Anil is Founder and Managing Partner at Unicorn India Ventures an leading Venture Capital Fund focusing on early stage investments in tech focused companies. He has been one of the pioneers of angel investments in India as head of operations and President at Mumbai Angels and Bangalore Angels, leading Angel Investment forums in India. Anil's experience includes corporate management functions in medium and large organizations, early stage investment in start-ups, project management, joint ventures and business development. Anil has helped close over 100 venture-financing deals. Anil serves on the Board of 5 companies and is involved with various incubation centres as mentor in India as well as internationally.

Three members from the legal affairs committee are currently working to ensure the EU is prepared for the legal and ethical aspects of developments in artificial intelligence (AI). Find out more in our interview. German EPP member Axel Voss, the member responsible for issues relating to civil liability regime for artificial intelligence, speaks about how the EU can solve the legal uncertainties created by the use of AI. What problems does the Parliament wants to solve? Although Europe's existing civil liability framework covers most upcoming scenarios, new technologies based on AI will nevertheless expose several unsolved issues.

It is predicted that the precision agriculture market will reach $12.9 billion by 2027. With this increase, there is a need for sophisticated data-analysis solutions that are capable of guiding management decisions in real-time. A new methodology has been developed by an interdisciplinary group at the University of Illinois, and it aims to efficiently and accurately process precision agricultural data. Nicolas Martin is an assistant professor in the Department of Crop Sciences at Illinois and co-author of the study. "We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," he says.

IBM's Deep Blue wasn't supposed to defeat Chess grandmaster Gary Kasparov when the two of them had their 1997 rematch. Computer experts of the time said machines would never beat us at strategy games because human ingenuity would always triumph over brute-force analysis. After Kasparov's loss, the experts didn't miss a beat. They said Chess was too easy and postulated that machines would never beat us at Go. Champion Lee Sedol's loss against DeepMind's AlphaGo proved them wrong there. Then the experts said AI would never beat us at games where strategy could be overcome by human creativity, such as poker.

Defenses against adversarial attacks, which in the context of AI refer to techniques that fool models through malicious input, are increasingly being broken by "defense-aware" attacks. In fact, most state-of-the-art methods claiming to detect adversarial attacks have been counteracted shortly after their publication. To break the cycle, researchers at the University of California, San Diego and Google Brain, including Turing Award winner Geoffrey Hinton, recently described in a preprint paper an approach that deflects attacks in the computer vision domain. Their framework either detects attacks accurately or, for undetected attacks, pressures the attackers to produce images that resemble the target class of images. The proposed architecture comprises (1) a network that classifies various input images from a data set and (2) a network that reconstructs the inputs conditioned on parameters of a predicted capsule.

A Bristol start-up building the next generation of microchips for artificial intelligence technology has raised $150m (£116m) from investors including Edinburgh's Baillie Gifford. Graphcore, founded in 2016 by Icera co-founder Nigel Toon, is now valued at almost $2bn having raised $450m from investors. Its new investors include Baillie Gifford, which has more than £200bn of assets under management, Mayfair Equity Partners and M&G Investments. Previous backers include Microsoft, BMW and Silicon Valley venture capital fund Sequoia. Graphcore has developed an intelligence processing unit, a microchip designed for artificial intelligence software.

Artificial intelligence is expected to increase economic output by $13 trillion in the coming decade. As per McKinsey's report, organisations that fully absorb this technology will double their cash flow in that time, while firms that don't, could see a 20% decline. The prepackaged solutions of the top cloud providers like Google Cloud help integrate AI into products. Cloud is now being utilised for navigating ships carrying cargo, thereby connecting online and offline retail markets. It is also assisting companies with ML services in fintech for fraud detection and many more.

Researchers in Japan have created a new kind of biodegradable, internet-connected piece of paper that's just one millimeter thick. Called nanopaper, the team believes it could change the way a wide variety of data is collected in nature. The project was developed at Osaka University's Institute of Scientific and Industrial Research (ISIR) by a team of scientists led by doctoral researcher Takaaki Kasuga. The nanopaper is just one-1,000th the thickness of pulp paper and it has a smooth surface that's similar to plastic in texture, according to a report in The Asahi Shimbun. In spite of its feel, the nanopaper is designed to be biodegradable and have a minimal impact on the environment and not require any expensive maintenance. In tests, the nanopaper had degraded by around 95 percent after 40 days in the ground, and its metal parts had been covered in non-toxic rust.

Any sufficiently advanced technology is indistinguishable from magic. In the world of artificial intelligence & machine learning (AI & ML), black- and white-box categorization of models and algorithms refers to their interpretability. That is, given a model trained to map data inputs to outputs (e.g. And just as the software testing dichotomy is high-level behavior vs low-level logic, only white-box AI methods can be readily interpreted to see the logic behind models' predictions. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts that grok the models and algorithms, the conversation around interpretability has become an important one.

IBM has outlined principles to promote transparency -- and foster public trust -- in the way companies use artificial intelligence. The principles call on banks and other organizations to designate a lead AI official, own up to their use of the technology, explain it and test it for bias. Bankers say they're already on it. IBM unveiled the principles last month at Davos through its new IBM Policy Lab. The goal was to provide guidance for developing intelligent policy that will provide societal protections without stifling innovation.