DeepMind Technologies Ltd., the Google/Alphabet-owned elephant in the AI research room, has lost more than $1 billion over the past three years. And it owes another billion to creditors who’ll be looking for their money back, with interest, over the next year or so.
Do financial woes on this massive a scale in the AI world signal dark times ahead for everyone involved in AI research?
Don’t bet on it, advises Gary Marcus.
Writing for Wired, the NYU professor of psychology and neural science who founded the robotic AI company Robust.AI, asks and answers some probing questions about DeepMind’s money struggles and what they mean for the future of AI.
For starters, DeepMind has been concentrating too heavily on an AI technique called deep reinforcement learning, Marcus believes. This methodology has its strengths and weaknesses, he explains, but it’s only one approach.
Then too, “in the larger context of Alphabet, $500 million a year isn’t a huge bet,” Marcus observes. “Alphabet has (wisely) made other bets on AI, such as Google Brain, which itself is growing quickly.”
Moreover, while the hype around AI continues to outpace its real-world applications—or at least those packing truly transformative potential—the technology only has one way to go. And that’s up.
“The benefits from sound analysis of large data sets cannot be denied; even in limited form, AI is already a powerful tool,” Marcus writes. “The corporate world may become less bullish about AI, but it can’t afford to pull out altogether.”
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