• Synchron implanted its first device into a US patient on July 6 in New York.
• The chip is designed to allow patients with severe paralysis to communicate using their thoughts.
• Elon Musk’s Neuralink has a similar mission, but has yet to receive FDA approval.

One question at the core of AI has remained unanswered for 70 years—without giving any signs it’ll be resolved anytime soon:

“How much should artificial general intelligence (AGI) resemble the human brain?”

We know a lot more about the human brain than we did half a century ago. Yet, its deepest mysteries seem to be as out of reach as they were at the very beginning.

You may think it doesn’t matter if we don’t understand it—after all, we’re living in a golden era of AI research and development. But that’s only partially true.

It matters. Much more than many in the field think. Read More

Worried Alexa’s busy plotting our extinction while sitting on your kitchen counter? Don’t worry, artificial intelligence isn’t that smart… yet.

Artificial intelligence armageddon. We’ve all chatted about it in the pub, haven’t we? Even if it’s an ironic little, ​“Ah well, work won’t matter when AI’s doing everything and we’re all dead,” between drinks.

And to be fair, it has been a pretty big year for artificial intelligence. Back in July, an ex-Google engineer made headlines after claiming the company’s LaMDA technology had become sentient (it hadn’t). And earlier this month, an AI addressed parliament with a speech more authoritative than anything Liz Truss managed to say during her minuscule tenure as prime minister.

Maybe a world run by AI doesn’t seem too far-fetched, after all. Could this mysterious bit of technology really change the world as we know it? Read More

A new AI research company is launching out of stealth today with an ambitious goal: to research the fundamentals of human intelligence that machines currently lack. Called Generally Intelligent, it plans to do this by turning these fundamentals into an array of tasks to be solved and by designing and testing different systems’ ability to learn to solve them in highly complex 3D worlds built by their team.

“We believe that generally intelligent computers will someday unlock extraordinary potential for human creativity and insight,” CEO Kanjun Qiu told TechCrunch in an email interview. “However, today’s AI models are missing several key elements of human intelligence, which inhibits the development of general-purpose AI systems that can be deployed safely … Generally Intelligent’s work aims to understand the fundamentals of human intelligence in order to engineer safe AI systems that can learn and understand the way humans do.” Read More

Some objects grab our attention when we see them, even when we are not exactly looking for them. How precisely does this happen? And, more importantly, how can we incorporate this phenomena to improve our computer vision models? In this article, I will explain the process of paying attention to salient (i.e. noticeable) objects in the visual scene and their applications in Machine Learning as an AI researcher (or not only from the neuroscience perspective).

Visual perception, saliency, and attention have been active research topics in neuroscience for decades. The discoveries and advancements that these researchers have made have helped AI researchers understand and mimic the process(es) in the human brain. Indeed, saliency and attention are active research topics in the AI community, too. The outcome is a wide spectrum of applications ranging from better language understanding to autonomous driving. But before we can understand the AI perspective on attention, we’ll first have to understand it from the neuroscience perspective. Read More

Fundamental problems elude many strains of deep learning, says LeCun, including the mystery of how to measure information.

Yann LeCun, chief AI scientist of Meta Properties, owner of Facebook, Instagram, and WhatsApp, is likely to tick off a lot of people in his field. 

With the posting in June of a think piece on the Open Review server, LeCun offered a broad overview of an approach he thinks holds promise for achieving human-level intelligence in machines. 

Implied if not articulated in the paper is the contention that most of today’s big projects in AI will never be able to reach that human-level goal. Read More