My position feels more precarious than ever. It's a question that I sometimes toss out in the company of friends who--like me, and maybe like you--have a complicated relationship to their job. I've worked at WIRED as a writer for eight years, and with much success. Eight years is also an eternity in news media, and especially if you are Black. All industries suffer from unique growing pains. Ours just so happens to have laughably high turnover rates, a distaste for racial and gender diversity, and the dubious distinction of being perpetually on the verge of extinction. So on nights when friends and I gather, trading war stories of workplace microaggressions and corporate mismanagement under damp bar lighting, we wonder how we've lasted as long as we have. The only reason I've survived, I joke, is because I'm Black. It's a silly thing to say, particularly because I have no actual proof of it other than the occasional feeling. What I do know is that I've been The Only One in more spaces than I care to remember, and rarely by choice.

Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the transition of display advertising to first-price auctions. In this work, we propose a novel concave formulation for pure-strategy bidding in first-price auctions, and use it to analyze natural Gradient-Ascent-based algorithms for this problem. Importantly, our analysis goes beyond regret, which was the typical focus of past work, and also accounts for the strategic backdrop of online-advertising markets where bidding algorithms are deployed -- we prove that our algorithms cannot be exploited by a strategic seller and that they incentivize truth-telling for the buyer. Concretely, we show that our algorithms achieve $O(\sqrt{T})$ regret when the highest competing bids are generated adversarially, and show that no online algorithm can do better. We further prove that the regret improves to $O(\log T)$ when the competition is stationary and stochastic. Moving beyond regret, we show that a strategic seller cannot exploit our algorithms to extract more revenue on average than is possible under the optimal mechanism, i.e., the seller cannot do much better than posting the monopoly reserve price in each auction. Finally, we prove that our algorithm is also incentive compatible -- it is a (nearly) dominant strategy for the buyer to report her values truthfully to the algorithm as a whole.

Imagine having a digital twin that gets ill, and can be experimented on to identify the best possible treatment, without you having to go near a pill or a surgeon's knife. Scientists believe that within five to 10 years, "in silico" trials – in which hundreds of virtual organs are used to assess the safety and efficacy of drugs – could become routine, while patient-specific organ models could be used to personalise treatment and avoid medical complications. Digital twins are computational models of physical objects or processes, updated using data from their real-world counterparts. Within medicine, this means combining vast amounts of data about the workings of genes, proteins, cells and whole-body systems with patients' personal data to create virtual models of their organs – and eventually, potentially their entire body. "If you practise medicine today, a lot of it isn't very scientific," said Prof Peter Coveney, the director of the Centre for Computational Science at University College London and co-author of Virtual You.

Deep fake videos of President Joe Biden and Republican frontrunner Donald Trump highlight how the 2024 presidential race could be the first serious test of American democracy's resilience to artificial intelligence. Videos of Biden dressed as trans star Dylan Mulvaney promoting Bud Light and Trump teaching tax evasion inside a quiet Albuquerque nail salon show that not even the nation's most powerful figures are safe from AI identity theft. Experts say that while today it is relatively easy to spot these fakes, it will be impossible in the coming years because technology is advancing at such a fast pace. There have already been glimpses of the real-world harms of AI. Just earlier this week, an AI-crafted image of black smoke billowing out of the Pentagon sent shockwaves through the stock market before media factcheckers could finally correct the record.

Ready or not, the AI revolution is upon us and one of its most immediate impacts is the emergence of chatbots like ChatGPT. "It will be a boon to the societies that pick this up," said junior student leader and president of the Metropolitan State University of Denver Chess Club Paul Nelson. Nelson is talking about ChatGPT and its rapid emergence on college campuses throughout the U.S. One educated at MSU Denver said the first time he heard of the chatbot was in November and now, four months later, it's a part of almost every conversation he has. "My first reaction when I first saw ChatGPT was, 'Oh my God. We are in trouble,'" said Dr. David Merriam, assistant professor of biology.

"By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated." This is a prediction from Gartner that you will find in almost every single article, deck or press release related to synthetic data. We are repeating this quote here despite its ubiquity because it says a lot about the total addressable market of synthetic data. Let's unpack: First, describing synthetic data that is "synthetically generated" may seem tautologic, but it is also quite clear: We are talking about data that is artificial/fake and created, rather than gathered in the real world. Next, there's the core of the prediction -- that synthetic data will be used in the development of most AI and analytics projects.

We describe an incremental and adaptive approach to integrating hierarchical task network planning and constraint-based scheduling. The approach is grounded in the concept of approximating the'resource intensity' of planning options. A given planning problem is decomposed into a sequence of (not necessarily independent) subtasks, which are planned and then scheduled in turn. During planning, operators are rated according to a heuristic estimate of their expected resource requirements. Options are selected that best match a computed'target intensity' for planning. Feedback from the scheduler is used to adapt the target intensity after completion of each subplan, thus guiding the planner toward solutions that are tuned to resource availability.

This paper proposes a method for the development of AI for autonomous agents in game worlds modeled by fixed regular grids. This approach uses context behaviors driven by distance maps to support multiple agents in a dynamic environment. This paper introduces the notion of hierarchical distance maps which allow for higher-level goals to be easily specified by designers. We also discuss potential applications of our approach in the design and development of agents and behaviors in the block world genre.

In 2016 and 2017, chatbots were seen as one of the most important additions to the digital ecosystem, with some experts predicting that they would transform eCommerce, replace apps and save shopping malls, among myriad other capabilities. But lackluster artificial intelligence (AI) led most companies to put their plans on the backburner. Also see: Can Chatbot Concierges Save The Mall? Less than five years later, though, AI has advanced enough that some eCommerce executives are willing to give it another shot, looking at platforms such as Messenger and WhatsApp as the next step to reaching consumers. "The messaging ecosystem is giant -- lots of channels depending on where you are the world and what market you're in -- and eCommerce is shifting," Matt Ramerman, president of Sinch for Marketing, told PYMNTS in a recent conversation. "Will dot-com stores go away? No chance, but the share of balance moving toward wanting to be able to shop a catalog with a smart … AI system, and being able to shop in messaging, is the future."

A TikTok influencer has opened up about how a guy tricked her into buying 100 tacos when she went out on a date with him. Hilarious comments quickly poured in after TikToker Elyse Myers shared her "worst date ever." The now-viral clip, which she captioned, "I haven't been to a @tacobell since," was her response to a follower's question about her most disastrous dating experience, reported the New York Post. In the video, which has garnered more than 13 million views as of writing, Myers described how a man he met on a dating site messaged her out of the blue with the most unimaginable pickup line ever: "I like your face, let's go get some food." The man then asked her to drive up to his house, which she found odd.