Amazon on Friday announced it has agreed to acquire the vacuum cleaner maker iRobot for approximately $1.7 billion, scooping up another company to add to its collection of smart home appliances amid broader concerns about its market power. The move is part of Amazon's bid to own part of the home space through services and accelerate its growth beyond retail, said Neil Saunders, managing director at GlobalData Retail. A slew of home-cleaning robots adds to the company's tech arsenal, making it more involved in consumer's lives beyond static things like voice control. Amazon's Astro robot, which helps with tasks like setting an alarm, was unveiled last year at an introductory price of $1,000. But its rollout has been limited and has received a lackluster response.

Amazon on Friday announced it has agreed to acquire the vacuum cleaner maker iRobot for approximately $1.7 billion, scooping up another company to add to its collection of smart home appliances amid broader concerns about its market power. The move is part of Amazon's bid to own part of the home space through services and accelerate its growth beyond retail, said Neil Saunders, managing director at GlobalData Retail. A slew of home-cleaning robots adds to the company's tech arsenal, making it more involved in consumer's lives beyond static things like voice control. Amazon's Astro robot, which helps with tasks like setting an alarm, was unveiled last year at an introductory price of $1,000. But its rollout has been limited and has received a lackluster response.

Amazon announced it has agreed to acquire the vacuum cleaner maker iRobot for approximately $1.7bn, scooping up another company to add to its collection of smart home appliances amid broader concerns about its market power. The acquisition, announced on Friday, is part of Amazon's bid to own part of the home space through services and accelerate its growth beyond retail, said Neil Saunders, managing director at GlobalData Retail. The appliance would join the voice assistant Alexa, the Astro robot and Ring security cameras and others in the list of smart home features offered by the Seattle-based e-commerce and tech giant. So far, Amazon has not had much success with household robots. The company's Astro robot, which helps with tasks like setting an alarm, was unveiled last year at an introductory price of $1,000.

An iRobot Terra lawn mower is shown in Bedford, Mass., on Jan. 16, 2019. Amazon on Friday announced an agreement to acquire iRobot for approximately $1.7 billion. An iRobot Terra lawn mower is shown in Bedford, Mass., on Jan. 16, 2019. Amazon on Friday announced an agreement to acquire iRobot for approximately $1.7 billion. NEW YORK -- Amazon on Friday announced it has agreed to acquire the vacuum cleaner maker iRobot for approximately $1.7 billion, scooping up another company to add to its collection of smart home appliances amid broader concerns about its market power.

Patent data is an important source of knowledge for innovation research, while the technological similarity between pairs of patents is a key enabling indicator for patent analysis. Recently researchers have been using patent vector space models based on different NLP embeddings models to calculate the technological similarity between pairs of patents to help better understand innovations, patent landscaping, technology mapping, and patent quality evaluation. More often than not, Text Embedding is a vital precursor to patent analysis tasks. A pertinent question then arises: How should we measure and evaluate the accuracy of these embeddings? To the best of our knowledge, there is no comprehensive survey that builds a clear delineation of embedding models' performance for calculating patent similarity indicators. Therefore, in this study, we provide an overview of the accuracy of these algorithms based on patent classification performance and propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. In a detailed discussion, we report the performance of the top 3 algorithms at section, class, and subclass levels. The results based on the first claim of patents show that PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level. According to the first results, the performance of the models in different classes varies, which shows researchers in patent analysis can utilize the results of this study to choose the best proper model based on the specific section of patent data they used.

We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independently train subparts of a new class of LLMs on different subsets of the data, eliminating the massive multi-node synchronization currently required to train LLMs. BTM learns a set of independent expert LMs (ELMs), each specialized to a different textual domain, such as scientific or legal text. These ELMs can be added and removed to update data coverage, ensembled to generalize to new domains, or averaged to collapse back to a single LM for efficient inference. New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use. Experiments show that BTM improves in- and out-of-domain perplexities as compared to GPT-style Transformer LMs, when controlling for training cost. Through extensive analysis, we show that these results are robust to different ELM initialization schemes, but require expert domain specialization; LM ensembles with random data splits do not perform well. We also present a study of scaling BTM into a new corpus of 64 domains (192B whitespace-separated tokens in total); the resulting LM (22.4B total parameters) performs as well as a Transformer LM trained with 2.5 times more compute. These gains grow with the number of domains, suggesting more aggressive parallelism could be used to efficiently train larger models in future work.

This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitude corrections; since a human needs to carefully choose the magnitude of each correction, those methods can easily lead to over-corrections and learning inefficiency. The proposed method only requires human directional corrections -- corrections that only indicate the direction of an input change without indicating its magnitude. We only assume that each correction, regardless of its magnitude, points in a direction that improves the robot's current motion relative to an unknown objective function. The allowable corrections satisfying this assumption account for half of the input space, as opposed to the magnitude corrections which have to lie in a shrinking level set. For each directional correction, the proposed method updates the estimate of the objective function based on a cutting plane method, which has a geometric interpretation. We have established theoretical results to show the convergence of the learning process. The proposed method has been tested in numerical examples, a user study on two human-robot games, and a real-world quadrotor experiment. The results confirm the convergence of the proposed method and further show that the method is significantly more effective (higher success rate), efficient/effortless (less human corrections needed), and potentially more accessible (fewer early wasted trials) than the state-of-the-art robot learning frameworks.

Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions. Accordingly,enterprises are increasingly embracing this dynamic technology. A 2022 global study by IBM found that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a 2021 PwC study the COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises' impact on workforce planning, supply chain resilience, and demand projection.

Media expressed in the purely synthetic form will radically accelerate the process of content creation and delivery. Its accessibility and interactivity will usher in an exciting new era of digital media, one in which creativity, insight, and imagination determine content dissemination instead of the limitations imposed by physical space. Novel forms of synthetic media blur the distinction between physical and digital environments. This new creative expression category will unleash powerful user experiences built on a new dynamic relationship between media and human perception. When we are talking about this new world of AI-generated synthetic media, we are talking about a space that combines some of the most potent forces in our world: live video, visual content, and audio, along with the most advanced technology platform to drive them. Synthetic media is a new form of virtual media produced with the help of artificial intelligence (AI). It is characterized by a high degree of realism and immersiveness. Furthermore, synthetic media tends to be indistinguishable from other real-world media, making it very difficult for the user to tell apart from its artificial nature.

KUDLOW: The idea that it's inflation reduction is something out of George Orwell. By the way, the CBO came out with a preliminary scorecard, just preliminary stuff. But actually, the deficit gets worse for the next four years until it gets better after that. But, the getting better is full of gimmicks. So I don't believe a word of it.