The first SuperDataScience episode filmed with a live audience! Award-winning researcher Dr. Noam Brown from Meta AI was the guest, filling us in on A.I. systems that beat the world's best at poker and other games.
We shot this episode on stage at MLconf in New York. This means that you’ll hear audience reactions in real-time and, near the end of the episode, many great questions from audience members once I opened the floor up to them.
This episode has some moments here and there that get deep into the weeds of machine learning theory, but for the most part today’s episode will appeal to anyone who’s interested in understanding the absolute cutting-edge of A.I. capabilities today.
In this episode, Noam details:
• What Meta AI (formerly Facebook AI Research) is, how it fits into Meta.
• His award-winning no-limit poker-playing algorithms.
• What game theory is and how he integrates it into his models.
• The algorithm he recently developed that can beat the world’s best players at “no-press” Diplomacy, a complex strategy board game.
• The real-world implications of his game-playing A.I. breakthroughs.
• Why he became a researcher at a big tech firm instead of academia.
Noam:
• Develops A.I. systems that can defeat the best humans at complex games that computers have hitherto been unable to succeed at.
• During his Ph.D. in computer science at Carnegie Mellon University, developed A.I. systems that defeated the top human players of no-limit poker — earning him a Science Magazine cover story.
• Also holds a master’s in robotics from Carnegie Mellon and a bachelor’s degree in math and computer science from Rutgers.
• Previously worked for DeepMind and the U.S. Federal Reserve Board.
Thanks to Alexander Holden Miller for introducing me to Noam and to Hannah Gräfin von Waldersee for introducing me to Alex!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: Interview
Open-Access Publishing
This week Dr. Amy Brand, the pioneering Director of The MIT Press and executive producer of documentary films, leads discussion of the benefits of — and innovations in — open-access publishing.
In the episode, Amy details:
• What open-access means.
• Why open-access papers, books, data, and code are invaluable for data scientists and anyone else doing research and development.
• The new metadata standard she developed to resolve issues around accurate attribution of who did what for a given academic publication.
• How we can change the STEM fields to be welcoming to everyone, including historically underrepresented groups.
• What it’s like to devise and create an award-winning documentary film.
Amy:
• Leads one of the world’s most influential university presses as the Director and Publisher of the MIT Press.
• Created a new open-access business model called Direct to Open.
• Is Co-Founder of Knowledge Futures Group, a non-profit that provides technology to empower organizations to build the digital infrastructure required for open-access publishing.
• Launched MIT Press Kids, the first university+kids publishers collab.
• Was the executive producer of "Picture A Scientist", a documentary that was selected to premiere at the prestigious Tribeca Film Festival and was recognized with the 2021 Kavli Science Journalism Award.
• She holds a PhD in Cognitive Science from MIT.
Today’s episode is well-suited to a broad audience, not just data scientists.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AGI: The Apocalypse Machine
Jeremie Harris's work on A.I. could dramatically alter your perspective on the field of data science and the bewildering — perhaps downright frightening — impact you and A.I. could make together on the world.
Jeremie:
• Recently co-founded Mercurius, an A.I. safety company.
• Has briefed senior political and policy leaders around the world on long-term risks from A.I., including senior members of the U.K. Cabinet Office, the Canadian Cabinet, as well as the U.S. Departments of State, Homeland Security and Defense.
• Is Host of the excellent Towards Data Science podcast.
• He previously co-founded SharpestMinds, a Y Combinator-backed mentorship marketplace for data scientists.
• He proudly dropped out of his quantum mechanics PhD to found SharpestMinds.
• He hold a Master’s in biological physics from the University of Toronto.
In this episode, Jeremie details:
• What Artificial General Intelligence (AGI) is
• How the development of AGI could happen in our lifetime and could present an existential risk to humans, perhaps even to all life on the planet as we know it.
• How, alternatively, if engineered properly, AGI could herald a moment called the singularity that brings with it a level of prosperity that is not even imaginable today.
• What it takes to become an AI safety expert yourself in order to help align AGI with benevolent human goals
• His forthcoming book on quantum mechanics
• Why almost nobody should do a PhD
Today’s episode is deep and intense, but as usual it does still have a lot of laughs, and it should appeal broadly, no matter whether you’re a technical data science expert already or not.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Clem Delangue on Hugging Face and Transformers
In today's SuperDataScience episode, Hugging Face CEO Clem Delangue fills us in on how open-source transformer architectures are accelerating ML capabilities. Recorded for yesterday's ScaleUp:AI conference in NY.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Rock at Data Science — with Tina Huang
Can you tell I had fun filming this episode with Tina Huang, YouTube data science superstar (293k subscribers)? In it, we laugh while discussing how to get started in data science and her learning/productivity tricks.
