A creepy life-like robot called Erica is set to become a TV news anchor in Japan. According to her creator Hiroshi Ishiguro, the droid is warm and caring, and may soon have an'independent consciousness'. She has been described as so realistic she could'have a soul'. A robot called Erica (pictured) can not only make jokes but also has a'soul', according to her creator. Very few details have been revealed about Erica's new job, however Dr Ishiguro said she will use AI to read news put together by humans.

Google's billion-dollar deal to acquire a part of HTC (along with a non-exclusive license for its intellectual property) is done. The two companies announced its completion tonight, and the details appear to be the same as originally announced. While the team behind the Pixel phones is joining Google, HTC says it will continue to make mobile devices under its own brand name while using Vive products to compete in VR. It won't stop there either, as the company says it will pursue innovations in AR, AI and IoT technology. In a blog post, Google hardware SVP Rick Osterloh said: "Today, we start digging in with our new teammates, guided by the mission to create radically helpful experiences for people around the world, by combining the best of Google's AI, software and hardware." According to Osterloh, after this deal, Taipei will become the largest Google engineering site in the Asia-Pacific region.

OSAKA – The nation's first drone museum opened in the city of Osaka in December featuring a wide variety of drones on display and for sale, with visitors able to try them out. "There are not many places in Japan where people can see and touch drones. We hope to promote them from the Kansai region," said an official of Skyasky Co., the drone pilot school based in Toyonaka, Osaka Prefecture, that runs Drone Museum Horie. The museum, located in Osaka's Minamihorie district, exhibits 16 types of drones produced in Japan and abroad, ranging from an 18-gram palm-sized indoor drone to a gigantic crop-spraying drone equipped with a 10-liter tank. Visitors can learn the history of drones, from the earliest models for industrial use produced in New Zealand in 2011 to the latest model that sends live video footage to a smartphone.

In a cramped and dark venue in a sleazy Tokyo district, dozens of middle-aged men cheer at a performer on stage: The object of their adoration is a 6-year-old girl. Decked out in makeup with ribbons in her hair, Ai is dressed like an adult but still looks very much a child. Even though Ai is so young, she is technically considered an "idol" singer. More typically, idols are in their teens. The idol phenomenon is common in Japan, where rights groups have complained that society's sometimes permissive view of the sexualization of young girls puts minors at risk.

It is easy to confuse the current geopolitical situation with that of the 1980s. The United States and Russia each accuse the other of interfering in domestic affairs. Russia has annexed territory over U.S. objections, raising concerns about military conflict. As during the Cold War after World War II, nations are developing and building weapons based on advanced technology. AI can also be used to control non-nuclear weapons including unmanned vehicles like drones and cyberweapons, the expert says. Unmanned vehicles must be able to operate while their communications are impaired – which requires onboard AI control.

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allow the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction either use 3D structure of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which a high-level representation of a drug is constructed via CNNs and Smith-Waterman similarity is used for proteins achieved the best Concordance Index (CI) performance, outperforming KronRLS, a state-of-the-art algorithm for DT binding affinity prediction, with statistical significance.

The artificial neural network shows powerful ability of inference, but it is still criticized for lack of interpretability and prerequisite needs of big dataset. This paper proposes the Rule-embedded Neural Network (ReNN) to overcome the shortages. ReNN first makes local-based inferences to detect local patterns, and then uses rules based on domain knowledge about the local patterns to generate rule-modulated map. After that, ReNN makes global-based inferences that synthesizes the local patterns and the rule-modulated map. To solve the optimization problem caused by rules, we use a two-stage optimization strategy to train the ReNN model. By introducing rules into ReNN, we can strengthen traditional neural networks with long-term dependencies which are difficult to learn with limited empirical dataset, thus improving inference accuracy. The complexity of neural networks can be reduced since long-term dependencies are not modeled with neural connections, and thus the amount of data needed to optimize the neural networks can be reduced. Besides, inferences from ReNN can be analyzed with both local patterns and rules, and thus have better interpretability. In this paper, ReNN has been validated with a time-series detection problem.

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods designed mainly based on the convex optimization, say, the projected gradient descent method. However, they are generally time-consuming due to that the singular value decomposition (SVD) is commonly adopted during the update, especially when the data size is very large. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: (a) SVD-free via dropping convexity, with good scalability by the use of stochastic algorithm, i.e., stochastic variance reduced gradient (SVRG), and (b) adaptive step size choice via introducing a new stabilized Barzilai-Borwein (SBB) method as the original version for convex problems might fail for the considered stochastic \textit{non-convex} optimization problem. Moreover, we show that the proposed algorithm converges to a stationary point at a rate $\mathcal{O}(\frac{1}{T})$ in our setting, where $T$ is the number of total iterations. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm via comparing with the state-of-the-art methods, particularly, much lower computational cost with good prediction performance.

It is easy to confuse the current geopolitical situation with that of the 1980s. The United States and Russia each accuse the other of interfering in domestic affairs. Russia has annexed territory over U.S. objections, raising concerns about military conflict. As during the Cold War after World War II, nations are developing and building weapons based on advanced technology. During the Cold War, the weapon of choice was nuclear missiles; today it's software, whether its used for attacking computer systems or targets in the real world. Russian rhetoric about the importance of artificial intelligence is picking up – and with good reason: As artificial intelligence software develops, it will be able to make decisions based on more data, and more quickly, than humans can handle.

When Singapore decided that they needed a new smart water assessment network to track pollution in their reservoirs, they obviously went with a robot, because otherwise you wouldn't be reading about it here. They also decided that the robot had to be "aesthetically pleasing" in order to "promote urban livability." The answer came from researchers at the National University of Singapore (NUS), who proposed developing a Smart Water Assessment Network: Yes, that's right, a SWAN. The researchers, from NUS Environmental Research Institute and Tropical Marine Science Institute, had developed and tested a version of the swanbots back in 2016. Now they've decided to deploy them full time across five different reservoirs in Singapore, where water is a particularly precious resource.