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.

MLOps: 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.

What will humans do when machines are vastly more intelligent? With Aleksa Gordić

Aleksa Gordić — the famed A.I. educator and multilingual-LLM entrepreneur — is my guest today. Brilliant and widely-read, Aleksa opines on what it will take to realize Artificial Super Intelligence and the consequences for humans.

Aleksa:

• Is Founder & CEO of Runa AI, a startup focused on building multilingual LLMs.

• Is an online educator that has built a community of 160,000 people in the A.I. space, including through his A.I. Epiphany YouTube channel.

• Previously, he was an A.I. Research Engineer at Google DeepMind in London and a Machine Learning Software Engineer at Microsoft.

• He holds a degree in Electronics and Computer Science from the University of Belgrade in Serbia.

Today’s episode contains tidbits here and there that will appeal primarily to hands-on machine learning practitioners, but it mostly should be of great interest to anyone.

In this episode, wildly-intelligent Aleksa details:

• Why multilingual LLMs provide so much value despite the cutting-edge LLMs like Claude 3, Gemini Ultra and GPT-4 supporting so many languages.

• His frameworks for entrepreneurial success and for effective self-directed learning.

• His analogy for how humans are born as a checkpoint of a Bayesian model that’s fine-tuned with reinforcement learning from human feedback (RLHF).

• What he thinks it will take to realize artificial super intelligence and what it could mean for human society when it arrives.

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

Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas

Today, Prof. Barrett Thomas blends his rich technical understanding of Deep Reinforcement Learning with his commercial savviness to eloquently detail how Deep RL can be leveraged to minimize costs and maximize profits.

Barrett:

• Is Research Professor in Business Analytics and Senior Associate Dean at the University of Iowa’s College of Business.

• As will soon be unsurprising to you when you hear how well he communicates complex concepts, he’s won multiple teaching awards (amongst other academic prizes).

• He holds a PhD in Industrial and Operations Engineering from the University of Michigan.

Today’s episode is a technical one that will appeal primarily to hands-on practitioners like data scientists, software developers and machine learning engineers.

In this episode, Barrett details:

• What Markov Decision Processes are and how they relate to Deep Reinforcement Learning.

• How operations research leverages neural networks to minimize business costs and maximize business profits.

• How same-day delivery has been made possible by machine learning.

• How aerial drones and autonomous vehicles will revolutionize supply chains and transportation.

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

In Case You Missed It in March 2024

We're trying something novel on the SuperDataScience Podcast today: an ICMYI ("in case you missed it") episode that highlights the most gripping moments from my conversations with guests over the past month.

Please let me know what you think of this! Does it work for you? What would you change about it? Should we stop doing these entirely? Let me know right here on this post; your voice matters :)

For this inaugural ICYMI episode, conversation highlights include:

1. Sebastian Raschka, PhD on how Lightning AI makes LLM training and deployment easy (from Episode #767).

2. Dr. Travis Oliphant, creator of the ubiquitous NumPy and SciPy libraries, on the future of scientific computing (#765).

3. Award-winning, A.I.-focused venture capitalist Rudina Seseri letting us know what it takes to get a VC firm to invest in you (#763).

4. Prof. Zachary Lipton on his roadmap from AI startup to long-term commercial success (#769).

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

Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko

You wanted more of Kirill Eremenko, now you've got it! Kirill returns to the show today to detail Decision Trees, Random Forests and all three of the leading gradient-boosting algorithms: XGBoost, LightGBM and CatBoost 😸

If you don’t already know him, Kirill: 
• Is 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 2.7 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.

Today’s episode is a highly technical one focused specifically on Gradient Boosting methods and the foundational theory required to understand them. I expect this episode will be of interest primarily to hands-on practitioners like data scientists, software developers and machine learning engineers.