Tina:
• Creates YouTube videos with millions of views on data science careers, learning to code, SQL, productivity, and study techniques.
• Is a data scientist at one of the world's largest tech companies (she keeps the firm anonymous so she can publish more freely).
• Previously worked at Goldman Sachs and the Ontario Institute for Cancer Research.
• Holds a Masters in Computer and Information Technology from the University of Pennsylvania and a bachelors in Pharmacology from the University of Toronto
In this episode, Tina details:
• Her guidance for preparing for a career in data science from scratch.
• Her five steps for consistently doing anything.
• Her strategies for learning effectively and efficiently.
• What the day-to-day is like for a data scientist at one of the world’s largest tech companies.
• The software languages she uses regularly.
• Her SQL course.
• How her science and computer science backgrounds help her as a data scientist today.
Today’s episode should be appealing to a broad audience, whether you’re thinking of getting started in data science, are already an experienced data scientist, or you’re more generally keen to pick up career and productivity tips from a light-hearted conversation.
Thanks to Serg Masís, Brindha Ganesan and Ken Jee for providing questions for Tina... in Ken's case, a very silly question indeed.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Engineering Data APIs
How you design a data API from scratch and how a data API can leverage machine learning to improve the quality of healthcare delivery are topics covered by Ribbon Health CTO Nate Fox in this week's episode.
Ribbon Health is a New York-based API platform for healthcare data that has raised $55m, including from some of the biggest names in venture capital like Andreessen Horowitz and General Catalyst.
Prior to Ribbon, Nate:
• Worked as an Analytics Engineer at the marketing start-up Unified.
• Was a Product Marketing Manager at Microsoft.
• Obtained a mechanical engineering degree from the Massachusetts Institute of Technology and an MBA from Harvard Business School.
In this episode, Nate details:
• What APIs ("application programming interfaces") are.
• How you design a data API from scratch.
• How Ribbon Health’s data API leverages machine learning models to improve the quality of healthcare delivery.
• How to ensure the uptime and reliability of APIs.
• How scientists and engineers can make a big social impact in health technology.
• His favorite tool for easily scaling up the impact of a data science model to any number of users.
• What he looks for in the data scientists he hires.
Today’s episode has some technical data science and software engineering elements here and there, but much of the conversation should be interesting to anyone who’s keen to understand how data science can play a big part in improving healthcare.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
GPT-3 for Natural Language Processing
With its human-level capacity on tasks as diverse as question-answering, translation, and arithmetic, GPT-3 is a game-changer for A.I. This week's brilliant guest, Melanie Subbiah, was a lead author of the GPT-3 paper.
GPT-3 is a natural language processing (NLP) model with 175 billion parameters that has demonstrated unprecedented and remarkable "few-shot learning" on the diverse tasks mentioned above (translation between languages, question-answering, performing three-digit arithmetic) as well as on many more (discussed in the episode).
Melanie's paper sent shockwaves through the mainstream media and was recognized with an Outstanding Paper Award from NeurIPS (the most prestigious machine learning conference) in 2020.
Melanie:
• Developed GPT-3 while she worked as an A.I. engineer at OpenAI, one of the world’s leading A.I. research outfits.
• Previously worked as an A.I. engineer at Apple.
• Is now pursuing a PhD at Columbia University in the City of New York specializing in NLP.
• Holds a bachelor's in computer science from Williams College.
In this episode, Melanie details:
• What GPT-3 is.
• Why applications of GPT-3 have transformed not only the field of data science but also the broader world.
• The strengths and weaknesses of GPT-3, and how these weaknesses might be addressed with future research.
• Whether transformer-based deep learning models spell doom for creative writers.
• How to address the climate change and bias issues that cloud discussions of large natural language models.
• The machine learning tools she’s most excited about.
This episode does have technical elements that will appeal primarily to practicing data scientists, but Melanie and I put an effort into explaining concepts and providing context wherever we could so hopefully much of this fun, laugh-filled episode will be engaging and informative to anyone who’s keen to learn about the start of the art in natural language processing and A.I.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
SuperDataScience Podcast LIVE at MLconf NYC and ScaleUp:AI!
