A perfect day in Los Angeles starts with a stroll along the Venice Beach boardwalk. After that, Beverly Hills, then Hollywood to see the Walk of Fame, then Griffith Park for a hike, then Chinatown for dim sum, then downtown, perhaps to catch an evening show at the Walt Disney Concert Hall. Or at least, that's what a chatbot thinks a "perfect day" is. This agenda was custom-made for me by Microsoft Copilot after I told it I had one day in town to explore the sights and asked it to plan accordingly. Here's a jam-packed 24-hour itinerary," Copilot responded, before rattling off an eight-part answer. What I didn't tell Copilot is that I already live here--and know that such an itinerary is perfect only if your idea of bliss is spending most of the day traversing one of the country's most sprawling, traffic-clogged cities, frantically popping from landmark to landmark. I asked Copilot to make me a travel itinerary because Microsoft has trotted it out as an example of how people can use the ChatGPT-like assistant. It can supposedly help you pick a destination, compare flight prices, and settle on attractions that are "popular with tourists--or just a little more off the beaten path." Of all the things you might ask a chatbot, AI companies love to suggest you ask for help planning upcoming travel. Open up ChatGPT and you might see this hypothetical prompt: "Plan a trip to see the best of New York in 3 days." Google's Gemini chatbot offers similar ones. Meta's line of chatbot assistants on Instagram and Facebook includes "Lorena," your own personal travel expert. And Rabbit, the company behind a new AI gadget, pulled out the travel example for a keynote video last month. If one were to play AI-marketing bingo, "trip itinerary" would get crossed off basically every time. More than a year into the generative-AI revolution, companies so frequently suggest that people use their tools in this way that you'd think chatbots would excel at it. In theory, chatbots that can instantaneously create travel plans are a marketer's dream. The use case is easy to understand: Planning a vacation can be a real challenge for people. First, it involves toggling among flight listings, hotel availability, and ticketing websites for major attractions. Then, it requires more nuanced research, to figure out which local restaurants are actually good and which are overpriced tourist scams, or what time to set off for a big hike that won't leave you in the woods after sunset. Most of this travel information already lives on the internet or in books, meaning that it has likely already been incorporated into a chatbot's training data. "There are probably thousands of places on webpages that describe a trip to Boston," Kathleen Creel, a professor of philosophy and computer science at Northeastern University, told me. There's people on Reddit talking about living in Boston and what they like."

A whole bunch of factors have coalesced into a perfect storm of terribleness for support players. The competitive ladder is a hot mess. The rank reset resulted in a Wild West situation, with Grandmaster players placed alongside Golds. Support players are being shuffled into games with huge rank disparities -- possibly because there aren't enough supports queuing, leaving the matchmaking algorithm to cobble together whatever matches it can. On top of that, the loss of a second tank in the move from a 6v6 to a 5v5 format means it's much easier for opponents to dive on an enemy or punish a sloppy play.

AI needs to expand horizons. With the endless promotion of artificial intelligence by analysts, media, and vendors, one can be forgiven for assuming AI is proliferating across and running enterprises far and wide. However, the reality is beyond simply automating narrow applications -- such as credit scoring, upselling recommendations, chatbots, or managing machine performance -- AI still has a limited range, and barely begun to achieve its full potential as a true augment to human intelligence and talent. That's the takeaway from recent panel discussion hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School. "I am not that excited about the narrow applications of AI," says Sadun. "I'm not excited about the dumb approach of automating one process and them claiming that you are on a different tier of technology."

Rudi Asseer tells Inc. that his Nashville-based supply chain services company, IMI Material Handling Logistics, saves more than 42 hours per job posting thanks to A.I. To optimize those tools, Asseer recommends employers use them in a way that emulates the company's tone and voice if using A.I. to communicate with its workforce. The reason: It maintains consistency in communications but also helps broadcast what you're company is all about. That can contribute to better fit and happier employees who want to stay put.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In January 2020, OpenAI laid out the scaling law of language models: You can improve the performance of any neural language model by adding more training data, more model parameters, and more compute. Since then, there has been an arms race to train ever larger neural networks for natural language processing (NLP). And the latest to join the list is AI21 with its 178 billion parameter model. Before this, Amnon founded Mobileye, the NYSE-listed self-driving tech company that Intel acquired for $15.4 billion.

Most of those efforts haven't died, but the hype has faded considerably. Apple seemed to make the biggest reversal, reportedly laying off more than 200 workers last year from its autonomous-car effort called Project Titan. Google is still at it, with its Waymo car venture now offering a highly limited taxi service in Phoenix. But Waymo remains buried in parent company Alphabet Inc.'s GOOG -0.06% "other bets" segment, where it doesn't appear to be generating much actual business. The company's most recent quarterly filing said Other Bets revenue is still derived primarily from its broadband service once known as Google Fiber and licensing from its Verily Life Sciences venture. Intel, meanwhile, hasn't exactly revved up with Mobileye.

By now, everyone who is familiar with artificial intelligence knows its immense applications in healthcare. Making its way through one's body, AI has now reached the human being's mouth. AI already demands a certain amount of data to work better. Now, apart from data like fingerprints, retina scans or X-rays the AI will now have the chance to look at your teeth and everything inside your mouth using the smart toothbrush. This AI-based system will let the user know that there is something stuck in between their teeth, by telling the user that they missed a spot.

Autonomous vehicles will open up a wide range of benefits by removing the human from the driver's seat. They free up valuable time, enabling us to be more productive during trips or they can complete deliveries and other services 24 hours a day, seven days a week. While a range of different sensors and high performance AI supercomputers running redundant and diverse algorithms will safely pilot the vehicle, some tricky obstacles may pop up during neighborhood or delivery driving. A new construction site may not appear on a robotaxi's map, and a detour may not be readily apparent. A moving truck may block an autonomous delivery vehicle's path, and it can't see a way around.

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. In his latest book, The Book of Why: The New Science of Cause and Effect, he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is. Three decades ago, a prime challenge in artificial-intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria.

A few days after the crash, Gray told a safety inspector with the Federal Aviation Administration that the crash was his fault, according to court documents. Gray said lack of feeling on his right side because of the prosthetic leg caused the leg to become stuck on the aircraft's brake, causing it to spin out on landing, prosecutors said.