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.
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Filtering by Category: SuperDataScience
Deep Learning Classics and Trends, with Dr. Rosanne Liu
Today's guest is the amazing Google DeepMind research scientist, Dr. Rosanne Liu!
Rosanne:
• Is a Research Scientist at Google DeepMind in California.
• Is Co-Founder and Executive Director of ML Collective, a non-profit that provides global ML research training and mentorship.
• Was a founding member of Uber AI Labs, where she served as a Senior Research Scientist.
• She has published deep learning research in top academic venues such as NeurIPS, ICLR, ICML and Science, and her work has been covered in publications like WIRED and the MIT Tech Review.
• Holds a PhD in Computer Science from Northwestern University.
Today’s episode, particularly in the second half when we dig into Rosanne’s fascinating research, is relatively technical so will probably appeal most to hands-on practitioners like data scientists and ML engineers.
In today’s episode, Rosanne details:
• The problem she founded the ML Collective to solve.
• How her work on the “intrinsic dimension” of deep learning models inspired the now-standard LoRA approach to fine-tuning LLMs.
• The thorny problems with LLM evaluation benchmarks and how they might be solved.
• The pros and cons of curiosity- vs goal-driven ML research.
• The positive impacts of diversity, equity and inclusion in the ML community.
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.
Fast-Evolving Data and AI Regulatory Frameworks, with Dr. Gina Guillaume-Joseph
A.I. regulatory frameworks are proliferating globally, protecting personal privacy while unlocking "dark data" for A.I.-model training. In today's episode, Dr. Gina Guillaume-Joseph is our expert guide to these A.I. regulations.
Gina:
Was, until recently, the CTO responsible for Government at Workday, aligning the HRtech giant with the U.S. federal government’s tech transformation strategy.
Prior to Workday, was Director of Technology at financial giant Capital One.
Earlier, spent 16 years supporting the federal government as a contractor with leading firms like Booz Allen Hamilton and The MITRE Corporation.
Now works as a fractional Chief Information Officer and as Adjunct Faculty at The George Washington University.
Holds a PhD in Systems Engineering from George Washington University and a Bachelor’s in Computer Science from Boston College.
Today’s episode should be of interest to just about anyone who would listen to this podcast because it focuses on the data and A.I. regulatory frameworks that will transform our industry.
In today’s episode, Gina details:
The “dark data conundrum”.
The most important data and A.I. regulations of recent years as well as those that are coming soon.
The pros and cons of being or hiring a fractional executive.
What system engineering is and why it’s an invaluable background for implementing large-scale A.I. projects.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Exciting (and Frightening!) Trends in Open-Source AI
Friday's short episode of my podcast features four data-science luminaries (Emily Zabor, James David Long, Drew Conway and Jared Lander) explicating on the most exciting open-source A.I. trends they see.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Bayesian Methods and Applications, with Alexandre Andorra
Is he a man or a country? Find out in today's episode with Alexandre Andorra — developer of the leading Bayesian library for Python, implementer of commercial Bayesian models and leading Bayesian educator/podcaster!
More on Alex:
• Co-Founder and Principal Data Scientist at PyMC Labs, a firm that develops PyMC (the leading Python library for Bayesian statistics) and consults with their clients to implement profit-increasing Bayesian models.
• Co-Founder and Instructor at an online learning platform called Intuitive Bayes that provides free Bayesian stats education.
• Creator and Host of an excellent podcast called Learning Bayesian Statistics.
Today’s episode will probably appeal most to hands-on practitioners like statisticians, data scientists and machine learning engineers, but the episode also serves as an introduction to Bayesian statistics for anyone who’d like to learn about this important, unique and powerful field.
In today’s episode, Alex details:
• What Bayesian statistics is.
• The situations where Bayesian stats can solve problems that no other approach can.• Resources for learning Bayesian stats.
• The key Python libraries for implementing Bayesian models yourself.
• How Gaussian Processes can be incorporated into a Bayesian framework in order to allow for especially advanced and flexible models.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in May 2024
We had another incredible set of guests in May on the SuperDataScience Podcast I host. ICYMI, today's episode highlights the most fascinating moments of my conversations with them.
