AI is Eating Biology and Chemistry, with Dr. Ingmar Schuster

For today's exceptional episode, I traveled to Berlin to find out how the visionary Dr. Ingmar Schuster is using A.I. to transform biology and chemistry research, thereby helping solve the world's most pressing problems, from cancer to climate change.

Ingmar:

• Is CEO and co-founder of Exazyme, a German biotech startup that aims to make chemical design as easy as using an app.

• Previously he worked as a research scientist and senior applied scientist at Zalando, the gigantic European e-retailer.

• Completed his PhD in Computer Science at Leipzig University and postdocs at the Université Paris Dauphine and the Freie Universität Berlin, throughout which he focused on using Bayesian and Monte Carlo approaches to model natural language and time series.

Today’s episode is on the technical side so may appeal primarily to hands-on practitioners such as data scientists and machine learning engineers.

In this episode, Ingmar details:

• What kernel methods are and how he uses them at Exazyme to dramatically speed the design of synthetic biological catalysts and antibodies for pharmaceutical firms and chemical producers, with applications including fixing carbon dioxide more effectively than plants and allowing our own immune system to detect and destroy cancer.

• When “shallow” machine learning approaches are more valuable than deep learning approaches.

• Why the benefits of A.I. research far outweigh the risks.

• What it takes to become a deep-tech entrepreneur like him.

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

Engineering Biomaterials with Generative AI, with Dr. Pierre Salvy

Today, the brilliant Dr. Pierre Salvy details the "double deep-tech sandwich" that blends cutting-edge A.I. (generative LLMs) with cutting-edge bioengineering (creating new materials). This is a fascinating one, shot live at the Merantix AI Campus in Berlin.

Pierre:

• Has been at Cambrium for three years. Initially as Head of Computational Biology and then Head of Engineering for the past two years, growing the team from 2 to 7 to bridge the gap between wet-lab biology, data science, and scientific computing.

• Holds a PhD in Biotechnology from EPFL in Switzerland and a Master’s in Math, Physics and Engineering Science from Mines in Paris.

Today’s episode touches on technical machine learning concepts here and there, but should largely be accessible to anyone.

In it, Pierre details:

• How data-driven R&D allowed Cambrium to go from nothing to tons of physical product sales inside two years.

• How his team leverages Large Language Models (LLMs) to be the biological-protein analogue of a ChatGPT-style essay generator.

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

Scikit-learn’s Past, Present and Future, with scikit-learn co-founder Dr. Gaël Varoquaux

For today's massive episode, I traveled to Paris to interview Dr. Gael Varoquaux, co-founder of scikit-learn, the standard library for machine learning worldwide (downloaded over 1.4 million times PER DAY 🤯). In it, Gaël fills us in on sklearn's history and future.

More on Gaël:

• Actively leads the development of the ubiquitous scikit-learn Python library today, which has several thousand people contributing open-source code to it.

• Is Research Director at the famed Inria (the French National Institute for Research in Digital Science and Technology), where he leads the Soda ("social data") team that is focused on making a major positive social impact with data science.

• Has been recognized with the Innovation Prize from the French Academy of Sciences and many other awards for his invaluable work.

Today’s episode will likely be of primary interest to hands-on practitioners like data scientists and ML engineers, but anyone who’d like to understand the cutting edge of open-source machine learning should listen in.

In this episode, Gaël details:

• The genesis, present capabilities and fast-moving future direction of scikit-learn.

• How to best apply scikit-learn to your particular ML problem.

• How ever-larger datasets and GPU-based accelerations impact the scikit-learn project.

• How (whether you write code or not!) you can get started on contributing to a mega-impactful open-source project like scikit-learn yourself.

• Hugely successful social-impact data projects his Soda lab has had recently.

• Why statistical rigor is more important than ever and how software tools could nudge us in the direction of making more statistically sound decisions.

