Become Your Best Self Through AI Augmentation — feat. Natalie Monbiot

The deep-thinking and highly articulate Natalie Monbiot returns to my podcast today for a can't-miss episode (one of my favorite convos ever) on how A.I. will overhaul our lives, our work, our society in the coming years.

More on Natalie:

• Through her consultancy, Virtual Human Economy, she advises on virtual humans and A.I. clones, including to startups like Wizly and investment firms like Blue Tulip Ventures.

• Was previously Head of Strategy at Hour One, a leading virtual-human video-generation startup.

• Regularly speaks at the world's largest conferences, including Web Summit and SXSW.

• Holds a Master's in Modern Languages and Literature from the University of Oxford.

Today’s fascinating episode will be of great interest to all listeners. In it, Natalie details:

• How A.I. is making us dumber — and what we can do about it.

• Why the "virtual human economy" could be the next evolution of human civilization.

• The two states of being humans are seeking (and how A.I. could help us achieve them).

• Why focusing on merely 10x’ing our capabilities misses the much bigger opportunity of A.I.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

NoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake

In today's episode (#871), I'm joined by the gifted writer, speaker and ML developer Richmond Alake, who details what NoSQL databases are and why they're ideally suited for A.I. applications.

Richmond:

• Is Staff Developer Advocate for AI and Machine Learning at MongoDB, a huge publicly-listed database company with over 5000 employees and over a billion dollars in annual revenue.

• With Andrew Ng, he co-developed the DeepLearning.AI course “Prompt Compression and Query Optimization” that has been undertaken by over 13,000 people since its release last year.

• Has delivered his courses on Coursera, DataCamp, and O'Reilly.

• Authored 200+ technical articles with over a million total views, including as a writer for NVIDIA.

• Previously held roles as an ML Architect, Computer Vision Engineer and Web Developer at a range of London-based companies.

• Holds a Master’s in computer vision, machine learning and robotics from The University of Surrey in the UK.

Today's episode (filmed in-person at MongoDB's London HQ!) will appeal most to hands-on practitioners like data scientists, ML engineers and software developers, but Richmond does a stellar job of introducing technical concepts so any interested listener should enjoy the episode.

In today’s episode, Richmond details:

• How NoSQL databases like MongoDB differ from relational, SQL-style databases.

• Why NoSQL databases like MongoDB are particularly well-suited for developing modern A.I. applications, including Agentic A.I. applications.

• How Mongo incorporates a native vector database, making it particularly well-suited to RAG (retrieval-augmented generation).

• Why 2025 marks the beginning of the "multi-era" that will transform how we build A.I. systems.

• His powerful framework for building winning A.I. strategies in today's hyper-competitive landscape.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

AI Should Make Humans Wiser (But It Isn’t), with Varun Godbole

Today's trippy, brain-stimulating episode features Varun Godbole, a former Google Gemini LLM researcher who’s turned his attention to the future implications of the crazy-fast-moving exponential moment we're in.

Varun:

• Spent the past decade doing Deep Learning research at Google, across pure and applied research projects.

• For example, he was co-first author of a Nature paper where a neural network beat expert radiologists at detecting tumors.

• Also co-authored the Deep Learning Tuning Playbook (that has nearly 30,000 stars on GitHub!) and, more recently, the LLM Prompt Tuning Playbook.

• He's worked on engineering LLMs so that they generate code and most recently spent a few years as a core member of the Gemini team at Google.

• Holds a degree in Computer Science as well as in Electrical and Electronic Engineering from The University of Western Australia.

Varun mostly keeps today’s episode high-level so it should appeal to anyone who, like me, is trying to wrap their head around how vastly different society could be in a few years or decades as a result of abundant intelligence.

In today’s episode, Varun details:

• How human relationship therapy has helped him master A.I. prompt engineering.

• Why focusing on A.I. agents so much today might be the wrong approach — and what we should focus on instead.

• How the commoditization of knowledge could make wisdom the key differentiator in tomorrow's economy.

