TikTok appears to have avoided a US ban at the last minute... probably. President Trump has agreed to a deal "in concept" (via CNBC) that theoretically allays US security issues while letting it operate in the country. True to earlier discussions, Oracle and Walmart would claim a 20% investment stake in a newly formed TikTok Global company that will run the social video service's business in the US and "most of the users" worldwide. Oracle would become TikTok's "secure cloud provider" and hold on to American data, while Walmart would wield its e-commerce and advertising technology. The deal will also see TikTok Global pay over $5 billion in "new tax dollars" to the US Treasury, and join with Oracle, Walmart and investors like Coatue and Sequoia to launch an AI-powered educational video curriculum. The program would teach kids basics like math, reading and science, as well as more advanced subjects like computer engineering.
President Donald Trump said Saturday he has approved a deal in principle in which Oracle and Walmart will partner with the viral video-sharing app TikTok in the U.S., allowing the popular app to avoid a shutdown. "I have given the deal my blessing -- if they get it done that's great, if they don't that's okay too," Trump told reporters on the White House South Lawn before departing for North Carolina. "I approved the deal in concept." The U.S. Department of Commerce announced it would delay the prohibition of U.S. transactions with TikTok until next Sunday. Shortly after Trump's comments, Oracle announced it was chosen as TikTok's secure cloud provider and will become a minority investor with a 12.5% stake.
Throughout the world, dust storms wreak havoc on many aspects of human life including health, aviation, solar power generation, and agriculture, among others. Given the hazards that this natural phenomena causes, it is imperative that societies are prepared for the onset of these storms to minimize economic loss and save lives. Utilizing the data received from Earth observation satellites, it is possible for atmospheric scientists to detect developing dust storms; however, even for experts, it can be difficult to detect dust storms in satellite images obscured by clouds, smoke, or nighttime conditions. Furthermore, manual detection requires atmospheric scientists to gather together the relevant satellite images, which takes time before a complete analysis can be made. The ability to automatically detect dust is potentially a large boon for the Earth science community.
Artificial intelligence can be used to diagnose cancer, predict suicide, and assist in surgery. In all these cases, studies suggest AI outperforms human doctors in set tasks. But when something does go wrong, who is responsible? There's no easy answer, says Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University. At any point in the process of implementing AI in healthcare, from design to data and delivery, errors are possible.
A brain mechanism referred to as "replay" inspired researchers at Baylor College of Medicine to develop a new method to protect deep neural networks, found in artificial intelligence (AI), from forgetting what they have previously learned. The study, in the current edition of Nature Communications, has implications for both neuroscience and deep learning. Deep neural networks are the main drivers behind the recent fast progress in AI. These networks are extremely good at learning to solve individual tasks. However, when they are trained on a new task, they typically lose the ability to solve the previously learned task completely.
The Department of Energy's first artificial intelligence director is currently reviewing more than 600 AI projects across its agencies to identify "critical" technologies worth advancing and replicating. Earlier this month, Cheryl Ingstad was named head of DOE's new Artificial Intelligence and Technology Office (AITO), intended to prioritize department resources for AI projects as the coordinating agency. The Trump administration proposed funding AITO at $5 million in fiscal 2021 -- up from $2.5 million the previous fiscal year -- but the office will be tapping into other agencies' funds as well. "They have program and project resources available," Ingstad told FedScoop in an interview. Energy has 17 national laboratories developing and applying AI to power generation, cybersecurity, national security, and accelerating scientific discoveries.
One study estimated that pharmaceutical companies spent US$2·6 billion in 2015, up from $802 million in 2003, for the development of a new chemical entity approved by the US Food and Drug Administration (FDA). N Engl J Med. 2015; 372: 1877-1879 The increasing cost of drug development is due to the large volume of compounds to be tested in preclinical stages and the high proportion of randomised controlled trials (RCTs) that do not find clinical benefits or with toxicity issues. Given the high attrition rates, substantial costs, and low pace of de-novo drug discovery, exploiting known drugs can help improve their efficacy while minimising side-effects in clinical trials. As Nobel Prize-winning pharmacologist Sir James Black said, "The most fruitful basis for the discovery of a new drug is to start with an old drug". New uses for old drugs.
Ahead of the U.S. presidential election on November 3, IBM today announced it's working with states to put information into the hands of potential voters. Using the AI and natural language processing capabilities of Watson Assistant, IBM says it's helping field voter queries online and via phone by advising people on polling place locations, voting hours, procedures for requesting mail-in ballots, and deadlines. Research from the Pew Center indicates that nearly half of all U.S. voters expect to have difficulties casting a ballot due to the coronavirus pandemic. In a recent NPR/PBS NewsHour/Marist Poll, 41% of those surveyed said they believed the U.S. is not very prepared or not at all prepared to keep November's election safe and secure. IBM's election-focused Watson Assistant offering taps Watson Discovery to surface information about voting logistics from federal, state, and county websites; local news reports; and government documents.
The chief of Iran's paramilitary Revolutionary Guard threatened Saturday to go after everyone who had a role in a top general's January killing during a U.S. drone strike in Iraq. The guard's website quoted Gen. Hossein Salami as saying, "Mr. Our revenge for martyrdom of our great general is obvious, serious and real." U.S. President Donald Trump warned this week that Washington would harshly respond to any Iranian attempts to take revenge for the death of Gen. Qassem Soleimani, tweeting that "if they hit us in any way, any form, written instructions already done we're going to hit them 1000 times harder." The president's warning came in response to a report that Iran was plotting to assassinate the U.S. ambassador to South Africa in retaliation for Soleimani's killing at Baghdad's airport at the beginning of the year.
Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a "major, long-standing obstacle to increasing AI capabilities" by drawing inspiration from a human brain memory mechanism known as "replay." First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect--"surprisingly efficiently"--deep neural networks from "catastrophic forgetting;" upon learning new lessons, the networks forget what they had learned before. Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting. They write, "One solution would be to store previously encountered examples and revisit them when learning something new. Although such'replay' or'rehearsal' solves catastrophic forgetting," they add, "Constantly retraining on all previously learned tasks is highly inefficient and the amount of data that would have to be stored becomes unmanageable quickly."