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.

Humans Love A.I.-Crafted Beer

I recently recorded tipplers' reactions as they had their first taste of the A.I.-crafted "Krohn&Borg" lager I co-developed. Today's episode illustrates the result: Humans love A.I. beer! There's also cool content on using CRISPR-Cas9 to modify yeast genes.

Thanks again to Beau Warren, Head Brewer at Species X Beer Project, for the opportunity to collaborate on this delicious project. You can check out Episode #755 for tons of detail on the ML packages used and the models developed to craft beer with A.I.

And thanks to all of the guests/judges in today's episode:

• Rehgan Avon of AlignAI

• Alexandra Hagmeyer (Dauterman) of Path Robotics

• Kelsey Dingelstedt of Women in Analytics (WIA)

• William McFarland of Omega Yeast

• Jim Lachey of the Super Bowl XXVI-winning Washington Commanders

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.

How 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.

A 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.

Blend Any Programming Languages in Your ML Workflows, with Dr. Greg Michaelson

The revolutionary Zerve IDE for data science launches today! Zerve gives ML teams a unified space to collaborate, build and deploy projects... and is free for most use-cases! In today's episode, Zerve co-founder Dr Greg Michaelson explains all the details.

Greg is a super-insightful, crisp and clear communicator; I think you'll really enjoy our conversation. More on him:
• He's Co-Founder and Chief Product Officer of Zerve, which just raised $3.8m in pre-seed funding.
• Previously spent 7 years as DataRobot’s Chief Customer Officer and 4 years as Senior Director of Analytics & Research for Travelers.
• Was a baptist pastor while he obtained his PhD in Applied Statistics from The University of Alabama — that perhaps explains some of the variance in how he’s such a silver-tongued communicator!

Today’s episode will appeal most to hands-on practitioners like data scientists, machine learning engineers and software developers, but may also be of interest to anyone who wants to stay on top of the latest approaches to developing and deploying ML models.

In this episode, Greg details: 
• Why his swish new Zerve IDE is so sorely needed.
• How their open-source Pypelines project uniquely generates Python code for Automated Machine Learning.
• Why AutoML is not suitable to most commercial use cases.
• Why most commercial A.I. projects fail and how to ensure they succeed.
• The straightforward way you can develop speaking skills as slick as his.

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.

How to Found and Fund Your Own A.I. Startup, with Dr. Rasmus Rothe

Fresh off the heels of his triumphant AI House Davos debut at the World Economic Forum last week, Dr. Rasmus Rothe is my guest today, discussing how to successfully found your own A.I. startup, attract venture capital and scale up!

Rasmus is a co-Founder of Merantix, the comprehensive ecosystem that finances, incubates and scales A.I. companies, transforming existing industries and spawning new ones. Merantix includes:

• A "venture studio" that builds transformative A.I. startups from the founding team up.

• A venture-capital fund that invests in A.I. startups.

• Merantix Momentum, a consulting partner for A.I. development and operation.

• The Merantix AI Campus, a slick physical location in Berlin that is Europe's largest A.I. co-working hub. It houses over 1000 entrepreneurs, researchers, investors, and policymakers.

In addition to Merantix, Rasmus:

• Co-founded and co-leads the German A.I. Association, a role that has him regularly providing policy guidance to Europe’s top politicians.

• Scaled, to 150 million users, a deep-learning powered service that analyzes faces.

• Studied computer science at Oxford, Princeton and ETH Zürich, culminating in a PhD in machine vision.

Today’s episode will be of great interest to anyone interested in commercializing and scaling A.I.

In this episode, Rasmus details:

• What makes a great A.I. entrepreneur.

• How to best raise capital for your own A.I. company.

• How to ensure your A.I. company is well-defended from competitors.

• What the future of work could look like in the coming decades as A.I. and robotics overhaul industry after industry.

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

How A.I. is Transforming Science

A.I. is not just a tool, but a driving force in reshaping the landscape of science. In today's episode, I dive into the profound implications A.I. holds for scientific discovery, citing applications across nuclear fusion, medicine, self-driving labs and more.