• Why the future may belong to "full-stack employees" rather than traditional specialized roles.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

LLMs and Agents Are Overhyped, with Dr. Andriy Burkov

Andriy Burkov's ML books are mega-bestsellers and his newsletter has a wild 900,000 subscribers. He seldom does interviews so don't miss today's episode, in which he takes compelling, contrarian views on LLMs and agents.

More on Dr. Burkov:

• His indispensable "100-Page Machine Learning Book" seems to be on *every* data scientist / ML engineer's bookshelf.

• He also wrote "ML Engineering" and his latest book, "The 100-Page Language Model Book", was released this year to rave reviews.

• His "Artificial Intelligence" newsletter is subscribed to by 900,000 people on LinkedIn.

• He's the Machine Learning Lead at TalentNeuron, a global labor-market analytics provider.

• He runs his own book-publishing company, True Positive Inc.

• Previously held data science / ML roles at Gartner, Fujitsu and more.

• Holds a PhD in Computer Science (A.I.) from Université Laval in Quebec, where his doctoral dissertation focused on multi-agent decision-making — 15 years ago!

Despite Dr. Burkov being such a technical individual, most of today’s episode should appeal to anyone interested in A.I. (although some parts here and there will be particularly appealing to hands-on machine-learning practitioners).

In today’s episode, Andriy details:

• Why he believes AI agents are destined to fail.

• How he managed to create a chatbot that never hallucinates — by deliberately avoiding LLMs.

• Why he thinks DeepSeek AI crushed Bay Area A.I. leaders like OpenAI and Anthropic.

• What makes human intelligence unique from all other animals and why A.I. researchers need to crack this in order to attain human-level intelligence in machines.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How to Grow (and Sell) a Data Science Consultancy, with Cal Al-Dhubaib

Today, my ebullient long-time friend Cal Al-Dhubaib makes his debut on my podcast to spill the beans on how you can launch your own thriving (data science / A.I. / ML) consultancy and, eventually, sell it 💰

Cal:

• Is Head of AI & Data Science at Further, a data and A.I. company based in Atlanta that has hundreds of employees.

• Previously, he was founder and CEO of Pandata, an Ohio-based A.I. and machine learning consultancy that he grew for over eight years until it was acquired by Further a year ago.

• Delivers terrific talks — don’t miss him if you have the chance!

• Holds a degree in data science from Case Western Reserve University in Cleveland.

Today’s episode should appeal to any listener, particularly anyone that would like to drive revenue and profitability from data science or AI projects.

In it, Cal covers:

• Why his first startup was unsuccessful, but how the experience allowed him to discover an untapped market and build Pandata, a thriving data science consultancy.

• His unconventional strategy of requiring clients to make a sizable commitment up front that initially scared away clients but ultimately attracted the best ones.

• The way core values inspired by his "tin can to Mars" thought experiment shaped his hiring and company culture.

• How making data science "boring", helping his clients trust AI systems and delivering a clear return on investment became his formula for success.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

TabPFN: Deep Learning for Tabular Data (That Actually Works!), with Prof. Frank Hutter

I've been teaching Deep Learning for a decade. In that time, countless students have been disappointed by applying DL to tabular and time-series data. Finally, thanks to Prof. Frank Hutter, that will no longer be the case!

Frank:

• Is a tenured professor of machine learning and head of the Machine Learning Lab at the University of Freiburg, although he has been on leave since May to focus on…

• His fellowship on AutoML and Tabular Foundation Models at the ELLIS Institute Tübingen in Germany…

• As well as becoming Co-Founder and CEO of Prior Labs, a German startup that provides a commercial counterpart to his tabular deep-learning model research and open-source projects… and that has just announced a huge €9m pre-seed funding round.

• Holds a PhD in Computer Science from The University of British Columbia and his research has been extremely impactful: It has been cited over 87,000 times!

Today’s episode is on the technical side and will largely appeal to hands-on practitioners like data scientists, AI/ML engineers, software developers and statisticians (especially Bayesian statisticians)!

For a bit of context: Pretty much everyone works with tabular data, either primarily or occasionally. Tabular data are data stored in a table format, so structured into rows and columns, where the columns might be different data types, say, some numeric, some categorical and some text. For a decade, deep learning has ushered in the A.I. era by making huge advancements across many kinds of data — pixels from cameras, sound from microphones and of course natural language — but through all of this revolution, deep learning has struggled to be impactful on highly ubiquitous tabular data… until now.

