Offering no criteria by which to define unacceptable risk for AI systems and no method to add new high-risk applications to the Act if such applications are discovered to pose a substantial danger of harm. This is particularly problematic because AI systems are becoming broader in their utility. Only requiring that companies take into account harm to individuals, excluding considerations of indirect and aggregate harms to society. An AI system that has a very small effect on, e.g., each person's voting patterns might in the aggregate have a huge social impact. Permitting virtually no public oversight over the assessment of whether AI meets the Act's requirements.

Machines don't have an IQ. This is a common mistake that some commentators make, i.e., the machine IQ will exceed human IQ at some point of time. A trivial example is how the Google Search Engine remembers everything, but still can't plan its way out of a paper bag. Turing's 1950 paper, "Computing Machinery and Intelligence" is one of the stepping stones for AI, which introduced many of the core ideas of AI, including Machine Learning (ML). The paper also proposed what we now call the Turing Test as a thought experiment, and it demolished several standard objections to the very possibility of machine intelligence.

Among the 100 updates announced at Microsoft's developer event Build this week, an area that stood out above the noise was artificial intelligence (AI) and, in particular, Microsoft's growing push into higher-level services for applied AI and business scenarios. Microsoft has been gradually embedding business logic into more of its AI in an attempt to help enterprises get out of the "pilot purgatory" that has so often characterized AI projects over the past few years. But at Build this year, it took a big leap forward. Let's take a closer look at the major moves that came out of Build and what they mean for the market. According to a survey my team conducted, more than 80% of companies are now trialling or putting AI into production in their organizations, up from 55% in 2019, and we've seen adoption accelerate dramatically in several narrow areas such as contact centres, chat bots, and fraud detection.

A manager hires a new employee, and offers to pay her $1,000 a day. She replies: "I'll do you one better. Why don't you pay me one penny on my first day, and double my pay every day from there until the month is over?" Sensing a bargain, the manager agrees. Such is the price of failing to respect exponential growth.

"Just to rub it in, a version of AlphaGO, called AlphaZero recently learned to trounce AlphaGo at Go, and also to trounce Stockfish (the world's best chess program, far better than any human) and Elmo (the world's best shongi program, also better than any human). AlphaZero did all this in one day." I was reading "Human Compatible" this week and the above anecdote got me thinking. A computer crushing Chess and Go Grandmasters is impressive and feels ominous, but what does it mean for our everyday jobs? Every year computer chips get smaller and faster (Moore's Law) and experts predict Machine Learning, AI and automation will eviscerate our jobs.

In this first article, we highlight how intelligence and rationality are tightly coupled with the uncertainty present in the world. We also discuss how uncertainty plays a critical role in designing beneficial general-purpose artificial intelligence (AI), as described by the work of Stuart Russel and Peter Norvig on Modern AI [1][2]. Human intelligence, both social and individual, is what has been driving advances achieved by the human civilization. Having access to even greater intelligence in the form of machine artificial intelligence (AI) can potentially lead to even further advances, and will help us solve major problems such as eliminating poverty and disease, solving open scientific and mathematical problems, and offering personal assistance targeting billions of people worldwide. This is subject of course to the finite resources of land and raw material available on earth.

Tomohiro Inoue, an associate professor of economics at Komazawa University, graduated from the Faculty of Environment and Information Studies at Keio University before studying at the Waseda University Graduate School of Economics. His written works include "Jinko Chino to Keizai no Mirai" (Artificial intelligence and the future of the economy). Inoue speaks about the future of jobs in the age of artificial intelligence during an interview with The Yomiuri Shimbun. To what extent will AI be able to do human jobs? Groundbreaking new technologies are coming out, and artificial intelligence is becoming quite practical.

Artificial intelligence can boost business productivity and profitability -- as well as find ways to foster human happiness, according to the director of Hitachi's artificial intelligence laboratory. Interest in AI traditionally has centered on purpose-built technology crafted for a particular reason, such as Google's AI program that last year beat a champion player at the Chinese strategy game Go, said Kazuo Yano during a 14 December presentation at AAAS headquarters. The address was part of the Hitachi lecture series, which has brought speakers to AAAS for nearly a decade to examine a wide range of issues related to science and society. Yano, who serves as Hitachi's chief corporate scientist, noted that AI increasingly is being used to address the needs of business. To cope with changing variables like customer behavior and marketplace position, businesses now can take advantage of the flexibility offered by "general-purpose AI," which can be added to existing systems, he said.

The aim of this workshop is to bring to bear on the expertise of a diverse set of researchers to progress in the evaluation of general purpose AI systems. Up to now, most AI systems are tested on specific tasks. However, to be considered truly intelligent, a system should exhibit enough flexibility to be able to learn how to perform a wide variety of tasks, some of which may not be known until after the system is deployed. This workshop will examine formalisations, methodologies and test benches for evaluating the numerous aspects of this type of general AI systems. More specifically, we are interested in theoretical or experimental research focused on the development of concepts, tools and clear metrics to characterise and measure the intelligence, and other cognitive abilities, of general AI agents.