Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this work are as follows: (1) we motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (2) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate a higher prediction performance in comparison to traditional machine learning and thus direct value gains. (3) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (4) We finally discuss directions for future research in the field of business analytics.

Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in singlemachine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach. The source code is publicly available at github.com/deepmind/scalable

Epistemic logics are a primary formalism for multi-agent systems but major reasoning tasks in such epistemic logics are intractable, which impedes applications of multi-agent epistemic logics in automatic planning. Knowledge compilation provides a promising way of resolving the intractability by identifying expressive fragments of epistemic logics that are tractable for important reasoning tasks such as satisfiability and forgetting. The property of logical separability allows to decompose a formula into some of its subformulas and thus modular algorithms for various reasoning tasks can be developed. In this paper, by employing logical separability, we propose an approach to knowledge compilation for the logic Kn by defining a normal form SDNF. Among several novel results, we show that every epistemic formula can be equivalently compiled into a formula in SDNF, major reasoning tasks in SDNF are tractable, and formulas in SDNF enjoy the logical separability. Our results shed some lights on modular approaches to knowledge compilation. Furthermore, we apply our results in the multi-agent epistemic planning. Finally, we extend the above result to the logic K45n that is Kn extended by introspection axioms 4 and 5.

One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utilize prior knowledge on the domain. However, existing methods have limitations in their applicability and expressiveness. In this paper we propose a hierarchical reinforcement learning method based on abductive symbolic planning. The planner can deal with user-defined evaluation functions and is not based on the Herbrand theorem. Therefore it can utilize prior knowledge of the rewards and can work in a domain where the state space is unknown. We demonstrate empirically that our architecture significantly improves learning efficiency with respect to the amount of training examples on the evaluation domain, in which the state space is unknown and there exist multiple goals.

The new round is being led by asset manager Fidelity Investments, which has poured some $800 million into Lyft, and includes hedge fund Senator Investment Group LP and others. The investment should help Lyft keep apace of Uber, which raised $1.25 billion in new capital in January from SoftBank Group Corp. and has said it is planning to seek an IPO in next year's second half. Lyft has weighed its own IPO, according to people familiar with the matter, though it may not beat Uber to the punch. With Uber valued recently at $72 billion as part of a settlement granting equity to Alphabet Inc.'s Waymo, its IPO is likely to be one of the largest in recent memory. Both companies are battling for the future of transportation, investing billions in yet unproven self-driving vehicles and snapping up technology and competitors that offer rentable bicycles and scooters for shorter hops within urban centers.

A new artificial intelligence platform has demonstrated its ability to provide health advice that is as good as a human doctor's, according to research published on the preprint server arXiv.org. The technology, which has been developed by British company Babylon Health, takes the form of a mobile phone app, or website, that patients interact with via a chat service. The AI system has been put through rigorous testing that took place in collaboration with the U.K.'s Royal College of Physicians, as well as researchers from Stanford University and the Yale New Haven Health System. Part of this testing involved the system taking a medical diagnosis exam that trainee primary care physicians in the U.K. must pass to be able to practice independently. Remarkably, the AI doctor scored 81 percent on its first attempt.

From the Alan Turing Institute to DeepMind, the UK boasts a rich history and exciting present in machine learning and artificial intelligence research and development, led by academia and industry. According to recent research, AI is the largest commercial opportunity for Britain, projected to add £232 billion to the UK economy by 2030. SMEs and start-ups will play a significant role in grasping this. The segment highlighted by Theresa May during her speech at the World Economic Forum in Davos in January, where she told world leaders that the UK's strong start-up scene will be instrumental in making the UK a world leader in ethical AI. However, start-ups today still need to overcome some significant challenges before reaching their full potential.

This type of therapy works best, however, if the robot can smoothly interpret the child's own behavior -- whether he or she is interested and excited or paying attention -- during the therapy. Researchers at the MIT Media Lab have now developed a type of personalized machine learning that helps robots estimate the engagement and interest of each child during these interactions, using data that are unique to that child. Armed with this personalized "deep learning" network, the robots' perception of the children's responses agreed with assessments by human experts, with a correlation score of 60 percent, the scientists report June 27 in Science Robotics. It can be challenging for human observers to reach high levels of agreement about a child's engagement and behavior. Their correlation scores are usually between 50 and 55 percent.

Engineers at VW Group are working on the predictive capabilities stemming from analysis of large data volumes. At the Volkswagen Group IT Data Lab, a team is using human reasoning to analyse big data with the support of artificial intelligence. Their predictive analysis helps make many procedures and corporate processes even more efficient and sustainable, it is claimed. At the Data Lab, Volkswagen's competence center for artificial intelligence (AI) in Munich, a team of several experts is working on the data. "Our work is like a jigsaw puzzle," says Gabriele Compostella, an IT specialist.