As a young child, I used to imagine what life would be like if I had a chauffeur to drive me wherever and whenever I wanted. Of course, this was a luxury afforded by only the wealthy and remained well out of reach for most people -- myself included. Fast forward to today, and the rise of the ride-sharing economy has essentially leveled the playing field, giving everyone affordable access to on-demand transportation. Access to artificial intelligence (AI) is poised to undergo a similar shift. Traditionally, large corporations and government entities have been ahead of the adoption curve because they've had the capital to invest in and the talent to leverage the technology.

The South Western Command will be holding a two-day brain-storming session with top military officers, scientists and IT experts on "AI in mechanised (tanks and infantry combat vehicles) warfare" at Hissar next week Rajnath Singh is likely to announce "25 defence-specific AI products" that will be developed by 2024 The South Western Command will be holding a two-day brain-storming session with top military officers, scientists and IT experts on "AI in mechanised (tanks and infantry combat vehicles) warfare" at Hissar next week Rajnath Singh is likely to announce "25 defence-specific AI products" that will be developed by 2024 NEW DELHI: The Army now wants to harness the potential of artificial intelligence (AI) to bolster its combat lethality and survivability, even as the 13-lakh force is testing its new integrated battle groups (IBGs) geared towards mobilising fast and striking hard across the borders. With China taking huge strides in the ongoing global race to develop AI-powered weapon and surveillance systems for futuristic wars, with a special focus on developing lethal autonomous weapon systems (LAWS), India obviously does not want to miss the bus. "China is employing AI (basically simulation of human intelligence processes by computers) in the defence arena in a big way. But we can catch up because we have the required IT (information technology) brains in India. Our aim is to examine how AI can help us become more lethal and effective in our war-fighting in a flexible and dynamic battlefield," said South Western Command (SWC) chief Lt-General Alok Kler, speaking to TOI on Friday.

A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive. Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state. Past attempts to apply deep learning techniques to detect and monitor depression in social media posts have been shown to be tedious and expensive, explained Nawshad Farruque, a University of Alberta Ph.D. student in computing science who is leading the new study. He explained that a Twitter post saying that somebody is depressed because Netflix is down isn't really expressing depression, so someone would need to "explain" this to the algorithm.

Carnegie Mellon University AI researchers have created an AI agent that is able to translate words into physical movement. Called Joint Language-to-Pose, or JL2P, the approach combines natural language with 3D pose models. The pose forecasting joint embedding is trained with end-to-end curriculum learning, an approach that stresses shorter task completion sequences before moving on to harder objectives. JL2P animations are limited to stick figures today, but the ability to translate words into human-like movement can someday help humanoid robots do physical tasks in the real world or assist creatives in animating virtual characters for things like video games or movies. JL2P is in line with previous works that turn words into imagery -- like Microsoft's ObjGAN, which sketches images and storyboards from captions, Disney's AI that uses words in a script to create storyboards, and Nvidia's GauGAN, which lets users paint landscapes using paintbrushes labeled with words like "trees," "mountain," or "sky."

Two new and seemingly unrelated approaches to delivering healthcare are starting to take shape in the industry: the use of artificial intelligence, and the integration of social determinants of health in crafting care plans. Both trends are developing independently, but they're likely due to intersect; factoring in SDOH is possible due to data, and if AI shines in any one particular area, it's making sense of complex data sets. If the social determinants are comprised of the socioeconomic factors that can influence a person's health -- income, education, access to transportation, etc. -- then AI has the potential to allow providers to make the best possible use of that information. That becomes increasingly important as value-based care emerges. With reimbursement increasingly tied to health outcomes, providers have a real incentive to ensure they're delivering the best care possible.

If there's one area where there's real excitement about the improvements in our lives which advances in artificial intelligence could bring, it is healthcare. On Tech Tent this week we ask whether, amidst all the hope and hype, real innovations are beginning to transform the way patients are treated. At Oxford University's Said Business School, some of the leading thinkers in AI research - from the university and beyond - gathered this week. There were fascinating discussions about everything from autonomous cars to the way AI is transforming the finance industry. But the panels on healthcare drew big crowds and a sense that researchers were on the cusp of delivering concrete results which will soon begin to be seen in hospitals.

Economics has experienced an empirical turn in the last few decades. We have entered an era of big data, machine learning, and artificial intelligence. Experimental methods have greatly increased in importance in both the social and life sciences. And recent efforts at reforming the publication system promise to improve the replicability and credibility of published findings. One might be tempted to conclude that this increased availability of and reliance on quantitative evidence allows us to dispense with the normative judgements of earlier days. I will argue that the opposite is the case. The choice of objective functions, which define our goals, and of the set of policies to be considered matters ever more in all of these contexts. A famous example in debates about the dangers of artificial intelligence (AI) is the hypothetical AI system with the objective of producing as many paperclips as possible. If sufficiently capable, such an AI system might end up annihilating humanity in the pursuit of this objective. Another example is the design of experiments. The majority of experiments in the social and life sciences are designed based on the (implicit) objective of obtaining precise estimates of causal effects. Such experiments randomly assign treatments using fixed probabilities.

Finextra spoke to Amit Bhute, SVP & global head of the banking and financial services practice and Soumyendu Kishore Pal, co-head of the capital markets practice at Virtusa about what is driving digital transformation in financial services today. Bhute says that the rise of digitally native neobanks has showcased a significant gap in the market. "Banking legacy infrastructure that was built between the 1960s and 1980s is crumbling and is now unable to meet the demands of increasingly real-time and data-intensive customer demands. Banks have realised that sticking to the status quo is no longer enough and simply meeting regulatory demands will not support growth." Traditional banks must focus their digital transformation strategies on innovation, otherwise new revenue will captured by the digitally savvy neobanks.

SELF-DRIVING cars are in a constant state of development, with numerous companies including Tesla, Audi and Volvo (as well as technology giants such as Apple and Google) pouring millions of pounds into making the autonomous car technology roadworthy. Will it see a major shift in employment and work culture? Is the driver or the manufacturer liable in the event of an accident? How will legislation and layouts be changed to make self-driving cars compatible with UK roads? Auto Express has investigated the world of driverless car technology, to bring you the answers about the cutting edge of mobility.