In the near future, you may be able to eat a robot that will heal you or provide nutrients. It may sound like science fiction, but researchers are closing in on the creation of an ingestible robot that can perform a variety of functions from within the human body. At the International Conference on Intelligent Robots and Systems in Vancouver last week, researchers from Switzerland's École Polytechnique Fédérale de Lausanne (EPFL) presented a prototype of a gelatin-based actuator, according to the Institute of Electrical and Electronics Engineers' magazine, Spectrum. Actuators are the components that allow a mechanism to physically move. So, while doctors can already insert machines like pacemakers into your body, those are stationary and also require invasive surgery.

AI for Earth, a program that offers access to Microsoft's artificial intelligence (AI) technologies and cloud resources to researchers and organizations that are addressing environmental challenges, is now turning its attention to the world's oceans. When AI for Earth was first announced in July, its focus was on agriculture, biodiversity, climate change and water scarcity. Now a different sort of water management, that of the oceans, is in the company's crosshairs. The new AI for Earth European Union Oceans Award provides cloud computing resources to researchers focused on oceans and problems affecting them, which may include pollution, rising sea levels and increasing ocean acidity. "Covering nearly 70 percent of the Earth's surface, oceans play an outsized role in the health of our planet," said Lucas Joppa, chief environmental scientist at Microsoft, in an Oct. 6 announcement. "They generate much of the oxygen we breathe, provide food and livelihoods for billions of people around the world, and support a vast and incredible array of species, many of which have not yet been discovered or described."

We hear a lot about the under-representation of women in so-called STEM fields - science, technology, engineering and maths. But the proportion of women in economics is by some measures smaller. In the US, only about 13% of women hold permanent academic positions in economics; and in the UK the proportion is only slightly better at 15.5%. Only one woman has ever won the Nobel Prize in economics - American Elinor Ostrom in 2009. And there wasn't even a single woman on some of the lists floating about guessing who this year's prize winner would be - it went to the behavioural economist Richard Thaler.

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.

We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control. Empowerment, being the channel capacity between actions and states, maximises the influence of an agent on its near future. It has been shown to be a good model of biological behaviour in the absence of an extrinsic goal. But empowerment is also prohibitively hard to compute, especially in nonlinear continuous spaces. We introduce an efficient, amortised method for learning empowerment-maximising policies. We demonstrate that our algorithm can reliably handle continuous dynamical systems using system dynamics learned from raw data. The resulting policies consistently drive the agents into states where they can use their full potential.

In this paper, we study the Nystr{\"o}m type subsampling for large scale kernel methods to reduce the computational complexities of big data. We discuss the multi-penalty regularization scheme based on Nystr{\"o}m type subsampling which is motivated from well-studied manifold regularization schemes. We develop a theoretical analysis of multi-penalty least-square regularization scheme under the general source condition in vector-valued function setting, therefore the results can also be applied to multi-task learning problems. We achieve the optimal minimax convergence rates of multi-penalty regularization using the concept of effective dimension for the appropriate subsampling size. We discuss an aggregation approach based on linear function strategy to combine various Nystr{\"o}m approximants. Finally, we demonstrate the performance of multi-penalty regularization based on Nystr{\"o}m type subsampling on Caltech-101 data set for multi-class image classification and NSL-KDD benchmark data set for intrusion detection problem.

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning. Previous results used one-dimensional TNs in image recognition, showing limited scalability and a high bond dimension. In this work, we train two-dimensional hierarchical TNs to solve image recognition problems, using a training algorithm derived from the multipartite entanglement renormalization ansatz (MERA). This approach overcomes scalability issues and implies novel mathematical connections among quantum many-body physics, quantum information theory, and machine learning. While keeping the TN unitary in the training phase, TN states can be defined, which optimally encodes each class of the images into a quantum many-body state. We study the quantum features of the TN states, including quantum entanglement and fidelity. We suggest these quantities could be novel properties that characterize the image classes, as well as the machine learning tasks. Our work could be further applied to identifying possible quantum properties of certain artificial intelligence methods.

We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The method is based on the invariances or stability of predicted results when known data is represented as expansions in terms of various stochastic functions. The algorithm predicts the classification/regression values of new data by combining (via voting) the outputs of these numerous linear expansions in randomly chosen functions. The few parameters (typically only one parameter is used in all studied examples) that this model has may be automatically optimized. The algorithm has been tested on 10 diverse training data sets of various types and feature space dimensions. It has been shown to consistently exhibit high accuracy and readily allow for optimization of parameters, while simultaneously avoiding pitfalls of existing algorithms such as those associated with class imbalance. We very briefly speculate on whether spatial coordinates in physical theories may be viewed as emergent "features" that enable a robust machine learning type description of data with generic low order smooth functions.

We are moving rapidly towards a world where robots and artificial intelligence (AI) systems are connected to and influenced by social media, the Internet of Things (IoT) and big data. Technological developments are moving fast, and AI has many governments concerned. Given the pace of technological advancement, how do rule-makers set legislation for AI while allowing the safe evolution of technology? Who thinks about and enforces these guidelines, and what work is being done, or should be done, with governments to craft AI policy? Moves by the European Parliament to consider granting some form of legal status to AI have revived questions of liability and responsibility.