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

How to Officially Certify your AI Model, with Jan Zawadzki

In today's episode, learn from Jan Zawadzki how independent certification of A.I. models makes them safer and more reliable, gives you an advantage over your competitors, and, in the EU at least, will soon be mandatory!

Jan:

• Is CTO and Co-Managing Director of CertifAI, a startup that is an early mover in the fast-developing A.I. certification ecosystem.

• Was previously the Head of A.I. at CARIAD, the software development subsidiary of Volkswagen Group, where he grew the team from scratch to over 50 engineers.

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

A.I. Product Management, with Google DeepMind's Head of Product, Mehdi Ghissassi

The elite team at Google DeepMind cranks out one world-changing A.I. innovation after another. In today's episode, their affable Head of Product Mehdi Ghissassi shares his wisdom on how to design and release successful A.I. products.

Mehdi: 
• Has been Head of Product at Google DeepMind — the world’s most prestigious A.I. research group — for over four years.
• Spent an additional three years at DeepMind before that as their Head of A.I. Product Incubation and a further four years before that in product roles at Google, meaning he has more than a decade of product leadership experience at Alphabet. 
• Member of the Board of Advisors at CapitalG, Alphabet’s renowned venture capital and private equity fund.
• Holds five (!!!) Master’s degrees, including computer science and engineering Master’s degrees from the École Polytechnique, in International Relations from Sciences Po, and an MBA from Columbia Business School.

Today’s episode will be of interest to anyone who’s keen to create incredible A.I. products.

In this episode, Mehdi details: 
• Google DeepMind’s bold mission to achieve Artificial General Intelligence (AGI).
• Game-changing DeepMind A.I. products such as AlphaGo and AlphaFold.
• How he stays on top of fast-moving A.I. innovations.
• The key ethical issues surrounding A.I.
• A.I.’s big social-impact opportunities.
• His guidance for investing in A.I. startups.
• Where the big opportunities lie for A.I. products in the coming years.

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

Humanoid Robot Soccer, with the Dutch RoboCup Team

In today's unique episode, robots from the Dutch Nao Team (Naos are the little humanoids shown in the photo) compete against each other at football (⚽️) while Dário Catarrinho, a developer on the team, describes the machine learning involved.

The Dutch Nao Team is one of many international teams that competes annually in RoboCup Federation tournaments. The lofty goal of the RoboCup competitions is to develop a team of humanoid robots that is able to win against the human World Cup Championship team by the year 2050. Very cool.

Dario, my human guest in today's episode is Secretary of the Dutch Nao Team as well as a software developer on the team. He's also pursuing a degree in A.I. at the University of Amsterdam.

Most of today’s episode should be accessible to anyone but occasionally Dario and I talk a bit technically about ML algorithms so those brief parts might be most meaningful to hands-on practitioners.

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

OpenAssistant: The Open-Source ChatGPT Alternative, with Dr. Yannic Kilcher

Yannic Kilcher — famed Machine Learning YouTuber and creator of OpenAssistant, the best-known open-source conversational A.I. — is today's rockstar guest! Hear from this luminary where the biggest A.I. opportunities are in the coming years 😎

If you’re not already aware of him, Dr. Yannic: 
• Has over 230,000 subscribers on his machine learning YouTube channel.
• Is the CTO of DeepJudge, a Swiss startup that is revolutionizing the legal profession with AI tools.
• Led the development of OpenAssistant, a leading open-source alternative to ChatGPT, that has over 37,000 stars (⭐️⭐️⭐️!!!) on GitHub.
• Holds a PhD in A.I. from the outstanding Swiss technical university, ETH Zürich.

Despite being such a technical expert himself, most of today’s episode should be accessible to anyone who’s interested in A.I., whether you’re a hands-on practitioner or not.

