To use, or not to use, Bard? That is the Shakespearean question an Associated Press reporter sought to answer while testing out Google's artificially intelligent chatbot. The recently rolled-out bot dubbed Bard is the internet search giant's answer to the ChatGPT tool that Microsoft has been melding into its Bing search engine and other software. During several hours of interaction, the AP learned Bard is quite forthcoming about its unreliability and other shortcomings, including its potential for mischief in next year's U.S. presidential election. Even as it occasionally warned of the problems it could unleash, Bard repeatedly emphasized its belief that it will blossom into a force for good.

To use, or not to use, Bard? That is the Shakespearean question an Associated Press reporter sought to answer while testing out Google's artificially intelligent chatbot. The recently rolled-out bot dubbed Bard is the internet search giant's answer to the ChatGPT tool that Microsoft has been melding into its Bing search engine and other software. During several hours of interaction, the AP learned Bard is quite forthcoming about its unreliability and other shortcomings, including its potential for mischief in next year's US presidential election. Even as it occasionally warned of the problems it could unleash, Bard repeatedly emphasized its belief that it will blossom into a force for good.

Early in the pandemic, a number of researchers, startups and institutions developed AI systems that they claimed could diagnose COVID-19 from the sound of a person's cough. At the time, we ourselves were enthusiastic about the prospect of AI that could be yielded as a weapon against the virus; in one headline, we endorsed cough-scrutinizing AI as "promising." But a recent study (first reported on by The Register) suggests that some cough-analyzing algorithms are less accurate than we -- and the public -- were led to believe. It serves as a cautionary tale for machine learning tech in healthcare, whose flaws aren't always immediately apparent. Researchers from The Alan Turing Institute and Royal Statistical Society, commissioned by the U.K. Health Security Agency, conducted an independent review of audio-based AI tech as a COVID-19 screening tool.

On a May evening in 1959, C.P. Snow, a popular novelist and former research scientist, gave a lecture before a gathering of dons and students at the University of Cambridge, his alma mater. He called his talk "The Two Cultures and the Scientific Revolution." Snow declared that a gulf of mutual incomprehension divided literary intellectuals and scientists. "The non-scientists have a rooted impression that the scientists are shallowly optimistic, unaware of man's condition," Snow said. "On the other hand, the scientists believe that the literary intellectuals are totally lacking in foresight, peculiarly unconcerned with their brother men, in a deep sense anti-intellectual, anxious to restrict both art and thought to the existential moment." Snow didn't expect much of his talk.

After years of complaints that there were no rules to determine what constitutes a "sustainable" investment, investors are now fretting that there will soon be too many to navigate easily. More than 30 taxonomies outlining what is and isn't a green investment are being compiled by governments across Asia, Europe and Latin America, each one reflecting national economic idiosyncrasies that can jar with a global capital market that has seen trillions pour into sustainable funds. The European Union will introduce its green investment taxonomy, or common framework, in January to help asset managers inside the bloc and make green activities more visible and attractive to investors. The rules also aim to stamp out "green washing," whereby organizations overstate their environmental credentials. The U.K., which hosts the COP26 climate change conference from Oct. 31, is set to finalize its own taxonomy next year but has already signaled it will not just replicate what is drawn up across the channel.

The article proposes a new type of AI system that does not give solutions directly but rather points toward it, friendly prompting the user with questions and adjusting messages. Models of AI - human collaboration can be deduced from the classic literary example of interaction between Mr. Holmes and Dr. Watson from the stories by Conan Doyle, where the highly qualified expert Mr. Holmes, answers questions posed by Dr. Watson. Here Mr. Holmes, with his rule-based calculations, logic and memory management apparently plays the role of an AI system and Dr. Watson is the user. Looking into the same Holmes-Watson interaction, we find and promote another model in which the AI behaves like Dr. Watson, who, by asking questions and acting in a particular way, helps Holmes (the AI user) to make the right decisions. We call the systems based on this principle "Dr.Watson-type systems". The article describes the properties of such systems and introduces two particular - Patient Management System for intensive care physicians and Data Error Prevention System.

The press gushing over Dr. Fauci's leaked emails, Chris Mathews' accuser getting hate mail and journalist Andy Ngo saying he was beaten up by Antifa last week round out today's top media headlines Despite the fact that Democrats control the House of Representatives, the Senate and the White House, Politico's highly-read newsletters have dedicated more digital ink to former President Trump and GOP infighting on Capitol Hill. Politico Playbook, which describes itself as "The unofficial guide to official Washington," headlined 12 out of its 31 newsletters published in the month of May fixated on various Republican drama from the ouster of Rep. Liz Cheney, R-Wyo, from House leadership to Trump's potential legal woes. One newsletter additionally focused on the disgruntled staffers who formally worked for Florida Gov. Ron DeSantis. However, only 11 of the Playbook's newsletters were focused on the Biden agenda. The GOP obsession is even more severe in Politico's "Huddle" newsletter, which offers a "play-by-play preview of the day's congressional news."

A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. A black hole permanently scrambles information that can't be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment. A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling. "Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes," said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters. "Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results don't condemn quantum machine learning, but rather highlight the importance of understanding its limits," Holmes said.

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling. "Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes," said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published today in Physical Review Letters. "Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results don't condemn quantum machine learning, but rather highlight the importance of understanding its limits," Holmes said. In the classic Hayden-Preskill thought experiment, a fictitious Alice tosses information such as a book into a black hole that scrambles the text. Her companion, Bob, can still retrieve it using entanglement, a unique feature of quantum physics.