KOLKATA: Nobel laureate and microfinance pioneer Muhammad Yunus has expressed concern over the rapid evolution of artificial intelligence (AI) and said machines may outrun humans in terms of efficiency and usefulness in the next 25-30 years. AI robots will able to develop on their own without human intervention beyond a point, he told PTI yesterday on the sidelines of Tata Steel Literary Meet 2018. "There should be some global regulatory guidelines on the development and research on this technology, which is primarily driven by greed," Yunus, the founder of Grameen Bank in Bangladesh, said. AI should be used for social issues like healthcare, flood and drought, Yunus opined. "There is no gatekeeper, no social guidelines... Even when a new medicine is introduced, it has to seek regulatory approvals. Why can't same rules apply for AI?" he asked.

As was also clearly stated by Vladimir Putin on September 4, 2017: "whichever country leads the way in Artificial Intelligence research will be the ruler of the world". According to Thomas Kuhn's old, but still useful, epistemological model, every change of the scientific paradigm – rather than the emergence of new material discoveries – radically changes the visions of the world and hence strategic equilibria. Hence, first of all, what is Artificial Intelligence? It consists of a series of mathematical tools, but also of psychology, electronic technology, information technology and computer science tools, through which a machine is taught to think as if it were a human being, but with the speed and security of a computer. The automatic machine must representman's knowledge, namely show it, thus enabling an external operator to change the process and understand its results within the natural language. In practice, AI machines imitate the perceptual vision, the recognition and the reprocessing of language -and even of decision-making – but only when all the data necessary to perform it are available.

An African tale says that in Taubiland there lived in ancient, ancient times a man who possessed all the wisdom of the world. He hid all the wisdom in a jug. One day when he climbed a tree to hide the jug, his son gave him some advice about climbing ( a lesson of wisdom, which was supposed to be in the jug). His disappointment was so great that, with all of his might, he threw the jug of wisdom as far as he could. The jug hit a rock and broke into a million pieces.

Ordered weighted averaging (OWA) operators have been widely used in decision making these past few years. An important issue facing the OWA operators' users is the determination of the OWA weights. This paper introduces an OWA determination method based on truncated distributions that enables intuitive generation of OWA weights according to a certain level of risk and trade-off. These two dimensions are represented by the two first moments of the truncated distribution. We illustrate our approach with the well-know normal distribution and the definition of a continuous parabolic decision-strategy space. We finally study the impact of the number of criteria on the results.

Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy ( 79.27%) is best among the investigated classifiers. However, true positives rate i.e. malware detection rate is highest ( 99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihoods. The proposed method is recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why this approach works, showing (for the population setting) that the point estimate obtained with this method converges to the true parameter as recursion proceeds, under a certain assumption. We conduct a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that our method outperforms existing approaches in most cases.

Sparse subspace clustering (SSC) is one of the current state-of-the-art methods for partitioning data points into the union of subspaces, with strong theoretical guarantees. However, it is not practical for large data sets as it requires solving a LASSO problem for each data point, where the number of variables in each LASSO problem is the number of data points. To improve the scalability of SSC, we propose to select a few sets of anchor points using a randomized hierarchical clustering method, and, for each set of anchor points, solve the LASSO problems for each data point allowing only anchor points to have a non-zero weight (this reduces drastically the number of variables). This generates a multilayer graph where each layer corresponds to a different set of anchor points. Using the Grassmann manifold of orthogonal matrices, the shared connectivity among the layers is summarized within a single subspace. Finally, we use $k$-means clustering within that subspace to cluster the data points, similarly as done by spectral clustering in SSC. We show on both synthetic and real-world data sets that the proposed method not only allows SSC to scale to large-scale data sets, but that it is also much more robust as it performs significantly better on noisy data and on data with close susbspaces and outliers, while it is not prone to oversegmentation.

We introduce an application of the group lasso to design of experiments. Note that we are NOT trying to explain experimental design for the group lasso. Conversely, we explain how we can use the idea of the group lasso in experimental design, showing that the problem of constructing an optimal design matrix can be transformed into a problem of the group lasso. In some numerical examples, we show that we can obtain the orthogonal arrays as the solutions of the group lasso problems.

By 2050, humans will ditch speech and communicate using nothing but their thoughts. Called HIBA, which stands for Hybrid Intelligence Biometric Avatar, the consciousness will take on the personas of its users and exchange information between them. That's according to Marko Krajnovic, the producer of the exhibit in Dubai that is this week showcasing predictions by AI experts. Get Free Crypto Coins Daily, No strings Attached!