However, we have identified that the effectiveness of these rewards declines as the environmental complexity rises. Therefore, we present a novel USD algorithm, skill disco very with gui dance ( DISCO-DANCE), which (1) selects the guide skill that possesses the highest potential to reach unexplored states, (2) guides other skills to follow guide skill, then (3) the guided skills are dispersed to maximize their discriminability in unexplored states. Empirical evaluation demonstrates that DISCO-DANCE outperforms other USD baselines in challenging environments, including two navigation benchmarks and a continuous control benchmark.

Let's nitpick about the physics of Stranger Things, not its ending Feedback has seen all the fuss about the finale of Stranger Things, but would like to point out that if we're going to dissect the plot, we have bigger things to worry about In common, it seems, with a substantial fraction of the human species, Feedback spent part of our holiday watching the final episodes of Stranger Things . We laughed, we cried, we wondered if it would have even more endings than The Return of the King (it did). As is almost inevitable these days, a group of fans vocally disliked the finale, and went so far as to create a conspiracy theory about it. According to "Conformity Gate" (don't blame us, we didn't name it), the finale wasn't the real finale - despite lasting more than 2 hours, costing an enormous amount of money and being shown in cinemas. No, a super-secret final episode was going to air in January, which would reveal the true ending.

The world's fourth biggest company, Google-owner Alphabet, has announced a new £5bn ($6.8bn) investment in UK artificial intelligence (AI). The money will be used for infrastructure and scientific research over the next two years - the first of several massive US investments being unveiled ahead of US President Donald Trump's state visit. Google's President and Chief Investment Officer Ruth Porat told BBC News in an exclusive interview that there were profound opportunities in the UK for its pioneering work in advanced science. The company will officially open a vast $1bn (£735m) data centre in Waltham Cross, Hertfordshire, with Chancellor Rachel Reeves on Tuesday. The investment will expand this site and also include funding for London-based DeepMind, run by British Nobel Prize winner Sir Demis Hassabis, which deploys AI to revolutionise advanced scientific research.

Welcome back to In the Loop, TIME's new twice-weekly newsletter about the world of AI. If you're reading this in your browser, you can subscribe to have the next one delivered straight to your inbox. Let's say, sometime in the next few years, artificial intelligence automates most of the jobs that humans currently do. If that happens, how can we avoid societal collapse? This question, once the stuff of science fiction, is now very real.

Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work, we extend UDRL to the task of learning a command-conditioned generator of deep neural network policies. We accomplish this using Hypernetworks - a variant of Fast Weight Programmers, which learn to decode input commands representing a desired expected return into command-specific weight matrices. Our method, dubbed Upside Down Reinforcement Learning with Policy Generators (UDRLPG), streamlines comparable techniques by removing the need for an evaluator or critic to update the weights of the generator. To counteract the increased variance in last returns caused by not having an evaluator, we decouple the sampling probability of the buffer from the absolute number of policies in it, which, together with a simple weighting strategy, improves the empirical convergence of the algorithm. Compared with existing algorithms, UDRLPG achieves competitive performance and high returns, sometimes outperforming more complex architectures. Our experiments show that a trained generator can generalize to create policies that achieve unseen returns zero-shot. The proposed method appears to be effective in mitigating some of the challenges associated with learning highly multimodal functions. Altogether, we believe that UDRLPG represents a promising step forward in achieving greater empirical sample efficiency in RL. A full implementation of UDRLPG is publicly available at https://github.com/JacopoD/udrlpg_

Ever since Bear Blog's infancy, spam has been an issue. Free services tend to attract those seeking to exploit them for backlinks and the alleged SEO benefits (although this is debatable given updates to the Google algorithm). I've previously discussed this in a post, detailing the manual review process which has been holding up well for the past 3 years. But alas, change is upon us. Spam used to be quite easy to spot: poorly worded, low-effort paragraphs sprinkled with backlinks to products or services.

Bank of America strategists identified 20 stocks poised to benefit from the intense enthusiasm surrounding artificial technology, as a host of companies scramble to capitalize on ChatGPT's viral moment. Bank of America identified 20 stocks poised to cash in on the AI craze. Microsoft, partial owner of ChatGPT parent OpenAI, unsurprisingly headlined the picks outlined in the Tuesday note to clients, as the bank lauded the tech giant's "recent success with AI-driven offerings" and the upside the technology brings for its Bing search engine; the analysts set a $300 price target for the company's stock, indicating 20% upside. The strategists, led by Eric Lopez, also recommend buying Google-parent Alphabet, Facebook-parent Meta and Chinese Baidu, Microsoft and OpenAI's most direct competitors in the generative technology space, after each announced expansions to their respective units in recent weeks. Analysts identified American technology giants Adobe, Arista Networks, Nvidia, Palantir, and Shutterstock as firms who provide essential technology for artificial intelligence or who already use the technology in different end cases.

TWO YEARS ago, when Elinor Lobel was 16, a "smart" insulin pump was attached to her body. Powered by artificial intelligence (AI), it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is a miraculous innovation for diabetes sufferers and just one of myriad new ways that data and ai can help improve lives. Your browser does not support the audio element. Books that decry the dark side of data abound.

The continued disruption of supply chains suggests that the challenges of the COVID-19 era have been more than a blip in an otherwise stable period of global business. Supply chain professionals must consider that this instead marks the beginning of a new era of continuous disruption -- and it's time to take proactive action to prepare. Technology increasingly appears to offer promising answers to complex problems in business operations, and the supply chain is no different. Logistics professionals should explore integrating cutting-edge systems, particularly those centered around artificial intelligence, to fill gaps that the human workforce can't effectively manage. By combining human oversight and experience with the AI tools described below, leaders can better protect supply chains against current and future global challenges.