In this episode, Yannic details: 
• The behind-the-scenes stories and lasting impact of his OpenAssistant project.
• The technical and commercial lessons he’s learned while growing his A.I. startup.
• How he stays up to date on ML research.
• The important, broad implications of adversarial examples in ML. 
• Where the biggest opportunities are in A.I. in the coming years.

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

Data Science for Astronomy, with Dr. Daniela Huppenkothen

Our planet is a tiny little blip in a vast universe. In today's episode, the astronomical data scientist and talented simplifier of the complex, Dr. Daniela Huppenkothen, explains how we collect data from space and use ML to understand the universe.

Daniela: 
• Is a Scientist at both the University of Amsterdam and the SRON Netherlands Institute for Space Research.
• Was previously an Associate Director of the Institute for Data-Intensive Research in Astronomy and Cosmology at the University of Washington, and was also a Data Science Fellow at New York University.
• Holds a PhD in Astronomy from the University of Amsterdam.

Most of today’s episode should be accessible to anyone but there is some technical content in the second half that may be of greatest interest to hands-on data science practitioners.

In today’s episode, Daniela details: 
• The data earthlings collect in order to observe the universe around us.
• The three categories of ways machine learning is applied to astronomy.
• How you can become an astronomer yourself.

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

A.I. Agents Will Develop Their Own Distinct Culture, with Nell Watson

Nell Watson is the most insightful person I've spoken to on where A.I. is going in the coming decades and how it will overhaul our lives. In today's mind-bending episode, she conveys these insights with amusing analogies and clever literary references.

This sensational guest, Nell: 
• Is IEEE — the Institute of Electrical and Electronics Engineers’ — A.I. Ethics Certification Maestro, a role in which she engineers mechanisms into A.I. systems in order to safeguard trust and safety in algorithms.
• Also works for Apple as an Executive Consultant on philosophical matters related to machine ethics and machine intelligence.
• Is President of EURAIO - European Responsible Artificial Intelligence Office.
• Is renowned and sought-after as a public speaker, including at venerable venues like The World Bank and the United Nations General Assembly.
• On top of all that, she’s currently wrapping up a PhD in Engineering from the University of Gloucestershire in the UK.

Today’s episode covers rich philosophical issues that will be of great interest to hands-on data science practitioners but the content should be accessible to anyone. And I do highly recommend that everyone give this extraordinary episode a listen.

In this episode, Nell details: 
• The distinct, and potentially dangerous, new phase of A.I. capabilities that our society is stumbling forward into. 
• How you yourself can contribute to IEEE A.I. standards that can offset A.I. risks.
• How we together can craft regulations and policies to make the most of A.I.’s potential, thereby unleashing a fast-moving second renaissance and potentially bringing about a utopia in our lifetimes.

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

How GitHub Operationalizes AI for Teamwide Collaboration and Productivity, with GitHub COO Kyle Daigle

Today's episode features the exceptionally passionate GitHub COO Kyle Daigle detailing how generative A.I. tools improve not only the way individuals work, but also dramatically transform the way people across entire firms collaborate.

Kyle was my on-stage guest for a "fireside chat" live on stage at Insight Partners' ScaleUp:AI conference in New York. It was a terrifically slick conference and a ton of fun to collaborate on stage with Kyle! He's an energizing and inspiring speaker.

Check out the episode for all of our conversation; some of the key takeaways are:
• Generative AI tools like GitHub CoPilot are most useful and efficient when they’re part of your software-development flow.
• These kinds of in-flow generative AI tools can be used for collaboration (such as speeding up code review) not just on an individual basis.
• "Innersourcing" takes open-source principles but applies them within an organization on their proprietary assets.

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

Universal Principles of Intelligence (Across Humans and Machines), with Prof. Blake Richards

Today's episode is wild! The exceptionally lucid Prof. Blake Richards will blow your mind on what intelligence is, why the "AGI" concept isn't real, why AI doesn't pose an existential risk to humans, and how AI could soon directly update our thoughts.

