How to Integrate Generative A.I. Into Your Business, with Piotr Grudzień

Want to integrate Conversational A.I. ("chatbots") into your business and ensure it's a (profitable!) success? Then today's episode with Quickchat AI co-founder Piotr Grudzień, covering both customer-facing and internal use cases, will be perfect for you.

Piotr:

• Is Co-Founder and CTO of Quickchat AI, a Y Combinator-backed conversation-design platform that lets you quickly deploy and debug A.I. assistants for your business.

• Previously worked as an applied scientist at Microsoft.

• Holds a Master’s in computer engineering from the University of Cambridge.

Today's episode should be accessible to technical and non-technical folks alike.

In this episode, Piotr details:

• What it takes to make a conversational A.I. system successful, whether that A.I. system is externally facing (such as a customer-support agent) or internally facing (such as a subject-matter expert).

• What’s it’s been like working in the fast-developing Large Language Model space over the past several years.

• What his favorite Generative A.I. (foundation model) vendors are.

• What the future of LLMs and Generative A.I. will entail.

• What it takes to succeed as an A.I. entrepreneur.

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

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.

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.

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.

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.

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.

Computational Mathematics and Fluid Dynamics, with Prof. Margot Gerritsen

Today, the extremely intelligent and super delightful Prof. Margot Gerritsen returns to the show to introduce what Computational Mathematics is, detail countless real-world applications of it, and relate it to the field of data science.

Margot:
• Has been faculty at Stanford University for more than 20 years, including eight years as Director of the Institute for Computational and Mathematical Engineering.
• In 2015, co-founded Women in Data Science (WiDS) Worldwide, an organization that supports, inspires and lowers barriers to entry for women across over 200 chapters in over 160 countries.
• Hosts the corresponding Women in Data Science podcast.
• Holds a PhD from Stanford in which she focused on Computational Fluid Dynamics — a passion she has retained throughout her academic career.

Today’s episode should appeal to anyone.

In it this episode, Margot details:
• What computational mathematics is.
• How computational math is used to study fluid dynamics, with fascinating in-depth examples across traffic, water, oil, sailing, F1 racing, the flight of pterodactyls and more.
• Synaesthesia, a rare perceptual phenomenon, which in her case means she sees numbers in specific colors and how this relates to her lifelong interest in math.
• The genesis of her Women in Data Science organization and the impressive breadth of its global impact today.

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

Overcoming Adversaries with A.I. for Cybersecurity, with Dr. Dan Shiebler

Recently in Detroit, my hotel randomly had a podcast studio complete with "ON AIR" sign haha. From there, I interviewed the wildly intelligent Dr. Dan Shiebler on how machine learning is used to tackle cybercrime.

Dan:
• As Head of Machine Learning at Abnormal Security, a cybercrime detection firm that has grown to over $100m in annual recurring revenue in just four years, manages a team of over 50 engineers.
• Previously worked at Twitter, first as a Staff ML Engineer and then as an ML Engineering Manager.
• Holds a PhD in A.I. Theory from the University of Oxford and obtained a perfect 4.0 GPA in his Computer Science and Neuroscience joint Bachelor’s from Brown University.

Today’s episode is on the technical side so might appeal most to hands-on practitioners like data scientists and ML engineers, but anyone who’d like to understand the state-of-the-art in cybersecurity should give it a listen.

In this episode, Dan details:
• The machine learning approaches needed to tackle the uniquely adversarial application of cybercrime detection.
• How to carry out real-time ML modeling.
• What his PhD research on Category Theory entailed and how it applies to the real world.
• The major problems facing humanity in the coming decades that he thinks A.I. will be able to help with… and those that he thinks A.I. won’t.

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.

Llama 2, Toolformer and BLOOM: Open-Source LLMs with Meta’s Dr. Thomas Scialom

Thomas Scialom, PhD is behind many of the most popular Generative A.I. projects including Llama 2, the world's top open-source LLM. Today, the Meta A.I. researcher reveals the stories behind Llama 2 and what's in the works for Llama 3.

Thomas:
• Is an A.I. Research Scientist at Meta.
• Is behind some of the world’s best-known Generative A.I. projects including Llama 2, BLOOM, Toolformer and Galactica.
• Is contributing to the development of Artificial General Intelligence (AGI).
• Has lectured at many of the top A.I. labs (e.g., Google, Stanford, MILA).
• Holds a PhD from Sorbonne University, where he specialized in Natural-Language Generation with Reinforcement Learning.

