Tegmark, president of the Future of Life Institute at MIT, made this rather grandiose statement: "In creating AI [artificial intelligence], we're birthing a new form of life with unlimited potential for good or ill." A study by Sir Nigel Shadbolt and Roger Hampson entitled The Digital Ape carries the subtitle How to Live (in Peace) with Smart Machines. They are optimistic that humans will still be in charge, provided we approach the process sensibly. But is this optimism justified? The director of Cambridge University's Centre for the Study of Existential Risk said: "We live in a world that could become fraught with . . .
Researchers at two U.K. universities have developed a way to predict battery health with 10 times greater accuracy than the current industry standard. A machine learning method developed by researchers at the University of Cambridge and Newcastle University in the U.K. can predict battery health with 10 times greater accuracy than the current industry standard. The new method could help develop safer, more reliable batteries for electric vehicles and consumer electronics. The researchers trained the model by performing more than 20,000 experimental measurements. Said Cambridge's Alpha Lee, "By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance."
Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach. Now, thanks to a study that combines the power of supercomputing with data science and experimental methods, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel "design to device" approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.
The University of Cambridge is set to receive a new AI supercomputer as part of a £10 million partnership between the Engineering and Physical Sciences Research Council (EPSRC), the Science and Technology Facilities Council (STFC). The system, which is supported by Cambridge's Research Computing Service, aims to help companies to create real business value from the use of advanced computing infrastructure. The supercomputer is part of the UK government's AI Sector Deal, which involves more than 50 leading technology companies and organisations. The deal is worth almost £1 billion, including around £300 million of private sector investment in AI. 'AI research requires supercomputing capacity capable of processing huge amounts of data at very high speeds, said Dr Paul Calleja, Director of the University's Research Computing Service. 'Cambridge's supercomputer provides researchers with the fast and affordable supercomputing power they need for AI work.' UK Secretary of State for Digital, Culture, Media and Sport Matt Hancock stated: 'The UK must be at the forefront of emerging technologies, pushing boundaries and harnessing innovation to change people's lives for the better.
A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.
Stephen Hawking predicted the Earth would turn into a hothouse planet like Venus as a result of President Donald Trump's decision to pull the United States out of the Paris climate change agreement. In an interview with BBC on Sunday, the Cambridge University professor and physicist said Trump's action could lead to irreversible climate change, pushing "Earth over the brink." "We are close to the tipping point where global warming becomes irreversible," Hawking told BBC News. "Trump's action could push the Earth over the brink, to become like Venus, with a temperature of 250 degrees, and raining sulphuric acid." Since 2009, NASA has been working to discover Earth-like planets that can support life.
Research Scientist – Bayesian Machine Learning Location: Cambridge - United Kingdom The Schlumberger Gould Research Centre offers a stimulating research environment with real-world problems that push the limits of scientific knowledge. We are committed to be at the leading edge of science and to incorporate new emerging technologies in Schlumberger's activities. To achieve that we recruit the most talented scientists from a variety of scientific and engineering backgrounds, and give them opportunities to advance their fields of expertise as well as to develop solutions of significant industrial impact. The Schlumberger Gould Research Centre strongly encourages the self-development of its scientists, offers high-end experimental facilities and scientific resources, and maintains strong collaborations with academic and industrial research groups worldwide. Schlumberger is currently developing new drilling systems with a high degree of autonomy and intelligence which will change the way the industry drills wells.
How humanity will meet its end is a an endless source of fascination in science fiction. But scientists claim many of the scenarios depicted in films - such as an asteroid strike and killer robots - may not be as far fetched as you might think. Now researchers at Cambridge University's Study of Existential Risk (CESR) have come up with a list of 10 threats that may some day trigger an apocalypse. Humanity faces an uncertain future as technology learns to think for itself and adapt to its environment. Artificial Intelligence, disguised as helpful digital assistants and self-driving vehicles, is gaining a foothold and it could one day spell the end for mankind if allowed to develop without strict controls.
This year, several leading researchers have sounded warnings about the risks of using the CRISPR gene-editing technique to modify human1 and other species' genomes in ways that could have "unpredictable effects on future generations"2 and "profound implications for our relationship to nature" (see go.nature.com/jq5sik). Concerns are coming from the silicon sector as well. Last year, the physicist Stephen Hawking proclaimed that rapidly advancing artificial intelligence (AI) could destroy the human race. And in 2013, former Royal Society president Martin Rees co-founded the Centre for the Study of Existential Risk at the University of Cambridge, UK, in part to study threats from advanced AI. Leaders of the scientific community are ready to share the responsibility for these powerful technologies with the public. George Church, a geneticist at Harvard University in Cambridge, Massachusetts, and others wrote last year of CRISPR that "the decision of when and where to apply this technology, and for what purposes, will be in our collective hands".
The origins of robotics go back to the automata invented by ancient civilisations. The word robot entered our vocabulary only in 1920 with Czech writer Karel Čapek's play R.U.R (Rossum's Universal Robots). Over the past 20 years robots have been developed to work in settings that range from manufacturing industry to space. At Cambridge University, robotics is a rapidly developing field within many departments, from theoretical physics and computing to engineering and medical science. Lord Martin Rees is Emeritus Professor of Cosmology and Astrophysics at the University of Cambridge.