It's finally happening: the first-ever SuperDataScience episodes filmed with a live audience! On March 31 and April 7 in New York, you'll be able to react to guests and ask them questions in real-time. I'm excited 🕺
The first live, in-person episode will be filmed at MLconf NYC on March 31st. The guest will be Alexander Holden Miller, an engineering manager at Facebook A.I. Research who leads bleeding-edge work at mind-blowing intersections of deep reinforcement learning, natural language processing, and creative A.I.
A week later on April 7th, another live, in-person episode will be filmed at ScaleUp:AI. I'll be hosting a panel on open-source machine learning that features Hugging Face CEO Clem Delangue.
I hope to see you at one of these conferences, the first I'll be attending in over two years! Can't wait. There are more live SuperDataScience episodes planned for New York this year and hopefully it won't be long before we're recording episodes live around the world.
Effective Pandas
Seven-time bestselling author Matt Harrison reveals his top tips and tricks to enable you to get the most out of Pandas, the leading Python data analysis library. Enjoy!
Matt's books, all of which have been Amazon best-sellers, are:
1. Effective Pandas
2. Illustrated Guide to Learning Python 3
3. Intermediate Python
4. Learning the Pandas Library
5. Effective PyCharm
6. Machine Learning Pocket Reference
7. Pandas Cookbook (now in its second edition)
Beyond being a prolific author, Matt:
• Teaches "Exploratory Data Analysis with Python" at Stanford
• Has taught Python at big organizations like Netflix and NASA
• Has worked as a CTO and Senior Software Engineer
• Holds a degree in Computer Science from Stanford University
On top of Matt's tips for effective Pandas programming, we cover:
• How to squeeze more data into Pandas on a given machine.
• His recommended software libraries for working with tabular data once you have too many data to fit on a single machine.
• How having a computer science education and having worked as a software engineer has been helpful in his data science career.
This episode will appeal primarily to practicing data scientists who are keen to learn about Pandas or keen to become an even deeper expert on Pandas by learning from a world-leading educator on the library.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Sports Analytics and 66 Days of Data with Ken Jee
Ken Jee — sports analytics leader, originator of the ubiquitous #66daysofdata hashtag, and data-science YouTube superstar (190k subscribers) — is the guest for this week's fun and candid episode ⛳️🏌️
In addition to his YouTube content creation, Ken:
• Is Head of Data Science at Scouts Consulting Group LLC.
• Hosts the "Ken's Nearest Neighbors" podcast.
• Is Adjunct Professor at DePaul University.
• Holds a Masters in Computer Science with an AI/ML concentration.
• Is renowned for starting #66daysofdata, which has helped countless people create the habit of learning and working on data science projects every day.
Today’s episode should be broadly appealing, whether you’re already an expert data scientist or just getting started.
In this episode, Ken details:
• What sports analytics is and specific examples of how he’s made an impact on the performance of athletes and teams with it.
• Where the big opportunities lie in sports analytics in the coming years.
• His four-step process for how someone should get started in data science today.
• His favorite tools for software scripting as well as for production code development.
• How the #66daysofdata can supercharge your capacity as a data scientist whether you’re just getting started or are already an established practitioner.
Thanks to Christina, 🦾 Ben, Serg, Arafath, and Luke for great questions for Ken!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Statistics and Machine Learning Quests of Dr. Josh Starmer
Holy crap, it's here! Joshua Starmer, the creative genius behind the StatQuest YouTube channel (over 675k subscribers!) joins me for an epic episode on stats, ML, and his learning and communication secrets.
Dr. Starmer:
• Provides uniquely clear statistics and ML education via his StatQuest You Tube channel.
• Is Lead A.I. Educator at Grid.ai, a company founded by the creators of PyTorch Lightning that enables you to take an ML model you have on your laptop and train it seamlessly on the cloud.
• Was a researcher at the University of North Carolina at Chapel Hill for 13 years, first as a postdoc and then as an assistant professor, applying statistics to genetic data.
• Holds a PhD in Biomathematics and Computational Biology.
• Holds two bachelor degrees, one in Computer Science and another in Music.
In this episode filled with silliness and laughs from start to finish, Josh fills us in on:
• His learning and communication secrets.
