Novel categories names:['High_Jump', 'Front_Crawl', 'Pole_V ault', 'Hammer_Throw', All experiments are conducted under the 16-shot setting. An incremental bayesian approach tested on 101 object categories. Conditional prompt learning for vision-language models.

Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant actions, yet they fail to provide quantitative and reliable evidence to determine redundancy. In this paper, we propose a method to improve exploration efficiency by estimating the causal effects of actions. Unlike prior methods, our approach offers quantitative results regarding the causality of actions for one-step transitions. We first pre-train an inverse dynamics model to serve as prior knowledge of the environment. Subsequently, we classify actions across the entire action space at each time step and estimate the causal effect of each action to suppress redundant actions during exploration. We provide a theoretical analysis to demonstrate the effectiveness of our method and present empirical results from simulations in environments with redundant actions to evaluate its performance. Our implementation is available at https://github.com/agi-brain/cee.git.

If you're shopping for yourself or checking off a holiday wishlist, these Black Friday deals under $50 can help you get it done without breaking the bank. There are popular kitchen gadgets and cookware deals, plenty of toys on sale, and even a streaming discount that'll make binge-watching your favorite shows next year a more budget-friendly experience. Check out our top picks for Black Friday deals under $50 and keep more cash in your pocket this holiday. A smart speaker with bonafide benefits, like high-quality sound, voice recognition, and hands-free Alexa control, the Amazon Echo (4th Gen) can make all your smart home dreams come true. It's easy to set up and can control compatible lights, locks, and sensors, acting as a smart hub.

Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrate with symbolic knowledge, and often efficient, but requires an up-front logical domain specification and is sensitive to noise; RL only requires specification of rewards and is robust to noise but is sample inefficient and not easily supplied with external knowledge. We propose an integrative approach that combines high-level planning with RL, retaining interpretability, transfer, and efficiency, while allowing for robust learning of the lower-level plan actions. Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP). Options are learned by adding intrinsic rewards to encourage consistency between the MDP and AIP transition models. We demonstrate the benefit of our integrated approach by comparing the performance of RL and HRL algorithms in both MiniGrid and N-rooms environments, showing the advantage of our method over the existing ones.

Our homes are, as comedian George Carlin put it, just a place for our stuff. But what if, asks a new generation of startups, all that stuff could just…disappear? Inventors, architects and designers all over the world have lately converged on ways to do just that. Their technology can make parts of apartments and homes, and all their contents, slide out of view at the touch of a button. Former researchers at the Massachusetts Institute of Technology, ex-Apple and Tesla engineers toiling in San Francisco and a design and architectural firm in Spain are among those devising what can only be described as robotic furniture.

The concept of AI has been around for many decades. British mathematician Alan Turing proposed in 1950 that it might be possible for machines to use information to reason, solve problems, and make decisions. His framework is the basis of the Turing Test, which says an AI system learns until indistinguishable from a human being in its ability to hold a conversation. In 1956, a team presented proof of concept on AI at the Dartmouth Summer Research Project on Artificial Intelligence. Also in the 1950s, a group of researchers at Massachusetts Institute of Technology (MIT) began work that would become the MIT Computer Science and Artificial Intelligence Laboratory.

Robot vacuums typically fall into one of two categories: pricey premium models that come with cool features like app control, mapping navigation, and smart-home support, and budget-minded models that that, well, don't. The reasonably priced Deebot N79S bridges that chasm a bit by offering a simple but elegant app experience and Alexa-enabled voice control. The N79S sticks pretty close to the blueprint of other Deebot robot vacuums we've tested: a simple circular design--13.9 inches in diameter in this case--in a modest black color. The Auto button and Wi-Fi indicator sit on the brushed-metal top and there's a power switch on the side. Underneath are a single roller brush, two spin brushes, a pair of treads and a nose wheel.

One of IBM's first partners Harman will demonstrate Watson Assistant at the event through a digital cockpit aboard a Maserati GranCabrio, though the companies didn't elaborate on what it can do. In fact, IBM already released a Watson-powered voice assistant for cybersecurity early last year. You'll be able to access Watson Assistant via text or voice, depending on the device and how IBM's partner decides to incorporate it. So, you'll definitely be using voice if it's a smart speaker, but you might be able to text commands to a home device. Speaking of commands, it wasn't just designed to follow them -- it was designed to learn from your actions and remember your preferences.

With less than 72 hours until its release, the first HomePod reviews are in from a hand-picked group of media outlets, and they're very positive--as long as you're buying Apple's $349 smart speaker for sound quality. The Homepod's "smarts," though, leave much to be desired. That's not a total surprise, since Siri is woefully inadequate on other devices and Apple hadn't given us any indication that it has enhanced Siri for HomePod. But in action it's even worse than we expected. Brian X. Chen of The New York Times laments Siri's capabilities on the new speaker and concludes that "Siri doesn't even work as well on HomePod as it does on the iPhone."

The Fifth Annual AAAI Mobile Robot Competition and Exhibition was held in Portland, Oregon, in conjunction with the Thirteenth National Conference on Artificial Intelligence. The competition consisted of two events: (1) Office Navigation and (2) Clean Up the Tennis Court. The first event stressed navigation and planning. The second event stressed vision sensing and manipulation. In addition to the competition, there was a mobile robot exhibition in which teams demonstrated robot behaviors that did not fit into the competition tasks.