Assessing the fastest-growing job is tricky. For example, using job-posting data isn’t great because there could be lots of duplicate postings out there or a lot of the postings could be going unfilled. Another big issue is defining exactly what a job is: The exact same responsibilities could be associated with the job title “data scientist”, “data engineer” or “ML engineer”, depending on the particular job titles a particular company decides to go with. So, whoever’s evaluating job growth is going to end up bucketing groups of related jobs and responsibilities into one particular, standardized job-title bucket, probably these days in a largely automated, data-driven way; if you dug into individual examples, I’m sure you’d find lots of job-title standardizations you disagreed with but some kind of standardization approach is essential to ensuring identical roles with slightly different job titles get counted as the same thing.
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Filtering by Category: Five-Minute Friday
The Six Epochs of Intelligence Evolution
The six epochs of intelligence evolution. I came across the definition of these six stages in the futurist Ray Kurzweil’s latest book, The Singularity is Nearer. Per Kurzweil, each of the six stages of intelligence builds on the complexity of the information processing in the preceding stage, so the third epoch depends on the second one happening and the second epoch depends on the first.
Read MoreIn Case You Missed It in December 2024
Today's "In Case You Missed it Episode"... is one not to miss! Several of the most fascinating conversations I've ever had on the SuperDataScience Podcast I host happened in December.
The specific conversation highlights included in today's episode are:
1. The legendary Dr. Andrew Ng on why LLM cost doesn't matter for your A.I. proof of concept.
2. Building directly on Andrew's segment, CTO (and my fellow Nebula.io co-founder) Ed Donner on how to choose the right LLM for a given application.
3. Extremely intelligent and clear-spoken Dr. Eiman Ebrahimi (CEO of Protopia AI) on the future of autonomous systems and data security in our Agentic A.I. future.
4. From our 2024 recap episode, Sadie St. Lawrence's three biggest A.I. "wow" moments of the year... as well as the biggest flop of the year. (One company was behind both!)
5. Harvard/MIT humanist chaplain Greg Epstein (and bestselling author on tech in society) on the ethics of accelerating A.I. advancements. Should we, for example, consider slowing A.I. progress down?
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Continuous Calendar for 2025
Back in Episode #482, I provided a detailed introduction to continuous calendars — a calendar format that I personally find vastly superior to the standard weekly or monthly calendars. With today’s episode, we’re updating the calendar for the new year — for 2025.
Read MoreHappy Holidays from the SuperDataScience Podcast
2024 was unquestionably the fastest-moving year yet for A.I. innovation. In particular, we witnessed the meteoric rise of generative AI from its largely-proof-of-concept phase to being commercially indispensable. According to survey results, nearly two-thirds of organizations are now regularly using generative A.I. – a number that has almost doubled since a year earlier. From enhancing product development to facilitating medical breakthroughs, generative AI has become a cornerstone of innovation across industries. For those of who practice data science hands-on, GenAI has proved itself to be near-magical at composing functional code and debugging our errors.
Indeed, as we’ll discuss in detail in next Tuesday’s episode with Sadie St. Lawrence, this year GenAI models crossed reliability and accuracy thresholds, enabling it to power independently acting AI agents, even multi-agent systems that can tackle complex tasks without human supervision. 2025 looks set to be the year Agentic AI takes center stage, the next phase in A.I. transforming every industry and overhauling our way of life; if we get the tricky parts right, then for the better for all of us on this planet.
I hope you’ve enjoyed our exploration of these developments (and much more!) in depth over the course of the year through our podcast episodes, allowing you to hear directly from leading experts and practitioners like Andrew Ng, Bernard Marr and Sol Rashidi. Our discussions have covered a wide range of topics, from the industrialization of data science processes to the ethical considerations surrounding AI implementation.
Through exploring the tricky bits like ethics and equity alongside the breathtaking technological breakthroughs, I hope that overall we’ve left you feeling optimistic about our capacity as a species to get this tech revolution right and have it benefit all of us. This holiday season, I hope you’ll also be able to sit with these positive vibes, get some time away from your screened devices and enjoy the wonder of life — including how lucky we are to be alive at this extraordinary time in history — with your loved ones.
