Latest Episode
by Peter High, published on Forbes
4-18-2016
Greg Brockman is co-founder and CTO at OpenAI, a non-profit artificial intelligence research company that also includes Elon Musk and Y-Combinator’s Sam Altman among other Silicon Valley luminaries as co-founders. OpenAI was founded to ensure that artificial intelligence benefits humanity as a whole, which has defined its non-profit status and long-term perspective. When I asked Brockman who influenced him, he listed Alan Kay of Xerox PARC among others, and highlighted the he hopes to foster a comparable idea lab to PARC. We also discussed how the organization’s bold mission and unique structure acts as a magnet for world-class talent, the trend of open sourcing AI development, how AI may impact jobs and society more broadly, and the promise versus the peril of AI, among other topics.
Prior to OpenAI, Greg was the CTO of Stipe, a FinTech company that builds tools enabling web commerce. Greg was the fourth employee at Stripe, a company that now has a valuation of over $5 billion.
(To listen to an unabridged audio version of this interview, please click this link. This is the fourth interview in my artificial intelligence series. Please visit these links to interviews with Mike Rhodin of IBM Watson, Sebastian Thrun of Udacity, Scott Phoenix of Vicarious, and Antoine Blondeau of Sentient Technologies. To read future articles in the series, including with Neil Jacobstein of Singularity University, Oren Etzioni of the Allen Institute for Artificial Intelligence, and Nick Bostrom of Oxford University, please click the “Follow” link above.)
Peter High: The stated goal of OpenAI is to advance digital intelligence in a way that is likely to benefit humanity as a whole, unconstrained by a need to generate financial return. What advances in digital intelligence are most likely to benefit humanity as a whole, in your mind?
Greg Brockman: I think there is something special going on right now in the field of artificial intelligence (AI) where, for the first time, systems that are based on deep learning and statistical methods suddenly start to have extremely good performance, and you are able to start building computer vision systems, for example, that can classify objects in a certain sense much better than humans can. Rather than having humans spend time understanding “how do I write down the code to specifically solve this problem?”, you build this general architecture, and the architecture learns from the data. We are getting better at writing these algorithms that are able to learn, to understand the world, and operate within it. At the same time, I do not think the world has changed in a significant way as a result yet. It has only been a short period of time that these algorithms started to be best in class – it dates back to a 2012 paper that showed that if you scale up this neural network architecture in the right way, the system starts to perform significantly better in a wide variety of domains. I think we are going to see these techniques mature and start to be baked into a wide variety of products, both at big companies and at new companies, and in a variety of applications.
We are already starting to get a sense of this if you think about self-driving cars. They are basically here. There is a lot of engineering left to do and lot of hard work and a lot of societal questions to answer, but it is a just a question of when; it is not a question of if. I think that is the tip of the iceberg. Robotics, I think, is poised to start working. Imagine you have a robot in your house that can clean things. A couple of years ago that was not something on the horizon. Now it is not even extrapolation anymore to say that it is going to start having an impact.
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