Companies in India say the use of artificial intelligence (AI) have added jobs in their firms and see little to no job losses as a result of the technology, a report by IT and consulting firm Capgemini showed. Out of the 86 companies surveyed in India, over 92% have created new job roles related to artificial intelligence, though a significant portion of the additional jobs are in upper management levels. Over 60% do not expect any job losses from AI. "There is a kind of renaissance in artificial intelligence that is currently going on... This creates a whole series of job roles and job roles will always evolve. In the short-to-medium term, which is what the report focuses on, there will not be massive kinds of job losses," Ron Tolido, executive vice-president & Global CTO for Capgemini's Insights & Data Practice, told ET.

As Dubai welcomes RoboCop, drones land in the palm of your hand and cars park themselves, are we getting carried away with'maybes'? This week saw a plethora of tangible internet of things (IoT) developments, which is actually a rarity when looking at this space. For example, in Dubai, the city's police force has recruited its latest member: a robot police officer. Weighing in at 100kg and measuring 170cm in height, the robot will patrol the city's streets, offering advice to those who need it. The robot's hardware will enable it to scan a person's face to determine their emotions from up to 1.5 metres away, then changing its mood accordingly to help them. In the event of a crime, its facial recognition software will record a criminal's face and live-stream it back to police headquarters.

In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learn- ing from demonstration method of an autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.

Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special network can take interference into consideration, which is likely to be an efficient way to describe the underlying mechanism. With the assistance of belief entropy, named as Deng entropy, the paper proposes Belief Distance to render the model practical. Tested with empirical data, the proposed model is proved to be predictable and effective.

Explicitly or implicitly, most of dimensionality reduction methods need to determine which samples are neighbors and the similarity between the neighbors in the original highdimensional space. The projection matrix is then learned on the assumption that the neighborhood information (e.g., the similarity) is known and fixed prior to learning. However, it is difficult to precisely measure the intrinsic similarity of samples in high-dimensional space because of the curse of dimensionality. Consequently, the neighbors selected according to such similarity might and the projection matrix obtained according to such similarity and neighbors are not optimal in the sense of classification and generalization. To overcome the drawbacks, in this paper we propose to let the similarity and neighbors be variables and model them in low-dimensional space. Both the optimal similarity and projection matrix are obtained by minimizing a unified objective function. Nonnegative and sum-to-one constraints on the similarity are adopted. Instead of empirically setting the regularization parameter, we treat it as a variable to be optimized. It is interesting that the optimal regularization parameter is adaptive to the neighbors in low-dimensional space and has intuitive meaning. Experimental results on the YALE B, COIL-100, and MNIST datasets demonstrate the effectiveness of the proposed method.

BENGALURU: Companies in India say the use of artificial intelligence (AI) have added jobs in their firms and see little to no job losses as a result of the technology, a report by IT and consulting firm Capgemini showed. Out of the 86 companies surveyed in India, over 92% have created new job roles related to artificial intelligence, though a significant portion of the additional jobs are in upper management levels. Over 60% do not expect any job losses from AI. "There is a kind of renaissance in artificial intelligence that is currently going on... This creates a whole series of job roles and job roles will always evolve. In the short-to-medium term, which is what the report focuses on, there will not be massive kinds of job losses," Ron Tolido, executive vice-president & Global CTO for Capgemini's Insights & Data Practice, told ET.

Artificial intelligence and machine-learning algorithms are a useful predictor of schizophrenia with 74% accuracy, according to research at IBM and the University of Alberta in Edmonton, Canada. The retrospective study, which just appeared in Schizophrenia – published by the journal Nature – shows that the technology is capable of predicting the severity of certain symptoms in schizophrenia.

MAKUHARI, Japan-- Nissan Motor Co. NSANY 0.90% introduced its new Leaf electric car here Wednesday, with improved range, autonomous-driving technology and a price tag that undercuts rivals in a bid to jump-start slowing sales. Nissan said the car would go on sale in Japan on Oct. 2 and in the U.S. and Europe in January. When it goes on sale in the U.S. the vehicle will start at $29,990, slightly less expensive than the current Leaf. With bulked-up battery power, the Leaf will go 150 miles on a single charge, according to the company, up from 107 miles in the previous version. It also plans to introduce a version in the next year with a range of more than 300 miles.

IBM is making a 10-year, $240 million investment in artificial intelligence research through a new lab it's creating in partnership with the Massachusetts Institute of Technology. The investment will support research by IBM and MIT scientists at the newly created MIT-IBM Watson AI Lab in Cambridge, Mass., the two partners announced today. "Through this collaboration, we will target innovations that will move us beyond specialized tasks to more general approaches to solving more complex problems, with the added capability of robust, continuous learning," Dario Gil, IBM Research's vice president of AI and IBM Q, said in a blog post. Gil and MIT engineering dean Anantha Chandrakasan will be co-chairs of the lab, which will bring together more than 100 AI scientists, professors and students for joint research into AI hardware and software. The aim will be to take advantage of big data and find better ways to augment human intelligence, Gil said.

Tesla and SpaceX CEO Elon Musk has told conference attendees that humans must merge with AI if we are to survive against the dawn of the robot. "Over time I think we will probably see a closer merger of biological intelligence and digital intelligence," Musk told an audience at the World Government Summit in Dubai, according to CNBC. He explained that AI presents quite a danger to humans because they can process data at speeds of a trillion bits per second, while humans can only process information at ten bits per second. Our slow uptake of information could put us in danger and, therefore, Musk believes we must join forces with AI rather than work against it. "Some high bandwidth interface to the brain will be something that helps achieve a symbiosis between human and machine intelligence and maybe solves the control problem and the usefulness problem," he added.