This episode was adapted from a post that I wrote for the GQR blog.
In today’s Five-Minute Friday episode, I continue last week’s theme. We overview exponentially accelerating technological progress and examine today’s augmentation of human intelligence: the first babysteps of the AI revolution.
In Part 3, coming next month, we'll speculate on how data and automation will dramatically change work and life over the coming decades.
Listen or watch here.
In this week's episode, Stephen Welch joined me to discuss 10 big global problems worth trying to solve with data science and machine learning. Perhaps you'll be inspired to tackle one or two yourself!
2020 presented a lot of challenges to a lot of folks around the world. For Stephen, the challenging year was compounded by intense delivery targets for him and his team on a machine vision-enabled system that automatically detects defective industrial products. After delivering this fascinating edge-deployed software into production, Stephen was able to finally come up for air and he reflected on what's most important in one's life — culminating in his list of 10 global problems worth solving.
Stephen is VP of Data Science at Mariner, where he leads a team developing deep learning-based solutions to manufacturing problems. Prior to working with Mariner, Stephen was VP of Machine Learning at Autonomous Fusion, a firm specialized in self-driving cars. He’s also an adjunct professor at University of North Carolina at Charlotte and the creator of the gorgeous math-focused YouTube channel WelchLabs, which has over 200k subscribers and 10M views.
You can listen or watch here.
Six new YouTube videos from my Machine Learning Foundations series are live today! These round out a thematic segment of linear algebra videos focused on matrix determinants, eigenvectors, and eigenvalues.
The specific videos are:
Up next will be a few videos that focus on real-world, hands-on applications of linear algebra to ML problems, including with singular value decomposition, Moore-Penrose pseudoinversion, and principal component analysis. That will wrap up all of the linear algebra content in the series and then we'll move on to calculus!
The playlist for my entire Machine Learning Foundations series is here.
The series is full of hands-on demos in Python and all of the code is available open-source in GitHub.
This podcast episode is based on a blog post I published for GQR. See here.
For millennia, you might not have witnessed a single innovation in your lifetime. In the 20th Century, any job changed dramatically. In the coming decades, automation will dramatically change all aspects of life many times over.
In this week's FiveMinuteFriday, I examine this acceleration — this staggering pace of progress — through the lens of the recruitment industry that is facilitating a lot of the change.
Listening and viewing options here.
Dan Shiebler has a pretty unambitious hobby: a full-time University of Oxford PhD 😂! Dan researches Category Theory, a math branch that categorizes items by their behavior. By day, he's Staff Machine Learning Engineer at Twitter.
In this week's SuperDataScience episode, Dan explains to me how relatively pure mathematical research translates to improving real-world ML applications.
We also discuss:
• How Twitter labels huge datasets
• How Revenue Science can boost ad performance
• What the responsibilities of a staff software engineer at a big tech company are
• What skills are sought after in data science hires at Twitter
Listening and viewing options here.
This episode features the world-renowned yogi Steve Fazzari. He gives me (and you!) a crash course on Yoga Nidra, a practice of deep relaxation that impacts brain structure, leading to better sleep and ability to respond to stress.
Steve lives up in the mountains of British Columbia as an explorer and instructor of yoga instructors. A ravenous reader and critical thinker, he has a rich understanding of science, the body, and the mind. I learned a ton from Steve during the filming of this episode.
If you like the sound of his explanation (and two-minute demo of) yoga nidra, please let me know because I'd love to bring him back on the SuperDataScience show to lead us through a full-length yoga nidra session.
Machines increasingly decide on critical aspects of your life, including medical treatment, mortgages, and job applications. Disturbingly, many such algorithms reinforce historical biases against particular gender and ethnic groups.
This week, Ayodele Odubela joins me on SuperDataScience to discuss the importance of equitability, introspection, and transparency when modeling data (and even in hardware development!), plus a bit about Ayodele’s own personal journey as a data scientist.
Ayodele works at Comet (a machine learning company), is the founder of FullyConnected (a brilliantly-named platform for black and brown data scientists), and the author of Getting Started in Data Science as well as the forthcoming book Uncovering Bias in Machine Learning. I learned a lot from Ayodele; she knows a ton and conveys her knowledge beautifully.