In today’s episode, Prof. Hutter details:

• How his revolutionary transformer architecture, TabPFN, has finally cracked the code on using deep learning for tabular data and is outperforming traditionally leading approaches like gradient-boosted trees on tabular datasets.

• How version 2 of TabPFN, released last month to much fanfare thanks to its publication in the prestigious journal Nature, is a massive advancement, allowing it to handle orders of magnitude more training data.

• How embracing Bayesian principles allowed TabPFN v2 to work "out of the box" on time-series data, beating specialized models and setting a new state of the art on the key time-series analysis benchmark.

• The breadth of verticals that TabPFN has already been applied to and how you can now get started with this (conveniently!) open-source project on your tabular data today.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

BAML: The Programming Language for AI, with Vaibhav Gupta

Today's guest, Vaibhav Gupta, has developed BAML, the programming language for AI. If you are calling LLMs, you've gotta check BAML out for instant accuracy improvements and big (20-30%) cost savings.

More on charming and terrifically brilliant Vaibhav:

• Founder & CEO of Boundary (YC W23), a Y Combinator-backed startup that has developed a new programming language (BAML) that makes working with LLMs easier and more efficient for developers.

• Across his decade of experience as a software engineer, he built predictive pipelines and real-time computer vision solutions at Google, Microsoft and the renowned hedge fund The D. E. Shaw Group.

• Holds a degree in Computer Science and Electrical Engineering from The University of Texas at Austin.

This is a relatively technical episode. The majority of it will appeal to folks who interact with LLMs or other model APIs hands-on with code.

In today’s information-dense episode, Vaibhav details:

• How his company pivoted 13 times before settling upon developing a programming language for A.I.

• Why creating a programming language was "really dumb" but why it’s turning out to be brilliant, including by BAML already saving companies 20-30% on their AI costs.

• Fascinating parallels between today's A.I. tools and the early days of web development.

• His unconventional hiring process (I’ve never heard of anything remotely close to it) and the psychology behind why it works.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

In Case You Missed It in December 2024

Today's "In Case You Missed it Episode"... is one not to miss! Several of the most fascinating conversations I've ever had on the SuperDataScience Podcast I host happened in December.

The specific conversation highlights included in today's episode are:

1. The legendary Dr. Andrew Ng on why LLM cost doesn't matter for your A.I. proof of concept.

2. Building directly on Andrew's segment, CTO (and my fellow Nebula.io co-founder) Ed Donner on how to choose the right LLM for a given application.

3. Extremely intelligent and clear-spoken Dr. Eiman Ebrahimi (CEO of Protopia AI) on the future of autonomous systems and data security in our Agentic A.I. future.

4. From our 2024 recap episode, Sadie St. Lawrence's three biggest A.I. "wow" moments of the year... as well as the biggest flop of the year. (One company was behind both!)

5. Harvard/MIT humanist chaplain Greg Epstein (and bestselling author on tech in society) on the ethics of accelerating A.I. advancements. Should we, for example, consider slowing A.I. progress down?

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Quantum ML: Real-World Applications Today, with Dr. Florian Neukart

In today's episode, extremely clear-spoken Dr. Florian Neukart — a world-leading expert on quantum computing — details real-world problems we can tackle with Quantum ML and how you can get started with QML today!

Florian:

• Is Chief Product Officer and Member of the Board at Terra Quantum AG, a leading quantum-computing startup headquartered in Switzerland and Germany.

• Assistant Professor of quantum computing at Leiden University in the Netherlands.

• Holds a PhD in Quantum Computing and Machine Learning.

Today’s episode gets a bit technical at some parts — particularly near the beginning — with respect to the mechanics of quantum computing and Quantum ML (QML) but, by and large, the episode should be fascinating to any interested listener.

In today’s episode, Florian details:

• How a new generation of hybrid quantum-classical systems has made quantum computing practical for real-world applications.

• An overview of the available quantum computing chips, including Willow, Google’s new quantum chip, which has been making a big splash.