From all of us here at the SuperDataScience Podcast, happy holidays!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Delicate Viticultural Robotics
I’ve been excited all year this year about the potential for AI to revolutionize agricultural robotics and help us feed the planet with high-quality nutrition. So, I’m jazzed today to be digging into an innovative application of computer vision and robotics in agriculture, specifically in viticulture — the delicate cultivation of super-expensive grapes for making wine. And, yeah, wine may not provide the world with high-quality nutrition, but the same technologies developed for delicate wine grapes will be transferrable to other plants as well.
Read MoreHow to Become Happier, with Dr. Nat Ware
On most metrics, it's never been a better time to be alive. And yet, many of us are unhappy. In today's episode, Dr. Nat Ware explains why we're unhappy... and, mercifully, what we can do about it!
Nat:
• Is a renowned keynote speaker; he has one TEDx talk alone that has over 2 million views on YouTube (it forms the basis of the content in today’s episode).
• Is the social-impact entrepreneur behind 180 Degrees Consulting (the world's largest consultancy for non-profits) as well as Forté (a startup that facilitates cost-free reskilling of workforces).
• Holds both a doctorate in economics and an MBA from the University of Oxford.
Today’s episode should be fascinating to anyone. In it, Nat details:
• Why, despite life on this planet being better than ever before, humans are so unhappy.
• Concrete guidance on what you can do to become happier.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The “A.I.” Nobel Prizes (in Physics and Chemistry??)
A.I. was center stage at the 2024 Nobel Prizes, with Demis Hassabis sharing the Chemistry prize and Geoff Hinton sharing the Physics prize. Chem and Physics seems weird for A.I. though, no? Today's episode explains.
Read MoreIn Case You Missed It in September 2024
Another month, another set of invaluable conversations on the SuperDataScience Podcast I host. ICYMI, today's episode highlights the most fascinating moments from September.
The specific conversation highlights included in today's episode are:
Posit PBC engineering manager Dr. Julia Silge explains why Positron, the next-generation IDE she's leading development of, is better-suited to data scientists than any existing IDE.
PyTorch expert Luka Anicin provides his top tips for training more accurate and compute-efficient ML models.
Exceptional open-source developer Marco Gorelli on why Polars is anywhere from 10 to 100x faster than Pandas, the incumbent Python library for working with DataFrames.
Microsoft's Marck Vaisman on what companies hiring data scientists should be looking for... as opposed to what the typically (and mistakenly!) look for today.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
NotebookLM: Jaw-Dropping Podcast Episodes Generated About Your Documents
Today’s episode topic is on Google’s newly-released (and frankly sensational) product NotebookLM. All you need is a Google login, which is as easy as having a Gmail account. Use of NotebookLM is likewise totally free.
Read MoreOpenAI's o1 "Strawberry" Models
Today’s episode, which, given the gravity of the event, could of course be none other than OpenAI’s new o1 series of models, which represent a tremendous leap forward in AI capabilities.
Read MoreSummer Reflections
This week, I’m enjoying the tail end of the northern-hemisphere summer by spending time with my family.
Read MoreThe Five Levels of Self-Driving Cars
Back in Episode #748 earlier this year, I covered the five levels of Artificial General Intelligence. Well, today, inspired by my first-ever experience in an autonomous vehicle (a Waymo ride while in San Francisco recently), we’ve got an episode on the five levels of motor-vehicle automation.
Read MoreLlama 3.1 405B: The First Open-Source Frontier LLM
Meta releasing its giant (405-billion parameter) Llama 3.1 model is a game-changer: For the first time, an "open-source" LLM competes at the frontier (against proprietary models GPT-4o and Claude).
Read MoreA 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.
Claude 3.5 Sonnet: Frontier Capabilities & Slick New “Artifacts” UI
Anthropic’s latest publicly released model, Claude 3.5 Sonnet. This might not seem like a big deal because it’s not a “whole number” release like Claude 3 was or Claude 4 eventually will be, but in fact, it’s quite a big deal as this model now appears to actually represent the state of the art for text-in/text-out generative LLM, outcompeting the other frontier models like OpenAI’s GPT-4o and Google’s Gemini.
Read MoreEarth’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.
Multi-Agent Systems: How Teams of LLMs Excel at Complex Tasks
Groundbreaking multi-agent systems (MAS, for short) are transforming the way AI models collaborate to tackle complex challenges.
Read MoreThe 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.