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Alphabet’s DeepMind lost $572 million last year. What does it mean?
DeepMind, likely the world’s largest research-focused artificial intelligence operation, is losing a lot of money fast, more than $1 billion in the past three years. DeepMind also has more than $1 billion in debt due in the next 12 months.
Mean - AI
Does this mean that AI is falling apart?
Gary Marcus is founder and CEO of Robust.AI and a professor of psychology and neural science at NYU. He is the author, with Ernest Davis, of the forthcoming Rebooting AI: Building Artificial Intelligence We Can Trust.
Research - Money - DeepMind - Research - Year
Not at all. Research costs money, and DeepMind is doing more research every year. The dollars involved are large, perhaps more than in any previous AI research operation, but far from unprecedented when compared with the sums spent in some of science’s largest projects. The Large Hadron Collider costs something like $1 billion per year and the total cost of discovering the Higgs Boson has been estimated at more than $10 billion. Certainly, genuine machine intelligence (also known as artificial general intelligence), of the sort that would power a Star Trek–like computer, capable of analyzing all sorts of queries posed in ordinary English, would be worth far more than that.
Still, the rising magnitude of DeepMind’s losses is worth considering: $154 million in 2016, $341 million in 2017, $572 million in 2018. In my view, there are three central questions: Is DeepMind on the right track scientifically? Are investments of this magnitude sound from Alphabet’s perspective? And how will the losses affect AI in general?
Question - Reason - Skepticism - DeepMind - Eggs
On the first question, there is reason for skepticism. DeepMind has been putting most of its eggs in one basket, a technique known as deep reinforcement learning. That technique combines deep learning, primarily used for recognizing patterns, with reinforcement learning, geared around learning based on reward...
(Excerpt) Read more at: WIRED
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