• The race to develop "quantum-proof" encryption.

• The breadth of real-world problems that can be tackled with quantum machine learning today.

• How quantum ML could unlock personalized medicine, nuclear fusion energy and revolutionary space technologies.

• How you can get started with quantum ML yourself today.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar

Today's episode features execs (from fast-growing, VC-backed A.I. startups) Anu Jain and Mahesh Kumar elucidating how enterprises can prepare and manage their data for powerful A.I. applications.

In a bit more detail, today's guests are:

• Anu Jain — CEO of Nexus Cognitive.

• Mahesh Kumar — CMO (with an engineering background and he still writes code!) of Acceldata.

This episode was filmed live at Insight Partners' ScaleUp:AI conference in New York last month.

The episode features highlights of a session I hosted at ScaleUp:AI on "Managing Data to Embrace an A.I.-First Mindset for Enterprises”. It should be interesting to folks looking to make A.I. implementations effective in large organizations that have lots of data.

In the episode, Anu and Mahesh detail:

• How a tiny data error can lead to millions of dollars in losses for an enterprise.

• Why data storage isn't a major cost driver anymore (and what is!)

• What the heck data governance actually is and why it matters.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Tech is Our New Religion And It Needs Reformation, with Greg Epstein

In today's fascinating episode, Harvard/MIT's humanist chaplain (and bestselling author!) Greg Epstein details how tech has become our foremost religion... and why this new religion needs reformation 😈

More on Greg:

• Serves as humanist chaplain at both Harvard University and the Massachusetts Institute of Technology.

• Wrote the bestselling book “Good without God: What a Billion Nonreligious People Do Believe."

Today’s episode focuses largely on Greg’s latest book, "Tech Agnostic", and should be interesting to everyone. In today’s episode, Greg details:

• How technology has supplanted traditional religion as society's most powerful belief system.

• Why Silicon Valley's promise of technological salvation parallels religious prophecies.

• The concerning parallel between AI's "singularity" and religious end-of-times narratives.

• How we can embrace technological progress while maintaining our humanity.

• Why building genuine human connections matters more than chasing technological utopias.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Safe, Fast and Efficient AI, with Protopia’s Dr. Eiman Ebrahimi

Nearly all A.I. projects get stuck in "POC Purgatory" because of complex trade-offs between cost, speed and security. Thankfully, today's guest — Dr. Eiman Ebrahimi — cogently provides a path to production A.I. heaven.

Eiman is extremely intelligent and well-spoken; don't miss this episode! It was a delight developing this episode with him and I learned a ton from his gifted mind throughout the process.

Eiman:

• Is CEO of Protopia AI, venture capital-backed startup based in Austin that converts sensitive data into a special, stochastic format that improves A.I. model accuracy, protects privacy and reduces compute costs.

• Prior to founding Protopia, spent a decade at NVIDIA as Senior Research Scientist and Computer Architect.

• Holds a PhD in Computer Engineering from The University of Texas at Austin.

Today’s episode is relatively technical so might appeal most to technical listeners, but Eiman is such a terrific communicator that anyone interested in A.I. might love it.

In today’s episode, Eiman details:

• How he went from optimizing GPU performance at NVIDIA to revolutionizing A.I. data security.

• Why many promising A.I. projects get stuck in what he calls "proof of concept purgatory" - and how to escape it.

• Gripping, deep detail on the real-world tradeoffs between the cost, speed and security of running A.I. models in production.

• How to make your enterprise A.I. products profitable.

• Why having your own private server doesn't make your A.I. system as secure as you think.

• What Alan Watts' philosophy teaches us about entrepreneurship and innovation.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Andrew Ng on AI Vision, Agents and Business Value

My guest today is the inimitable Andrew Ng! In his trademark, clear-spoken style, Andrew gives us a glimpse of the Agentic A.I. future, particularly how the coming Vision Agent tsunami will change the world.

I suspect pretty much everyone knows Dr. Ng already, but just in case:

• As director of Stanford University's AI Lab, his research group played a key role in the development of deep learning (which led to him to founding the influential Google Brain team) as well as educating millions on machine learning (and leading to him co-founding Coursera).

