In her fireside chat at Startup Grind Global with Mike Abbott, a partner at VC firm KPCB, Dr Fei Fei Li - the Chief Scientist of AI & Machine Learning at Google Cloud and the Director of Stanford’s AI Lab (SAIL) - shared that her personal mission is to democratize AI and evolve it from its niche, academic origins.
I would not want [the cloud] to be in the hands of a few elites. The cloud is the biggest computing technology Man has created and the marriage between Cloud and AI is the perfect vehicle to democratize it.
This is a growing pie and not a zero-sum game, she suggests, and we need to be growing this pie together.
Three Factors Have Caused Recent Advances In AI
When asked by Abbott and what had caused the recent advances in AI, Dr Li points to the convergence of three factors:
1. Better computing power and hardware such as GPUs.
2. Twenty years of labeled big data & annotated consumer data.
3. The advances in the algorithms themselves.
These are the landscape changes Dr. Li has covered in more detail earlier in February at the Women in Data Science conference at Stanford where she'd made the connection between Alan Turing’s work of the 1940s and 1950s with her neighbor and the founder of SAIL, Professor Terry Winograd’s, 1970s frameworks for integrating Language, Vision, Syntax, Semantics and Inferencing.
How can small startups compete against big players?
"Part of it is that the big companies with the tagged data have advantages but they won’t go into deep verticals. And, if you’re a startup you have to be creative.
How do you snowball your data? So design the snowballing of that data. Importantly, develop an empathetic understanding of the problem you’re solving for the customer."
Dr. Li's comments are timely because empathic AI that understands our natural language and that has a personality and emotional capabilities are seen by AI researchers as important for the smoother interaction between humans and machines and better decision-making.
Last December, research from Brian Uzzi, a Kellogg School professor and faculty director of the Kellogg Architectures of Collaboration Initiative, showed that:
When traders are low in emotional states, they're very cool-headed, they tend to make bad decisions. They're too slow in taking advantage of an opportunity in the market, and they tend to hold on to bad trades too long. Exactly what you don't want to do. We also found that when they were in a very high emotional state, they did the same thing. When they were at an intermediate level of emotion, somewhere between being cool-headed and being highly emotional, they made their best trades.
This is interesting because, since the days of Descartes in the mid-C17th, there's been a belief that the best reasoning involves the "separation of mind from body from emotions" and this is the autistic, logical basis upon which today's AI has been created and built.
If emotions and empathy are vital to the decision-making process and the big companies don't yet have this type of data and algorithms, that suggests opportunities for startups to explore and to create new pies here. And to leverage the Cloud to democratize the data and algorithms as they do so.