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.
Oxford Insights prepare and published Government Artificial Intelligence Readiness Index 2019 where author analyzed word prepare to AI in different countries.
Artificial intelligence (AI) technologies are forecast to add US$15 trillion to the global economy by 2030. According to the findings of our Index and as might be expected, the governments of countries in the Global North are better placed to take advantage of these gains than those in the Global South. There is a risk, therefore, that countries in the Global South could be left behind by the so-called fourth industrial revolution. Not only will they not reap the potential benefits of AI, but there is also the danger that unequal implementation widens global inequalities.
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).
Bias and prejudice remains a serious issue across many societies, take away human input and the result could be disastrous.
IBM is stepping in with a tool it calls ‘Fairness 360’ which scans for signs of bias in algorithms to recommend adjustments on how to correct them.
AIs already have a documented bias problem. It’s rarely intentional, but typically a result of their developers coming from the predominant part of each society.
Take facial recognition software, for example.
DARPA shows it’s not just about creepy robots with a $2 billion funding announcement for various AI projects over the next five years.
The so-called ‘Defense Advanced Research Projects Agency’ researches a range of innovative new technologies. Many of these advancements will have an impact beyond defence.
As such, DARPA’s funding initiative will be open to applications beyond the defence community. That means any AI project you’re involved with could be in the running to receive a nice cash boost.
The research, from Capgemini’s Digital Transformation Institute, found close to three-quarters (73 percent) of consumers have interacted via AI.
Satisfaction with those who have experienced AI interactions is slightly lower, at 69 percent. Over two-thirds satisfaction is quite surprisingly high, especially when you consider how dissatisfied people typically are with traditional automated systems.
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.