Listening/viewing options, as well as full transcript, available here.
The more leadership responsibility you take on as a data scientist or engineer, the more you accept that can't stay on top of the cutting-edge innovations as much as you might like. Nor do you get to spend as much time as you'd like writing code.
...and that's for the best. It's what what your team and your company need from you.
In this week’s SuperDataScience guest episode, the staggeringly experienced Dr. Michael Segala fills us in on how commercial opportunities for applying machine learning to real-world problems are everywhere.
Michael is co-founder and CEO of SFL Scientific, a world-leading A.I.-consulting firm that brings state-of-the-art deep-learning possibilities into production across the public and private sectors alike. We survey opportunities for transformative ML applications in the coming decades across a broad range of industries before focusing on the medical industry and governments in particular.
As NVIDIA’s Partner of the Year for AI services, as well as a consultant to firms as diverse as the US Navy, the Smithsonian Institution, and Johnson & Johnson, Dr. Segala may be better-positioned than anyone to be optimistic about the huge positive social impact data and models will have in our lifetimes.
Listening/viewing options, as well as full transcript, available here.
In 2013, I sat on a crowded bus next to a guy with the same (bald) haircut as me. Luckily, we struck up a conversation because that talk was the catalyst for a drastic, ever-developing U-turn across my entire life, both personally and professionally.
Following what he today calls the Atomic Habits approach, that guy on the bus -- James Clear -- now has a million subscribers to his weekly email newsletter and a New York Times #1-bestseller that's been translated into over 40 languages.
Via analogy to specific data science and software development examples, in today’s FiveMinuteFriday on the SuperDataScience Podcast I describe how “atomic habits” have the capacity to dramatically transform your career and your very identity.
Read MoreThis week, I released four new YouTube videos from my Machine Learning Foundations series -- these happen to be the first videos from Subject 2 of the series, which is called Linear Algebra II: Matrix Operations.
The four videos are:
Up next -- probably as soon as next week -- I'll be releasing more Linear Algebra videos from Subject 2, covering matrix determinants, eigendecomposition, and the interplay between the two concepts. After that, we'll start releasing videos from Subjects 3 and 4, which are focused on Calculus.
The YouTube playlist for my entire Machine Learning Foundations series is here. The series is full of hands-on demos in Python (particularly the NumPy, TensorFlow, and PyTorch libraries) and all of the code is available open-source in GitHub.
Thanks as always to Sangbin Lee for incredible production. He’s been lately outdoing his already outstanding self.
I recently released three new YouTube videos on linear algebra from my Machine Learning Foundations series. I created these in response to thoughtful questions from students who'd worked through the existing videos in the series -- thank you for the inspiration!
The videos are:
The YouTube playlist for the entire ML Foundations series is here.
On last Friday’s episode, I answered questions from podcast listeners on the “futureproof-ness” of a data science career. This week, I’m answering a few more listener-submitted questions about getting start in ML, particularly if you’re completely new to the field!
From MOOCs, to free videos, to graduate level textbooks, I cover a wide or materials that you can use to become an expert in machine learning.
Read MoreSinan Ozdemir may be the most articulate explainer of complex concepts I’ve ever met. In the latest SuperDataScience episode, he fills me in on how to design a Conversational A.I. (aka "chatbot") so it's effective for users and businesses alike.
In addition, we discussed AutoML, how a background in pure mathematics is a huge asset in applied data science, and the hard and soft skills you need for a career in natural language processing at a cutting-edge tech company.
Sinan co-founded Kylie.ai, a conversational-AI company, which was acquired by Directly, where Sinan now serves as Director of Data Science. He has a deep well of both technical knowledge and business savvy that I found wildly informative.
Listening/viewing options, as well as full transcript, available here.
At the beginning of 2021, I asked the following on Twitter: “What questions do you have about machine learning as a science or as a career?”
In response, I was asked some terrific questions about data science, many of which are popular ones that I’ve been asked time and again. In today’s FiveMinuteFriday, I’ll answer the ones I thought would be most valuable for everyone to hear the answer to.
