Speaking at Google Cloud Platform in San Francisco, the chairman of Alphabet and former Google CEO highlighted artificial intelligence as the "next evolution" in computing. This will will spur the creation of new apps and services, he said. "Machine learning and crowdsourcing data will be the basis and fundamentals of every successful, huge IPO win in five years, in the same sense that the transition to [mobile] apps five years ago created the modern corporations of Uber, SnapChat and others." Schmidt highlighted Google AlphaGo - which defeated Go champion Lee Sedol last week - as an example of the power of artificial intelligence. See also: Google DeepMind: What is it, how does it work and should you be scared? He also cited Google's Photos image search software as a practical example of how machine learning can be applied to create new products and services when applied to large crowd-sourced datasets.

Following in the wake of the recent trouncing of humans by artificial intelligence platform AlphaGo, Google has announced the launch of a cloud-based machine learning platform. The search giant's new large-scale platform will be able to learn and make predictions'across a whole variety of scenarios', and is reminiscent of the fictional Skynet service from Terminator. A limited preview of the service is now available for users to build their own machine-learning models'that work on any type of data, of any size'. Following in the wake of the recent trouncing of humans by artificial intelligence platform AlphaGo, Google has announced the launch of a cloud-based machine learning platform. The search giant's new large-scale platform will be able to learn and make predictions'across a whole variety of scenarios' A limited preview of the service is now available for users to build their own machine-learning models'that work on any type of data, of any size'.

Supercomputers are about to achieve a quantity and level of intelligence which allows them to grow hyper-exponentially over a short period of time. We call them superintelligences and this sudden growth poses some risks because they will be much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills (Nick Bostrom). I don't question that this will happen, as Ray Kurzweil said "Our intuition about the future is linear. But the reality of information technology is exponential, and that makes a profound difference. If I take 30 steps linearly, I get to 30. If I take 30 steps exponentially, I get to a billion.".

Global technology leaders and senior executives from around the world spoke on a range of topics, including Digital India, Smart Cities, Make in India, Skill India and cutting-edge technologies such as artificial intelligence, machine learning, 3D printing, drones, robotics, robotic surgeries and genomics, at the two-day EmTech India 2016 event, held in New Delhi on 18 and 19 March. The event was organized by Mint and MIT Technology Review, published by the Massachusetts Institute of Technology (MIT). The speakers included Jack Hidary, senior adviser at Google X Labs; Bhaskar Pramanik, chairman of Microsoft India; and Sharad Sharma, co-founder of think tank iSPIRT. The full list can be accessed at emtech.livemint.com/speakers. Here are edited excerpts from their speeches. A moonshot is an initiative that accompanies a goal that was previously thought to be near impossible. Moonshot philosophy sounds like it is quite radical and risky, but actually it is low-risk. That is because it attracts the best human capital and finance. Moonshot approaches do a few things. First, they attract the best human capital, which is a key driver of growth. They attract the best financial capital as well; capital from big and long-term thinkers. One describes India as a moonshot nation. India itself is going through a radical transformation--the likes of what we have never seen. This is very different to what is happening in China or any other country in the world. It is a combination of smartphones, digital payments, broadband and power of energy storage coming together. Smartphones ease the access to the Internet and open up users to mobile apps and that really changes the name of the game.

In the neo-noir sci-fi classic, protagonist Neo is able to learn kung fu in seconds after the martial art is'uploaded' straight to his brain. Researchers from HRL Laboratories, based in California, say they have found a way to amplify learning, only on a much smaller scale than seen in the Hollywood film. They studied the electric signals in the brain of a trained pilot and then fed the data into novice subjects as they learned to pilot an aeroplane in a realistic flight simulator. The study, published in the journal Frontiers in Human Neuroscience, found that subjects who received brain stimulation via electrode-embedded head caps improved their piloting abilities and learnt the task 33 per cent better than a placebo group.

Organisations have begun to shift their analytical efforts to back office operations and away from more customer-facing processes in a search for greater competitive advantage, according to a new report. More than two thirds of companies, based on feedback from 600 operations executives surveyed as part of a report from Capgemini Consulting's digital transformation institute, revealed there is now a greater focus on operational analytics initiatives, in order to boost efficiency behind the scenes. Anne-Laure Thieullent, head of big data, Europe, for Capgemini's insights and data global practice, said: "Organisations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office. However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams." The four levels of maturity for operational analytics, as decided by Capgemini Consulting, are'Game Changers' - those who have both integrated analytics and realised its benefits, 'Optimisers' - those who have seen basic success but not yet scaled-up efforts, 'Strugglers' - who have adopted the analytics strategy without seeing the benefits, and'Laggards' - who are just beginning to focus on operational analytics.

Depending on who you talk to, artificial intelligence (AI) will either be the saviour or nemesis of mankind. Elon Musk, of PayPal, Tesla Cars and SpaceX (sometimes described as a real life Iron Man) has likened AI to "summoning a demon" – i.e. once it's truly here it can't be controlled. Meanwhile, Demis Hassabis (himself referred to as a superhero of AI), founder of DeepMind Technologies – a British AI start-up acquired by Google in 2014 for 400 million – is on a quest for what he calls "the meta-solution to any problem" through AI. Debate will rightly rage about the future of AI, but what about the now? Research and development (R&D) into artificial intelligence has been going on for decades.

Somewhere in a Cupertino warehouse, a giant labors with robotic precision, its 29 arms singularly focused on one thing: an iPhone. But instead of putting pieces together, this robot is pulling pieces apart. It disassembles iPhones at the rate of one handset every 11 seconds--less time than it takes you to fish your phone out of an overcrowded bag. Apple calls the machine Liam. The project was kept secret for three years, says Mashable deputy tech editor Samantha Murphy Kelly, who was allowed a sneak preview of Liam in action.

We hear a lot about the Internet of Things on the consumer side. The oft trotted out example of the'smart' refrigerator that tells consumers when they've run out of the milk, and so on. But more serious potential for IoT -- and potentially seriously big wins -- are likely to be on the enterprise side where connected sensors can be deployed to automate at scale. The company took in a 6 million Series A round in December, which it's just announcing now -- and which includes, on top of that, a 3 million convertible seed, bringing its total raised to date to 9 million. Investors in the business include Jabil Circuit, Stanford StartX Fund, Founder.org

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him.