The government of Japan is planning to introduce English-speaking Artificial Intelligence (AI) robots in classrooms to help children improve their English speaking skills, considered one of the worst in the world. The Japanese education ministry would be launching a pilot programme to test the effectiveness of the initiative in April 2019, reports Efe news. The initiative will be initially rolled out in 500 schools throughout the country with the aim of fully implementing it in two years, public broadcaster NHK reported Saturday. The programme also includes study apps and online conversation sessions with native English speakers. Japan has proposed improving English skills ahead of the surge in tourists expected during the 2020 Summer Olympics in Tokyo.

As "great power competition" ramps up, signs of arms races in America's strategic relationships with both Russia and China are everywhere apparent. In this respect, Russian President Vladimir Putin's March 1 speech made a big splash in the press, but readers may not be aware of the late tests in May when the Russian Navy simultaneously test launched four new Bulava submarine-launched ballistic missiles (SLBMs). These missiles were designed no doubt for nuclear strikes on the American heartland. Likewise, China recently announced the tenth test of its new road-mobile intercontinental ballistic missile (ICBM), called the DF-41. Furthermore, this test was to be of a missile-defense evading hypersonic warhead with the same general purpose in mind.

Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.

Japan's education ministry is planning to place English-speaking artificial intelligence robots in schools to help children improve their English oral communication skills. Japanese students are generally not good at writing in English or speaking the language. Curriculum guidelines that are due to be fully implemented in 2 years will focus on nurturing those skills. In April, the ministry will launch the robot initiative on a trial basis at about 500 schools nationwide. Some schools have already adopted similar robots to enable students to have fun while honing their English pronunciation and conversation skills.

"I spent dozens of hours just to produce a document that board members would not pay much attention to. It was not exciting," recalled a man in his 30s working for a consultant in Tokyo, about his 10 years or so working at a major bank. Graduating from a top national university, he joined the bank hoping to help grow companies beneficial to society. Much of what he ended up doing, however, was paperwork, including putting together reports on next generation systems. He does not regret his decision to throw away the stability of the bank and move to his current job.

As the World Economic Forum publishes the most extensive and in-depth report yet on artificial intelligence's transformation of the financial services sector, Internet of Business editor Chris Middleton presents a 3,500-word breakdown of the document's key findings and highlights. The financial services sector is in the vanguard of deploying artificial intelligence (AI) worldwide. However, the technology has the potential to be either a transformative and beneficial force, or a destabilising, even existential threat to the global financial system, according to the World Economic Forum. This risks of economic contagion spreading via the technology are real, it says. The WEF has published an in-depth report, The New Physics of Financial Services: Understanding how artificial intelligence is transforming the financial ecosystem, produced in collaboration with C-level executives, analysts, and technology specialists from across every part of the industry. The 166-page sector analysis finds that the bonds that have historically held financial institutions together are weakening as a result of new technologies. This is creating new threats, new opportunities, and new centres of gravity where emerging and established capabilities are being combined in unexpected ways.

The Nature Morte gallery in New Delhi opens its doors to a unique, one of kind show today. Titled'Gradient Descent', the show is the country's first Artificial Intelligence (AI) art exhibition. Curated by 64/1, a Bengaluru-based curation and research collective (founded by Raghava KK and Karthik Kalyanaraman that focuses on raising awareness on AI's place in the realm of contemporary art), 'Gradient Descent' showcases the works of seven, carefully picked artists from the US, Japan, Germany, Turkey, India, UK and New Zealand. Each of these artists, equipped with a strong foundational background in artificial neural networking, has collaborated with AI to produce art. Conceptualised and planned meticulously since February, the show will run till 15 September.

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.

Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar $K$ source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.

This paper proposes a new architecture for speaker adaptation of multi-speaker neural-network speech synthesis systems, in which an unseen speaker's voice can be built using a relatively small amount of speech data without transcriptions. This is sometimes called "unsupervised speaker adaptation". More specifically, we concatenate the layers to the audio inputs when performing unsupervised speaker adaptation while we concatenate them to the text inputs when synthesizing speech from text. Two new training schemes for the new architecture are also proposed in this paper. These training schemes are not limited to speech synthesis, other applications are suggested. Experimental results show that the proposed model not only enables adaptation to unseen speakers using untranscribed speech but it also improves the performance of multi-speaker modeling and speaker adaptation using transcribed audio files.