Today’s episode should be equally appealing to hands-on machine learning practitioners as well as folks who may not be hands on but are nevertheless keen to understand the state-of-the-art in A.I. from someone who’s right on the cutting edge of it all.

In this episode, Thomas details: 
• Llama 2, today’s top open-source LLM, including what is what like behind the scenes developing it and what we can expect from the eventual Llama 3 and related open-source projects.
• The Toolformer LLM that learns how to use external tools.
• The Galactica science-specific LLM, why it was brought down after a few days, and how it might eventually re-emerge in a new form.
• How RLHF — reinforcement learning from human feedback — shifts the distribution of generative A.I. outputs from approximating the average of human responses to excellent, often superhuman quality.
• How soon he thinks AGI — artificial general intelligence — will be realized and how.
• How to make the most of the Generative A.I. boom as an entrepreneur.

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

Large Language Model Leaderboards and Benchmarks

Llamas, Alpacas, Koalas, Falcons... there is a veritable zoo of LLMs out there! In today's episode, Caterina Constantinescu breaks down the LLM Leaderboards and evaluation benchmarks to help you pick the right LLM for your use case.

Caterina:
• Is a Principal Data Consultant at GlobalLogic, a full-lifecycle software development services provider with over 25,000 employees worldwide.
• Previously, she worked as a data scientist for financial services and marketing firms.
• Is a key player in data science conferences and Meetups in Scotland.
• Holds a PhD from The University of Edinburgh.

In this episode, Caterina details:
• The best leaderboards (e.g., HELM, Chatbot Arena and the Hugging Face Open LLM Leaderboard) for comparing the quality of both open-source and proprietary Large Language Models (LLMs).
• The advantages and issues associated with LLM evaluation benchmarks (e.g., evaluation dataset contamination is an big issue because the top-performing LLMs are often trained on all the publicly available data they can find... including benchmark-evaluation datasets).

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

How Data Happened: A History, with Columbia Prof. Chris Wiggins

Today, Chris Wiggins — Chief Data Scientist at The New York Times and faculty at Columbia University — provides an enthralling, witty and rich History of Data Science. Chris is an extraordinarily gifted orator; don't miss this episode!

Chris:
• Is an Associate Professor of Applied Math at Columbia University.
• Has been CDS at The NY Times for nearly a decade.
• Co-authored two fascinating recently-published books: "How Data Happened: A History from the Age of Reason to the Age of Algorithms" and "Data Science in Context: Foundations, Challenges, Opportunities"

The vast majority of this episode will be accessible to anyone. There are just a couple of questions near the end that cover content on tools and programming languages that are primarily intended for hands-on practitioners.

In the episode, Chris magnificently details: 
• The history of data and statistics from its infancy centuries ago to the present.
• Why it’s a problem that most data scientists have limited exposure to the humanities.
• How and when Bayesian statistics became controversial.
• What we can do to address the key issues facing data science and ML today.
• His computational biology research at Columbia.
• The tech stack used for data science at the globally revered New York Times.

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

Generative A.I. without the Privacy Risks (with Prof. Raluca Ada Popa)

Consumers and enterprises dread that Generative A.I. tools like ChatGPT breach privacy by using convos as training data, storing PII and potentially surfacing confidential data as responses. Prof. Raluca Ada Popa has all the solutions.

Today's guest, Raluca:
• Is Associate Professor of Computer Science at University of California, Berkeley.
• Specializes in computer security and applied cryptography.
• Her papers have been cited over 10,000 times.
• Is Co-Founder and President of Opaque Systems, a confidential computing platform that has raised over $31m in venture capital to enable collaborative analytics and A.I., including allowing you to securely interact with Generative A.I.
• Previously co-founded PreVeil, a now-well-established company that provides end-to-end document and message encryption to over 500 clients.
• Holds a PhD in Computer Science from MIT.

Despite Raluca being such a deep expert, she does such a stellar job of communicating complex concepts simply that today’s episode should appeal to anyone that wants to dig into the thorny issues around data privacy and security associated with Large Language Models (LLMs) and how to resolve them.

