Researchers in Zurich are teaching a robot how to balance on wheels attached to its four legs.

Apple is reportedly coming out with a cheaper version of its $349 HomePod, Business Insider reports. The company is following in the footsteps of Google and Amazon, both of which released'discount' versions of their own smart speakers, making the technology more accessible to a wider audience. The cheaper HomePod would cost somewhere between $150 and $200, and it would be smaller. The new report says it could be available as soon as this fall. Apple might be releasing a cheaper, smaller version of its HomePod speaker.

Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene pairs like human? In this paper, we propose a novel unsupervised algorithm to address the problem of localizing the sound source in visual scenes. A two-stream network structure which handles each modality, with attention mechanism is developed for sound source localization. Moreover, although our network is formulated within the unsupervised learning framework, it can be extended to a unified architecture with a simple modification for the supervised and semi-supervised learning settings as well. Meanwhile, a new sound source dataset is developed for performance evaluation. Our empirical evaluation shows that the unsupervised method eventually go through false conclusion in some cases. We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

The widespread growth of high-dimensional biological data in particular has spurred a renewed interest in the use of graphical models to aid in the discovery of novel biological mechanisms (Bühlmann, Kalisch, and Meier 2014). While the past decade has witnessed tremendous developments towards understanding undirected graphical models (Meinshausen and Bühlmann 2006; Ravikumar, Wainwright, and Lafferty 2010; Yang, Ravikumar, Allen, and Liu 2015), there has been less progress towards understanding directed graphical models--also known as Bayesian networks (BNs) or structural equation models (SEM)--for high-dimensional data with p n. A BN is represented by a directed acyclic graph (DAG), whose structure contains a richer and different set of conditional independence relations than an undirected graph. Moreover, DAGs are commonly used 2 Learning Large-Scale Bayesian Networks with the sparsebn Package in causal inference where the direction of an edge encodes causality. Consequently, there have been continuing efforts in structure learning of directed graphs from data.

A common approach for Bayesian computation with big data is to partition the data into smaller pieces, perform local inference for each piece separately, and finally combine the results to obtain an approximation to the global posterior. Looking at this from the bottom up, one can perform separate analyses on individual sources of data and then combine these in a larger Bayesian model. In either case, the idea of distributed modeling and inference has both conceptual and computational appeal, but from the Bayesian perspective there is no general way of handling the prior distribution: if the prior is included in each separate inference, it will be multiply-counted when the inferences are combined; but if the prior is itself divided into pieces, it may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma, we propose expectation propagation (EP) as a general prototype for distributed Bayesian inference. The central idea is to factor the likelihood according to the data partitions, and to iteratively combine each factor with an approximate model of the prior and all other parts of the data, thus producing an overall approximation to the global posterior at convergence. In this paper, we give an introduction to EP and an overview of some recent developments of the method, with particular emphasis on its use in combining inferences from partitioned data. In addition to distributed modeling of large datasets, our unified treatment also includes hierarchical modeling of data with a naturally partitioned structure. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.

European policymakers have asked for help unravelling the "patchwork" of ethical and societal challenges as the use of artificial intelligence increases. The European Commission's group on ethics in science and new technologies on Friday issued a statement (PDF) warning that existing efforts to develop solutions to the ethical, societal and legal challenges AI presents are a "patchwork of disparate initiatives." It added that "uncoordinated, unbalanced approaches in the regulation of AI" risked "ethics shopping," resulting in the "relocation of AI development and use to regions with lower ethical standards." Instead, the group wants to start a process that will "pave the way towards a common, internationally recognized ethical and legal framework for the design, production, use and governance of artificial intelligence, robotics, and'autonomous' systems." The Commission said in a separate statement that it wanted to kick off a "wide, open and inclusive discussion on how to use and develop artificial intelligence both successfully and ethically sound."

A senior clergyman and government advisor has written what he calls "the Ten Commandments of AI", to ensure the technology is applied ethically and for social good. AI has been put forward as the saviour of businesses and national economies, but how to ensure that the technology isn't abused? The Rt Rev the Lord Bishop of Oxford (pictured below), a Member of the House of Lords Select Committee on Artificial Intelligence, set out his proposals at a policy debate in London, attended by representatives of government, academia, and the business world. Speaking on 27 February at a Westminster eForum Keynote Seminar, Artificial Intelligence and Robotics: Innovation, Funding and Policy Priorities, the Bishop set out his ten-point plan, after chairing a debate on trust, ethics, and cybersecurity. AI should be designed for all, and benefit humanity.

The autonomous vehicle division of Google's parent company will start hauling cargo using self-driving trucks, capping a busy week for next-generation shipping technology. Waymo, the driverless vehicle unit of Alphabet, announced a pilot programme that will have self-driving big rigs transport cargo to the company's data centres in Georgia. Several companies are vying to dominate the nascent self-driving vehicle industry, believing the technology will reshape how humans and goods travel. Waymo has already extensively tested autonomous cars intended to ferry people around. "Now we're turning our attention to things as well", the company said in a blog post, noting that driverless trucks pose unique tech challenges.

These duties are shared across the organizing team.