Here are some of the ways A.I. is transforming science that are covered in today's episode:

• Antibiotics: MIT researchers uncovered two new antibiotics in a single year (antibiotic discovery is very rare so this is crazy!) by using an ML model trained on the efficacy of known antibiotics to sift through millions of potential antibiotic compounds.

• Batteries: Similar sifting was carried out by A.I. at the University of Liverpool to narrow down the search for battery materials from 200,000 candidates to just five highly promising ones.

• Weather: Huawei's Pangu-Weather and NVIDIA's FourCastNet use ML to offer faster and more accurate forecasts than traditional super-compute-intensive weather simulations — crucial for predicting and managing natural disasters.

• Nuclear Fusion: AI is simplifying the once-daunting task of controlling plasma in tokamak reactors, thereby contributing to advancements in clean energy production.

• Self-Driving Labs: Automate research by planning, executing, and analyzing experiments autonomously, thereby speeding up scientific experimentation and unveiling new possibilities for discovery.

• Generative A.I.: Large Language Models (LLMs) tools are pioneering new frontiers in scientific research. From improving image resolution to designing novel molecules, these tools are yielding tangible results, with several A.I.-designed drugs currently in clinical trials. Tools like Elicit are streamlining the process of scientific literature review over vast corpora, allowing connections within or between fields to be uncovered automatically and suggesting new research directions.

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

Data Science for Clean Energy, with Emily Pastewka

How can data science and machine learning power the transition toward a sustainable global economy? The ML leader (and exceptional communicator of technical concepts!) Emily Pastewka is my guest today to fill us in on Green Data Science.

Emily:

• Leads the data function at Palmetto, a cleantech startup focused on home electrification.

• Prior to Palmetto, spent more than 10 years building consumer data products and solving marketplace problems as a data science and ML leader at huge fast-growing tech companies like Uber and Rent The Runway.

• Holds a Masters degree in Computer Science from Columbia University and undergraduate degrees in Economics and Environmental Policy from Duke.

Today’s episode should be accessible to technical and non-technical folks alike because whenever Emily got technical, she did an exquisite job of explaining the concepts.

In this episode, Emily details:

• How data science and A.I. can make the world greener by shifting us to clean energy.

• The team of people needed to bring cleantech data solutions to life.

• How econometrics plays a key role in nudging consumers toward greener decisions.

• Her top tips for excelling as a data leader.

• What she looks for in the scientists and engineers she hires.

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

Technical Intro to Transformers and LLMs, with Kirill Eremenko

For today's episode, the indefatigable SuperDataScience Founder Kirill Eremenko gives a detailed technical intro to Transformers and how they're scaled up to allow Large-Language Models like GPT-4, Llama 2 and Gemini to have their mind-blowing abilities.

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 SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins three years ago.

• Has reached more than 2.6 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.

Today’s episode is perhaps the most technical episode of this podcast ever so it will probably appeal mostly to hands-on practitioners like data scientists and ML engineers, particularly those who already have some understanding of deep learning.

In this episode, Kirill details:

• The history of the Attention mechanism in natural-language models.

• How compute-efficient Attention is enabled by the Transformer, a transformative deep-neural-network architecture.

• How Transformers work, across each of five distinct data-processing stages.

• How Transformers are scaled up to power the mind-blowing capabilities of LLMs such as modern Generative A.I. models.

• Why knowing all of this is so helpful — and lucrative — in a data science career.

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

A 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.

To a Peaceful 2024

Today I reflect on the wild advances in A.I. over the past year, opine on how A.I. could make the world more peaceful, and wrap 2023 up by singing a tune. Thanks to all eight humans of the Super Data Science Podcast for their terrific work all year 'round:

• Ivana Zibert: Podcast Manager

• Natalie Ziajski: Operations & Revenue

• Mario Pombo: Media Editor

• Serg Masís: Researcher

• Sylvia Ogweng: Writer

• Dr. Zara Karschay: Writer

• Kirill Eremenko: Founder

It's these terrifically talented and diligent people that make it possible for us to create 104 high-quality podcast episodes per year for now over seven years running 🙏

I'm looking forward to the next 104 episodes with awesome guests and (no doubt!) oodles of revolutionary new machine learning breakthroughs to cover. To a wonderful and hopefully much more peaceful 2024 🥂

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

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.