In the episode, Raluca details:
• What confidential computing is and how to do it without sacrificing performance.
• How you can perform inference with an LLM (or even train an LLM!) without anyone — including the LLM developer! — being able to access your data.
• How you can use commercial generative models OpenAI’s GPT-4 without OpenAI being able to see sensitive or personally-identifiable information you include in your API query.
• The pros and cons of open-source versus closed-source A.I. development.
• How and why you might want to seamlessly run your compute pipelines across multiple cloud providers.
• Why you should consider a career that blends academia and entrepreneurship.

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

The Modern Data Stack, with Harry Glaser

Today, eloquent Harry Glaser details the Modern Data Stack, including cloud collab tools (like Deepnote), running ML from data warehouses (like Snowflake), using dbt Labs for model orchestration, and model deployment best-practices.

Harry:
• Is Co-Founder and CEO of Modelbit, a San Francisco-based startup that has raised $5m in venture capital to make the productionization of machine learning models as fast and as simple as possible.
• Previously, was Co-Founder and CEO of Periscope Data, a code-driven analytics platform that was acquired by Sisense for $130m.
• And, prior to that, was a product manager at Google.
• Holds a degree in Computer Science from the University of Rochester.

Today’s episode is squarely targeted at practicing data scientists but could be of interest to anyone who’d like to enrich their understanding of the modern data stack and how ML models are deployed into production applications.

In the episode, Harry details:
• The major tools available for developing ML models.
• The best practices for model deployment such as version control, CI/CD, load balancing and logging.
• The data warehouse options for running models.
• What model orchestration is.
• How BI tools can be leveraged to collaborate on model prototypes across your organization.

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

Brain-Computer Interfaces and Neural Decoding, with Prof. Bob Knight

In today's extraordinary episode, Prof. Bob Knight details how ML-powered brain computer interfaces (BCIs) could allow real-time thought-to-speech synthesis and the reversal of cognitive decline associated with aging.

This is a rare treat as "Dr. Bob" doesn't use social media and has only made two previous podcast appearances: on Ira Flatow's "Science Friday" and a little-known program called "The Joe Rogan Experience".

Dr. Bob:
• Is Professor of Neuroscience and Psychology at University of California, Berkeley.
• Is Adjunct Professor of Neurology and Neurosurgery at UC San Francisco.
• Over his career, has amassed tens of millions of dollars in research funding, 75 patents, and countless international awards for neuroscience and cognitive computing research.
• His hundreds of papers have together been cited over 70,000 times.

In this episode, Bob details:
• Why the “prefrontal cortex” region of our brains makes us uniquely intelligent relative to all the other species on this planet. 
• The invaluable data that can be gathered by putting recording electrodes through our skulls and directly into our brains.
• How "dynamic time-warping" algorithms allow him to decode imagined sounds, even musical melodies, through recording electrodes implanted into the brain.
• How BCIs are life-changing for a broad range of illnesses today.
• The extraordinary ways that advances in hardware and machine learning could revolutionize medical care with BCIs in the coming years.

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

A.I. Accelerators: Hardware Specialized for Deep Learning

Today we’ve got an episode dedicated to the hardware we use to train and run A.I. models (particularly LLMs) such as GPUs, TPUs and AWS's Trainium and Inferentia chips. Ron Diamant may be the best guest on earth for this fascinating topic.

Ron:
• Works at Amazon Web Services (AWS) where he is Chief Architect for their A.I. Accelerator chips, which are designed specifically for training (and making inferences with) deep learning models.
• Holds over 200 patents across a broad range of processing hardware, including security chips, compilers and, of course, A.I. accelerators.
• Has been at AWS for nearly nine years – since the acquisition of the Israeli hardware company Annapurna Labs, where he served as an engineer and project manager.
• Holds a Masters in Electrical Engineering from Technion, the Israel Institute of Technology.

Today’s episode is on the technical side but doesn’t assume any particular hardware expertise. It’s primarily targeted at people who train or deploy machine learning models but might be accessible to a broader range of listeners who are curious about how computer hardware works.

In the episode, Ron details: 
• CPUs versus GPUs.
• GPUs versus specialized A.I. Accelerators such as Tensor Processing Units (TPUs) and his own Trainium and Inferentia chips.
• The “AI Flywheel” effect between ML applications and hardware innovations.
• The complex tradeoffs he has to consider when embarking upon a multi-year chip-design project.
• When we get to Large Language Model-scale models with billions of parameters, the various ways we can split up training and inference over our available devices.
• How to get popular ML libraries like PyTorch and TensorFlow to interact optimally with A.I. accelerator chips.

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