Blake: 
• Is Associate Professor in the School of Computer Science and Department of Neurology and Neurosurgery at the revered McGill University in Montreal. 
• Is a Core Faculty Member at Mila, one of the world’s most prestigious A.I. research labs, which is also in Montreal. 
• His lab investigates universal principles of intelligence that apply to both natural and artificial agents and he has received a number of major awards for his research. 
• He obtained his PhD in neuroscience from the University of Oxford and his Bachelor’s in cognitive science and AI from the University of Toronto.

Today’s episode contains tons of content that will be fascinating for anyone. A few topics near the end, however, will probably appeal primarily to folks who have a grasp of fundamental machine learning concepts like cost functions and gradient descent.

In this episode, Blake details: 
• What intelligence is.
• Why he doesn’t believe in Artificial General Intelligence (AGI).
• Why he’s skeptical about existential risks from A.I.
• The many ways that A.I. research informs our understanding of how the human brain works. 
• How, in the future, A.I. could practically and directly influence your thoughts and behaviors through brain-computer interfaces (BCIs).

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

Unmasking A.I. Injustice, with Dr. Joy Buolamwini

Today, the inimitable Dr. Joy Buolamwini reveals how she uncovered staggering racial and gender biases in widely used Amazon, Microsoft and IBM algorithms, the firms' varying (sometimes shocking) responses and how to address these A.I. issues.

Joy has so many huge achievements, I struggled to pare them down but here's my best shot:
• During her Ph.D. at MIT, her research uncovered extensive racial and gender biases in the A.I. services of big-tech firms including Amazon, Microsoft and IBM.
• The "Coded Bias" documentary she stars in that follows this research has a crazy 100% fresh rating on Rotten Tomatoes.
• Her TED Talk on algorithmic bias has over a million views.
• She founded The Algorithmic Justice League to create a world with more equitable and accountable technology.
• Has been recognized in the Bloomberg 50, Tech Review 35 under 35, Forbes 30 under 30, TIME Magazine’s A.I. 100 and was the youngest person included in Forbes' Top 50 Women in Tech.
• In addition to her MIT Ph.D, holds a Master's from the University of Oxford (where she studied as a Rhodes Scholar) and she holds a Bachelor's in Computer Science from the Georgia Institute of Technology.

Today’s episode should be fascinating to just about anyone! In it, Joy details: 
• The research that led her to uncover startling racial and gender biases in widely-used commercial A.I. systems.
• How firms reacted to her discoveries, including which big tech companies were receptive and which were disparaging. 
• What we can do to ensure our own A.I. models don’t reinforce historical stereotypes. 
• Whether she thinks our A.I. future will be bleak or brilliant.

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

Seven Factors for Successful Data Leadership

Today's episode is a fun one with the jovial EIGHT-time book author, Ben Jones. In it, Ben covers the seven factors of successful data leadership — factors he's gleaned from administering his data literacy assessment to 1000s of professionals.

Ben:
• Is the CEO of Data Literacy, a firm that specializes in training and coaching professionals on data-related topics like visualization and statistics.
• Has published eight books, including bestsellers "Communicating Data with Tableau" (O'Reilly, 2014) and "Avoiding Data Pitfalls" (Wiley, 2019).
• Has been teaching data visualization at the University of Washington for nine years.
• Previously worked for six years as a director at Tableau.

Today’s episode should be broadly accessible to any interested professional.

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

Neuroscience + Machine Learning, with Google DeepMind’s Dr. Kim Stachenfeld

Today's episode with is one of my favorite conversations ever. In it, the hilarious and fascinating Dr. Kimberly Stachenfeld (of both DeepMind and Columbia) blows my mind by detailing relationships between human neuroscience and A.I.

More on Kim:
• Research Scientist at Google DeepMind, the world’s leading A.I. research group.
• Affiliate Professor of Theoretical Neuroscience at Columbia University.
• Research interests include deep learning, reinforcement learning, representation learning, graph neural networks and a brain structure called the hippocampus.
• Holds a PhD in Computational Neuroscience from Princeton.

