Human communication is based on a variety of inferences that we draw from sentences, often going beyond what is literally said. While there is wide agreement on the basic distinction between entailment, implicature, and presupposition, the status of many inferences remains controversial. In this paper, we focus on three inferences of plain and embedded disjunctions, and compare them with regular scalar implicatures. We investigate this comparison from the novel perspective of the predictions of state-of-the-art large language models, using the same experimental paradigms as recent studies investigating the same inferences with humans. The results of our best performing models mostly align with those of humans, both in the large differences we find between those inferences and implicatures, as well as in fine-grained distinctions among different aspects of those inferences.

At the Warner Bros Games studio in Montreal, a 7ft Batman statue greets visitors at reception. Comics are crammed into every shelf between each desk. And rather than images of lattes and flat whites, the coffee machine proudly displays the bat symbol. So, it might surprise you to learn that the people who work here have killed Batman. For the team behind the new video game Gotham Knights, their murder was the answer to a big question: how do you do something new with a character who has been throwing the Joker in jail for 83 years? If you're thinking that Batman has been done to death, you'd be right.

Artificial intelligence and machine learning are often touted in IT as crucial tools for automated detection, response, and remediation. Enrich your defenses with finely honed prior knowledge, proponents insist, and let the machines drive basic security decisions at scale. This year's RSA Conference had an entire track dedicated to security-focused AI, while the virtual show "floor" featured no fewer than 45 vendors hawking some form of AI or machine learning capabilities. While the profile of AI in security has evolved over the past five years from a dismissible buzzword to a legitimate consideration, many question its efficacy and appropriateness -- and even its core definition. This year's conference may not have settled the debate, but it did highlight the fact that AI, ML, and other deep-learning technologies are making their way deeper into the fabric of mainstream security solutions.

Data is everywhere, and with data being so prevalent in all aspects of the world, it was only a matter of time that data, along with machine learning and artificial intelligence, would help basketball players hone their skills and better their game. The new technologies can specifically help players improve their shooting, as well as help coaches draw up plays for their team. One major technology that is aiding basketball players is Noah Basketball. It tracks shots as well as produces data on those shots, which help improve both player and team shooting statistics. It gives players data on their shot -- such as the arc and trajectory of their shot -- and gives coaches access to data that allows them to figure out from where, how often and how well their players shoot on the court.

When six-foot-four inch Marty first rolled into Stop & Shop, the robot walked into history. Social robot experts say it is among the first instance of a robot deployed in a customer environment, namely supermarkets in the Northeast. Marty rolls around the store looking for spills with its three cameras. It does take the place of the human worker, called an associate, that did the same thing, but it means the associate can do something else. Doing the walk-around of the store is seen as a mundane task.

As the DeLorean rolled to a stop and the cloud of tire smoke cleared, Jon Goh peeked out the sliver of the passenger-side window to see dozens of gathered spectators cheering and high-fiving the successful test. The crowd, and anticipation, had built throughout the afternoon, while Goh, a recent mechanical engineering Ph.D. graduate from Stanford, had been outlining a kilometer-long obstacle course in traffic cones at Thunderhill Raceway in Northern California. The sun was setting fast, but Goh and his co-pilot, another grad student named Tushar Goel, couldn't wait until morning to take a shot at the twisty course. Besides, MARTY, the driver, didn't need to see the track--it needed only GPS coordinates and the algorithms on Goh's laptop to chart its path. MARTY is a 1981 DeLorean that Goh and his colleagues at Stanford's Dynamic Design Lab converted into an all-electric, autonomous drift car.

Stanford engineers have redesigned an iconic 1981 DeLorean with a futuristic feature that Dr. Emmett Brown didn't think of –self-driving capabilities. The vehicle, referred to as MARTY, has been converted into an all-electric, self-driving machine that boasts custom suspension and onboard computers. The team designed the vintage car to also drift in a bid to develop automated vehicles that can use all of the friction between the tires in order to'avoid any accident that is avoidable within the laws of physics'. The MARTY, which stands for Multiple Actuator Research Test bed for Yaw control, was develop by a team at Stanford's Dynamic Design Lab, which first designed to car to drift – a style of driving where the car moves forward but in a sideways position. Stanford engineers have redesigned an iconic 1981 DeLorean with a futuristic feature that Dr. Emmett Brown didn't think of –self-driving capabilities.

Some areas of AI are further along in adoption than others. One of those areas is in recruiting. Already, there are companies that are marketing services to review hundreds (or thousands) of applicants and give each candidate a "score" based on multiple factors.The potential pitfall is that the output from some of these systems may have a disparate impact on a protected group. The most notable example was a system being developed (and rejected) by Amazon that did not like women. Thus, HR needs to have a seat at the table when these systems are being considered.

Technological advances such as cashless stores and apps that help fill more restaurant seats with hungry diners aren't the only latest trends. Some of the latest innovations we've seen at Foodable are introducing technological advances, like robots, to the grocery store space. Grocery chain Stop and Shop, is partnering with mobile market startup Robomart to bring a new method of grocery delivery to Boston this Spring. Instead of having customers order their groceries and deliver them to the door, customers will be able to order a remote-operated Robomart vehicle to their door via an app and pick out their own produce from a pre-stocked vehicle. Another way technology is becoming more prevalent in the grocery store space is shown by Giant Food Stores.

A common refrain in the media is that people don't like their boss and people are scared of robots. So I wondered about the truth and nuance to these emotions: how many people would prefer a robot to their boss? The old saying goes, "People join a company, but they leave a bad boss." As Gallup research demonstrates, 70% of how we feel about work--our emotional commitment--is driven by who our manager is. The ongoing employee engagement crisis is largely about managers who know how to manage tasks, but don't know how to lead people. And there is also growing press coverage about automation and the emergence of robots in the workplace.