Many-time bestselling author and prolific open-source R developer Hadley Wickham is our guest today. In it, we discuss Posit's rebrand and why the Tidyverse needs to be in every data scientist's toolkit.
More on Hadley:
• Chief Scientist at Posit PBC
• Adjunct Professor of Statistics at Stanford University, Rice University and The University of Auckland.
• Is best-known as the creator of the Tidyverse suite of open-source R libraries for data science, including the essential libraries dplyr and ggplot2.
• Has written seminal books on R programming for O'Reilly, Springer and CRC Press, including the mega-bestselling "R for Data Science".
Today’s episode will primarily be of interest to hands-on practitioners like data scientists and machine learning engineers. In it, Hadley details:
• Why the iconic open-source company RStudio rebranded to Posit.
• The philosophy of the tidyverse, amusing backstories on its most iconic packages and why the tidyverse is invaluable for all data scientists to be familiar with.
• The open-source projects he’s most excited about today.
• How you can easily get involved with career-bolstering open-source projects yourself.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: SuperDataScience
Deep Utopia: AI Could Solve All Human Problems in Our Lifetime
Today’s episode focuses on Nick Bostrom's latest book, Deep Utopia. Published a couple of weeks ago, it delves into the possibilities of a future where artificial intelligence has solved humanity's deepest problems.
Read MoreWhat will humans do when machines are vastly more intelligent? With Aleksa Gordić
Aleksa Gordić — the famed A.I. educator and multilingual-LLM entrepreneur — is my guest today. Brilliant and widely-read, Aleksa opines on what it will take to realize Artificial Super Intelligence and the consequences for humans.
Aleksa:
• Is Founder & CEO of Runa AI, a startup focused on building multilingual LLMs.
• Is an online educator that has built a community of 160,000 people in the A.I. space, including through his A.I. Epiphany YouTube channel.
• Previously, he was an A.I. Research Engineer at Google DeepMind in London and a Machine Learning Software Engineer at Microsoft.
• He holds a degree in Electronics and Computer Science from the University of Belgrade in Serbia.
Today’s episode contains tidbits here and there that will appeal primarily to hands-on machine learning practitioners, but it mostly should be of great interest to anyone.
In this episode, wildly-intelligent Aleksa details:
• Why multilingual LLMs provide so much value despite the cutting-edge LLMs like Claude 3, Gemini Ultra and GPT-4 supporting so many languages.
• His frameworks for entrepreneurial success and for effective self-directed learning.
• His analogy for how humans are born as a checkpoint of a Bayesian model that’s fine-tuned with reinforcement learning from human feedback (RLHF).
• What he thinks it will take to realize artificial super intelligence and what it could mean for human society when it arrives.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
RFM-1 Gives Robots Human-like Reasoning and Conversation Abilities
Today’s episode is all about an LLM trained for robotics applications called RFM-1 that completely blows my mind because of the implications for what can now suddenly be accomplished so easily with robotics.
Read MoreDeep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas (Copy)
Today, Prof. Barrett Thomas blends his rich technical understanding of Deep Reinforcement Learning with his commercial savviness to eloquently detail how Deep RL can be leveraged to minimize costs and maximize profits.
Barrett:
• Is Research Professor in Business Analytics and Senior Associate Dean at the University of Iowa’s College of Business.
• As will soon be unsurprising to you when you hear how well he communicates complex concepts, he’s won multiple teaching awards (amongst other academic prizes).
• He holds a PhD in Industrial and Operations Engineering from the University of Michigan.
Today’s episode is a technical one that will appeal primarily to hands-on practitioners like data scientists, software developers and machine learning engineers.
In this episode, Barrett details:
• What Markov Decision Processes are and how they relate to Deep Reinforcement Learning.
• How operations research leverages neural networks to minimize business costs and maximize business profits.
• How same-day delivery has been made possible by machine learning.
• How aerial drones and autonomous vehicles will revolutionize supply chains and transportation.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in March 2024
We're trying something novel on the SuperDataScience Podcast today: an ICMYI ("in case you missed it") episode that highlights the most gripping moments from my conversations with guests over the past month.
