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.

AGI Could Be Near: Dystopian and Utopian Implications, with Dr. Andrey Kurenkov

In today's episode, you can immerse yourself in one my favorite on-air convos ever: with the exceptional Andrey Kurenkov, on how soon AGI could be realized and the potential utopian/dystopian implications.

Andrey:

• Founded and co-hosts my favorite podcast, "Last Week in A.I.", a weekly program that recaps all of the A.I.-related news you need to know about.

• Is an ML Scientist at Astrocade, an NVIDIA-backed generative AI platform that converts your natural-language prompt into a functional video game.

• Holds a PhD in Computer Science from Stanford University, with research focused on robotics and reinforcement learning.

Today’s episode should be of interest to just about anyone!

In it, Andrey details":

• The genesis of the wide range of A.I. publications and podcasts he’s founded.

• What the future of text-to-video-game generative A.I. could look like.

• Why “A.I. as a product” rarely works commercially, but what you can succeed at with A.I. instead.

• Why A.I. robotics is suddenly progressing so rapidly.

• In one of my favorite on-air conversations ever, how soon AGI could be realized and the potentially dystopian or utopian implications.

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

In Case You Missed It in May 2024

We had another incredible set of guests in May on the SuperDataScience Podcast I host. ICYMI, today's episode highlights the most fascinating moments of my conversations with them.

Specifically, conversation highlights include:

1. Dr. Luis Serrano, a math- and ML-education YouTuber with 150k subscribers, explaining what language embeddings are, how they function, and how essential they are for running semantic search queries.

2. Sol Rashidi, serial C-suite data-role executive at Fortune 100s and bestselling author of "Your A.I. Survival Guide", on her approach to building data teams.

3. Co-founder of the MLOps Community, Demetrios Brinkmann, on the differences between ML Engineering and MLOps roles.

4. Navdeep Martin, an entrepreneur blending climate tech and generative A.I. in her latest startup, on opportunities where you can tackle climate change with technological innovation yourself.

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

Q*: OpenAI’s Rumored AGI Breakthrough

Today’s episode is all about a rumored new model out of OpenAI called Q* (pronounced “Q star”) that has been causing quite a stir, both for its purported role in Altmangate and its implications for Artificial General Intelligence (AGI).

Key context:
• Q* is reported to have advanced capabilities in solving complex math problems expressed in natural language, indicating a significant leap in A.I.
• The rumors about Q* emerged during OpenAI's corporate drama involving the firing and re-hiring of CEO Sam Altman.
• Reports suggested a connection between Q*'s development and the OpenAI upheaval, with staff expressing concerns about its potential dangers to humanity (no definitive evidence links Q* to the OpenAI CEO controversy, however, leaving its role in the incident ambiguous).

Research overview:
• OpenAI's recent published research on solving grade-school word-based math problems (e.g., “The cafeteria had 23 apples. They used 20 for lunch and bought 6 more. How many apples do they have?”) hints at broader implications of step-by-step reasoning in A.I.
• While today's Large Language Models (LLMs) show better results on logical problems when we use chain-of-thought prompting ("work through the problem step by step"), the contemporary LLMs do so linearly (they don't go back to correct themselves or explore alternative intermediate steps), which limits their capability.
• To develop a model that can be trained and evaluated at each intermediate step, OpenAI gathered tons of human feedback on math-word problems, amassing a dataset of 800,000 individual intermediate steps across 75,000 problems.
• Their approach involves an LLM generating solutions at each step and a second model acting as a verifier.

The Q* connection:
• The above research merges LLM reasoning abilities with search-tree methods, inspired by Google DeepMind's AlphaGo algorithm and its ilk.
• The decades-old Q* concept is used for training models to simulate and evaluate prospective moves, a concept from reinforcement learning.
• Q*'s potential for automated self-play could lead to significant advancements in AGI, particularly by reducing reliance on (expensive) human-generated training data.

Implications:
• Q* could yield significant societal benefits (e.g., by solving mathematical proofs humans can't or discovering new physics), albeit with potentially high inference costs.
• Q* raises concerns about security and the unresolved challenges in achieving AGI.
• While Q* isn't the final leap towards AGI, it would represent a major milestone in general reasoning abilities.

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.

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.

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.

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.

Using A.I. to Overcome Blindness and Thrive as a Data Scientist

Today's guest is the remarkable Tim Albiges, who lost the ability to see as an adult. Thanks to A.I. tools, as well as learning how to learn by sound and touch, he is now thriving as a data scientist and pursuing a fascinating PhD!

Tim was working as a restaurant manager eight years ago when he tragically lost his sight.

In the face of countless alarming and discriminatory acts against him on account of his blindness, he taught himself Braille and auditory learning techniques (and to raise math equations and diagrams using a special thermoform machine so that he can feel them) in order to be able to return to college and study computing and data science.

Not only did he succeed in obtaining a Bachelor’s degree in computing (with First-Class Honours), he is now pursuing a PhD at Bournemouth University full-time, in which he’s applying machine learning to solve medical problems. His first paper was published in the peer-reviewed journal Sensors earlier this year.

Today’s inspiring episode is accessible to technical and non-technical listeners alike.

In it, Tim details:
• Why a career in data science can be ideal for a blind person.
• How he’s using ML to automate the diagnosis of chronic respiratory diseases.
• The techniques he employs to live a full and independent life, with a particular focus on the A.I. tools that assist him both at work and at leisure.
• A keen athlete, how he’s adapted his approach to fitness in order to run the London marathon and enjoy a gripping team sport called goalball.