• The single tool he uses to create YouTube videos with over a million views.
• The software languages he uses daily as a data scientist.
• His forthcoming book, "The StatQuest Illustrated Guide to Machine Learning".
• Why he left his academic career.
• A question you might want to ask yourself to check in on whether you’re following the right life path yourself.
Today’s epic episode is largely high level and so will appeal to anyone who likes to giggle while hearing from one of the most intelligent and creative minds in education on data science, machine learning, music, genetics, and the intersection of all of the above.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Thanks to Serg, Nikolay, Phil, Jonas, and Suddhasatwa for great audience questions!
Engineering Natural Language Models — with Lauren Zhu
Zero-shot multilingual neural machine translation, how to engineer natural language models, and why you should use PCA to choose your job are topics covered this week by the fun and brilliant Lauren Zhu.
Lauren:
• Is an ML Engineer at Glean, a Silicon Valley-based natural language understanding company that has raised $55m in venture capital.
• Prior to Glean, she worked as an ML Intern at both Apple and the autonomous vehicle subsidiary of Ford Motor Company; as a software engineering intern at Qualcomm; and as an A.I. Researcher at The University of Edinburgh.
• Holds BS and MS degrees in Computer Science from Stanford
• Served as a teaching assistant for some of Stanford University’s most renowned ML courses such as "Decision Making Under Uncertainty" and "Natural Language Processing with Deep Learning".
In this episode, Lauren details:
• Where to access free lectures from Stanford courses online.
• Her research on Zero-Shot Multilingual Neural Machine Translation.
• Why you should use Principal Component Analysis to choose your job.
• The software tools she uses day-to-day at Glean to engineer natural language processing ML models into massive-scale production systems.
• Her surprisingly pleasant secret to both productivity and success.
There are parts of this episode that will appeal especially to practicing data scientists but much of the conversation will be of interest to anyone who enjoys a laugh-filled conversation on A.I., especially if you’re keen to understand the state-of-the-art in applying ML to natural language problems.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How Genes Influence Behavior — with Prof. Jonathan Flint
How do genes influence behavior? This week's guest, Prof. Jonathan Flint, fills us in, with a particular focus on how machine learning is uncovering connections between genetics and psychiatric disorders like depression.
In this episode, Prof. Flint details:
• How we know that genetics plays a role in complex human behaviors incl. psychiatric disorders like anxiety, depression, and schizophrenia.
• How data science and ML play a prominent role in modern genetics research and how that role will only increase in years to come.
• The open-source software libraries that he uses for data modeling.
• What it's like day-to-day for a world-class medical sciences researcher.
• A single question you can ask to prevent someone committing suicide.
• How the future of psychiatric treatments is likely to be shaped by massive-scale genetic sequencing and everyday consumer technologies.
Jonathan:
• Is Professor-in-Residence at the University of California, Los Angeles, specializing in Neuroscience and Genetics.
• Leads a gigantic half-billion dollar project to sequence the genomes of hundreds of thousands of people around the world in order to better understand the genetics of depression.
• Originally trained as a psychiatrist, he established himself as a pioneer in the genetics of behavior during a thirty-year stint as a medical sciences researcher at the University of Oxford.
• Has authored over 500 peer-reviewed journal articles and his papers have been cited an absurd 50,000 times.
• Wrote a university-level textbook called "How Genes Influence Behavior", which is now in its second edition.
Today’s episode mentions a few technical data science details here and there but the episode will largely be of interest to anyone who’s keen to understand how your genes influence your behavior, whether you happen to have a data science background or not.
Thanks to Mohamad, Hank, and Serg for excellent audience questions!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Scaling Data-Intensive Real-Time Applications — with Matthew Russell
This week's guest is indefatigable Matthew Russell. An Air Force veteran and author of four data science books, Matthew is now Founder/CEO of Strongest AI, a leading tech platform for fitness.
In this episode, Matthew covers:
• The tech stack he uses to make it possible to provide data from fitness competitions to millions of spectators all over the world in real-time.
• How he rapidly tests machine learning models for deployment into portable devices like the iPhone and the Apple Watch.
• Multi-objective ML functions and why they’re so widely useful in real-world applications.
• The three critical traits he looks for in anyone he hires.
• The values instilled in him by pursuing a military education.
• The key skills he wishes he’d learned earlier in his career.