Please let me know what you think of this! Does it work for you? What would you change about it? Should we stop doing these entirely? Let me know right here on this post; your voice matters :)
For this inaugural ICYMI episode, conversation highlights include:
1. Sebastian Raschka, PhD on how Lightning AI makes LLM training and deployment easy (from Episode #767).
2. Dr. Travis Oliphant, creator of the ubiquitous NumPy and SciPy libraries, on the future of scientific computing (#765).
3. Award-winning, A.I.-focused venture capitalist Rudina Seseri letting us know what it takes to get a VC firm to invest in you (#763).
4. Prof. Zachary Lipton on his roadmap from AI startup to long-term commercial success (#769).
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko
You wanted more of Kirill Eremenko, now you've got it! Kirill returns to the show today to detail Decision Trees, Random Forests and all three of the leading gradient-boosting algorithms: XGBoost, LightGBM and CatBoost 😸
If you don’t already know him, Kirill:
• Is Founder and CEO of SuperDataScience, an e-learning platform that is the namesake of this very podcast.
• Launched the SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins four years ago.
• Has reached more than 2.7 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.
Today’s episode is a highly technical one focused specifically on Gradient Boosting methods and the foundational theory required to understand them. I expect this episode will be of interest primarily to hands-on practitioners like data scientists, software developers and machine learning engineers.
In this episode, Kirill details:
• Decisions Trees.
• How Decision Trees are ensembled into Random Forests via Bootstrap Aggregation.
• How the AdaBoost algorithm formed a bridge from Random Forests to Gradient Boosting.
• How Gradient Boosting works for both regression and classification tasks.
• All three of the most popular Gradient Boosting approaches — XGBoost, LightGBM and CatBoost — as well as when you should choose them.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Generative AI for Medicine, with Prof. Zack Lipton
Generative A.I. is rapidly transforming medicine. My guest today is brilliant, inspiring Prof. Zachary Lipton — Chief Scientific Officer and CTO of Abridge, a startup that has quickly raised $208m to lead the transformation!
More on Zack:
• Assoc. Prof. in the Machine Learning Dept. of Carnegie Mellon University's Computer Science school.
• Highly-cited (23k+ citations) with research spanning core ML methods and theory, as well as applications in healthcare and NLP.
• Directs the Approximately Correct Machine Intelligence (ACMI) Lab at CMU, where they build robust systems for the real world.
• Is also a jazz saxophonist! 🎷
Despite Zack being such a deep technical expert, most of today’s content will be of interest to anyone who’d like to hear about the cutting edge of generative A.I. applications in healthcare.
The tech that Zack is leading development of at Abridge, which you can hear about in today's episode:
• Initial deployment uses ambient listening and generative A.I. to reduce the cognitive burden of clinical documentation, reducing burnout as well as enabling clinicians to spend less time with computers and more with patients.
• Industry-leading automatic speech recognition engine specifically designed for healthcare applications; can accurately transcribe speech in challenging environments, e.g., when there is background noise or when multiple people are speaking.
• Supports 14+ languages including handling code-switching (where speakers shift between languages) and interpreter-mediated conversations.
• In-house LLM development allows greater customization and responsible-use features, such as transparency (e.g., links to source transcript/audio) and evidence extraction (verification process).
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant
Huge episode today with iconic Dr. Travis Oliphant, creator of NumPy and SciPy, the standard libraries for numeric operations (downloaded 8 million and 3 million times PER DAY, respectively). Hear about the future of open-source, including the impact of GenAI.
More on Travis:
• Founded Anaconda, Inc., the company behind the also-ubiquitous Python package manager.
• Founded the massive PyData conferences and communities as well as its associated non-profit foundation, NumFOCUS.
• Currently serves as the CEO of two firms: OpenTeams and Quansight.
• Holds a PhD in biomedical engineering from the Mayo Clinic in Minnesota.
Today’s episode will primarily be of interest to hands-on practitioners like data scientists, software developers and machine learning engineers.