Today’s episode should be fascinating for anyone (🧠 + 🤖 = 🤯).

In it, Kim details:
• Her research on computer-based simulations of how the human brain simulates the real world.
• What today’s most advanced A.I. systems (like Large Language Models) can do… and what they can’t.
• How language serves as an efficient compression mechanism for both humans and machines.
• How a leading neuroscience theory called the dopamine reward-prediction error hypothesis relates to reinforcement learning in machines.
• The special role of our brain’s hippocampus in memory formation.
• The best things we personally can do to improve our cognitive abilities.
• What it might take to realize Artificial General Intelligence (AGI)

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

Decoding Speech from Raw Brain Activity, with Dr. David Moses

Dr. David Moses and his colleagues have pulled off a miracle with A.I.: allowing paralyzed patients to "speak" through a video avatar in real time — using brain waves alone. In today's episode, David details how ML makes this possible.

David:
• Is an adjunct professor at the University of California, San Francisco.
• Is the project lead on the BRAVO (Brain-Computer Interface Restoration of Arm and Voice) clinical trial.
• The success of this extraordinary BRAVO project led to an article in the prestigious journal Nature and YouTube video that already has over 3 million views.

Today’s episode does touch on specific machine learning (ML) terminology at points, but otherwise should be fascinating to anyone who’d like to hear how A.I. is facilitating real-life miracles.

In this episode, David details: 
• The genesis of the BRAVO project.
• The data and the ML models they’re using on the BRAVO project in order to predict text, speech sounds and facial expressions from the brain activity of paralyzed patients.
• What’s next for this exceptional project including how long it might be before these brain-to-speech capabilities are available to anyone who needs them.

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

Mathematical Optimization, with Jerry Yurchisin

Mathematical Optimization complements Machine Learning and Statistics in the data scientist's tool belt, but before today's episode with Mathematical Optimization guru Jerome Yurchisin, I knew almost nothing about the powerful technique.

Jerry: 
• Works as a Data Science Strategist at Gurobi Optimization, a leading decision-intelligence company that provides mathematical optimization solutions to the likes of Uber, Air France and the National Football League. 
• Spent eight years as a mathematical consultant at Booz Allen Hamilton where he paired mathematical optimization with ML, statistics and simulation to inform decision-making.
• Was also previously an instructor at the University of North Carolina at Chapel Hill, where he obtained his Master’s in Operations Research and Statistics.
• Also holds an additional Master’s in Applied Math from Ohio University.

Today’s episode will appeal most to hands-on data science practitioners such as data scientists and ML engineers.

In this episode, Jerry details: 
• What mathematical optimization is and how it works. 
• Specific real-world examples where mathematical optimization is a better choice than a statistical or machine learning approach. 
• His recommended resources for getting started with mathematical optimization in Python (or whatever your preferred programming language is) today.

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

AI Emits Far Less Carbon Than Humans (Doing the Same Task)

There's been a lot of press about Large Language Models (LLMs), such as those behind ChatGPT, using vast amounts of energy per query. In fact, however, a person doing the same work emits 12x to 45x more carbon from their laptop alone.

Today’s "Five-Minute Friday" episode is a quick one on how “The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans”. Everything in today’s episode is based on an ArXiV preprint paper with that title by researchers from UC Irvine, the Massachusetts Institute of Technology and other universities.

For writing a page of text, for example, the authors estimate:
• BLOOM open-source LLM (including training) produces ~1.6g CO2/query.
• OpenAI's GPT-3 (including training) produces ~2.2g CO2/query.
• Laptop usage for 0.8 hours (average time to write page) emits ~27g CO2 (that's 12x GPT-3).
• Desktop for same amount of writing time emits ~72g CO2 (32 x GPT-3).

