Doctors using Infervision's AI powered CT diagnosis at Shanghai Changzheng Hospita in China. A digital health company from the UK wants to change the way a patient interacts with a doctor through the creation of an artificial intellignce (AI) doctor in the form of a AI chatbot. Babylon Health raised close to $60 million in April 2017 to diagnose illnesses with an AI chatbot on your smartphone. Around the same time, Berlin and London based start up Ada, announced its push into the AI chat bot space. "The news that Babylon Health has raised near £50M to build an'AI doctor' is a promising development for the health industry; trials are currently ongoing in London, where Babylon's tech is being used as an alternative to the non-emergency 111 number," said Conway Kosi, Head of Managed Infrastructure Services, Fujitsu EMEIA.

A former Google China president and now venture capitalist says Elon Musk uses shiny cars and the promise of medical implants as bait for his real goals: Distributing energy away from traditional power companies and turning humans into cyborgs. First, says Kai-Fu Lee, were Tesla cars. "By selling us fancy, beautiful Teslas--luxury cars that none of us can say no to, it seems to have changed to distributed energy," Lee, the CEO of Sinovation Ventures, told Quartz in an interview today. As Tesla CEO, Musk has acquired the solar energy startup SolarCity that he previously helped lead as chairman, then he began sharing a vision where a battery in the home stores energy from the sun (preferably using SolarCity's new solar panels). That energy will be used to power the home and charge electric cars.

Artificial intelligence has already stolen the headlines in 2017 by defeating poker champions and ordering us cups of coffee. Besides this, there are ways the technology is being used to improve the world. AI is the most important investment for companies in the near future. IDC predicts a widespread adoption of cognitive systems and AI will drive worldwide revenues from nearly $8bn in 2016 to more than $47bn in 2020. The industries with the most lucrative short-term opportunities are identified as banking, securities and investments and manufacturing.

Walt Disney World in Florida appears poised to launch the highest-profile commercial deployment of driverless passenger vehicles to date, testing a fleet of driverless shuttles that could cart passengers through car parks and around its theme parks. According to sources with direct knowledge of Disney's plans, Walt Disney is in late-stage negotiations with at least two manufacturers of autonomous shuttles. The sources, who asked not be identified to avoid offending Disney, said the company plans a pilot program this year to transport employees in the electric-drive robot vehicles. If that goes well, they said, the shuttles would begin transporting park visitors sometime next year. There are no plans for driverless shuttles at Disneyland in Anaheim, according to the sources.

In this work, we present a methodology that enables an agent to make efficient use of its exploratory actions by autonomously identifying possible objectives in its environment and learning them in parallel. The identification of objectives is achieved using an online and unsupervised adaptive clustering algorithm. The identified objectives are learned (at least partially) in parallel using Q-learning. Using a simulated agent and environment, it is shown that the converged or partially converged value function weights resulting from off-policy learning can be used to accumulate knowledge about multiple objectives without any additional exploration. We claim that the proposed approach could be useful in scenarios where the objectives are initially unknown or in real world scenarios where exploration is typically a time and energy intensive process. The implications and possible extensions of this work are also briefly discussed.

A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES parameters and improve its performance. The proposed framework is then employed to attain the performance of a human expert used to predict earthquakes in the Zagros area based on the idea of coupled earthquakes. While the prediction results are promising in parts of the testing set, the general performance indicates that prediction methodology based on coupled earthquakes needs more investigation and more complicated reasoning procedure to yield satisfactory predictions.

Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2,846 human whole-body motions and 6,187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.

In a series of recent works, we have generalised the consistency results in the stochastic block model literature to the case of uniform and non-uniform hypergraphs. The present paper continues the same line of study, where we focus on partitioning weighted uniform hypergraphs---a problem often encountered in computer vision. This work is motivated by two issues that arise when a hypergraph partitioning approach is used to tackle computer vision problems: (i) The uniform hypergraphs constructed for higher-order learning contain all edges, but most have negligible weights. Thus, the adjacency tensor is nearly sparse, and yet, not binary. (ii) A more serious concern is that standard partitioning algorithms need to compute all edge weights, which is computationally expensive for hypergraphs. This is usually resolved in practice by merging the clustering algorithm with a tensor sampling strategy---an approach that is yet to be analysed rigorously. We build on our earlier work on partitioning dense unweighted uniform hypergraphs (Ghoshdastidar and Dukkipati, ICML, 2015), and address the aforementioned issues by proposing provable and efficient partitioning algorithms. Our analysis justifies the empirical success of practical sampling techniques. We also complement our theoretical findings by elaborate empirical comparison of various hypergraph partitioning schemes.

Automation isn't new to display campaigns on the Google Display Network, but with the new Smart display campaigns, it's not just the creative that's automated. Targeting, bidding and the ads all run on autopilot powered by machine learning. The system pulls advertiser-provided headlines, descriptions, logos and images to create responsive text, display and native ads. Bids are set, based on Target CPAs, for each auction as the system determines the likelihood of conversion. Trivago, Hulu Japan and Credit Karma were among the beta testers for this new campaign type.

Artificial intelligence is a booming business in 2017, but one that also comes with significant baggage in the form of public misunderstanding, potential job losses, and fear. Last fall, A.I. competitors Amazon, Microsoft, Facebook, IBM, and Google banded together to form the Partnership on AI to Benefit People and Society, an industry-led attempt to get ahead of the many social, ethical, and economic issues presented by the advent of technology with increasingly human-like capabilities. Apple joined the group as another founding member earlier this year. On Tuesday, the Partnership on AI (PAI) announced nearly two dozen new members, including more of the tech industry's biggest names--Intel, eBay, Salesforce, and SAP among them--and many of the world's foremost A.I. research institutions, such as the Seattle-based Allen Institute for Artificial Intelligence. Also joining are nonprofits focused on digital privacy, human rights, and freedom.