In it, Travis details:
• How his journey creating open-source software began and how NumPy and SciPy grew to become the most popular foundational Python libraries for working with data.
• How he identifies commercial opportunities to support his vast open-source efforts and communities.
• How AI, particularly generative AI, is transforming open-source development.
• Where open-source innovation is headed in the years to come.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri
How should an A.I. startup find product-market fit? How do some A.I. startups become spectacularly successful? The renowned (and highly technical!) A.I. venture-capital investor Rudina Seseri answers these questions and more in today's episode.
Rudina:
• Founder and Managing Partner of Glasswing Ventures in Boston.
• Led investments and/or served on the Board of Directors of more than a dozen SaaS startups, many of which were acquired.
• Was named Startup Boston's 2022 "Investor of the Year" amongst many other formal recognitions.
• Is a sought-after keynote speaker on investing in A.I. startups.
• Executive Fellow at Harvard Business School.
• Holds an MBA from Harvard University.
Today’s episode will be interesting to anyone who’s keen on scaling their impact with A.I., particularly through A.I. startups or investment.
In this episode, Rudina details:
• How data are used to assess venture capital investments.
• What makes particular AI startups so spectacularly successful.
• Her "A.I. Palette" for examining categories of machine learning models and mapping them to categories of training data.
• How Generative AI isn’t a fad, but it is still only a component of the impact that AI more broadly can make.
• The automated systems she has built for staying up to date on all of the most impactful AI developments.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google’s Lisa Cohen
Google recently released Gemini Ultra, their largest language model. I love Ultra and now use it instead of GPT-4 on many tasks. Today's guest, Lisa Cohen, leads Gemini's rollout; hear from her how a company with billions of users rolls out new A.I. products.
More on Gemini Ultra:
• The only LLM with comparable capabilities to GPT-4 (in my experience as well as on benchmark evaluations, although I know benchmarking has plenty of issues!)
• Ultra maintains attention across large context windows (Gemini 1.5 Pro has a million-token context, btw!), competently generating natural language and code.
• Like GPT-4V, Ultra is multi-modal and so accepts both an image and text as input at the same time.
• Piggybacking on Google's excellence at search, I’ve found Gemini Ultra to be particularly effective at tasks that involve real-time search (the Google "Bard" project that focused on real-time information retrieval was renamed "Gemini" when Gemini Ultra was released).
Lisa Cohen is perhaps the best person on the planet to be speaking to about the momentous Gemini releases because Lisa is Director of Data Science & Engineering for Google's Gemini, Assistant and Search Platforms. In addition, she:
• Was previously Senior Director of Data Science at Twitter and Principal Director of Data Science at Microsoft.
• Holds a Master's in Applied Math from Harvard University.
In this episode, Lisa details:
• The three LLMs in Google’s Gemini family and how the largest one, Gemini Ultra, fits in.
• The many ways you can access Gemini models today.
• How absolutely enormous LLM projects are carried out and how they’re rolled out safely and confidently to literally billions of users.
• How LLMs like Gemini Ultra are transforming life and work for everyone from data scientists to educators to children, and how this transformation will continue in the coming years.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko
Last month, Kirill Eremenko was on the show to detail Decoder-Only Transformers (like the GPT series). It was our most popular episode ever, so he's come right back today to detail an even more sophisticated architecture: Encoder-Decoder Transformers.
If you don’t already know him, Kirill:
• Is Founder and CEO of SuperDataScience, an e-learning platform that is the namesake of this podcast.
• Founded the Super Data Science Podcast in 2016 and hosted the show until he passed me the reins a little over three years ago.
• Has reached more than 2.7 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.
Kirill was most recently on the show for Episode #747 to provide a technical introduction to the Transformer module that underpins all the major modern Large Language Models (LLMs) like the GPT, Gemini, Llama and BERT architectures. We received an unprecedented amount of positive feedback from that episode, demanding more! So here we are.
That episode, #747, as well as today’s, are perhaps the two most technical episodes of this podcast ever so they probably appeal mostly to hands-on practitioners like data scientists and ML engineers, particularly those who already have some understanding of deep neural networks.
