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).
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.
Research has found most consumers have interacted with AI and would prioritise businesses with human-like implementations.
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.)