Subhalata Pradhan, a Gram Vikas fieldworker, talks to Raja Pradhan about the chatbot and addresses concerns over sharing his details. Subhalata Pradhan, a Gram Vikas fieldworker, talks to Raja Pradhan about the chatbot and addresses concerns over sharing his details. India's scattered workforce: the chatbot keeping families in touch during emergencies Covid exposed the lack of data on the country's 140 million mobile migrant workers, but a new project in Odisha is helping to fill in the gaps Mon 16 Mar 2026 02.00 EDTLast modified on Mon 16 Mar 2026 02.03 EDT Raja Pradhan is sitting cross-legged, scrolling on his phone in his village in eastern India when a green WhatsApp chat bubble pops up on the screen. Are you going outside for work? He reads the message twice, unsure whether to respond.

Tech firm International Business Machines Corp. (IBM) has said that artificial intelligence (AI), blockchain and Internet of Things will be deployed across larger projects, now that their pilot stage is over. "Last year, we saw banks and other partners do a lot of pilots and experiments in areas like AI, blockchain, and the Internet of Things. However, most of these experiments are now complete and we will be seeing these technologies being adopted in larger production-ready projects" Prashant Pradhan, South Asia and India chief technology officer, was quoted as saying by The Economic Times (ET). "What a lot of our legacy clients are recognising is the fact that while they always had the advantage of having a lot of customer data, newer digital companies have done a much better job of anchoring the whole business on insights from that data. Hence, you will see a lot of AI workloads with what you do with enterprise data." he added.

A camera can be compared to the human eye in the sense that both can capture an image. While the camera can only store the image, the nerve network and brain cells that help the human eye see can recognise as well as reconstruct it. The human brain has the power to memorise, recollect and think but even advanced machines cannot think for themselves. Recent breakthroughs in research, however, may just have made possible a machine that can "see". Led by a scientist who calls Calcutta home, researchers at the University of Central Florida (UCF), US, devised a minute gadget that exhibited the ability to recollect and recognise human faces in a way that mimics human brain cells.

Namita Pradhan sat at a desk in downtown Bhubaneswar, India, about 40 miles from the Bay of Bengal, staring at a video recorded in a hospital on the other side of the world. The video showed the inside of someone's colon. Pradhan was looking for polyps, small growths in the large intestine that could lead to cancer. When she found one -- they look a bit like a slimy, angry pimple -- she marked it with her computer mouse and keyboard, drawing a digital circle around the tiny bulge. She was not trained as a doctor, but she was helping to teach an artificial intelligence system that could eventually do the work of a doctor. Pradhan was one of dozens of young Indian women and men lined up at desks on the fourth floor of a small office building. They were trained to annotate all kinds of digital images, pinpointing everything from stop signs and pedestrians in street scenes to factories and oil tankers in satellite photos. AI, most people in the tech industry would tell you, is the future of their industry, and it is improving fast thanks to something called machine learning. But tech executives rarely discuss the labor-intensive process that goes into its creation.

Machine learning or artificial techniques has been rapidly transforming many areas related to GIS and spatial applications. One example is using web GIS with machine learning algorithms to predict or forecast the success of given potential hotel sites. This has been created into an application called Hotel Location Selection and Analyzing Toolset (HoLSAT). Techniques such as pursuit regression, artificial neural network, and support vector regression allow the tool to determine beneficial hotel locations based on a variety of criteria.[1] Determining where landslides might occur is also another potential application for machine learning techniques such as decision trees (DT) and adaptive neuro-fuzzy inference methods.

New Delhi: Tech honchos in Silicon Valley are deeply worried at China's rapid progress in harnessing Artificial Intelligence (AI) technology that has shown encouraging results in changing the way we work and live. Measured by start-up financing deals and dollars from venture capitalists, the United States' AI start-up ecosystem currently dominates -- followed by China, says a recent Accenture analysis titled "Rewire for Growth". When it comes to India, the number of AI start-ups has increased since 2011 at a compounded annual growth rate of 86 per cent. But the size of funding till date is substantially smaller in India than in the US and China, reflecting the limited success of India's AI start-ups in achieving scale so far, the report noted. "According to our analysis, AI has the potential to add $957 billion, or 15 per cent of current gross value added, to India's economy in 2035," said Accenture.

Tech honchos in Silicon Valley are deeply worried at China's rapid progress in harnessing Artificial Intelligence (AI) technology that has shown encouraging results in changing the way we work and live. Measured by start-up financing deals and dollars from venture capitalists, the United States' AI start-up ecosystem currently dominates -- followed by China, says a recent Accenture analysis titled "Rewire for Growth". When it comes to India, the number of AI start-ups has increased since 2011 at a compounded annual growth rate of 86 per cent. But the size of funding till date is substantially smaller in India than in the US and China, reflecting the limited success of India's AI start-ups in achieving scale so far, the report noted. "According to our analysis, AI has the potential to add $957 billion, or 15 per cent of current gross value added, to India's economy in 2035," said Accenture.

As I sit down in Nissan's simulator, I prepare myself for the fact that a cohort of researchers could scrutinize my skills as a wheelman with more rigor than the most aggravating backseat driver. And, I accept that this process involves wearing what looks like a too-small, sideways bicycle helmet, which holds 11 electrodes poking through my hair. "For each corner, there'll be an evaluation of your driving smoothness," says Lucian Gheorghe, the Nissan researcher in charge of this rig. Gheorghe is interested in motor related potentials, a specific pattern of activity the brain creates as it prepares to move a limb. It takes half a second for the body to translate that signal to the wave of an arm or kick of a leg, and Nissan wants to exploit the gap.

Life and stories are so closely intertwined that, at times, it's hard to know where one ends and the other begins. On Flipboard, stories flow from one to the next, weaving a rich tapestry that does an amazing job of showcasing all that life has to offer. The Galaxy Note 7 was the best phone available when it was released in August. A culmination of all of the company's strongest technologies to date, coupled with some fascinating new arrivals, Samsung somehow made the whole thing work in harmony, fit into a beautifully crafted 5.7-inch design. Even a war as pitiless as Syria's can have a low point.

The fundamental assumption being, any hypothesis found to approximate well over a sufficiently large Surface Electromyogram (SEMG) signals are physiological set of training examples will also approximate well over signals processed to assess the intensity of activity and the other unobserved examples (Mitchell 1997), belonging to fatigue state of the muscles, non-invasively (Kumar, Pah, the same distribution as the training data. But if this basic and Bradley 2003; Georgakis, Stergioulas, and Giakas 2003; assumption is violated as in the case of SEMG data over Koumantakis et al. 2001; Gerdle, Larsson, and Karlsson multiple subjects, direct application of traditional data mining 2000). However researches observed significant difference and machine learning methods would not work. Figure 1 between the data collected from different subjects shows a typical distribution of SEMG data for three different though they performed the same activity under similar experimental subjects, collected over a fatiguing exercise at varying speed conditions (Contessa, Adam, and Luca 2009; representing the four physiological phases corresponding to Gerdle, Larsson, and Karlsson 2000). Because of their four classes (l) low intensity of activity and low fatigue, (2) highly subject specific nature the SEMG based fatigue assessment high intensity of activity and moderate fatigue, (3) low intensity requires subject specific calibration and are hence of activity and moderate fatigue and (4) high intensity confined to clinical environments related to training and rehabilitation. of activity and high fatigue.