In this episode, Kirill:
• Reviews the key Transformer theory that we covered in Episode #747, namely the individual neural-network components of the Decoder-Only architecture that prevails in generative LLMs like the GPT series models.
• Builds on that to detail the full, Encoder-Decoder Transformer architecture that is used in the original Transformer by Google, in their “Attention is All You Need” paper, as well as in other models that excel at both natural-language understanding and generation such as T5 and BART.
• Discusses the performance and capability pros and cons of full Encoder-Decoder architectures relative to Decoder-Only architectures like GPT and Encoder-Only architectures like BERT.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Mamba Architecture: Superior to Transformers in LLMs
Modern, cutting-edge A.I. basically depends entirely on the Transformer. But now, the first serious contender to the Transformer has emerged and it’s called Mamba; we’ve got the full paper—called "Mamba: Linear-TimeSequence Modeling with Selective State Spaces" and written by researchers at Carnegie Mellon and Princeton.
Read MoreHow to Speak so You Blow Listeners’ Minds, with Cole Nussbaumer Knaflic
Cole Nussbaumer Knaflic's book, "storytelling with data", has sold over 500k copies... wild! In today's episode, Cole details the best tricks from her latest book, "storytelling with you" — a goldmine on how to inform and profoundly engage people.
Cole:
• Is the author of “storytelling with data”, which has sold half a million copies, been translated into over 20 languages and is used by more than 100 universities. Nearly a decade old, the book is the #1 bestseller still today in several Amazon categories.
• Also wrote the follow-on, hands-on “storytelling with data: let’s practice!” a bestseller in its own right.
• Serves as the Founder and CEO of the storytelling with data company, which provides data-storytelling workshops and other resources.
• Previously she was a People Analytics Manager at Google.
• Holds a degree in math as well as an MBA from the University of Washington.
Today’s episode will be of interest to anyone who’d like to communicate so effectively and compellingly that people are blown away.
In this episode, Cole details:
• Her top tips for planning, creating and delivering an incredible presentation.
• A few special tips for communicating data effectively for all of you data nerds like me.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AlphaGeometry: AI is Suddenly as Capable as the Brightest Math Minds
Google DeepMind's open-sourced AlphaGeometry blends "fast thinking" (like intuition) with "slow thinking" (like careful, conscious reasoning) to enable a big leap forward in A.I. capability and match human Math Olympiad gold medalists on geometry problems.
KEY CONTEXT
• A couple weeks ago, DeepMind published on AlphaGeometry in the prestigious journal peer-reviewed Nature.
• DeepMind focused on geometry due to its demand for high-level reasoning and logical deduction, posing a unique challenge that traditional ML models struggle with.
MASSIVE RESULTS
• AlphaGeometry tackled 30 International Mathematical Olympiad problems, solving 25. This outperforms human Olympiad bronze and silver medalists' averages (who solved 19.3 and 22.9, respectively) and closely rivals gold medalists (who solved 25.9).
• This new system crushes the previous state-of-the-art A.I., which solved only 10 out of 30 problems.
• Beyond solving problems, AlphaGeometry also generates understandable proofs, making A.I.-generated solutions more accessible to humans.
HOW?
• AlphaGeometry uses a new method of generating synthetic theorems and proofs, simulating 100 million unique examples to overcome the limitations of (expensive, laborious) human-generated proofs.
• It combines a neural (deep learning) language model for intuitive guesswork with a symbolic deduction engine for logical problem-solving, mirroring "fast" and "slow thinking" processes akin to human cognition (per Daniel Kahneman's "Thinking, Fast and Slow" book).
