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.

Happy Holidays from All of Us

Today's podcast episode is a quick one from all eight of us humans at the SuperDataScience Podcast, wishing you the happiest of holiday seasons ☃️

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

How to Visualize Data Effectively, with Prof. Alberto Cairo

The renowned data-visualization professor and many-time bestselling author Dr. Alberto Cairo is today's guest! Want a copy of his fantastic new book, "The Art of Insight"? I'm giving away ten physical copies; see below for how to get one.

Alberto:

• Is the Knight Chair in Infographics and Data Visualization at the University of Miami.

• Leads visualization efforts at the University of Miami’s Institute for Data Science and Computing.

• Is a consultant for Google, the US government and many more prominent institutions.

• Has written three bestselling books on data visualization, all in the past decade.

• His fourth book, "The Art of Insight", was just published.

Today’s episode will be of interest to anyone who’d like to understand how to communicate with data more effectively.

In this episode, which tracks the themes covered in his "The Art of Insight" book, Alberto details:

• How data visualization relates to the very meaning of life.

• What it takes to enter in a meditation-like flow state when creating visualizations.

• When the “rules” of data communication should be broken.

• His data visualization tips and tricks.

• How infographics can drive social change.

• How extended reality, A.I. and other emerging technologies will change data viz in the coming years.

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

Q*: OpenAI’s Rumored AGI Breakthrough

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

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

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

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

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

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

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.

Engineering Biomaterials with Generative AI, with Dr. Pierre Salvy

Today, the brilliant Dr. Pierre Salvy details the "double deep-tech sandwich" that blends cutting-edge A.I. (generative LLMs) with cutting-edge bioengineering (creating new materials). This is a fascinating one, shot live at the Merantix AI Campus in Berlin.

Pierre:

• Has been at Cambrium for three years. Initially as Head of Computational Biology and then Head of Engineering for the past two years, growing the team from 2 to 7 to bridge the gap between wet-lab biology, data science, and scientific computing.

• Holds a PhD in Biotechnology from EPFL in Switzerland and a Master’s in Math, Physics and Engineering Science from Mines in Paris.

Today’s episode touches on technical machine learning concepts here and there, but should largely be accessible to anyone.

In it, Pierre details:

• How data-driven R&D allowed Cambrium to go from nothing to tons of physical product sales inside two years.

• How his team leverages Large Language Models (LLMs) to be the biological-protein analogue of a ChatGPT-style essay generator.

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

Scikit-learn’s Past, Present and Future, with scikit-learn co-founder Dr. Gaël Varoquaux

For today's massive episode, I traveled to Paris to interview Dr. Gael Varoquaux, co-founder of scikit-learn, the standard library for machine learning worldwide (downloaded over 1.4 million times PER DAY 🤯). In it, Gaël fills us in on sklearn's history and future.

More on Gaël:

• Actively leads the development of the ubiquitous scikit-learn Python library today, which has several thousand people contributing open-source code to it.

• Is Research Director at the famed Inria (the French National Institute for Research in Digital Science and Technology), where he leads the Soda ("social data") team that is focused on making a major positive social impact with data science.

• Has been recognized with the Innovation Prize from the French Academy of Sciences and many other awards for his invaluable work.

Today’s episode will likely be of primary interest to hands-on practitioners like data scientists and ML engineers, but anyone who’d like to understand the cutting edge of open-source machine learning should listen in.

In this episode, Gaël details:

• The genesis, present capabilities and fast-moving future direction of scikit-learn.

• How to best apply scikit-learn to your particular ML problem.

• How ever-larger datasets and GPU-based accelerations impact the scikit-learn project.

• How (whether you write code or not!) you can get started on contributing to a mega-impactful open-source project like scikit-learn yourself.

• Hugely successful social-impact data projects his Soda lab has had recently.

• Why statistical rigor is more important than ever and how software tools could nudge us in the direction of making more statistically sound decisions.

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

How to Officially Certify your AI Model, with Jan Zawadzki

In today's episode, learn from Jan Zawadzki how independent certification of A.I. models makes them safer and more reliable, gives you an advantage over your competitors, and, in the EU at least, will soon be mandatory!

Jan:

• Is CTO and Co-Managing Director of CertifAI, a startup that is an early mover in the fast-developing A.I. certification ecosystem.

• Was previously the Head of A.I. at CARIAD, the software development subsidiary of Volkswagen Group, where he grew the team from scratch to over 50 engineers.

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

A.I. Product Management, with Google DeepMind's Head of Product, Mehdi Ghissassi

The elite team at Google DeepMind cranks out one world-changing A.I. innovation after another. In today's episode, their affable Head of Product Mehdi Ghissassi shares his wisdom on how to design and release successful A.I. products.

Mehdi: 
• Has been Head of Product at Google DeepMind — the world’s most prestigious A.I. research group — for over four years.
• Spent an additional three years at DeepMind before that as their Head of A.I. Product Incubation and a further four years before that in product roles at Google, meaning he has more than a decade of product leadership experience at Alphabet. 
• Member of the Board of Advisors at CapitalG, Alphabet’s renowned venture capital and private equity fund.
• Holds five (!!!) Master’s degrees, including computer science and engineering Master’s degrees from the École Polytechnique, in International Relations from Sciences Po, and an MBA from Columbia Business School.

Today’s episode will be of interest to anyone who’s keen to create incredible A.I. products.

In this episode, Mehdi details: 
• Google DeepMind’s bold mission to achieve Artificial General Intelligence (AGI).
• Game-changing DeepMind A.I. products such as AlphaGo and AlphaFold.
• How he stays on top of fast-moving A.I. innovations.
• The key ethical issues surrounding A.I.
• A.I.’s big social-impact opportunities.
• His guidance for investing in A.I. startups.
• Where the big opportunities lie for A.I. products in the coming years.

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

Humanoid Robot Soccer, with the Dutch RoboCup Team

In today's unique episode, robots from the Dutch Nao Team (Naos are the little humanoids shown in the photo) compete against each other at football (⚽️) while Dário Catarrinho, a developer on the team, describes the machine learning involved.

The Dutch Nao Team is one of many international teams that competes annually in RoboCup Federation tournaments. The lofty goal of the RoboCup competitions is to develop a team of humanoid robots that is able to win against the human World Cup Championship team by the year 2050. Very cool.

Dario, my human guest in today's episode is Secretary of the Dutch Nao Team as well as a software developer on the team. He's also pursuing a degree in A.I. at the University of Amsterdam.

Most of today’s episode should be accessible to anyone but occasionally Dario and I talk a bit technically about ML algorithms so those brief parts might be most meaningful to hands-on practitioners.

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.