Artificial intelligence turns brain activity into speech

Kelly Servick
ScienceMag.org
Originally published January 2, 2019

Here is an excerpt:

Finally, neurosurgeon Edward Chang and his team at the University of California, San Francisco, reconstructed entire sentences from brain activity captured from speech and motor areas while three epilepsy patients read aloud. In an online test, 166 people heard one of the sentences and had to select it from among 10 written choices. Some sentences were correctly identified more than 80% of the time. The researchers also pushed the model further: They used it to re-create sentences from data recorded while people silently mouthed words. That's an important result, Herff says—"one step closer to the speech prosthesis that we all have in mind."

However, "What we're really waiting for is how [these methods] are going to do when the patients can't speak," says Stephanie Riès, a neuroscientist at San Diego State University in California who studies language production. The brain signals when a person silently "speaks" or "hears" their voice in their head aren't identical to signals of speech or hearing. Without external sound to match to brain activity, it may be hard for a computer even to sort out where inner speech starts and ends.

Decoding imagined speech will require "a huge jump," says Gerwin Schalk, a neuroengineer at the National Center for Adaptive Neurotechnologies at the New York State Department of Health in Albany. "It's really unclear how to do that at all."

One approach, Herff says, might be to give feedback to the user of the brain-computer interface: If they can hear the computer's speech interpretation in real time, they may be able to adjust their thoughts to get the result they want. With enough training of both users and neural networks, brain and computer might meet in the middle.

The info is here.

The ethics of computer science: this researcher has a controversial proposal

Elizabeth Gibney
www.nature.com
Originally published July 26, 2018

In the midst of growing public concern over artificial intelligence (AI), privacy and the use of data, Brent Hecht has a controversial proposal: the computer-science community should change its peer-review process to ensure that researchers disclose any possible negative societal consequences of their work in papers, or risk rejection.

Hecht, a computer scientist, chairs the Future of Computing Academy (FCA), a group of young leaders in the field that pitched the policy in March. Without such measures, he says, computer scientists will blindly develop products without considering their impacts, and the field risks joining oil and tobacco as industries whose researchers history judges unfavourably.

The FCA is part of the Association for Computing Machinery (ACM) in New York City, the world’s largest scientific-computing society. It, too, is making changes to encourage researchers to consider societal impacts: on 17 July, it published an updated version of its ethics code, last redrafted in 1992. The guidelines call on researchers to be alert to how their work can influence society, take steps to protect privacy and continually reassess technologies whose impact will change over time, such as those based in machine learning.

The rest is here.