A Transformative Century of Technological Progress, with Annie P.

For today's special episode (#800), I learned from my 94-year-old grandmother the tricks to living before electricity or running water... and how the wild tech transformation of the past century has impacted her.

In a bit more detail, in this episode, Annie covers:

• What work and life were like growing up on a farm with no electricity or running water.

• How education, communication, security, entertainment and food storage have evolved over her lifetime.

• Similarities between geopolitical events in the 1930s and events transpiring today.

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

Earth’s Coming Population Collapse and How AI Can Help, with Simon Kuestenmacher

Worried about overpopulation? Excessive immigration? In today's episode, demographer Simon Kuestenmacher reveals the data on why we should be more concerned about the opposite: the coming global-population collapse.

Simon:

• Is Co-Founder and Director of The Demographics Group, a firm that provides advice on demographic data to businesses and governments.

• Writes a regular column on demographics for The Australian, the antipodean country’s most widely-read newspaper.

• He holds a Master’s in Urban Geography from the University of Melbourne.

Today’s episode should be of great interest to anyone! In it, Simon details:

• Why demography is the closest thing we have to a crystal ball.

• Why the world is at a greater risk of underpopulation than overpopulation by humans this century.

• How, in less than a decade, developed nations that depend on migrants to prevent their populations from declining will run out of immigrants.

• How A.I. and automation may solve both the coming low-migration crisis and the later global underpopulation crisis.

• The implications of vastly life-extending healthcare breakthroughs.

• What you can do in your career to prepare for the coming demographic and technological shifts.

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

The Six Keys to Data Scientists’ Success, with Kirill Eremenko

For today's episode, Kirill Eremenko — who has taught more than 2.8 million people data science — fills us in on his six most valuable insights about data science careers.

More on Kirill:

• 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 2.8 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.

At a high level, Kirill's six data science insights are:

1. Unlike many other careers, there’s no need for formal credentials to become a data scientist.

2. Mentors can be invaluable guides in a DS career, but you should also try to give back to your mentors when you can.

3. Portfolios are the key to landing the DS job of your dream because they showcase your DS abilities for all to see.

4. Hands-on labs are a fun, interactive way to develop your portfolio and are a great complement to classes.

5. Collaborations can make lots of aspects of DS career development fun, including learning new materials, completing labs and developing your portfolio.

6. Data scientists can come from any background and work from anywhere in the world with an Internet connection.

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

Aligning Large Language Models, with Sinan Ozdemir

For today’s quick Five-Minute Friday episode, the exceptional author, speaker and entrepreneur Sinan Ozdemir provides an overview of what it actually means for an LLM to be “aligned”.

More on Sinan:
• Is Founder and CTO of LoopGenius, a generative AI startup.
• Has authored several excellent books, including, most recently, the bestselling "Quick Start Guide to Large Language Models".
• Is a serial AI entrepreneur, including founding a Y Combinator-backed generative AI startup way back in 2015 that was later acquired.

This episode was filmed live at the Open Data Science Conference (ODSC) East in Boston last month. Thanks to ODSC for providing recording space.

The Super Data Science Podcast is available on all major podcasting platforms and a video version is on YouTube. This is episode #784!

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

In Case You Missed It in April 2024

Other than excessive maleness and paleness*, April 2024 was an excellent month for the podcast, packed with outstanding guests. ICYMI, today's episode highlights the most fascinating moments of my convos with them.

Specifically, conversation highlights include:

1. Iconic open-source developer Dr. Hadley Wickham putting the "R vs Python" argument to bed.

2. Aleksa Gordić, creator of a digital A.I.-learning community of 160k+ people, on the movement from formal to self-directed education.

3. World-leading futurist Bernard Marr on how we can work with A.I. as opposed to it lording over of us.

4. Educator of millions of data scientists, Kirill Eremenko, on why gradient boosting is so powerful for making informed business decisions.

5. Prof. Barrett Thomas on how drones could transform same-day delivery.

*Remedied in May!

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

How to Become a Data Scientist, with Dr. Adam Ross Nelson

Today's episode features Dr. Adam Ross Nelson providing his #1 most useful piece of guidance on "How to Become a Data Scientist" from his book of that very name!

This was filmed live at the Open Data Science Conference (ODSC) East in Boston last week — thanks ODSC East for providing valuable conference space for us to shoot podcast episodes.

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 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.

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.

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.

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.