Utopians believe that once AI far surpasses human intelligence, it will provide us with near-magical tools for alleviating suffering and realizing human potential. In this vision, super-intelligent AI systems will so deeply understand the universe that they will act as omnipotent oracles, answering humanity's most vexing questions and conjuring brilliant solutions to problems such as disease and climate change. But not everyone is so optimistic. The best-known member of the dystopian camp is the technology entrepreneur Elon Musk, who has called super-intelligent AI systems "the biggest risk we face as a civilization," comparing their creation to "summoning the demon." This group warns that when humans create self-improving AI programs whose intellect dwarfs our own, we will lose the ability to understand or control them.

This endowment will empower the renowned academic institution with industry specific knowledge and resources to help create solutions to accelerate the growth and adoption of Data Science and AI globally, the company said in a statement. Through this endowment, Mindtree will help accelerate the development of technology innovation in fields like AI, data analytics, and machine learning, it added. "AI and Data Science are key priorities for our clients as these technologies offer immense potential to create new business opportunities. IIT Madras is one of the global leaders in this field and the collaboration between Mindtree and IIT Madras will help accelerate innovation and push the boundaries of knowledge," the company's CEO and Managing Director Rostow Ravanan said. Mindtree will further extend the partnership with IIT Madras to include research projects focusing on related topics such as personalisation, conversational interfaces and natural language generation, the statement said.

That's the point of virtual testing and adjustment in robots before they start real-world operations.The correct sequence of commands is as follows: An articulated arm performs a series of instructions stored in its memory. These actions can be as simple as welding parts on an assembly line.

That's how quickly brain damage happen when the cells get no oxygen in a stroke or in some brain injuries. Both can have tragic consequences -- paralysis, memory loss, speech difficulties and even death. But doctors can't start treatment without an initial diagnosis, and that requires reading a CT scan as soon as the test's completed. Unfortunately, that's not what usually happens, said Prashant Warier, co-founder of Qure.ai, a member of our Inception startup accelerator program. "Radiologists typically have a backlog of cases," he said.

Beijing: A little more than a year ago, China released an aggressive plan to become the world's leading artificial intelligence (AI) player. But with its technological dependence on the US laid bare, it's now promoting a softer approach, calling for all nations to join hands to develop the technology. Chinese leaders, including vice premier Liu He, joined business mogul Jack Ma and executives from Google at the World Artificial Intelligence Conference in Shanghai to support a borderless approach to AI research. He called for foreign investment in the country and pledged to foster "an environment of free thinking" to support development. The tone struck by He is a far cry from the aggressive plan issued by the State Council last year with the aim of China becoming the world leader in AI by 2030, in part through government support.

Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely accepted that words with similar semantics should be close to each other in the embedding space, we find that word embeddings learned in several tasks are biased towards word frequency: the embeddings of high-frequency and low-frequency words lie in different subregions of the embedding space, and the embedding of a rare word and a popular word can be far from each other even if they are semantically similar. This makes learned word embeddings ineffective, especially for rare words, and consequently limits the performance of these neural network models. In this paper, we develop a neat, simple yet effective way to learn \emph{FRequency-AGnostic word Embedding} (FRAGE) using adversarial training. We conducted comprehensive studies on ten datasets across four natural language processing tasks, including word similarity, language modeling, machine translation and text classification. Results show that with FRAGE, we achieve higher performance than the baselines in all tasks.

Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuousaction domains, multiagent coordination domains with continuous actions have received relatively few investigations. In this paper, we propose an independent learner hierarchical method, named Sample Continuous Coordination with recursive Frequency Maximum Q-Value (SCC-rFMQ), which divides the cooperative problem with continuous actions into two layers. The first layer samples a finite set of actions from the continuous action spaces by a re-sampling mechanism with variable exploratory rates, and the second layer evaluates the actions in the sampled action set and updates the policy using a reinforcement learning cooperative method. By constructing cooperative mechanisms at both levels, SCC-rFMQ can handle cooperative problems in continuous action cooperative Markov games effectively. The effectiveness of SCC-rFMQ is experimentally demonstrated on two well-designed games, i.e., a continuous version of the climbing game and a cooperative version of the boat problem. Experimental results show that SCC-rFMQ outperforms other reinforcement learning algorithms. A large number of multiagent coordination domains involve continuous action spaces, such as robot soccer [1] and multiplayer online battle arena game [2]. In such environments, agents not only need to coordinate with other agents towards desirable outcomes efficiently but also have to deal with infinitely large action spaces.

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. In experiments, we construct a real-world civil case dataset for LRC. Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. We will publish all source codes and datasets of this work on \urlgithub.com for further research.

Last Monday, we covered the new, updated, and way way better guidelines for the ANA Avatar XPRIZE. Since we were mostly talking with the folks over at XPRIZE, we didn't realize that ANA (All Nippon Airways) is putting a massive amount of effort into this avatar concept-- they're partnering with JAXA, the Japan Aerospace Exploration Agency, "to create a new space industry centered around real-world avatars." AVATAR X aims to capitalize on the growing space-based economy by accelerating development of real-world Avatars that will enable humans to remotely build camps on the Moon, support long-term space missions and further explore space from afar. These avatars will be essentially the same sorts of things that the Avatar XPRIZE is looking to advance: Robotic systems designed to operate with a human in the loop through immersive telepresence, allowing them to complete tasks like a human could without a human needing to be physically there. JAXA says that they're interested in the usual stuff, like remote construction in space and maintenance, but also in "space-based entertainment and travel for the general public," so use your imagination on that one.