When it comes to artificial intelligence and machine learning, there’s a lot to know. But remember the old adage, “It’s not about what you know. It’s about who you know.” Okay, that’s a very cringey phrase, but it applies. It’s easier to learn about a new topic when you follow the people who are making it happen.
Here are seven AI and Machine Learning experts and influencers you should follow to keep up-to-date with what’s happening in the spaces.
1. Andrew Ng
As the founding lead of the Google Brain team and former director of the Stanford Artificial Intelligence Laboratory, now Chief Scientist for Baidu’s AI team of some 1,200 people, Andrew has some major chops in machine learning and deep learning.
Andrew is also an EdTech pioneer, serving as Chairman and Co-Founder of Coursera and currently teaching at Stanford. His passion for teaching makes him a great follow for folks who want to know more about ML. He breaks things down in articles like What Artificial Intelligence Can and Can’t Do Right Now.
This chart of his is a great example of a complex idea broken down into simple-to-understand parts:
Andrew is one of Dataconomy.com’s 10 ML experts you need to know. He’s authored or co-authored more than 100 ML/AI papers.
2. Andrej Karpathy
Andrej Karpathy is a Research Scientist at OpenAI who likes to, in his words, “train Deep Neural Nets on large datasets,” and is “on a quest to solve intelligence.” In my spare time, I like to watch Santa Clarita Diet.
The OpenAI Blog is super interesting, with articles like “Attacking machine learning with adversarial examples” breaking complex issues down to the point where non-programmers can understand them.
3. Michael Jordan
Michael Jordan is a “renowned statistician from Berkeley,” according to FastML.com. More specifically, Michael is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. Before that, he taught at MIT.
Michael focuses on probabilistic graphical models, spectral methods, natural language processing, statistical genetics, and much more.
According to NPR, he is a “leading figure in machine learning and Bayesian nonparametrics — a statistical approach that supports flexible models that can ’grow’ as more data becomes available. The computational models he’s developed have been applied to learning, memory, natural language processing, semantics, and vision, among other facets of natural and artificial intelligence.”
Dataconomy credits Michael with helping to popularize Bayesian networks in machine learning applications. He is also one of the luminaries who first brought up and popularized the ways in which statistics and machine learning overlap.
Also, a gifted teacher, Michael’s students are luminaries themselves in the world of ML. Andrew Ng, who is also on this list, is one of his students.
4. Yann LeCun
Yann LeCun and his buddies invented the convolutional neural networks that make image recognition work. NBD. In his spare time, Yann is Director of AI Research at Facebook,
Founding Director of the NYU Center for Data Science, a Silver Professor of Computer Science, Neural Science, and Electrical and Computer Engineering at NYU.
Yann focuses on machine learning and its applications, including vision, speech, language, data mining, and bioinformatics. In addition, Yann studies computer vision, mobile robotics, and computational neuroscience.
Follow him on Twitter for 45-minute documentaries on deep learning from Radio Canada in French, which he says are “amazing,” but tbh I have my doubts. There are also interesting articles about the future of manufacturing, for instance, that are written in English. But there’s a lot of French. Check out his personal website and Google Scholar page as well.
5. Geoffrey E. Hinton
After spending three decades inventing Boltzmann machines, backpropagation, and contrastive divergence with his colleagues in academia, Geoffrey Hinton became an intern at Google at age 64. His mostly Millennial cohort thought of him, in his estimation, as a “geriatric imbecile.”
Geoffrey is an Engineering Fellow at Google and an Emeritus Distinguished Professor at the University of Toronto. He and his buddies invented deep learning. He’s also the only ML influencer on this list, AFAIK, to have a Twitter fan account. Not sure which is the bigger accomplishment, but I bet the two are related.
“It’s incredibly hard, to sum up the career of any of these extraordinary minds in a few sentences, but with Hinton, this proves particularly challenging,” writes Dataconomy of Geoffrey. He’s been in the game for three decades now, but it wasn’t until computing power got better that he’s been recognized outside the academy.
He started at Google when they acquired his company DNNResearch. Today he works on the Google “Brain” neural network project. He co-founded Neural Computation and Adaptive Perception, an invite-only club for hand-picked physics, neuroscience, and engineering researchers.
Check out his academic page.
6. James Cham
“I think that from our perspective, the dirty secret around machine learning right now is that nobody knows what they’re doing.” James Cham keeps it real.
James Cham is a Partner at investment firm Bloomberg Beta and an early stage VC “obsessed with the new world of work.” I first heard about James when he was a guest on the CXO Talk podcast, where I thought he did a great job dispelling some myths about machine learning and also deflating some of the hype.
I loved his point about how everyone is focused on the big questions like how will AI impact the future of work, but no one is focusing on the nitty gritty implementation stuff, which is what’s required for AI to impact the future of work. It’s definitely a goal of my blog to help fill in those gaps and explain how businesses can actually use ML in the here and now to make their businesses better.
Another great point he made on the Too Embarrassed to Ask podcast is that the moment that we figure something out, it just becomes another feature. “Figuring out how to toast bread was really, really difficult,” Cham said. “And people talked about a robot that would actually figure out exactly how to get bread just right. And the moment it’s doable, it’s a toaster, and I think that’s true about a lot of technology.”
He doesn’t write or Tweet a lot but when he gives an interview, it’s worth your time.
7. Bob Poekert
“All of the ‘machine learning’/algorithms’ that it’s sensical to talk about being biased are rebranded actuarial science.”
Bob Poekert is one of the more interesting writers in the ML space. He’s a software engineer who went from Justin.tv to Priceonomics. His blog post on neural networks is extremely easy-to-understand, even for beginners. He mixes in programming with economics and culture in a fun, accessible way.
There are obviously more than seven movers and shakers in the AI space. Most of the names on this list come from people I know. In addition to these influencers, my friends mentioned Yoshua Bengio, one of the “best young researchers on reinforcement learning,” according to my friend. Yoshua is also a research scientist at OpenAI.
Yoav Artzi is an assistant professor of Computer Science at Cornell who is working on situated language understanding. Follow him on Twitter.
Killian Weinberger is another Associate Professor at Cornell University. He focuses on Machine Learning and its applications, particularly learning under resource constraints, metric learning, machine learned web search ranking, computer vision, and deep learning.
If you have others, please let me know in the comments!
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