In this episode, Kirill details: 
• Decisions Trees.
• How Decision Trees are ensembled into Random Forests via Bootstrap Aggregation.
• How the AdaBoost algorithm formed a bridge from Random Forests to Gradient Boosting.
• How Gradient Boosting works for both regression and classification tasks.
• All three of the most popular Gradient Boosting approaches — XGBoost, LightGBM and CatBoost — as well as when you should choose them.

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

Generative AI for Medicine, with Prof. Zack Lipton

Generative A.I. is rapidly transforming medicine. My guest today is brilliant, inspiring Prof. Zachary Lipton — Chief Scientific Officer and CTO of Abridge, a startup that has quickly raised $208m to lead the transformation!

More on Zack:

• Assoc. Prof. in the Machine Learning Dept. of Carnegie Mellon University's Computer Science school.

• Highly-cited (23k+ citations) with research spanning core ML methods and theory, as well as applications in healthcare and NLP.

• Directs the Approximately Correct Machine Intelligence (ACMI) Lab at CMU, where they build robust systems for the real world.

• Is also a jazz saxophonist! 🎷

Despite Zack being such a deep technical expert, most of today’s content will be of interest to anyone who’d like to hear about the cutting edge of generative A.I. applications in healthcare.

The tech that Zack is leading development of at Abridge, which you can hear about in today's episode:

• Initial deployment uses ambient listening and generative A.I. to reduce the cognitive burden of clinical documentation, reducing burnout as well as enabling clinicians to spend less time with computers and more with patients.

• Industry-leading automatic speech recognition engine specifically designed for healthcare applications; can accurately transcribe speech in challenging environments, e.g., when there is background noise or when multiple people are speaking.

• Supports 14+ languages including handling code-switching (where speakers shift between languages) and interpreter-mediated conversations.

• In-house LLM development allows greater customization and responsible-use features, such as transparency (e.g., links to source transcript/audio) and evidence extraction (verification process).

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

NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant

Huge episode today with iconic Dr. Travis Oliphant, creator of NumPy and SciPy, the standard libraries for numeric operations (downloaded 8 million and 3 million times PER DAY, respectively). Hear about the future of open-source, including the impact of GenAI.

More on Travis:

• Founded Anaconda, Inc., the company behind the also-ubiquitous Python package manager.

• Founded the massive PyData conferences and communities as well as its associated non-profit foundation, NumFOCUS.

• Currently serves as the CEO of two firms: OpenTeams and Quansight.

• Holds a PhD in biomedical engineering from the Mayo Clinic in Minnesota.

Today’s episode will primarily be of interest to hands-on practitioners like data scientists, software developers and machine learning engineers.

In it, Travis details:

• How his journey creating open-source software began and how NumPy and SciPy grew to become the most popular foundational Python libraries for working with data.

• How he identifies commercial opportunities to support his vast open-source efforts and communities.

• How AI, particularly generative AI, is transforming open-source development.

• Where open-source innovation is headed in the years to come.

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

The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri

How should an A.I. startup find product-market fit? How do some A.I. startups become spectacularly successful? The renowned (and highly technical!) A.I. venture-capital investor Rudina Seseri answers these questions and more in today's episode.

Rudina:

• Founder and Managing Partner of Glasswing Ventures in Boston.

• Led investments and/or served on the Board of Directors of more than a dozen SaaS startups, many of which were acquired.

• Was named Startup Boston's 2022 "Investor of the Year" amongst many other formal recognitions.

• Is a sought-after keynote speaker on investing in A.I. startups.

• Executive Fellow at Harvard Business School.

• Holds an MBA from Harvard University.

Today’s episode will be interesting to anyone who’s keen on scaling their impact with A.I., particularly through A.I. startups or investment.

In this episode, Rudina details:

• How data are used to assess venture capital investments.

• What makes particular AI startups so spectacularly successful.

• Her "A.I. Palette" for examining categories of machine learning models and mapping them to categories of training data.

• How Generative AI isn’t a fad, but it is still only a component of the impact that AI more broadly can make.

• The automated systems she has built for staying up to date on all of the most impactful AI developments.

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