In a new international collaborative study between The Chinese University of Hong Kong and Ann & Robert H. Lurie Children's Hospital of Chicago, researchers created a machine learning algorithm that uses brain scans to predict language ability in deaf children after they receive a cochlear implant. This study's novel use of artificial intelligence to understand brain structure underlying language development has broad reaching implications for children with developmental challenges. It was published in the Proceedings of the National Academy of Sciences of the United States of America. "The ability to predict language development is important because it allows clinicians and educators to intervene with therapy to maximize language learning for the child," said co-senior author Patrick C. M. Wong, PhD, a cognitive neuroscientist, professor and director of the Brain and Mind Institute at The Chinese University of Hong Kong. "Since the brain underlies all human ability, the methods we have applied to children with hearing loss could have widespread use in predicting function and improving the lives of children with a broad range of disabilities" said Wong.

Microsoft researchers have created technology that uses artificial intelligence to read a document and answer questions about it about as well as a human. It's a major milestone in the push to have search engines such as Bing and intelligent assistants such as Cortana interact with people and provide information in more natural ways, much like people communicate with each other. A team at Microsoft Research Asia reached the human parity milestone using the Stanford Question Answering Dataset, known among researchers as SQuAD. It's a machine reading comprehension dataset that is made up of questions about a set of Wikipedia articles. According to the SQuAD leaderboard, on Jan. 3, Microsoft submitted a model that reached the score of 82.650 on the exact match portion.

What will enterprise AI look like in 2018? In SearchCIO interviews with IT leaders at DBS Bank, Dun & Bradstreet, State Street and the city of Boston on 2018 plans, the strong consensus was for more, not less, investment in AI projects, suggesting AI's enterprise trajectory is still on an upward slope. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.

In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.

Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

Voice, connectivity and AI took center stage at the Consumer Electronics Show last week. If this year's CES is any indication, these three building blocks will compose the holy trinity of consumer electronics devices that will drive the market in 2018 and further into the future. Voice assistants are now poised to move into wearables, headphones, baby monitors, lamps, TV remotes and vehicles. Paul Beckmann, founder and chief technology officer of DSP Concepts, told EE Times, "We are witnessing a Cambrian explosion around voice." At CES, Baidu, known as "China's Google," shouted out most loudly for voice by unveiling and opening to developers its Duer OS-based platform.

In 2014, three young men from far-flung parts of the world teamed up in Finland with an audacious plan that could soon help doctors save more lives, not to mention money, and chart a new course for healthcare. Oguzhan Gencoglu, who hails from Turkey, is an AI and machine-learning whiz currently working on his Ph.D. in computer science, Hung Ta is a Vietnamese math prodigy with a doctorate in biotechnology, and Timo Heikkinen is a Finnish entrepreneur with a software industry background. Together, they launched Top Data Science, an AI startup based in Helsinki that's developing software that can make sense of millions of data points, alert doctors to unseen medical patterns, help them diagnose disease and track patients during treatment. Their "intelligent" code is already analyzing thousands of MRI images and could one day help radiologists at the Helsinki University Central Hospital diagnose prostate cancer. Another set of algorithms is crunching data from the hospital's intensive care unit and using it to identify high-risk cases that may soon need urgent medical care, as well as flag patients who are progressing well and who could be released to standard hospital care.

Whether you fear it or embrace it, the A.I. revolution is coming--and it promises to have an enormous impact on the world economy. PwC estimates that artificial intelligence could add $15.7 trillion to global GDP by 2030. To identify which private companies are set to make the most of it, research firm CB Insights recently released its 2018 "A.I. 100," a list of the most promising A.I. startups globally (grouped by sector in the graphic above). They were chosen, from a pool of over 1,000 candidates, by CB Insights' algorithm, based on factors like investor quality and momentum. China's Bytedance leads in funding with $3.1 billion, but 76 of the 100 startups are U.S.-based.

AI Robot, Sophia, became the first robot citizen. But was there more to the story and, although impressive and featuring genuine AI, do the animatronic features of Sophia suggest the goal here is the appearance of humanity – rather than an extension of it? This caused widespread uproar amongst human rights groups and on social media, and it was picked up by many of the big media outlets, but it turns out that the whole affair was mainly a PR stunt. The eye-grabbing headlines were a well strategised ploy to promote a tech summit in Saudi Arabia, but some experts say this sort of approach to robot rights is actively damaging, both to public understanding of technology and to civil society itself. So what about Sophia's amazing conversational abilities that led to the robot having an argument with Elon Musk?