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.

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.

ChatGPT Custom Instructions: A Major, Easy Hack for Data Scientists

Thanks to Shaan Khosla for tipping me off to a crazy easy hack to get markedly better results from GPT-4: providing Custom Instructions that prompt the algorithm to iterate upon its own output while critically evaluating and improving it.

Here's Shaan's full Custom Instructions text, which he himself has been iterating on in recent months:

"I need you to help me with a task. To help me with the task, first come up with a detailed outline of how you think you should respond, then critique the ideas in this outline (mention the advantages, disadvantages, and ways it could be improved), then use the original outline and the critiques you made to come up with your best possible solution.

"Overall, your tone should not be overly dramatic. It should be clear, professional, and direct. Don't sound robotic or like you're trying to sell something. You don't need to remind me you're a large language model, get straight to what you need to say to be as helpful as possible. Again, make sure your tone is clear, professional, and direct - not overly like you're trying to sell something."

Try it out! If you haven't used Custom Instructions before, in today's episode I talk you through how to set it up and explain why this approach is so effective. In the video version, I provide a screenshare that makes getting started foolproof.

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.

Happiness and Life-Fulfillment Hacks

Today, my 94-year-old grandmother shares the secrets behind her radiant happiness. Annie talks about the importance of community, relationships and setting daily intentions, blending time-tested wisdom with forward-thinking optimism.

In today’s episode, Annie discusses: 
• Her secret to happiness.
• How she maintains flourishing long-term relationships.
• The routines and mindset she has to still be living independently, including driving herself everywhere, at 94 years old.
• The pace of technological progress in her lifetime and how A.I. could enrich her life in the years to come.

This episode is something different from the usual pure tech focus so I encourage you to provide feedback if you had strong feelings on this episode one way or another. As always, your feedback is invaluable for shaping the direction of the show.

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

Make Better Decisions with Data, with Dr. Allen Downey

Today's episode with many-time bestselling author Allen Downey is incredible. Learn a ton from him about making better decisions with data, including how to prepare for Black Swan events and how your core beliefs will shift over your life.

Allen:
• Is a Professor Emeritus at Olin College and Curriculum Designer at the learning platform Brilliant.org.
• He was previously a Visiting Professor of Computer Science at Harvard University and a Visiting Scientist at Google.
• He has written 18 books (which he has made available for free online but which are also published in hard copy by major publishers. For example, his books "Think Python" and "Think Bayes" were bestsellers published by O'Reilly).
• His next book, "Probably Overthinking It", is available for pre-order now.
• Holds a PhD in Computer Science from University of California, Berkeley and Bachelor's and Masters degrees from the Massachusetts Institute of Technology.

Today’s episode focuses largely on content from Allen’s upcoming book — his first book intended for a lay audience — and so should appeal to anyone who’s keen to learn from an absolutely brilliant writer and speaker on “How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions.”

In this episode, Allen details: 
• Underused techniques like Survival Analysis that can be uniquely powerful in lots of ordinary circumstances.
• How to better prepare for rare “Black Swan” events.
• How to wrap your head around common data paradoxes such as Preston’s Paradox, Berkson’s Paradox and Simpson’s Paradox.
• What the Overton Window is and how our core beliefs shift relative to it over the course of our lifetime (this is extra trippy).

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.