IMPACT
• A.I. that can "think fast and slow" like AlphaGeometry could generalize across mathematical fields and potentially other scientific disciplines, pushing the boundaries of human knowledge and problem-solving capabilities.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Brewing Beer with A.I., with Beau Warren
In today's episode, Beau Warren of the innovative "Species X" brewery, details how we collaborated together on an A.I. model to craft the perfect beer. Dubbed "Krohn&Borg" lager, you can join us in Columbus, Ohio on Thursday night to try it yourself! 🍻
Read MoreA Code-Specialized LLM Will Realize AGI, with Jason Warner
Don't miss this mind-blowing episode with Jason Warner, who compellingly argues that code-specialized LLMs will bring about AGI. His firm, poolside, was launched to achieve this and facilitate an "AI-led, developer-assisted" coding paradigm en route.
Jason:
• Is Co-Founder and CEO of poolside, a hot venture capital-backed startup that will shortly be launching its code-specialized Large Language Model and accompanying interface that is designed specifically for people who code like software developers and data scientists.
• Previously was Managing Director at the renowned Bay-Area VC Redpoint Ventures.
• Before that, held a series of senior software-leadership roles at major tech companies including being CTO of GitHub and overseeing the Product Engineering of Ubuntu.
• Holds a degree in computer science from Penn State University and a Master's in CS from Rensselaer Polytechnic Institute.
Today’s episode should be fascinating to anyone keen to stay abreast of the state of the art in A.I. today and what could happen in the coming years.
In today’s episode, Jason details:
• Why a code-generation-specialized LLM like poolside’s will be far more valuable to humans who code than generalized LLMs like GPT-4 or Gemini.
• Why he thinks AGI itself will be brought about by a code-specialized ML model like poolside’s.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI is Disadvantaging Job Applicants, But You Can Fight Back
In today's important episode, the author, professor and journalist Hilke Schellmann details how specific HR-tech firms misuse A.I. to facilitate biased hiring, promotion, and firing decisions. She also covers how you can fight back and how A.I. can be done right!
Hilke’s book, "The Algorithm: How A.I. Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now", was published earlier this month. In the exceptionally clear and well-written book, Hilke draws on exclusive information from whistleblowers, internal documents and real‑world tests to detail how many of the algorithms making high‑stakes decisions are biased, racist, and do more harm than good.
In addition to her book, Hilke:
• Is Assistant Professor of Journalism and A.I. at New York University.
• Previously worked in journalism roles at The Wall Street Journal, The New York Times and VICE Media.
• Holds a Master’s in investigative reporting from Columbia University.
Today’s episode will be accessible and interesting to anyone. In it, Hilke details:
• Examples of specific HR-technology firms that employ misleading Theranos-like tactics.
• How A.I. *can* be used ethically for hiring and throughout the employment lifecycle.
• What you can do to fight back if you suspect you’ve been disadvantaged by an automated process.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Five Levels of AGI
Artificial General Intelligence (AGI) is a term thrown around a lot, but it's been poorly defined. Until now!
Read MoreA Continuous Calendar for 2024
Today's super-short episode provides a "Continuous Calendar" for 2024. In my view, far superior to the much more common Weekly or Monthly calendar formats, a Continuous Calendar can keep you on top of all your projects and commitments all year 'round.
I know I’m not the only one who Continuous Calendars because my annual blog post providing an updated continuous calendar for the new year is reliably one of my most popular blog posts. The general concept is that Continuous Calendars enable you to:
1. Overview large blocks of time at a glance (I can easily fit six months on a standard piece of paper).
2. Get a more realistic representation of how much time there is between two given dates because the dates don’t get separated by arbitrary 7-day or ~30-day cutoffs.
The way they work so effectively is that continuous calendars are a big matrix where every row corresponds to a week and every column corresponds to a day of the week.
So if you’d like to get started today with your own super-efficient Continuous Calendar in 2024, simply head to jonkrohn.com/cal24.
At that URL, you’ll find a Google Sheet with the full 52 weeks of the year, which will probably suit most people’s needs. If you print it on standard US 8.5” x 11” paper, it should get split exactly so that the first half of the year is on page one and the second half of the year is on page two.
The calendar template is simple: It’s all black except that we’ve marked U.S. Federal Holidays with red dates. If you’re in another region, or you’d like to adapt our continuous calendar for any reason at all, simply make a copy of the sheet or download it, and then customize it to your liking.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.