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US Senate Candidate Caught Insider Trading on Kalshi Says He Did It on Purpose
Mark Moran, an underdog Senate candidate from Virginia, claims he wanted to get caught violating the prediction market platform's rules. Kalshi announced Wednesday that it had taken action against three US politicians for violating the prediction market platform's rules on insider trading. One of the candidates, Mark Moran, a former investment banker and contestant on the reality dating show, is running a long-shot campaign for US Senate in Virginia against incumbent Mark Warner. According to Moran, getting caught was actually his plan all along: "I bet $100 on myself, not denying that, I did do it," he tells WIRED. "I wanted to see if they would enforce it."
This shoe is made entirely from mushroom 'brains'
Science This shoe is made entirely from mushroom'brains' Fungi footwear may offer a solution. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Two types of fungi were used to create the boot. Breakthroughs, discoveries, and DIY tips sent six days a week. The fashion industry is ecologically tacky, to put it mildly.
Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in GPSSMs has not been satisfactorily addressed yet: system identification, i.e., learning the model. To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network. Inference with this structure allows us to recover a posterior smoothed over sequences of data. We provide a practical algorithm for efficiently computing a lower bound on the marginal likelihood using the reparameterisation trick. This further allows for the use of arbitrary kernels within the GPSSM. We demonstrate that the learnt GPSSM can efficiently generate plausible future trajectories of the identified system after only observing a small number of episodes from the true system.
AI Tools Are Helping Mediocre North Korean Hackers Steal Millions
One group of hackers used AI for everything from vibe coding their malware to creating fake company websites--and stole as much as $12 million in three months. The advent of AI hacking tools has raised fears of a near future in which anyone can use automated tools to dig up exploitable vulnerabilities in any piece of software, like a kind of digital intrusion superpower. Here in the present, however, AI seems to be playing a more mundane, if still concerning, role in hackers' toolkit: It's helping mediocre hackers level up and carry out broad, effective malware campaigns. That includes one group of relatively unskilled North Korean cybercriminals who've been discovered using AI to carry out virtually every part of an operation that hacked thousands of victims to steal their cryptocurrency. On Wednesday, cybersecurity firm Expel revealed what it describes as a North Korean state-sponsored cybercrime operation that installed credential-stealing malware on more than 2,000 computers, specifically targeting the machines of developers working on small cryptocurrency launches, NFT creation, and Web3 projects.
New York Bans Government Employees from Insider Trading on Prediction Markets
A new executive order seen by WIRED prohibits New York state employees from using insider knowledge to enrich themselves with prediction market bets. New York has banned state employees from using insider information to trade on prediction markets . In an executive order signed today and viewed by WIRED, Governor Kathy Hochul forbade the state's government workforce from using "any nonpublic information obtained in the course of their official duties" to participate on prediction market platforms, or to help others profit using those services. "Getting rich by betting on inside information is corruption, plain and simple," Hochul said in a statement provided to WIRED. "Our actions will ensure that public servants work for the people they represent, not their own personal enrichment. While Donald Trump and DC Republicans turn a blind eye to the ethical Wild West they've created, New York is stepping up to lead by example and stamp out insider trading."
Sony AI table tennis robot outplays elite human players
In an article published today in Nature, Sony AI introduce Ace, the first robot to beat elite human players in competitive physical sport. Although AI systems have shown advanced performance in digital domains and board games (such as complex video games, chess and Go), translating this to physical performance has remained a significant challenge. Such a feat requires perception, planning, and control to work in a high-speed domain on the scale of milliseconds. Table tennis is a demanding and complex real-world test for robotics, requiring rapid decision-making, precise physical execution, and continuous adaptation to an unpredictable opponent. The ball's high speed, spin, and complex trajectories are central to competitive play.
AI-powered robot beats elite table tennis players
In feat hailed as milestone in robotics, Sony AI's Ace wins three out of five matches played under official rules An AI-powered robot has beaten elite players at table tennis in a significant achievement for a machine faced with human athletes in a real-world competitive sport. Named Ace, the robotic system developed by Sony AI, won three out of five matches against elite players, but lost the two it played against professionals, clawing back only one game in the seven contests. The feat has been hailed as a milestone for robotics, a field that has long seen table tennis - and the lightning-fast reactions, perception and skill it demands - as one of the toughest tests of how far the technology has advanced. In the matches, played under official competition rules, Ace displayed a mastery of spin, handled difficult shots, such as balls catching on the net, and pulled off one rapid backspin shot that a professional had thought impossible. A research paper on the robot was published in Nature on Wednesday, but scientists working on the project said Ace had improved since the report was submitted.