A bit more on Matthew... he's:
• Founder and CEO of Strongest, the leading technology platform for global fitness events, which is growing into an application that uses ML models to make you stronger, faster, and fitter than ever before.
• Author of four books published by O'Reilly Media, including the classic "Mining the Social Web", which is now in its third edition.
• Prior to founding Strongest, served as CTO at several firms.
• Holds a BS in Computer Science from the US Air Force Academy as well as an MS in Computer Science and Machine Learning from the US Air Force Institute of Technology.
Parts of today’s episode, particularly in the first half, do get fairly technical as we dig into the open-source software stack that enables the scalable deployment of data-intensive real-time applications. That said, much of the episode will appeal to anyone who’s excited about physical fitness or commercializing A.I.
Shout out to Austin Ogilvie for introducing me to Matthew 😀
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Sparking A.I. Innovation — with Nicole Büttner
Looking for ideas on how to spark A.I. innovation in your organization? Nicole Büttner, the eloquent and effervescent Founder/CEO of Merantix Labs, has concrete A.I. innovation frameworks for you in this week's guest episode.
Merantix Labs is a renowned Berlin-based consultancy that enables companies to unlock the value of A.I. across all industries.
In addition to being Founder and CEO of Merantix Labs, Nicole:
• Is a member of the Management Board of Merantix Labs’ parent company Merantix, an A.I. Venture Studio that has raised $30m in funding from the likes of SoftBank Group Corp. to serially originate successful ML start-ups.
• Holds a Masters in Quantitative Economics and Finance from the University of St.Gallen, the world’s leading German-language business school.
• Was a visiting researcher in Economics at Stanford University.
In this episode, Nicole details:
• What an A.I. Venture Studio is and how she founded a thriving A.I. consultancy within it
• How to spark A.I. innovation in a company of any size
• How to effectively use the unlabelled, unbalanced data sets that abound in business
• How to engineer reusable data and software components to tackle related projects efficiently
• The three distinct types of founders she looks for when she puts together the founding team of an A.I. start-up
Today’s episode touches on a few technical details here and there but the episode will largely be of interest to anyone who’s keen to make the most of A.I. innovation in a commercial organization, whether you happen to have a deep technical background today or not.
Special shout-out to the St. Gallen Symposium (Svenja, Rolf), which Nicole and I discuss our love for (as well as how you can get free flights, accommodation, and access — deadline to apply is Feb 1) starting at the 34-minute mark.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Observability — with Dr. Kevin Hu
This week's guest is the fun and wildly intelligent entrepreneur, Kevin Hu, PhD. Inspired by his doctoral research at MIT, he co-founded Metaplane, a Y-Combinator-backed data observability platform.
In a bit more detail, Kevin:
• Is Co-Founder/CEO of Metaplane, a platform that observes the quality of data flows, looks for abnormalities in the data, and reports issues
• Completed a PhD in machine learning and data science from the Massachusetts Institute of Technology
In this episode, Kevin covers:
• What data observability is and how it can help identify data quality issues immediately as well as more quickly resolve the source of the issue
• His PhD research on automating data science systems using ML
• How he identified the problem his start-up Metaplane would solve
• His experience in Y-Combinator accelerating Metaplane
• Pros and cons of an academic career relative to the start-up hustle
• The surprising complexity of the software tools he uses daily as a CEO
• What he looks for in the data engineers that he hires
This episode gets a little technical here and there but I think Kevin and I were pretty careful to define technical concepts when they came up, so today’s episode should largely be appealing to anyone who’s keen to learn a lot from a brilliant entrepreneur, especially if you’d like to found or grow a data science start-up yourself. Enjoy!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Interpretable Machine Learning — with Serg Masís
This week's guest is Serg Masís, an absolutely brilliant data scientist who's specialized in modeling crop yields and climate change. He's also a world-leading author and expert on Interpretable Machine Learning.
Serg:
• Is a Climate & Agronomic Data Scientist at Syngenta.
• Wrote "Interpretable Machine Learning with Python", an epic hands-on guide to techniques that enable us to interpret, improve, and remove biases from ML models that might otherwise be opaque black boxes.
• Holds a Data Science Masters from the Illinois Institute of Technology.
In this episode, Serg details:
• What Interpretable Machine Learning is.
• Key interpretable ML approaches we have today / when they're useful.
• Social and financial ramifications of getting model interpretation wrong.