Gabriel, who appears to be Brazillian, but indicates his location is “Lost + Found” asked me:
“Is a career in data science really future-proof? What are the odds of another AI winter and a crisis in this career?”
Read MoreWe’ve all heard the claim: “Robots will take our jobs.” But what actually happens when work is fundamentally changed by technology? Jeff Wald, author of The End of Jobs and co-founder of WorkMarket, joins me on this episode to discuss the future of our 9-to-5’s including how data science, automation, and other macroeconomic factors will reshape work around the globe in the coming decades.
Jeff’s work involves a careful examination of the data around the history of work, and builds on his own experience as co-founder of WorkMarket, a company focused on helping companies manage contractors. We discuss the pandemic, remote work, and the obligations of society towards the people caught up in the churn.
Listening/viewing options, as well as full transcript, available here.
Things can get silly when Kate and I get together 🙃 BUT we also covered a lot in this week’s guest episode of the SuperDataScience, including:
The utter importance of communicating data effectively
Concrete tips for doing so
And how to build a giant social-media presence (like Kate's 150k following!)
Kate is the founder of Story by Data & DATAcated Academy, and author of four books including The Disruptors: Data Science Leaders and Journey to Data Scientist.
Listening/viewing options, as well as full transcript, available here.
My Machine Learning Foundations tutorial series – as suggested by the diagram above – covers Linear Algebra, Calculus, Probability/Stats, and Computer Science (more detail on the series is available in GitHub).
Last month, with a big sigh of joy, I finished recording the last Linear Algebra video for the series, meaning that my filming journey for the first quarter of the ML Foundations series is now complete. I've recorded the footage three different ways:
If you have a subscription to the O'Reilly learning platform, a studio-recorded version has been available since late December as my complete, standalone Linear Algebra for Machine Learning curriculum.
If you have a subscription to the Ai+ training platform, live recordings of my entire linear algebra curriculum are available to view there on-demand as of January.
The first half of my linear algebra content is available on my YouTube channel and via my Udemy course today. This is the version I finished filming most recently. My producer Sangbin is currently editing these videos and I'll post here as they’re released (I recommend signing up for my email newsletter on my homepage to be pushed notifications).
With Linear Algebra in my rear-view mirror, I’m currently tackling the Calculus content. The studio-recorded version of my calculus materials is currently available as a “sneak peak” in O'Reilly. I'm teaching my calculus materials live in the Ai+ platform from now through February 24th. And calculus videos should be published on YouTube and in my Udemy course in March and April.
This article was adapted from a podcast. Listening/viewing options, as well as a full transcript, available here.
On last week's Five-Minute-Friday episode, I introduced the concept of artificial general intelligence ( or AGI, for short), a theoretical algorithm that one day could have all of the intellectual capacities of a human being. I also introduced the company DeepMind and the landmark deep reinforcement learning algorithms they developed over the past decade, each one a stepping stone on the road to creating AGI. If any of AGI, DeepMind, or deep reinforcement learning are unfamiliar terms to you, you might want to check out last week's Five Minute Friday episode to brush up.
Last week's coverage of DeepMind's deep reinforcement learning advances bring me now to MuZero, an algorithm that David Silver and his DeepMind research team published on in the final days of 2020 in the journal Nature, arguably the most prestigious academic science journal.
Read MoreIn the latest SuperDataScience episode, the brilliant A.I. researcher Deblina Bhattacharjee fills me in on the state of the art in machine vision. From predicting earthquakes to 3D VR experiences based on ordinary 2D art, she's seemingly in on it all!
We also covered:
Automatic detection of biological cells in medical images
The typical workday of, and software tools used by, A.I. researchers working at the cutting edge
The critical math subjects necessary to be an outstanding machine learning practitioner (to my delight, she reeled off the subjects covered in my ML Foundations curriculum)
The ever-increasing utility of unsupervised learning approaches
Her top productivity tips (Deblina is unbelievably productive so I greatly value her two cents on this!)
Prog rock music
The intelligence of plants (yep, plants!)
This is an outstanding episode. I learned a ton while filming and had a lot of laughs too, so I think you'll enjoy it too.
Listening/viewing options, as well as full transcript, available here.
