The last night of tech mogul Mike Lynch's life has become fodder for conspiracy theories. For the first time, the whole story can be told. In the predawn hours of August 19, 2024, bolts of lightning began to fork through the purple-black clouds above the Mediterranean. From the rail of a 184-foot vessel, a 22-year-old named Matthew Griffiths took out his phone to record a video. The British deckhand was just a week and a half into his first official yacht job, and he wasn't on just any boat. The yacht, the $40 million, was a star of the superyacht world, considered to be a feat of minimal design and precision engineering. As thunder rolled toward the anchored vessel, Griffiths set the video to AC/DC's "Thunderstruck" and posted it to Instagram. In the video, the's aluminum mast, one of the tallest in the world, is briefly visible against the roiling sky. Below deck, the yacht's owner, Michael Lynch, had every reason to be sleeping soundly. The boat trip had been organized as a celebration. Months earlier, Lynch had walked out of a San Francisco federal courthouse a free man, acquitted of all charges in one of the largest fraud cases in Silicon Valley history. Lynch had built his fortune on understanding probability, on turning the unlikely into the possible. He had named his yacht in honor of the statistical theorem that made him a billionaire, after the sale, in 2011, of his company Autonomy. The British tech giant sold software that could find meaningful signals amid the flood of unstructured data in emails, videos, and phone calls, but it would be better known as the company that allegedly defrauded, and nearly destroyed, Hewlett-Packard. The cabins aboard the contained the people who had stood by Lynch through his 13-year-long legal ordeal. Beside him in the master suite was his wife of 22 years, Angela Bacares, a former vice president in the investment division of Deutsche Bank who had caught his eye while working an Autonomy deal. Other cabins housed the Clifford Chance attorneys who had orchestrated Lynch's legal victory, as well as longtime colleagues, their partners, and a 1-year-old baby, all supported by 10 crew members. Also onboard was Lynch's younger daughter, Hannah, 18, who was about to begin her studies at Oxford.

One day last spring, in a high school classroom in Texas, students were arguing about who to kill off first. It was a thought experiment with a sci-fi premise: A global zombie outbreak has decimated major cities. One hundred frozen embryos meant to reboot humanity are safe in a bomb shelter, but the intended adult caretakers never made it. Instead, 12 random civilians stumbled in. The students had to decide who would die and who would live to raise the future of the human race.

This paper presents an evaluation of 18 children's in-the-wild experiences with the autonomous robot arm performer NED (Never-Ending Dancer) within the Thingamabobas installation, showcased across the UK. We detail NED's design, including costume, behaviour, and human interactions, all integral to the installation. Our observational analysis revealed three key challenges in child-robot interactions: 1) Initiating and maintaining engagement, 2) Lack of robot expressivity and reciprocity, and 3) Unmet expectations. Our findings show that children are naturally curious, and adept at interacting with a robotic art performer. However, our observations emphasise the critical need to optimise human-robot interaction (HRI) systems through careful consideration of audience's capabilities, perceptions, and expectations, within the performative arts context, to enable engaging and meaningful experiences, especially for young audiences.

Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included. Yet, the approaches proposed to scale up anaphoric annotation haven't so far resulted in datasets overcoming these limitations. In this paper, we introduce a new release of a corpus for anaphoric reference labelled via a game-with-a-purpose. This new release is comparable in size to the largest existing corpora for anaphoric reference due in part to substantial activity by the players, in part thanks to the use of a new resolve-and-aggregate paradigm to 'complete' markable annotations through the combination of an anaphoric resolver and an aggregation method for anaphoric reference. The proposed method could be adopted to greatly speed up annotation time in other projects involving games-with-a-purpose. In addition, the corpus covers genres for which no comparable size datasets exist (Fiction and Wikipedia); it covers singletons and non-referring expressions; and it includes a substantial number of long documents (> 2K in length).

Team-based multiplayer shooter Overwatch is getting a sequel: and interestingly for fans, it'll bring story missions into the game for the first time. According to Blizzard, it will also "redefine what a sequel means". Which is quite a claim for an online shooter. Unveiled with a crowd-pleasing cinematic trailer at annual fan convention BlizzCon last week, Overwatch 2 will introduce PvE missions in an all-new story mode, as well as a new core competitive mode, Push, a six-versus-six PvP team battle, which sees teams compete to have a robot push the map's objective to their opponent. Before now, the original 2016 first person shooter focused on PvP gameplay, with spin-off comic books and animated shorts filling in backstories for the popular crew of ragtag leads.

In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.

Searching for people to fill jobs requires a lot of research. Experience, skill sets, salary expectations, coordinating interviews, and numerous other issues must be addressed to match worker to employer. Artificial intelligence and machine learning are making all of those calculations easier, and the future of hiring will likely only get more streamlined due to those technologies. "One very specific example of this is coordinating and scheduling for incoming (job) candidates," says Dr. Andrew Chamberlain, chief economist with Glassdoor.com, "There are companies like X.AI who now offer chatbots that handle scheduling totally."

On top of the technology, Talenya hires and trains folks with experience in a particular industry to use the firm's tools and act as part-time recruiters to help screen talent to fill specific roles for Talenya's clients, which include companies like robotics firm Universal Robots, ad-tech player DoubeVerify and gift registration service, MyRegistry.com. "Ultimately, what we're trying to do is deliver three to five amazing candidates that are interested, qualified and evaluated within two weeks," says Cosentino. A firm addressing the issue of bias in the job markets is Textio, which uses language to boost the effectiveness of job posts. The company's software examines language for possible biases that may exist under the radar that could alienate certain job candidates, Chamberlain explained, identifying "overly feminine or overly masculine terms, or terms that have been associated with having trouble recruiting diverse candidates based on their past experience and their training data." Textio's programs also highlight phrases found to be attractive to job seekers, and suggests alternatives to problematic phrases to improve communication.

Searching for people to fill jobs requires a lot of research. Experience, skill sets, salary expectations, coordinating interviews, and numerous other issues must be addressed to match worker to employer. Artificial intelligence and machine learning are making all of those calculations easier, and the future of hiring will likely only get more streamlined due to those technologies. "One very specific example of this is coordinating and scheduling for incoming (job) candidates," says Dr. Andrew Chamberlain, chief economist with Glassdoor.com, "There are companies like X.AI who now offer chatbots that handle scheduling totally."

Artificial intelligence (AI) will soon impact nearly every industry in some way, but the technology is poised to make waves in one industry in particular in 2018: Human resources. A growing list of vendors in the HR sector are offering AI solutions to help HR professionals find and retain top talent, according to a recent Glassdoor report--especially in competitive fields such as tech. Vendors include Entelo, Textio, Textkernal, HiringSolved, and x.ai offer AI solutions that help recruiters sort through resumes, make predictive matches between job seekers and positions using data, correct biases in the language used in job descriptions, and use bots to schedule candidate interviews. But don't expect to see an android HR agent at your next job interview. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) "Rather than replacing HR experts, revolutionary new AI tools are complementing people's skills," wrote Andrew Chamberlain, Glassdoor's chief economist, in the report.