• Is Managing General Partner of AI Fund, a world-leading A.I. venture studio.

• Was CEO (is now Executive Chairman) of LandingAI, a computer-vision platform that specializes in domain-specific Large Vision Models (analogous to LLMs for language).

• Founded DeepLearning.AI, which provides excellent technical training on ML, deep learning (of course!), generative A.I. and many other associated subjects.

• Was co-CEO (as well as co-founder and chairman) of Coursera, which brought online learning from 300 leading universities to over 100 million students.

This episode was recorded live at the ScaleUp:AI conference in New York a few weeks ago. Thanks to George Mathew and Jennifer Jordan for inviting me back to the conference to interview Andrew :)

In today’s, Andrew details:

• Why a cheaper A.I. model with smart agentic A.I. workflow might outperform more expensive, more advanced models.

• The surprising truth about A.I. API costs that most businesses don't realize.• How Marvin Minsky's "Society of Mind" theory from the 1980s is making an unexpected comeback in modern A.I.

• A groundbreaking new way to process visual data that goes beyond traditional computer vision.

• Why unstructured data will be the key to A.I.'s next big revolution.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Consciousness and Machines, with Jennifer K. Hill

Something different for you: In today's episode, Bella Shing interviews both Jennifer Hill and me on a broad range of questions related to A.I., intelligence, neuroscience and (*gasp*) consciousness!

Looking forward to hearing what you think of this episode format, which is unlike anything we've ever done before. If you like it, I'll do it again :)

Jennifer Hill is:

• Serial entrepreneur currently building OptiMatch, where she serves as CEO.

• Exceptional speaker (to audiences as large as 100,000!), including regular collaborations with the renowned Deepak Chopra.

• Host of the "Regarding Consciousness" podcast.

Bella Shing is a film producer (including working with Tarantino on "Kill Bill"!) and entrepreneur who co-founded Coherence Education. She leads the Lisbon chapter of Light DAO, the organization that hosted this recording in Portugal last week.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Career Success in the AI Era, with Deepali Vyas

Rapid A.I. advances can be intimidating: How can you approach your career so you flourish in the coming A.I. era? Find out from Deepali Vyas — a world-leading A.I.-exec headhunter — in today's episode.

Deepali is:

• Senior Partner and Global Head of the Data, A.I. and Financial Technology Practice of Korn Ferry, one of the world’s largest executive-search firms.

• Founder of ProFolios.ai, a video-centric, A.I.-enhanced professional-branding platform.

• Founder of Fearless+, a platform that empowers tens of thousands of young people for career success.

• Holds a Bachelor’s in Financial Mathematics and a Master’s in International Finance from the London School of Economics.

Today’s episode should be interesting to everyone. In it, Deepali details:

• How A.I. has driven a 10x increase in applications per position and how you can compete in this high-volume climate.

• Why technical skills are becoming "table stakes" and what will differentiate the best candidates in the A.I. era.

• An insider's view on the talent flows between Wall Street and Silicon Valley, and how you can capitalize on these flows in your career.

• The "green flags" to look for in potential bosses and employers.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How to Become Happier, with Dr. Nat Ware

On most metrics, it's never been a better time to be alive. And yet, many of us are unhappy. In today's episode, Dr. Nat Ware explains why we're unhappy... and, mercifully, what we can do about it!

Nat:

• Is a renowned keynote speaker; he has one TEDx talk alone that has over 2 million views on YouTube (it forms the basis of the content in today’s episode).

• Is the social-impact entrepreneur behind 180 Degrees Consulting (the world's largest consultancy for non-profits) as well as Forté (a startup that facilitates cost-free reskilling of workforces).

• Holds both a doctorate in economics and an MBA from the University of Oxford.

Today’s episode should be fascinating to anyone. In it, Nat details:

• Why, despite life on this planet being better than ever before, humans are so unhappy.

• Concrete guidance on what you can do to become happier.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

AI Systems as Productivity Engines, with You.com’s Bryan McCann

Today, wildly intelligent Bryan McCann describes the Agentic A.I. behind his skyrocketing startup You.com and how it will lead to scientific discoveries human scientists couldn't dream of making. Don't miss this episode!

