As email continues to be not only an important means of communication but also an official record of information and a tool for managing tasks, schedules, and collaborations, making sense of everything moving in and out of our inboxes will only get more difficult. The good news is there's a method to the madness of staying on top of your email, and Microsoft researchers are drawing on this behavior to create tools to support users. Two teams working in the space will be presenting papers at this year's ACM International Conference on Web Search and Data Mining February 11–15 in Melbourne, Australia. "Identifying the emails you need to pay attention to is a challenging task," says Partner Researcher and Research Manager Ryen White of Microsoft Research, who manages a team of about a dozen scientists and engineers and typically receives 100 to 200 emails a day. "Right now, we end up doing a lot of that on our own."

Most business leaders and entrepreneurs today realise that artificial intelligence (AI) is essential for the growth and competitiveness of their organizations. In fact, the AI technology will allow the rate of innovation and employee productivity improvements in Asia Pacific to nearly double (1.9 times, to be precise) by 2021, according to business leaders in the Asia-Pacific (APAC) region. These were some of the findings of a study from Microsoft and IDC Asia/Pacific, "Future Ready Business: Assessing Asia's Growth Potential Through AI", which surveyed over 1,600 business leaders and over 1,580 workers across 15 markets, including Australia, China, Hong Kong, Indonesia, India, Japan, Korea, Malaysia, New Zealand, Philippines, Singapore, Sri Lanka, Taiwan, Thailand and Vietnam. Among the industries polled included agriculture, automotive, education, financial services, government, healthcare, manufacturing, retail, services and telco/media. Eight in 10 business leaders from companies with more than 250 staff agreed that AI is instrumental for their organization's competitiveness, the study said.

Most business leaders and entrepreneurs today realise that artificial intelligence (AI) is essential for the growth and competitiveness of their organizations. In fact, the AI technology will allow the rate of innovation and employee productivity improvements in Asia Pacific to nearly double (1.9 times, to be precise) by 2021, according to business leaders in the Asia-Pacific (APAC) region. These were some of the findings of a study from Microsoft and IDC Asia/Pacific, "Future Ready Business: Assessing Asia's Growth Potential Through AI", which surveyed over 1,600 business leaders and over 1,580 workers across 15 markets, including Australia, China, Hong Kong, Indonesia, India, Japan, Korea, Malaysia, New Zealand, Philippines, Singapore, Sri Lanka, Taiwan, Thailand and Vietnam. Among the industries polled included agriculture, automotive, education, financial services, government, healthcare, manufacturing, retail, services and telco/media. Eight in 10 business leaders from companies with more than 250 staff agreed that AI is instrumental for their organization's competitiveness, the study said.

After years of quiet percolation, the art world is suddenly waking up to the creative and market potential of AI-generated art. Earlier this year, the Grand Palais museum in Paris staged a show examining the medium, and this month, Christie's announced it will be auctioning off a work made by an artificial intelligence in October. Now, one of the largest contemporary commercial galleries in India, Nature Morte, has become the first mainstream gallery to take the nascent art form seriously. "Gradient Descent," on view through September 15 at the New Delhi gallery, is a group show including works created entirely by computers in collaboration with seven international artists: Harshit Agrawal, Memo Akten, Jake Elwes, Mario Klingemann, Anna Ridler, Nao Tokui, and Tom White. Gallery director Aparajita Jain tells artnet News that it couldn't afford to ignore the field of AI-made art because of how she believes it is going to impact the art world. And while she was initially shocked to find out how far AI has already come in the creative field, Jain wants to dispel the idea that it will replace artists in the same way it is replacing human workers in other fields.

As artificial intelligence (AI) and chatbots start to infiltrate the digital workplace it's been interesting to watch the emergence of the "enterprise digital assistant" concept. While "digital assistant" may conjure up cute images of robot helpers, they are effectively apps that act as an interface with other systems to aid in task completion and search. In some cases, they include a chat interface and possibly even a little machine learning thrown in for good measure. The concept is persuasive -- who wouldn't want a friendly, convenient digital assistant that works quietly in the background to help you get things done? Offering an enterprise digital assistant can tick the "we are doing something about AI" box for potential new hires, who arguably might find this attractive.

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.

This paper presents DIALOG (Digital Investigation Ontology); a framework for the management, reuse, and analysis of Digital Investigation knowledge. DIALOG provides a general, application independent vocabulary that can be used to describe an investigation at different levels of detail. DIALOG is defined to encapsulate all concepts of the digital forensics field and the relationships between them. In particular, we concentrate on the Windows Registry, where registry keys are modeled in terms of both their structure and function. Registry analysis software tools are modeled in a similar manner and we illustrate how the interpretation of their results can be done using the reasoning capabilities of ontology

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In these cases, the estimation of the actual query location is also subject to uncertainty. In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain. The algorithm employs a Gaussian process model that takes probability distributions as inputs. Theoretical results are provided for both the proposed algorithm and a conventional UCB approach within the uncertain-inputs setting. Finally, we evaluate each method's performance experimentally, comparing them to other input noise aware BO approaches on simulated scenarios involving synthetic and real data.

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms

Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, there have been few theoretical results investigating the reasons behind the improvements distributional RL provides. In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings. We prove that in many realizations of the tabular and linear approximation settings, distributional RL behaves exactly the same as expected RL. In cases where the two methods behave differently, distributional RL can in fact hurt performance when it does not induce identical behaviour. We then continue with an empirical analysis comparing distributional and expected RL methods in control settings with non-linear approximators to tease apart where the improvements from distributional RL methods are coming from.