• What agronomy is and how it’s increasingly integral to being able to feed the growing population on our warming planet.
• What it’s like to be a Climate & Agronomic Data Scientist day-to-day and why you might want to consider getting involved in this fascinating, high-impact field.
• His productivity tips for excelling when you have as many big commitments as he does.
Today’s episode does get technical in parts but Serg and I made an effort to explain many technical concepts at a high level where we could, so today’s episode should be equally appealing to both practicing data scientists and anyone who’s keen to understand the importance and impact of interpretable ML or agronomic data science. Enjoy!
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Data Science Trends for 2022
Happy New Year! To kick it off, this week's episode features the marvelous Sadie St. Lawrence predicting the data science trends for 2022. Topics include AutoML, Deep Fakes, model scalability, NFTs, and data literacy.
In a bit more detail, we discuss:
• How the SDS podcast predictions for 2021 panned out (pretty well!)
• The AutoML tools that are automating parts of data scientists’ jobs.
• The social implications of Deep Fakes, which are becoming so lifelike and easy to create.
• Principles for making A.I. models infinitely scalable in production.
• The impact of the remote-working economy on data science employment.
• Practical uses of blockchain and non-fungible token tech in data science.
• Improving the data literacy of the global workforce across all industries.
Sadie:
• Has taught over 300,000 students data science and machine learning.
• Is the Founder and CEO of WomenInData.org, a community of over 20,000 data professionals across 17 countries.
• Is remarkably well-read on the future of technology across industries.
This episode is relatively high-level. It will be of interest to anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2022, but also in the years beyond.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How to Found, Grow, and Sell a Data Science Start-up
This week's guest is terrifically witty Austin Ogilvie, a prodigiously successful data science entrepreneur. He was founder and CEO of the iconic start-up Yhat and is now founder/CEO of rapidly-scaling Laika.
Austin:
• Was the Founder and CEO of Yhat, a start-up that built tools for data scientists and had a loyal cult following in the data science community.
• In 2018, Yhat was acquired by Alteryx, an analytics automation company.
• More recently he founded Laika, a “compliance-as-a-service” company that dramatically improves your capacity to sell your products.
• Laika last month closed a $35m Series B funding round, bringing the total raised by the firm over two years to a staggering $48m.
In this episode, Austin describes:
• His journey from an arts degree studying foreign languages to teaching himself programming and machine learning, and then bootstrapping a data science start-up into a respected brand and acquisition target.
• His unique take on what makes a great data scientist.
• The hands-on data science tools he finds great value in coding with day-to-day as the founder and CEO of fast-growing tech start-ups.
• His practical tips for growing into a successful technical founder, whether you have a technical background yourself today or not.
Today’s episode will be of great interest to anyone who’s interested in founding, growing, and/or successfully exiting a tech start-up, particularly if you’re thinking of incorporating data or A.I. elements.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Fusion Energy, Cancer Proteomics, and Massive-Scale Machine Vision — with Dr. Brett Tully
This week's guest is Dr. Brett Tully, who leverages his rich cross-domain experience to detail how data science is applied to the fields of nuclear fusion energy, cancer biology, and massive-scale aerial machine vision.
In today’s episode, Brett details for us:
• What nuclear fusion is, how harnessing fusion power commercial could be a pivotal moment in the history of humankind, and how data simulations may play a critical role in realizing it
• How the study of the healthy proteins versus the proteins present in someone with a particular cancer type is accelerating the availability and impact of personalized cancer treatment
• What it means to be a Director of A.I. Output Systems and how this role fits in with other A.I. activities in an organization, such as model research and development
• His favorite software tools for working with geospatial data
• His tricks for the effective management of a team of ML Engineers
• His take on the big A.I. opportunities of the coming years
Brett:
• Is the Director of A.I. Output Systems at Nearmap, a world-leading aerial imagery company that uses massive-scale machine vision to detect and annotate vast images of urban and rural areas with remarkable detail
• As the Head of Simulation at First Light Fusion, he developed state-of-the-art data simulations that could be a key stepping stone toward enabling commercial nuclear fusion reactors
• As the Group Leader of Software Engineering at a major research hospital, he worked to characterize the differences in the proteome — the complete catalog of proteins in your body — between cancer patients and healthy individuals
• As a PhD student at the University of Oxford, he simulated how the cerebrospinal fluid present in our brains flows in order to better understand neurological abnormalities
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.