Bryan:

• Co-Founder and CTO of You.com, a prominent Bay Area A.I. startup that has raised $99m in venture capital (including a $50m Series B in September that valued the firm at nearly a billion dollars).

• Was previously Lead Research Scientist at Salesforce and an assistant on courses at Stanford such as Andrew Ng’s wildly popular machine learning course.

• Holds a Master’s in Computer Science, a Bachelor’s in Computer Science and a Bachelor’s in Philosophy, all from Stanford University.

Today’s episode should be fascinating to anyone interested in AI. In it, extremely well-spoken Bryan details:

• The philosophical underpinnings of the breakthroughs that led to the leading A.I. models we have today as well as the ones that will emerge in the coming years.

• How a coding mistake he made serendipitously revealed fundamental insights about meaning and language model alignment.

• Why he believes humanity is entering an existential crisis due to A.I., but nevertheless remains optimistic about the future.

• The fascinating connection between language models and biological proteins.

• Why A.I. systems might soon be able to make scientific discoveries humans could never dream of making.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

In Case You Missed It in October 2024

It's unreal to be able to speak to folks like the guests I hosted on the SuperDataScience Podcast last month. ICYMI, today's episode highlights the most riveting moments from October.

The specific conversation highlights included in today's episode are:

1. UC San Diego neuroscience professor Dr. Bradley Voytek on how data science facilitates breakthroughs in our understanding of the brain.

2. Eloquent Natalie Monbiot on how lifelike, digital versions of ourselves can scale up our public-facing work.

3. Lightning AI CTO Dr. Luca Antiga on where he sees generative A.I. being most useful in our professional lives.

4. Gable CEO Chad Sanderson on how, when we work with data, we always need to think about how downstream users might come to interpret our data... which is why he finds data contracts so important that he's writing an O'Reilly book about it.

5. Polars CEO Ritchie Vink on the incredible specs (e.g., efficiency speedups) of his open-source DataFrame-operations library for Python.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The 10 Reasons AI Projects Fail, with Dr. Martin Goodson

Most A.I. projects fail. In today's episode, the brilliant (and hilarious) Dr. Martin Goodson details the top 10 reasons why A.I. projects fail and how to avoid these common pitfalls.

Martin:

• Is CEO and Chief Scientist at Evolution AI, a firm that uses generative A.I. to extract information from millions of documents a day for their clients.

• Is Founder and Organizer of the London ML Meetup, which (with >15,000 members) is the largest community of AI/ML experts in Europe.

• Previously led data science at startups that apply ML to billions of data points daily.

• Was a statistical geneticist at the University of Oxford (where we shared a small office together)!

Today’s episode will be of interest to anyone even vaguely interested in data science, ML or AI. In today’s episode, Martin details:

• The 10 reasons why data science projects fail and how to avoid these common pitfalls.

• His insights on building A.I. startups that serve large enterprises.

• The importance of open-source A.I. development.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Neuroscience Fueled by ML, with Prof. Bradley Voytek

Today's guest is the extraordinarily intelligent and well-spoken UC San Diego theoretical neuroscience professor, Bradley Voytek. He reveals how AI/ML is accelerating our understanding of the brain.

More on Brad:

• Professor in UC San Diego's Department of Cognitive Science, Data Science Institute, and the Neurosciences Graduate Program.

• Joined Uber as their first data scientist, when it was a 10-person startup, helping build their data science strategy and team.

• Outreach work has appeared in Scientific American, NPR... and Comic-Con!

• Co-authored the amusing book "Do Zombies Dream of Undead Sheep?"

Today’s episode has some brief exchanges that will appeal most to hands-on practitioners, but should overall be fascinating to anyone.

In today’s episode, Brad details:

• How large-scale data science and machine learning are accelerating neuroscience research.

• Discoveries his lab has recently made that overturn nearly a century of neuroscience doctrine.

• Insights on structuring data science education to balance technical skills with creative, practical problem-solving.

• Lessons from using data science to optimize Uber's early ride-prediction algorithms.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.