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

Big A.I. R&D Risks Reap Big Societal Rewards, with Meta’s Dr. Laurens van der Maaten

By making big research bets, the prolific Meta Senior Research Director Dr. Laurens van der Maaten has devised or supported countless world-changing machine-learning innovations across healthcare, climate change, privacy and more.

Laurens:
• Is a Senior Research Director at Meta, overseeing swathes of their high-risk, high-reward A.I. projects with application areas as diverse as augmented reality, biological protein synthesis and tackling climate change.
• Developed the "CrypTen" privacy-preserving ML framework.
• Pioneered web-scale weakly supervised training of image-recognition models.
• Along with the iconic Geoff Hinton, created the t-SNE dimensionality reduction technique (this paper alone has been cited over 36,000 times).
• In aggregate, his works have been cited nearly 100,000 times!
• Holds a PhD in machine learning from Tilburg University in the Netherlands.

Today’s episode will probably appeal primarily to hands-on data science practitioners, but there is tons of content in this episode for anyone who’d like to appreciate the state of the art in A.I. across a broad range of socially impactful, super-cool applications.

In this episode, Laurens details: 
• How he pioneered learning across billions of weakly labeled images to create a state-of-the-art machine-vision model.
• How A.I. can be applied to the synthesis of new biological proteins with implications for both medicine and agriculture.
• Specific ways A.I. is being used to tackle climate change as well as to simulate wearable materials for enhancing augmented-reality interactivity.
• A library just like PyTorch but where all the computations are encrypted.
• The wide range of applications of his ubiquitous dimensionality-reduction approach.
• His vision for the impact of A.I. on society in the coming decades.

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

ChatGPT Code Interpreter: 5 Hacks for Data Scientists

The ChatGPT Code Interpreter is surreal: It creates and executes Python code for whatever task you describe, debugs its own runtime errors, displays charts, does file uploads/downloads, and suggests sensible next steps all along the way.

Whether you write code yourself today or not, you can take advantage of GPT-4's stellar natural-language input/output capabilities to interact with the Code Interpreter. The mind-blowing experience is equivalent to having an expert data analyst, data scientist or software developer with you to instantaneously respond to your questions or requests.

As an example of these jaw-dropping capabilities (and given the data science-focused theme of my show), I use today's episode demonstrate the ChatGPT Code Interpreter's full automation of data analysis and machine learning. If you watch the episode on YouTube, you can even see the Code Interpreter hands-on in action while I interact with it solely with natural language.

Over the course of today's episode/video, the Code Interpreter:
1. Receives a sample data file that I provide it.
2. Uses natural language to describe all of the variables that are in the file.
3. Performs a four-step Exploratory Data Analysis (EDA), including histograms, scatterplots that compare key variables and key summary statistics (all explained in natural language).
4. Preprocesses all of my variables for machine learning.
5. Selects an appropriate baseline ML model, trains it and quantitatively evaluates its performance.
6. Suggests alternative models and approaches (e.g., grid search) to get even better performance and then automatically carries these out.
7. Optionally provides Python code every step of the way and is delighted to answer any questions I have about the code.

The whole process is a ton of fun and, again, requires no coding abilities to use (the "Code Interpreter" moniker could be misleadingly intimidating to non-coding folks). Even as an experienced data scientist, however, I would estimate that in many everyday situations use of the Code Interpreter could decrease my development time by a crazy 90% or more.

The big caveat with all of this is whether you're comfortable sharing your code with OpenAI. I wouldn't provide proprietary company code to it without clearing it with your firm first and — if you do use proprietary code with it — turn "Chat history & training" off in your ChatGPT Plus settings. To circumnavigate the data-privacy issue entirely, you could alternatively try Meta's newly-released "Code Llama — Instruct 34B" Large Language Model on your own infrastructure. Code Llama won't, however, be as good as the Code Interpreter in many circumstances and will require some technical savvy to get it up and running.

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

Vicuña, Gorilla, Chatbot Arena and Socially Beneficial LLMs, with Prof. Joey Gonzalez

Vicuna, Gorilla and the Chatbot Arena are all critical elements of the new open-source LLM ecosystem — the extremely knowledgeable and innovative Prof. Joseph Gonzalez is behind all of them. Get the details in today's episode

Joey:
• Is an Associate Professor of Electrical Engineering and Computer Science at the University of California, Berkeley.
• Co-directs the Berkeley RISE Lab, which studies Real-time, Intelligent, Secure and Explainable systems.
• Co-founded Turi (acquired by Apple for $200m) and more recently Aqueduct.
• His research is integral to major software systems including Apache Spark, Ray (for scaling Python ML), GraphLab (a high-level interface for distributed ML) and Clipper (low-latency ML serving).
• His papers—published in top ML journals—have been cited over 24,000 times.
• Developed Berkeley's upper-division data science class, which he now teaches to over 1000 students per semester.

Today’s episode will probably appeal primarily to hands-on data science practitioners but we made an effort to break down technical terms so that anyone who’s interested in staying on top of the latest in open-source Generative A.I. can enjoy the episode.

In it, Prof. Gonzalez details:
• How his headline-grabbing LLM, Vicuña, came to be and how it arose as one of the leading open-source alternatives to ChatGPT.
• How his Chatbot Arena became the leading proving ground for commercial and open-source LLMs alike.
• How his Gorilla project enables open-source LLMs to call APIs making it an open-source alternative to ChatGPT’s powerful plugin functionality.
• The race for longer LLM context windows.
• How both proprietary and open-source LLMs will thrive alongside each other in the coming years.
• His vision for how A.I. will have a massive, positive societal impact over the coming decades.

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