"I was determined to do it precisely because I was told it was impossible." So says Yasuo Kuniyoshi, professor at the University of Tokyo's Graduate School of Information Science and Technology, in a quiet tone. However, the sharp glint in his eye betrays his grand ambition of developing a truly clever artificial intelligence to benefit humankind. Some current forms of artificial intelligence (AI), such as speech recognition and automated driving, are just as competent as humans--if not better--at carrying out their given tasks.However, just as AI developed for speech recognition cannot play chess, and chess-playing AI cannot drive a car, existing forms of AI are incapable of any actions beyond those intended by their creators. Because AI does not "think" the same way humans do, it cannot adapt to conditions besides the preconceived context it was programmed for in advance.For AI to be truly intelligent and highly adaptable, it must be able to think in the same way as humans.

From cloud platforms for medical data and hospital smart rooms to artificial intelligence and patient-engagement technologies, the giants of the digital world are threatening to disrupt healthcare. Leading the pack is IBM and its centerpiece offering Watson Health. In just the last six months, the company has announced major initiatives into healthcare including a partnership with clinical consultation provider Best Doctors to add Watson's cancer suite to employee benefits packages, a population health management alliance with Siemens Healthineers and an effort linking IBM's PowerAI deep learning software toolkit with NVIDIA's NVLink interconnect technology. The PowerAI is already being used improve diagnoses and care plans by sifting through patient data. In October, Big Blue announced a $200 million investment in its Watson Internet of Things global headquarters in Munich, Germany.

MOSUL, IRAQ – Iraqi forces said Monday that they have taken more territory from jihadists and were searching for militants and bombs on the edge of the Old City as they press an offensive in west Mosul. They are also striking IS with armed drones as part of a renewed push launched on March 5 that has forced the jihadis out of several neighborhoods and key sites, including the famed Mosul museum. West Mosul is the most-populated urban area still held by the jihadis, followed by Syria's Raqa, which is also a key target in the U.S.-led anti-IS campaign. Iraq's Joint Operations Command announced additional gains on Monday, saying that forces from the elite Counter-Terrorism Service had recaptured the Al-Nafat and Mosul al-Jadida neighborhoods. Lt. Gen. Raed Shakir Jawdat said that forces from the Rapid Response Division, another special forces unit, and the federal police were working to search and clear territory on the edge of Mosul's Old City.

It is a truism that the knowledge of groups of people, particularly experts, outperforms that of individuals [43] and there is increasing call to use the dispersed judgments of the crowd in policy making [42]. There is a large literature spanning multiple disciplines on methods for aggregating beliefs (for reviews see [9, 6, 7]), and previous applications have included political and economic forecasting [3, 27], evaluating nuclear safety [10] and public policy [28], and assessing the quality of chemical probes [31]. However, previous approaches to aggregating beliefs have implicitly assumed'kind' (as opposed to'wicked') environments [16]. In a previous paper, [35] we proposed an algorithm for aggregating beliefs using not only respondent's answers but also their prediction of the answer distribution, and proved that for an infinite number of non-noisy Bayesian respondents, it would always determine the correct answer if sufficient evidence was available in the world. 1 Here, we build on this approach but treat the aggregation problem as one of statistical inference. We propose a model of how people formulate their own judgments and predict the distribution of the judgments of others, and use this model to infer the most probable world state giving rise to the observed data from people. The model can be applied at the level of a single question but also across multiple questions, to infer the domain expertise of respondents. The model is thus broader in scope than other machine learning models for aggregation in that it accepts unique questions, but can also be compared to their performance across multiple questions. We do not assume that the aggregation model has access to correct answers or to historical data about the performance of respondents on similar questions. By using a simple model of how people make such judgments, we are able to increase the accuracy of the group's aggregate answer in domains ranging from estimating art prices to diagnosing skin lesions.

This paper analyses the DeGroot-Friedkin model for evolution of the individuals' social powers in a social network when the network topology varies dynamically (described by dynamic relative interaction matrices). The DeGroot-Friedkin model describes how individual social power (self-appraisal, self-weight) evolves as a network of individuals discuss a sequence of issues. We seek to study dynamically changing relative interactions because interactions may change depending on the issue being discussed. In order to explore the problem in detail, two different cases of issue-dependent network topologies are studied. First, if the topology varies between issues in a periodic manner, it is shown that the individuals' self-appraisals admit a periodic solution. Second, if the topology changes arbitrarily, under the assumption that each relative interaction matrix is doubly stochastic and irreducible, the individuals' self-appraisals asymptotically converge to a unique non-trivial equilibrium.

When we say "I know why he was late", we know not only the fact that he was late, but also an explanation of this fact. We propose a logical framework of "knowing why" inspired by the existing formal studies on why-questions, scientific explanation, and justification logic. We introduce the Ky_i operator into the language of epistemic logic to express "agent i knows why phi" and propose a Kripke-style semantics of such expressions in terms of knowing an explanation of phi. We obtain two sound and complete axiomatizations w.r.t. two different model classes depending on different assumptions about introspection.

We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinate vectors and associated WLAN/WiFi signal strengths of the available access points, a BPNN can be trained to output the expected signal strengths for any input position within the region of interest (BPNN-RM). This can be used to provide a continuous representation of the radio map and to filter, densify or decimate a discrete radio map. Correspondingly, the TRM can also be used to train another BPNN to output the expected position within the region of interest for any input vector of recorded signal strengths and thus carry out localization (BPNN-LA).Key aspects of the design of such artificial neural networks for a specific application are the selection of design parameters like the number of hidden layers and nodes within the network, and the training procedure. Summarizing extensive numerical simulations, based on real measurements in a testbed, we analyze the impact of these design choices on the performance of the BPNN and compare the results in particular to those obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest neighbors approaches to FWIPS.

Intel CEO Brian Krzanich, BMW CEO Harald Krueger, and Mobileye CTO and cofounder Amnon Shashua pose after a press conference in Munich on July 1, 2016. Intel started making lots of noise about the autonomous car market last year. But it's a long slog getting into a market like automotive, where it can take years to get designed into a vehicle. On Monday, the chip giant announced it would just buy its way into the market with a $15.3 billion acquisition of Mobileye, a leading provider of advanced driver assistant systems based in Israel. A massive consolidation spree is sweeping the semiconductor industry.

What is the state of AI Research in China? AI has witnessed rapid progress in China. This year, for the first time, the term AI has been mentioned in the government work report, indicating the significance of developing AI in China. Most internet companies including Baidu have invested heavily on AI. Many other companies across the industry have also established branches to develop and adopt AI in their businesses.

Thousands have flooded into Austin, Texas to experience the 31st Annual South by Southwest Convention and Festivals. Check out some of the sights and sounds from the first day. Osaka University roboticist Hiroshi Ishiguro returned SXSW, this time bringing two robots, shown at left. Don't be too concerned about the rise of humanoid robots, because they're still not ready for prime time. But you might want to keep a wary eye on the machine-learning systems that power them.