Model Speed vs Model Accuracy

In the vast majority of real-world, commercial cases, the speed of a machine learning algorithm is more important than it's accuracy. Hear why in today's Five-Minute Friday episode!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Bayesian, Frequentist, and Fiducial Statistics in Data Science

Harvard stats prof Xiao-Li MENG founded the trailblazing Harvard Data Science Review. We cover that and why BFFs (Bayesians, frequentists and fiducial statisticians) should be BFFs (best friends forever).

Xiao-Li:
• Is the Founding Editor-in-Chief of the Harvard Data Science Review, a new publication in the vein of the renowned Harvard Business Review.
• Has been a full professor in Harvard’s Dept of Statistics for 20+ years.
• Chaired the Harvard Stats Dept for 7 years.
• Was Dean of Harvard’s Grad School of Arts and Sciences for 5 years.
• Has published 200+ journal articles on statistics, machine learning, and data science, and been cited over 25,000 times.
• Holds a PhD in Statistics from — yep! — Harvard.

Today’s episode will be of interest to anyone who’s keen to better understand the biggest challenges and most fascinating applications of data science today.

In the episode, Xiao-Li details:
• What the Harvard Data Science Review is, why he founded it, and the most popular topics covered by the Review so far.
• The concept of “data minding”.
• Why there’s no “free lunch” with data — tricky trade-offs abound no matter what.
• The surprising paradoxical downside of having lots of data.
• What the Bayesian, Frequentist, and Fiducial schools of statistics are and when each of them is most useful in data science.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Collecting Valuable Data

Recently, I've been covering strategies for getting business value from machine learning. In today's episode, we dig into the most effective ways to obtain and label *commercially valuable* data.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Transforming Dentistry with A.I.

Engineer and computer scientist Dr. Wardah Inam has raised $79m in venture capital to transform dentistry with machine learning. Hear about it, as well as her tips for scaling an A.I. company, in this week's episode.

Wardah:
• Is Co-Founder/CEO of Overjet, which is transforming dentistry with ML.
• Co-founded uLink Technologies, a start-up behind A.I.-driven power grids.
• Served as Lead Product Manager at Q Bio, a healthcare A.I. start-up.
• Was a Postdoc in MIT’s renowned CSAIL (Computer Science and A.I. Lab).
• Holds an MIT PhD in electrical engineering and computer science.

Today’s episode focuses more on practical applications of ML and growing an A.I. company than getting into the nitty-gritty of ML models themselves, so it should be broadly appealing to both technically-oriented and business-oriented folks.

In the episode, Wardah details:
• How Overjet not only classifies images but quantifies dental diagnoses with computer vision, enabling models to answer questions like “how large is this cavity?”
• How natural language processing can be essential for determining the correct dental diagnosis.
• The data-labeling challenges firms like Overjet need to overcome to enable ML models to learn from noisy, real-world data.
• Her tips for building a successful A.I. business.
• What she looks for in the data scientists and software engineers she hires.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Identifying Commercial ML Problems

The importance of effectively identifying a commercial problem *before* starting data collection or machine learning model development is the focus of this week's Five-Minute Friday.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Scaling A.I. Startups Globally

Sensational A.I. entrepreneur Husayn Kassai co-founded Onfido while an undergrad and served as its CEO for ten years, raising $200m in venture capital. Hear his tips for scaling your own A.I. firm in this week's episode.

Husayn:
• Co-founded the ML company Onfido in 2010, while he was an undergraduate student at the University of Oxford.
• Served as Onfido’s CEO for ten years, overseeing $200m in venture capital raised, the team growing to over 400 employees, and the client base growing to over 1500 firms.
• Holds a degree in economics and management from Oxford.
• Served as the full-time President of the Oxford Entrepreneurs student society, which is how I got to know him more than a decade ago.

Today’s episode is non-technical and will appeal to anyone who’s interested in hearing tips and tricks for building a billion-dollar A.I. start-up from scratch.