Specifically, conversation highlights include:
1. Dr. Luis Serrano, a math- and ML-education YouTuber with 150k subscribers, explaining what language embeddings are, how they function, and how essential they are for running semantic search queries.
2. Sol Rashidi, serial C-suite data-role executive at Fortune 100s and bestselling author of "Your A.I. Survival Guide", on her approach to building data teams.
3. Co-founder of the MLOps Community, Demetrios Brinkmann, on the differences between ML Engineering and MLOps roles.
4. Navdeep Martin, an entrepreneur blending climate tech and generative A.I. in her latest startup, on opportunities where you can tackle climate change with technological innovation yourself.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Reinforcement Learning from Human Feedback (RLHF), with Dr. Nathan Lambert
In today's episode, the renowned RLHF thought-leader Dr. Nathan Lambert digs into the origins of RLHF, its role today in fine-tuning LLMs, emerging alternatives to RLHF... and how GenAI may democratize (human) education!
Nathan:
• Is a Research Scientist at the Allen Institute for AI (AI2) in Seattle, where he’s focused on fine-tuning Large Language Models (LLMs) based on human preferences as well as advocating for open-source AI.
• He’s renowned for his technical newsletter on AI called "Interconnects".
• Previously helped build an RLHF (reinforcement learning from human feedback) research team at Hugging Face.
• Holds a PhD from University of California, Berkeley in which he focused on reinforcement learning and robotics, and during which he worked at both Meta AI and Google DeepMind.
Today’s episode will probably appeal most to hands-on practitioners like data scientists and machine learning engineers, but anyone who’d like to hear from a talented communicator who works at the cutting edge of AI research may learn a lot by tuning in.
In today’s episode, Nathan details:
• What RLHF is and how its roots can be traced back to ancient philosophy and modern economics.
• Why RLHF is the most popular technique for fine-tuning LLMs.
• Powerful alternatives to RLHF such as RLAIF (reinforcement learning from A.I. feedback) and direct distilled preference optimization (dDPO).
• Limitations of RLHF.
• Why he considers AI to often be more alchemy than science.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Open-Source Libraries for Data Science at the New York R Conference
For today's short episode, I asked four data-science luminaries about their favorite open-source libraries. Hear what Emily Zabor, James David Long, Drew Conway and Jared Lander chose, live on stage!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
ML for Wind-Powered Energy Generation, with Dr. Jason Yosinski
One of my all-time favorite A.I. researchers, Dr. Jason Yosinski, is my guest today! He details how his startup is using ML to collect wind energy more efficiently and digs into visualizing/understanding deep neural networks.
Jason:
• Is Co-Founder and CEO of Windscape AI, a startup using ML to increase the efficiency of energy generation via wind turbines.
• Is Co-Founder and President of the ML Collective, a research group that’s open to ML researchers anywhere.
• Was a Co-Founder of the A.I. Lab at the ride-share company Uber.
• Holds a PhD in Computer Science from Cornell, during which he worked at the NASA Jet Propulsion Laboratory, Google DeepMind and with the eminent Yoshua Bengio in Montreal.
• His work has been featured in The Economist, on the BBC and, coolest of all, in an XKCD comic!
Today’s episode gets fairly technical in parts so may be of greatest interest to hands-on practitioners like data scientists and ML engineers, although there are also parts that will appeal to anyone keen to hear how ML is being used to produce more clean energy.
In today’s episode, Jason details:
• How ML can make wind direction more predictable, thereby making wind turbines and power grids in general more efficient.
• How to infer what individual neurons in a deep learning model are doing by using visualizations.
• Why freezing a particular layer of a neural net prior to doing any training at all can lead to better results.
• How you can get involved in a cutting-edge research community no matter where you are in the world.
• What traits make for successful A.I. entrepreneurs.
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 MoreMLOps: The Job and The Key Tools, with Demetrios Brinkmann
Today, global MLOps community leader Demetrios Brinkmann details why MLOps is essential, how it differs from related roles like LLMOps, DevOps and A.I. Engineering, and the best tools for deploying and scaling LLMs.
