Thanks to data science legend Hilary Mason and the engaging audience at the New York R Conference for making Friday's live-filmed episode of the SuperDataScience podcast an exhilarating and illuminating success ⚡️
Look out for Hilary's episode as #589, which will be released on July 5th.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This article was originally adapted from a podcast, which you can check out here.
At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Then, we had a series of Five-Minute Fridays that revolved around daily habits and we’ve been returning to this daily-habit theme periodically since.
The habits we covered in January and February were related to my morning routine. In March, we began coverage of habits on intellectual stimulation and productivity, such as reading and carrying out a daily math or computer science exercise.
Read MoreIn recent weeks, my YouTube videos have covered Probability concepts like Events, Sample Spaces, and Combinatorics. Today's video features exercises to test and cement your understanding of those concepts.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
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.
OpenAI just released their "DALL-E 2" multimodal model that defines "state of the art" A.I.: Provide it with (even extremely bizarre) natural-language requests for an image and it generates it! Hear about it in today's episode, and check out this interactive post from OpenAI that demonstrates DALL-E 2's mind-boggling capabilities.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Combinatorics is a field of math devoted to counting. In this week's YouTube video, we use examples with real numbers to bring Combinatorics to life and relate it to Probability Theory.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
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.
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.
In this week's YouTube tutorial, we consider probabilistic events where we have multiple independent observations — such as flipping a coin two or more times instead of just once.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
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.
In this week's YouTube tutorial, I introduce the most fundamental atoms of probability theory: events and sample spaces. Enjoy 😀
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
