.
• Intel's CFO says the server-CPU shortfall "starts with a B" — billions in unmet demand so server CPU prices are up 10–20% in just the past couple of months.
4. Electricity: Hyperscaler build-outs are now gated by grid interconnect (18–36 months) and transformer lead times.
THE BIG MISMATCH
• The top 5 hyperscalers alone (Alphabet, Amazon, Meta, Microsoft and Oracle) are on track for ~$725B in combined 2026 capex.
• That's roughly 6x the hyperscalers' 2022 spend, with ~75% going to A.I. infrastructure.
• Hardware suppliers, however, have grown capex by only ~50%.... a 6x increase in demand met by only a 50% increase in supply is a big mismatch!
REASONS FOR OPTIMISM
Demand will continue to be high but I'm optimistic we'll continue to squeeze more juice from every lemon because, e.g.:
• Algorithmic efficiency keeps improving — Google's TurboQuant recently briefly tanked memory stocks by promising to materially cut inference memory needs.
• LLM efficiency gains via mixture-of-experts and smarter inference scheduling continue to compound.
• The tokenmaxxing trend is a corporate farce that will fade.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Pair Programming with AI in Your Python Notebook, with Dr. Trevor Manz
Exceptional technical episode today with Dr. Trevor Manz on "marimo Pair", an actually!) game-changing pair-programming A.I.-agent companion that lifts heavy loads within your Python data-science notebook.
More on Trevor:
• 27-time NCAA Swimming All-American & National Champion.
• Master's in Computational Biology from University of Cambridge.
• PhD in Bioinformatics from Harvard University.
• Creator of the popular open-source "anywidget" project (amongst many others, particularly in visualizing bioinformatics data, e.g., genomics data).
• Now a founding engineer at marimo.io, where he is leading the charge on marimo Pair.
Seriously, marimo Pair is unreal. A complete reimagining of what's possible in a Jupyter notebook-style environment in the agentic A.I. era. You will hear (and see) my mind explode in this episode!
We also discuss:
• Agent skills.
• Recursive language models.
• A number of other open-source projects, largely in data viz/analysis.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Security for Mythos-Era Agentic Risks, with Rubrik’s Anneka Gupta and Cal Al-Dhubaib
Mythos finds security vulnerabilities at ~100X the rate of publicly available models, and comparable open-weight models are ~6 months away. Scary? Thankfully my guests today, Anneka and Cal, have solutions!
Anneka:
• Chief Product Officer at Rubrik.
• Lecturer in Product Management at Stanford University.
• Climbed the ladder from software engineer to President (!!) during an 11-year tenure at LiveRamp.
• Holds a degree in math and computational sciences from Stanford.
Cal:
• Principal Technologist at Rubrik.
• Formerly founder and CEO of Pandata, which was acquired by Further.
• Highly sought-after keynote speaker.
• Holds a degree in data science from Case Western Reserve University.
This is an exceptional episode with two brilliant, entertaining and highly knowledgeable guests. It can be enjoyed by anyone! In it, they cover:
• How Anthropic's Mythos model can be pointed at a code repository and autonomously surface every vulnerability inside it, and how Anthropic itself estimates Mythos-class capabilities will reach other labs within six to eighteen months, with open-weight versions likely to follow.
• How code-gen models make it easy for attackers by scaling up their capabilities... and by vibe-coders not being aware of vulnerabilities they have!
• How Rubrik's Agent Cloud delivers three pillars of resilience: visibility into every agent in your environment, governance and runtime control through the SAGE small language model, and remediation through Agent Rewind.
• Why the next wave of knowledge work is inherently cross-functional, with A.I. attorneys, security pros, and data scientists all needing shared literacy in A.I. risk.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in April 2026
Whoa, it's May Day... and our podcast-production team was *on the ball* with getting our ICYMI-in-April episode together lickety-split. In case you missed it, these were the best bits of my on-air convos last month:
1. Oracle's Director of A.I. Developer Experience Richmond Alake defines the four types of memory A.I. agents can have... and the biological inspiration for each of them.
2. Matthew J. Glickman, co-founder/CEO of Genesis Computing, describes how A.I. agents allow data engineers to dramatically scale up their impact in an enterprise.
3. The A.I. infrastructure engineer Linda Haviv has amassed a following of over 250,000 folks on social media. In her clip from last month, she combines both worlds — detailing why A.I. infrastructure has now become everyone's problem while also discussing her work in lowering the barrier to access A.I. education.
4. Traci Walker Griffith, principal of The Eliot School in Boston, shares her novel perspective on what critical thinking is... in the context of how fifth-graders are leveraging A.I. to evaluate their work and prepare for tests.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Infrastructure, Ray, and Why Nonlinear Careers Win, with Linda Haviv
For folks in A.I., software, data science, things are moving so fast, it's easy to be overwhelmed. Luckily, A.I. engineer Linda Haviv makes it a joy to stay up to date! Today, we discuss career tips as well as open-source A.I. tech like Ray.
More on Linda:
• Until recently, was Staff Developer Advocate at Anyscale, makers of Ray, an open-source framework for managing, executing and optimizing A.I. compute.
• Previously was A.I. Developer Advocate at Amazon Web Services (AWS).
• Before that, was a software developer at Fox Corporation.
• Was a professional singer in New York up until her second (of three!) children was born.
• Holds a degree in philosophy from Baruch College.
In this episode, Linda ebulliently covers:
• How "A.I. infrastructure" refers to the compute stack, tooling and frameworks purpose-built for A.I. and ML workloads.
• Ray is a Python-native open-source distributed computing framework that lets engineers distribute training, data processing and model serving across GPUs without needing to become distributed systems experts.
• How building in public, creating content and contributing to open source are not just career insurance... they're how you find your community, attract unexpected opportunities and learn faster through teaching.
• And much more!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.