Voices Of AI: 5 People You Should Know

Often the best way to learn about a new field -- especially one that is both complex and noisy, like AI -- is to watch people who are truly proficient at it. What do they talk about? What do they spend their time working on, and what things do they ignore? What kind of music do they listen to?

Someone working in marketing is not going to learn a lot about AI from another person working in marketing, for the simple reason that most marketers don’t understand the fundamentals of AI. They know the marketing-speak version of AI, which looks a lot like bullshit bingo. The term “AI” is just another entrant in the lexicon of agency word porn, a sexy-shiny thing to pull out during new-business pitches and client dinners, but ultimately signifying little.

To whom, then, should an autodidact turn in order to better understand where AI is really headed, and why industry pundits insist on saying ridiculous things like “artificial intelligence is eating the world”?

Below are five people I have found particularly insightful on topics ranging from neural networks, to self-driving cars, social robotics, natural language processing and AI startup investing. I highly recommend paying close attention to all of them this year.

(Note: at five, this list is necessarily — even hilariously — incomplete; my goal is to be concise and useful, not comprehensive. Plus, I have a thing for prime numbers).

Carla Echevarria, design lead, Google  
A masterful presenter, Echevarria is unrivaled in her ability to clearly explain neural networks to a nontechnical audience. After listening to her, you will understand how hierarchical thinking is different from linear thinking. You will understand why traditional programming is useful for teaching a computer how to recognize a specific cat, but utterly useless at enabling the computer to teach itself “catness.”  You will understand the preceding sentence without having to reread it three times.

Echevarria has a deep background in design, with previous lead design roles at MakerBot, Facebook, and R/GA. Part of her focus at Google is on nonvisual design challenges presented by voice-only interfaces, such as Google Home.

Shivon Zillis, partner, Bloomberg Beta
What LUMAscapes are to ad tech (remember ad tech?), Zillis' landscapes are to the field of machine intelligence. As a partner at Bloomberg Beta, Zillis is laser-focused on machine and enterprise intelligence. Her investments include Shield AI, Datalogue, and Domino Data Lab, among others.

If you follow AI long enough, you realize that some of the most important work comes out of Canadian universities. Zillis is on the advisory board of the Machine Learning Group at the University of Alberta, a leader in reinforcement learning.

Yann LeCun, director, AI research, Facebook; professor at NYU
When people talk about “personalization at scale,” they are talking about Facebook. With more than 1.2 billion users, Facebook Messenger has replaced SMS in the Western World and become the testing ground for bots and conversational commerce.

Sadly, most chatbots suck, due either to a lack of contextual awareness or emotional tone-deafness. If anyone can change that it’s Yann LeCun, one of the pioneers of deep learning.  In fact, Facebook’s public foray into AI began in 2013 when it hired LeCun.

Rumored developments at Facebook include speech recognition and enhanced emotional intelligence for its “M” assistant, the latter underscored by the company’s acquisition of conversational AI startup Ozlo just three days ago.  

Fei-Fei Li, director, Stanford Vision Lab
(Full disclosure: Fei-Fei Li and I attended Princeton University at the same time during the late 1990s. The similarities end there).

One month ago, Quartz featured Li in an article captioned “Stanford professor Fei-Fei Li changed everything.” Everything! Li is a rock star in the world of computer vision. In 2009, she pioneered a new paradigm in large-scale visual recognition called ImageNet; the annual competition that followed is credited as a major catalyst to the current AI boom.

Andrew Ng, co-founder of Coursera, former head of Baidu AI Group & Google Brain
As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and lead of Baidu’s AI team of 1,200 people (that’s not a a typo), Ng knows a thing or two about machine learning.  His early work included developing one of the most capable autonomous helicopters in the world (easy), and he’s also been a pioneer of online education, getting Stanford courses online in 2008 and founding Coursera in 2012 -- where you should head if you want to learn more about any of the AI topics discussed above.

I hope this short-ish list was helpful. I would love to hear what you think.

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