Medical researchers are employing AI to search through databases of known drugs to see if any can be associated with a treatment for the new COVID-19 coronavirus. An early success story comes from BenevolentAI of London, which using tools developed to search through medical literature, identified rheumatoid arthritis drug baricitinib as a possible treatment for COVID-19. In a pilot study at the end of March, 12 adults with moderate COVID-19 admitted to the hospital in either Alessandria or Prato, Italy, received a daily dose of baricitinib, along with an anti-HIV drug combination of lopinavir and ritonavir, for two weeks. Another study group of 12 received just lopinavir and ritonavir. After their two-week treatment, the patients who received baricitinib had mostly recovered, according to a recent account in The Scientist.
This June, 2020, NASA announced that intelligent computer systems will be installed on space probes to direct the search for life on distant planets and moons, starting with the 2022/23 ESA ExoMars mission, before moving beyond to moons such as Jupiter's Europa, and of Saturn's Enceladus and Titan. "This is a visionary step in space exploration." said NASA researcher Victoria Da Poian. "It means that over time we'll have moved from the idea that humans are involved with nearly everything in space, to the idea that computers are equipped with intelligent systems, and they are trained to make some decisions and are able to transmit in priority the most interesting or time-critical information". "When first gathered, the data produced by the Mars Organic Molecule Analyzer (MOMA) toaster-sized life-searching instrument will not shout out'I've found life here', but will give us probabilities which will need to be analyzed," says Eric Lyness, software lead in the Planetary Environments Lab at NASA Goddard Space Flight Center. "We'll still need humans to interpret the findings, but the first filter will be the AI system".
The graph represents a network of 3,936 Twitter users whose tweets in the requested range contained "#iot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 11 August 2020 at 21:01 UTC. The requested start date was Tuesday, 11 August 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 1-hour, 6-minute period from Sunday, 09 August 2020 at 22:54 UTC to Tuesday, 11 August 2020 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
In a win for transparency, a state court judge ordered the California Department of Corrections and Rehabilitation (CDCR) to disclose records regarding the race and ethnicity of parole candidates. This is also a win for innovation, because the plaintiffs will use this data to build new technology in service of criminal justice reform and racial justice. In Voss v. CDCR, EFF represented a team of researchers (known as Project Recon) from Stanford University and University of Oregon who are attempting to study California parole suitability determinations using machine-learning models. This involves using automation to review over 50,000 parole hearing transcripts and identify various factors that influence parole determinations. Project Recon's ultimate goal is to develop an AI tool that can identify parole denials that may have been influenced by improper factors as potential candidates for reconsideration.
Trail cameras are automatically triggered by animal movements. They are used by ecologists and wildlife managers around the world to study wild animal behavior and help preserve endangered species. I want to see if MATLAB image processing and deep learning can be used to identify individual animal species that visit trail cameras. We are going to start with off-the-shelf functionality--nothing specialized for this particular task. My partners on this project are Heather Gorr and Jim Sanderson. Heather is a machine learning expert at MathWorks.
Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startup to scale its technology and team as it expands its portfolio of joint ventures with researchers at the University of Toronto, Duke University School of Medicine, Charles River, Bayer, Eli Lilly, Merck, and others. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America. Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years.