Demetrios:
• Is Founder and CEO of MLOps Community, an organization dedicated to supporting MLOps professionals that has quickly grown to over 20,000 members.
• Was previously founder of the Data on Kubernetes community.
• Before that, worked in public-facing roles at a number of European tech startups.
Today’s episode will be of interest to anyone who’s keen to better understand the critical function of MLOps in bringing machine learning models to the real world.
In today’s episode, Demetrios details:
• What exactly MLOps is and how it relates to other jobs like LLMOps, DevOps and A.I. Engineer.
• The key MLOps tools and approaches.
• What it takes to build a thriving community of tens of thousands of professionals in just a few years.
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.
Math, Quantum ML and Language Embeddings, with Dr. Luis Serrano
Today, Dr. Luis Serrano (a master at making complex math and ML topics friendly) leads a mind-expanding discussion on embeddings in LLMs, Quantum ML and what the next big trends in A.I. will be. I wouldn't miss this one 🤯
Luis:
• Is the beloved creator behind the Serrano Academy, an educational YouTube channel on math and ML with over 146,000 subscribers.
• Until this month, he worked as Head of Developer Relations at Cohere, one of the world’s few A.I. labs that is actually at the frontier of LLMs.
• Prior to that, he was a Quantum A.I. Research Scientist at Zapata Computing, Lead A.I. Educator at Apple, Head of Content for A.I. at Udacity and ML Engineer at Google.
• Holds a PhD in Math from the University of Michigan.
Today’s episode should be appealing to just about anyone! In it, Luis details:
• How supposedly complex topics like math and A.I. can be made easy to understand.
• How Cohere’s focus on enterprise use cases for LLMs has led it to specialize in embeddings, the most important component of LLMs.
• The promising application areas for Quantum Machine Learning.
• What the next big trends in A.I. will be.
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.
Generative A.I. for Solar Power Installation, with Navdeep Martin
A startling 70% of solar-power projects fail. In today's episode, hear how Navdeep Martin's startup Flypower is using Generative A.I. to ensure we install renewable energy sources more effectively and efficiently.
Navdeep:
• Co-founder and CEO of Flypower, a generative A.I. startup dedicated to ensuring clean-energy projects, particularly solar-power projects, succeed.
• Previously held senior product leadership roles at VC-backed Bay Area AI startups as well as for AI products at Comcast and The Washington Post.
• Before that, was a software engineer for the CIA.
• Holds a degree in computer science from William & Mary and an MBA from the University of Virginia.
Today’s episode will appeal to anyone who’d like to hear about the evolution of generative A.I. technologies in products and applications, including how you can best make use of the various categories of Gen-A.I. technologies today and how, in particular, A.I. is being used to overcome the social and regulatory hurdles associated with combating climate change.
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.
Ensuring Successful Enterprise AI Deployments, with Sol Rashidi
Prodigious Sol Rashidi has deployed nearly 40 large-scale data and A.I. projects at Fortune 100 companies. Her rich insights on doing this successfully fill her new book and are distilled into today's fun episode.
Sol ☀:
• Has been a C-suite data/analytics/A.I. leader at Estée Lauder, Merck pharmaceuticals, Sony Music and Royal Caribbean Cruise Lines.
• Was Senior Partner leading the Digital and Innovation Practice at EY and was the Partner who led the Watson go-to-market at IBM.
• Has been involved in over three dozen large-scale data/A.I. deployments.
• Is recognized with a string of international awards for her leadership.
• Holds eight patents with many more pending.
Today’s episode will be invaluable to anyone who’d like to succeed at deploying A.I. models commercially. In it, Sol details:
• Her straightforward system for selecting the enterprise A.I. projects that will be successfully deployed.
• What kinds of A.I. projects should always be avoided.
• Why larger enterprises drag their feet on impactful A.I. projects and how to overcome such corporate logjams.
• When you should patent an innovation.
• Why Chief Data Officers and related C-suite roles have such high turnover.
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.
The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham
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.