Boat shoes and slippers often have exactly the same shape as certain loafers. The same can be seen for the attribute predictions, but as explained in Appendix A.3, the labels here

Breakthroughs, discoveries, and DIY tips sent every weekday. Fine artist Laura Shape uses quite an unexpected medium in her visual artwork. It lends striking patterns to her abstract canvases, while helping restore rivers, reefs, and wetlands. Shape uses the leather of invasive species--specifically lionfish, carp, and Burmese pythons. "I use those materials to make vibrant, textured, abstract acrylic pieces," she tells Popular Science via video call.

According to legend, Singer Vehicle Design founder and executive chairman Rob Dickinson was a young boy the first time his dad pointed out a Porsche 911. Dickinson turned that passion into a multi-million dollar business, reimagining classic Porsche models with his own twist. To be perfectly clear, Singer is not sponsored, approved, endorsed by, or in any way associated or affiliated with Porsche. Customers bring their own 911 to the Singer shop--not just any old 911, but an air-cooled 964 version model from 1989-1994--for a complete makeover. The cars are completely disassembled and modified around the original chassis with a process driven by Singer's obsessive attention to detail.

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.

In May, Sydney Leathers confessed to her tens of thousands of Twitter followers that she was smitten. Where'd she meet the guy? Not on a dating app, or through friends, but in the last place she ever expected to find a real connection: her mentions. "Still can't believe I fell in love with one of my reply guys. Apparently, things had progressed since December, when she last posted about him: "I had sex with someone who started as my reply guy and I hope this doesn't inspire confidence in the rest of you because frankly your replies are not that good," she wrote. Leathers is a writer, adult performer, and startup employee whose name you may recognize from her part in the Anthony Weiner sexting scandal--this wasn't exactly her first brush with online flirtation. But it was her first time falling for a reply guy, or someone who was, effectively, a fan. The term "reply guy" emerged on Twitter about five years ago to describe the behavior of a certain subset of people, usually with very few social media followers of their own, who stake out space in the mentions of prominent users. They can be counted on to reply promptly and frequently to the tweets of whomever they've chosen as their object of devotion, and they often seek attention by nitpicking, mansplaining, joke one-upping, and harassing them. Because of this, reply guys--who can also be girls, or people of any gender--are generally understood to be pathetic creatures, without a chance in hell of getting said person to like their replies, much less return their affections. So the revelation that this gambit actually worked for someone is … pretty noteworthy. Reply guy success stories may be happening more than we realize. Abby, a 25-year-old in Brooklyn who runs a meme page on Instagram with several thousand followers, told me that she got frisky with one of her reply guys last year. "I'm not the only person that I know that has hooked up with reply guys," she said. "It's not as uncommon as you might think." Now, Leathers' Twitter feed is a monument to her relationship, by turns adorable and lewd. "This definitely caught me by surprise," she told me. "But it's been the best, happiest relationship I've had." To attain this goal, a reply guy's first challenge is to stand out from the crowd. The meme account Abby is the admin for is about politics, so she likes when a guy can show not just that he's hot, but that they share a political sensibility. "I have to be attracted to them," she said. "And they have to have some sort of compelling thing to say." "I feel like I've never more than mildly acknowledged a reply guy before now," she said. "I generally don't even follow them back." But when her now-boyfriend started responding to her tweets last year after discovering her through a winding path that involved the singer of the band Eve 6, she took notice. "I'd seen him reply to my stuff a few times.

The ingrained principles of fairness in a dialogue system's decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. For example, misusing pronouns in a user interaction may cause ambiguity about the intended subject. Yet, there is no comprehensive study of equitable text generation in dialogue. Aptly, in this work, we use theories of computational learning to study this problem. We provide formal definitions of equity in text generation, and further, prove formal connections between learning human-likeness and learning equity: algorithms for improving equity ultimately reduce to algorithms for improving human-likeness (on augmented data). With this insight, we also formulate reasonable conditions under which text generation algorithms can learn to generate equitable text without any modifications to the biased training data on which they learn. To exemplify our theory in practice, we look at a group of algorithms for the GuessWhat?! visual dialogue game and, using this example, test our theory empirically. Our theory accurately predicts relative-performance of multiple algorithms in generating equitable text as measured by both human and automated evaluation.

Mobiltech is an AI-based startup, that has succeeded in developing a 3D scanner that can replicate spatial information to form a highly detailed copy. This is used to navigate self-driving vehicles. This new company uses energy epicycle technology to recycle EV batteries after they have been used, sourcing new energy. The creators of "AIWORKS", a platform that collects and processes data from crowd sourcing using AI. Their service leans on 3D data processing and evolved computer vision.