AWS introduced new services that use AI to allow more developers to apply machine learning to create better end user experiences, including new machine learning-powered enterprise search, code reviews and profiling, fraud detection, medical transcription, and human review of AI predictions. Machine learning continues to grow at a rapid clip, and today there are tens of thousands of customers doing machine learning on AWS (twice as many as the next largest cloud provider), including many customers that opt to use AWS's fully managed AI Services like Alfresco, Bayer Crop Science, Cerner, CJ Cox Automotive, C-SPAN, Deloitte, Domino's, Emirates NBD, Fred Hutchinson Cancer Research Center, FICO, FINRA, Gallup, Kelley Blue Book, Kia, Mainichi Newspapers Co, NASA, PricewaterhouseCoopers, White House Historical Association, and Zola. In the past year, AWS has introduced several new fully managed AI Services like Amazon Personalize and Amazon Forecast that allow customers to benefit from the same personalization and forecasting machine learning technology used by Amazon's consumer business to power its award-winning customer experiences. AWS customers are interested in learning from Amazon's vast experience using machine learning at scale to improve operations and deliver better customer experiences, without needing to train, tune, and deploy their own custom machine learning models. Today, AWS is announcing five new AI services that build upon Amazon's rich experience with machine learning, and allow organizations of all sizes across all industries to adopt machine learning in their enterprises – with no machine learning experience required. Despite many attempts over many years, internal search remains a vexing problem for today's enterprises, and most employees still frequently struggle to find the information they need. Organizations have vast amounts of unstructured text data, much of it incredibly useful if it can be discovered, stored in many formats and spread across different data sources (e.g.

Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just like humans, real-world AI systems have to coordinate and communicate with other agents in cooperative partially observable environments as well. These settings commonly require participants to both interpret the actions of others and to act in a way that is informative when being interpreted. Those abilities are typically summarized as theory of mind and are seen as crucial for social interactions. In this paper we propose two different search techniques that can be applied to improve an arbitrary agreed-upon policy in a cooperative partially observable game. The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy. In contrast, in multi-agent search all agents carry out the same common-knowledge search procedure whenever doing so is computationally feasible, and fall back to playing according to the agreed-upon policy otherwise. We prove that these search procedures are theoretically guaranteed to at least maintain the original performance of the agreed-upon policy (up to a bounded approximation error). In the benchmark challenge problem of Hanabi, our search technique greatly improves the performance of every agent we tested and when applied to a policy trained using RL achieves a new state-of-the-art score of 24.61 / 25 in the game, compared to a previous-best of 24.08 / 25. Introduction Real-world situations such as driving require humans to coordinate with others in a partially-observable environment with limited communication. In such environments, humans have a mental model of how other agents will behave in different situations (theory of mind). This model allows them to change their beliefs about the world based on why they think an agent acted as they did, as well as predict how their own actions will affect others' future behavior. Together, these capabilities allow humans to search for a good action to take while accounting for the behavior of others.

We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points of high dimensional non-Gaussian random functions. We obtain a rigorous explicit variational formula for the annealed complexity, which is the logarithm of the average number of critical points at fixed value of the empirical risk. This result is simplified, and extended, using the non-rigorous Kac-Rice replicated method from theoretical physics. In this way we find an explicit variational formula for the quenched complexity, which is generally different from its annealed counterpart, and allows to obtain the number of critical points for typical instances up to exponential accuracy.

Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large. We present a binarized CANDECOMP/PARAFAC(CP) decomposition algorithm, which we refer to as B-CP, where real-valued parameters are replaced by binary values to reduce model size. Moreover, we show that a fast score computation technique can be developed with bitwise operations. We prove that B-CP is fully expressive by deriving a bound on the size of its embeddings. Experimental results on several benchmark datasets demonstrate that the proposed method successfully reduces model size by more than an order of magnitude while maintaining task performance at the same level as the real-valued CP model.

This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another. When I studied Machine Learning (ML), my favorite figure among all was "The Table of Common Distributions" in Casella and Berger's 2002 book "Statistical Inference". It appeared in the book's appendix. Every time when I saw this figure, I was in awe.

"It's about saving as many lives as we possibly can," Tim Wood said. Wood spoke to Industrious en route to a meeting with USAID about its Global Health Supply Chain Program-Procurement and Supply Management project, implemented by Chemonics, a development contractor, and a consortium of partners, including IBM. Getting bed nets, HIV medication and other health supplies from medical storage facilities in Washington DC to remote parts of Africa is no small feat. But Wood, a global supply chain VP at IBM, and his GHSC-PSM consortium partners are doing just that. Global supply chains are crucial to any business or operation.

Technology, in all its guises, is changing the way we live and what exactly it means to be humans. From artificial intelligence (AI) to cryptocurrency and e-commerce, CIOs and IT leaders must ensure they are helping their organisations adapt in this rapidly changing world. In Japan, a restaurant is trialling AI robotics technology to allow employees with limited mobility to remotely pilot robotic waiters. Companies such as JPMorgan Chase, Microsoft and Ford are hosting virtual career fairs tailored to the needs of neurodiverse candidates. Enterprise Rent-A-Car has implemented braille-reader technology into its booking system for blind employees.

A new leak of highly classified Chinese government documents has uncovered the operations manual for running the mass detention camps in Xinjiang and exposed the mechanics of the region's Orwellian system of mass surveillance and "predictive policing." The China Cables, obtained by the International Consortium of Investigative Journalists, include a classified list of guidelines, personally approved by the region's top security chief, that effectively serves as a manual for operating the camps now holding hundreds of thousands of Muslim Uighurs and other minorities. The leak also features previously undisclosed intelligence briefings that reveal, in the government's own words, how Chinese police are guided by a massive data collection and analysis system that uses artificial intelligence to select entire categories of Xinjiang residents for detention. The manual, called a "telegram," instructs camp personnel on such matters as how to prevent escapes, how to maintain total secrecy about the camps' existence, methods of forced indoctrination, how to control disease outbreaks, and when to let detainees see relatives or even use the toilet. The document, dating to 2017, lays bare a behavior-modification "points" system to mete out punishments and rewards to inmates. The manual reveals the minimum duration of detention: one year -- though accounts from ex-detainees suggest that some are released sooner. Experts say the platform, which is used in both policing and military contexts, demonstrates the power of technology to help drive industrial-scale human rights abuses. The China Cables reveal how the system is able to amass vast amounts of intimate personal data through warrantless manual searches, facial recognition cameras, and other means to identify candidates for detention, flagging for investigation hundreds of thousands merely for using certain popular mobile phone apps.

In 2010, US authors in top-rated AI journals outnumbered Chinese counterparts by two to one. That ratio has now reversed. Last year, 1,073 AI experts based at Chinese universities were credited in AI journals such as the Institute of Electrical and Electronics Engineers's Transactions on Neural Networks, compared to 492 US authors. Australia and Israel also do well on this metric. When experts are ranked according to their'H-index' – a metric of productivity and the citation impact of the publications of a scientist or scholar – Americans occupy 626 of the 1,000 top spots, including all of the top ten spots at the time of our analysis. New Zealand, Saudi Arabia and Finland's AI academics are also highly ranked.