Three years ago Elon Musk created another company called Neuralink. And it is engaged in no less ambitious tasks than other enterprises of the billionaire.
Neuralink develops technologies to directly connect the human brain to a computer. Last year, we were exposed to a number of technological details, and today Musk announced that Neuralink will hold its first demonstration on Friday. True, Moscow time will be already 1:00 Saturday.
Neuralink, the Elon Musk-led startup that the multi-entrepreneur founded in 2017, is working on technology that’s based around ‘threads’ which it says can be implanted in human brains with much less potential impact to the surrounding brain tissue vs. what’s currently used for today’s brain-computer interfaces. “Most people don’t realize, we can solve that with a chip,” Musk said to kick off Neuralink’s event, talking about some of the brain disorders and issues the company hopes to solve.
During a panel discussion on transhumanism at this year’s MWC, one expert predicted AI could figure out how to make a human live forever.
‘If You’re Under 50, You’ll Live Forever: Hello Transhumanism’ was the name of the session and featured Alex Rodriguez Vitello of the World Economic Forum and Stephen Dunne of Telefonica-owned innovation facility Alpha.
Transhumanism is the idea that humans can evolve beyond their current physical and mental limitations using technological advancements. In some ways, this is already happening.
A deep learning program can identify cells with higher metastatic potential based on the way they look and move.
Scientists have developed a method to determine which tumor cells are most likely to metastasize efficiently to distant sites in the body. Assaf Zaritsky, now at Ben-Gurion University in Israel, and his colleagues in Gaudenz Danuser’s lab at UT Southwestern Medical Center designed a deep learning program that analyzes data from live phase-contrast imaging of melanoma cells taken from xenografts—mice implanted with patients’ tumor material. The program determined “the most representative morphological and behavioral properties of each melanoma cell and then demonstrated that this representation of the cell state can be used to predict in stage III melanoma excised from the lymphatic system the chances of progression to stage IV,” Zaritsky writes to The Scientist in an email. The scientists presented their “quantitative live cell histology” results at the American Society for Cell Biology / EMBOmeeting in San Diego on Monday (December 10).
You want to start an AI project – but what processes do you need to bear in mind, how do you manage the data, and what do you need to look at when it comes to team composition and testing? In this extract from Embracing the Power of AI, Javier Minhondo, Juan José López Murphy, Haldo Spontón, Martín Migoya, and Guibert Englebienne outline how to get through these crucial initial stages.
The most basic problem is that AI researchers often don’t share their source code. At the AAAI meeting, Odd Erik Gundersen, a computer scientist at the Norwegian University of Science and Technology in Trondheim, reported the results of a survey of 400 algorithms presented in papers at two top AI conferences in the past few years. He found that only 6% of the presenters shared the algorithm’s code. Only a third shared the data they tested their algorithms on, and just half shared “pseudocode”—a limited summary of an algorithm. (In many cases, code is also absent from AI papers published in journals, including Science and Nature.)
A few years ago, Google created a new kind of computer chip to help power its giant artificial intelligence systems. These chips were designed to handle the complex processes that some believe will be a key to the future of the computer industry.
On Monday, the internet giant said it would allow other companies to buy access to those chips through its cloud-computing service. Google hopes to build a new business around the chips, called tensor processing units, or T.P.U.s.
“We are trying to reach as many people as we can as quickly as we can,” said Zak Stone, who works alongside the small team of Google engineers that designs these chips.