In the episode, Husayn details:
• Tips for deciding on whether you need co-founders.
• How to choose your co-founders if you need them.
• Finding product-market fit.
• How to scale up a company.
• How to identify start-up opportunities.
• Why there’s never been a better time than now to found an A.I. startup.
• A look at his next startup, which is currently in stealth.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Tech Startup Dramas

Recently I was hooked on three series: The Dropout (on Theranos), WeCrashed (WeWork), and Super Pumped (Uber). The latter two even feature machine learning, but all three are educational and entertaining.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Optimizing Computer Hardware with Deep Learning

The polymath Dr. Magnus Ekman joins me from NVIDIA today to explain how machine learning is used to guide *hardware* architecture design and to provide an overview of his brilliant book "Learning Deep Learning".

Magnus:
• Is a Director of Architecture at NVIDIA (he's been there 12 years!)
• Previously worked at Samsung and Sun Microsystems.
• Was co-founder/CTO of the start-up SKOUT (acquired for $55m).
• Authored the epic, 700-page "Learning Deep Learning".
• Holds a Ph.D. in computer engineering from the Chalmers University of Technology and a masters in economics from Göteborg University.

Today’s episode has technical elements here and there but should largely be interesting to anyone who’s interested in hearing the latest trends in A.I., particularly deep learning, software and hardware.

In the episode, Magnus details:
• What hardware architects do.
• How ML can be used to optimize the design of computer hardware.
• The pedagogical approach of his exceptional deep learning book.
• Which ML users need to understand how ML models work.
• Algorithms inspired by biological evolution.
• Why Artificial General Intelligence won’t be obtained by increasing model parameters alone.
• Whether transformer models will entirely displace other deep learning architectures such as CNNs and RNNs.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Music for Deep Work

Five-Minute Friday this week is a fun one! My top music/audio recommendations for you while you "deep work" 🎶

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Automating ML Model Deployment

Relative to training a machine learning model, getting it into production typically takes multiple times as much time and effort. Dr Doris Xin, the brilliant co-founder/CEO of Linea, has a near-magical, two-line solution.

In the episode, Doris details:
• How Linea reduces ML model deployment to two lines of Python code.
• The surprising extent of wasted computation she discovered when she analyzed over 3000 production pipelines at Google.
• Her experimental evidence that the total automation of ML model development is neither realistic nor desirable.
• What it’s like being the CEO of an exciting, early-stage tech start-up.
• Where she sees the field of data science going in the coming years and how you can prepare for it.

Today’s episode is more on the technical side so will likely appeal primarily to practicing data scientists, especially those that need to — or are interested in — deploying ML models into production.

Doris:
• Is co-founder and CEO of Linea, an early start-up that dramatically simplifies the deployment of machine learning models into production.
• Her alpha users include the likes of Twitter, Lyft, and Pinterest.
• Her start-up’s mission was inspired by research she conducted as a PhD student in computer science at the University of California, Berkeley.
• Previously she worked in research and software engineering roles at Google, Microsoft, Databricks, and LinkedIn.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Collaborative, No-Code Machine Learning

Emerging tools allow real-time, highly visual collaboration on data science projects — even in ways that allow those who code and those who don't to work together. Tim Kraska fills us in on how ML models enable this.

Tim:
• Is Associate Professor in the revered CSAIL lab at the Massachusetts Institute of Technology.
• Co-founded Einblick, a visual data computing platform that has received $6m in seed funding.
• Was previous a professor at Brown University, a visiting researcher at Google, and a postdoctoral researcher at Berkeley.
• Holds a PhD in computer science from ETH Zürich in Switzerland.

Today’s episode gets into technical aspects here and there, but will largely appeal to anyone who’s interested in hearing about the visual, collaborative future of machine learning.

In this episode, Tim details:
• How a tool like Einblick can simultaneously support folks who code as well as folks who’d like to leverage data and ML without code.
• How this dual no-code/Python code environment supports visual, real-time, click-and-point collaboration on data science projects.
• The clever database and ML tricks under the hood of Einblick that enable the tool to run effectively in real time.
• How to make data models more widely available in organizations.
• How university environments like MIT’s CSAIL support long-term innovations that can be spun out to make game-changing impacts.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

A.I. For Crushing Humans at Poker and Board Games

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.

PaLM: Google's Breakthrough Natural Language Model

This month, Google announced a large natural language model called PaLM that provides staggering results on tasks like common-sense reasoning and solving Python-coding questions. Hear all about it in today's episode!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.