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Circa 2016


Taking vertical urban indoor farming efficiency to the next level, a new automated plant coming to Japan will be staffed entirely by robots and produce 30,000 heads of lettuce daily.

spread indoor farm

The so-called Vegetable Factory is a project of Spread, a Japanese company already operating vertical farms. Located in Kyoto, its small army of bots will various seed, water, trim and harvest the lettuce. Spread’s new automation technology will not only produce more lettuce, it will also reduce labor costs by 50%, cut energy use by 30%, and recycle 98% of water needed to grow the crops.

The hype about artificial intelligence is unavoidable. From Beijing to Seattle, companies are investing vast sums into these data-hungry systems in the belief that they will profoundly transform the business landscape. The stories in this special report will deepen your understanding of a technology that may reshape our world.


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Sundar Pichai, CEO of Google, calls for sensible regulation of AI. I agree. “Companies such as ours cannot just build promising new technology and let market forces decide how it will be used. It is equally incumbent on us to make sure that technology is harnessed for good and available to everyone.”


Companies cannot just build new technology and let market forces decide how it will be used.

A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.

We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.

Hassabis is about to be proven right again.

We all subconsciously learn complex behaviors in response to positive and negative feedback, but how that works in the brain remains a century-long mystery. By examining a powerful variant of reinforcement learning, dubbed distributional reinforcement learning, that outperforms original methods, the team suggests that the brain may simultaneously represent multiple predicted futures in parallel. Each future is assigned a different probability, or chance of actually occurring, based on reward.

Here’s the kicker: the team didn’t leave it as an AI-inspired hypothesis. In a collaboration with a lab at Harvard University, they recording straight from a mouse’s brain, and found signs of their idea encoded in its reward-processing neurons.