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.

Environment Conservation Land Scientists sacrifice delicious opossums to fight Florida's invasive pythons 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. Tracking them during digestion may help curb the snake population. Breakthroughs, discoveries, and DIY tips sent six days a week. Some of Florida's opossums may soon start dying for a noble cause. A few select marsupials fitted with tracking collars may begin to lead scientists to invasive Burmese pythons () slithering through the Everglades.

This study deals with neural networks in the sense of geometric transformations acting on the coordinate representation of the underlying data manifold which the data is sampled from. It forms part of an attempt to construct a formalized general theory of neural networks in the setting of Riemannian geometry. From this perspective, the following theoretical results are developed and proven for feedforward networks. First it is shown that residual neural networks are nite dierence approximations to dynamical systems of rst order dierential equations, as opposed to ordinary networks that are static. This implies that the network is learning systems of dierential equations governing the coordinate transformations that represent the data. Second it is shown that a closed form solution of the metric tensor on the underlying data manifold can be found by backpropagating the coordinate representations learned by the neural network itself. This is formulated in a formal abstract sense as a sequence of Lie group actions on the metric bre space in the principal and associated bundles on the data manifold. Toy experiments were run to conrm parts of the proposed theory, as well as to provide intuitions as to how neural networks operate on data.

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.

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."

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

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.