Alphabet subsidiary DeepMind lost $572 million in the past year, and its losses over the last three years amounted to more than $1 billion. AI contrarian Gary Marcus used the news as an opportunity to question the direction of AI as an industry.
What’s new: In an essay published by Wired, Marcus extrapolates DeepMind’s finances into an indictment of AI trends in recent years.
The blow-by-blow: He begins with a seeming defense of DeepMind, saying that the losses can be viewed as investments in cutting-edge research.
- But he quickly doubles back, suggesting the lack of a payoff indicates that DeepMind’s research focus, deep reinforcement learning, is a dead end.
- He calls out DRL’s failure to make progress towards artificial general intelligence and practical goals like self-driving vehicles.
- DeepMind’s expenditures aren’t just an expensive mistake, he claims. They rob research funding for worthier AI techniques, such as approaches based on cognitive science.
Behind the news: Marcus is a longtime critic of deep learning. He published a 10-point critique of deep learning’s shortcomings last year. He is currently promoting a book, Rebooting AI, arguing that the AI community should reorder its priorities to accommodate approaches that mimic human intelligence. In June, he announced a new venture, robust.ai, with roboticist Rodney Brooks.
Yes, but: As a tech company, Alphabet does well to invest in nascent technologies or risk being disrupted by them. As a public company, it has a fiduciary responsibility to do so. Moreover, DeepMind has achieved phenomenal successes at solving Go and StarCraft II and helped make Google’s data centers and Android devices run more efficiently.
What they’re saying: The essay created a stir on social media.
- Some voiced agreement with Marcus conclusions: “DeepMind struggles to achieve breakthrough results in transfer learning for at least two years. I believe part of them must see this as the key to AGI. I think deep nets are but one ingredient.” — @donbenham
- Others found Google’s investment well justified: “DeepMind may be over-invested in snake oil (DRL is lazy, brittle & struggles to scale past toy problems) but Google has 120B in cash sitting in the bank, w/ positive cash flow. DeepMind costs like 1% of profit, provides positive coverage, attracts talent, is a long odds bet, etc.” — @nicidob
- And many called out Marcus for ignoring DeepMind’s achievements: “What about @DeepMindAI’s protein folding success, using RL for data center cooling, WaveNet, etc, and their great neuroscience division?” — @blackHC
We’re thinking: Marcus warns that investors may abandon AI if big investments like DeepMind don’t start providing returns. But some AI approaches already are having a huge economic impact, and emerging techniques like DRL new enough that it makes little sense to predict doom for all approaches based on slow progress in one. Better to save such double-barreled criticism for AI that is malicious or inept. We disagree with Marcus’ views on deep learning, but cheer him on as he codes, tests, and iterates his own way forward.