For creating a digital illustration:
• Midjourney (including training) produces ~1.9g CO2/query.
• DALL-E 2 produces ~2.2g CO2/query.
• Human takes ~3.2 hours for the same work, emitting ~100g CO2 (45 x DALL-E 2) on a laptop or ~280g CO2 (127 x DALL-E 2) on a desktop.

There are complexities here, such as what humans do with their time instead of writing or illustrating; if it’s spent driving, for example, then the net impact would be worse. As someone who’d love to see the world at net negative carbon emissions ASAP through innovations like nuclear fusion and carbon capture, however, I have been getting antsy about how much energy state-of-the-art LLMs use, but this simple article turned that perspective upside down. I’ll continue to use A.I. to augment my work wherever I can... and hopefully get my day done earlier so I can get away from my machine and enjoy some time outdoors.

Hear more detail in today's episode or check out the video version to see figures as well.

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

Quantum Machine Learning, with Dr. Amira Abbas

Brilliant, eloquent Dr. Amira Abbas introduces us to Quantum Machine Learning in today's episode. She details the key concepts (like qubits), what's possible today (Quantum SVMs) and what the future holds (e.g., Quantum Neural Networks).

Amira: 
• Is a postdoctoral researcher at the University of Amsterdam as well as QuSoft, a world-leading quantum-computing research institution also in the Netherlands.
• Was previously on the Google Quantum A.I. team and did Quantum ML research at IBM.
• Holds a PhD in Quantum ML from the University of KwaZulu-Natal, during which she was a recipient of Google's PhD fellowship.

Much of today’s episode will be fascinating to anyone interested in how quantum computing is being applied to machine learning; there are, however, some relatively technical parts of the conversation that might be best-suited to folks who already have some familiarity with ML.

In this episode, Amira details: 
• What Quantum Computing is, how it’s different from the classical computing that dominates the world today, and where quantum computing excels relative to its classical cousin.
• Key terms such as qubits, quantum entanglement, quantum data and quantum memory.
• Where Quantum ML shows promise today and where it might in the coming years.
• How to get started in Quantum ML research yourself.
• Today’s leading software libraries for Quantum ML.

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

OpenAI’s DALL-E 3, Image Chat and Web Search

Today's episode details three big releases from OpenAI: (1) DALL-E 3 text-to-image model, which "exactly" adheres to your prompt. (2) Image-to-text chat. (3) Real-time web search integrated into ChatGPT (which seems to lag behind Google's Bard).

So, first, DALL-E 3 text-to-image generation:
• Appears to generate images that are on par with Midjourney V5, the current state-of-the-art.
• The big difference is that apparently DALL-E 3 will actually generate images that adhere “exactly” to the text you provide.
• In contrast, the incumbent models in the state of the art typically ignore words or key parts of the description even though the quality is typically stunning.
• This adherence to prompts extends even to language that you’d like to include in the image, which is mega.
• Watch today's YouTube version for examples of all the above.

In addition, using Midjourney is a really bizarre user experience because it's done through Discord where you provide prompts and get results alongside dozens of other people at the same time. DALL-E 3, in contrast, will be within the slick ChatGPT Plus environment, which could completely get rid of the need to develop text-to-image prompt-engineering expertise in order to get great results. Instead, you can simply have an iterative back-and-forth conversation with ChatGPT to produce the image of your dreams.

Next up is image-to-text chat in ChatGPT Plus:
• We've known this was coming for a while.
• Works stunningly well in the test I've done so far.
• Today's YouTube version also shows an example of this.

Finally, real-time web search with Bing is now integrated into ChatGPT Plus:
• In my personal (anecdotal tests), this lagged behind Google's Bard.
• Bard is also free, so if real-time web search is what you're after, there doesn't seem to be a reason to pay for ChatGPT Plus. That said, for state-of-the-art general chat plus now image generation and text-to-image chat (per the above), ChatGPT Plus is well worth the price tag.

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