Fears that artificial intelligence will wipe out human beings are completely overblown, according to the co-founder of Britain's DeepMind, who has insisted that the technology will help tackle some of the world's biggest problems including accessing clean water, financial inequality and stock market risks. Mustafa Suleyman, who with Demis Hassabis and Shane Legg set up the London-based machine learning company that was bought by Google in January 2014 for £400m, mounted a spirited defence of the company's successes. He told a conference on machine learning that "artificial intelligence, AI, has arrived. This isn't just some brief summer for this technology, and it's not about to go away again. High-profile figures including Elon Musk, Stephen Hawking and Bill Gates have all warned that the rise of AI poses a threat to humanity – a threat that has been echoed in recent Hollywood films such as Ex Machina, The Terminator and Transcendence. Yet Suleyman insisted that AI is, and will remain, a tool that humans can control and direct, rather than a threat. The best use for AI would be to help decisions about how to tackle some of the world's biggest problems such as lack of access to clean water, inequality of access to food and finance, and stock market risks, he suggested. DeepMind's systems use neural networks and "deep learning" methods that deploy low-level transistor networks to produce high-level effects so that they can, for instance, distinguish a cat's face from a human one – a trivial task for a human, but hard for a machine. That has been developed into "artificial general intelligence" (AGI) that can learn to solve tasks without prior programming, and have already been used to replace 60 hand-crafted systems across Google. The AGI system's deployment into speech recognition, now used in Android phones and Google Translate, had led to the biggest overall improvement in speech recognition in 20 years, Suleyman said, with a 30% reduction in transcription error rates. Yet training the program for the task took less than five days. Speaking to a conference on machine intelligence in London on Friday, Suleyman said that he was dismayed by the negative attitudes being shown towards AI. "It's sad how quickly we've adopted to the reality and don't acknowledge the magic and the good that these systems can bring.

There can't be many events where Neelie Kroes, vice-president of the European commission, rubs shoulders with Duran Duran's Simon Le Bon, or Google bon vivant Eric Schmidt with the reliably delightful Grayson Perry. For the one percenters who get an invitation to Founders Forum, the event is like the Davos of the tech industry. For an observer, the atmosphere combines the congratulatory backslapping of intense power network with an undercurrent of feisty rivalry. Secrets are told, processes explained, deals done late at night over very expensive drinks. There are modest panel discussions (not everyone goes; the best discussions are alwaysin the corridors, near the bar or clustered under an accommodating willow tree in the grounds), but even these sessions open the kimono a little wider than usual. There have been few recent tech acquisitions more intriguing than that of DeepMind, the British artificial intelligence firm bought by Google for about £400m in January.

Today we were greeted by the front page of Nature hailing a breakthrough in artificial intelligence: computers are now outperforming even the best humans at the Chinese game of Go, long been seen as the last preserve of human game-playing mastery. The breakthrough, from a team based at Google's DeepMind group in London, has come much earlier than many experts expected. The achievement is also being hailed as a breakthrough in understanding human intelligence, and a large step towards emulating it. However, so was Deep Blue's achievement when it first beat chess world champion, Gary Kasparov, nearly 20 years ago. So where does this latest success really bring us?

The holy grail of artificial intelligence--creating software that comes close to mimicking human intelligence--remains far off. But 2014 saw major strides in machine learning software that can gain abilities from experience. Companies in sectors from biotech to computing turned to these new techniques to solve tough problems or develop new products. The most striking research results in AI came from the field of deep learning, which involves using crude simulated neurons to process data. Work in deep learning often focuses on images, which are easy for humans to understand but very difficult for software to decipher.

A technique called deep learning has enabled Google and other companies to make breakthroughs in getting computers to understand the content of photos. Now researchers at Cornell University and the University of Wyoming have shown how to make images that fool such software into seeing things that aren't there. The researchers can create images that appear to a human as scrambled nonsense or simple geometric patterns, but are identified by the software as an everyday object such as a school bus. The trick images offer new insight into the differences between how real brains and the simple simulated neurons used in deep learning process images. Researchers typically train deep learning software to recognize something of interest--say, a guitar--by showing it millions of pictures of guitars, each time telling the computer "This is a guitar."

Demis Hassabis started playing chess at age four and soon blossomed into a child prodigy. At age eight, success on the chessboard led him to ponder two questions that have obsessed him ever since: first, how does the brain learn to master complex tasks; and second, could computers ever do the same? Now 38, Hassabis puzzles over those questions for Google, having sold his little-known London-based startup, DeepMind, to the search company earlier this year for a reported 400 million pounds ($650 million at the time). Google snapped up DeepMind shortly after it demonstrated software capable of teaching itself to play classic video games to a super-human level (see "Is Google Cornering the Market on Deep Learning?"). At the TED conference in Vancouver this year, Google CEO Larry Page gushed about Hassabis and called his company's technology "one of the most exciting things I've seen in a long time."

Facebook is set to get an even better understanding of the 700 million people who use the social network to share details of their personal lives each day. A new research group within the company is working on an emerging and powerful approach to artificial intelligence known as deep learning, which uses simulated networks of brain cells to process data. Applying this method to data shared on Facebook could allow for novel features and perhaps boost the company's ad targeting. Deep learning has shown potential as the basis for software that could work out the emotions or events described in text even if they aren't explicitly referenced, recognize objects in photos, and make sophisticated predictions about people's likely future behavior. The eight-person group, known internally as the AI team, only recently started work, and details of its experiments are still secret.

The first time Yann LeCun revolutionized artificial intelligence, it was a false dawn. It was 1995, and for almost a decade, the young Frenchman had been dedicated to what many computer scientists considered a bad idea: that crudely mimicking certain features of the brain was the best way to bring about intelligent machines. But LeCun had shown that this approach could produce something strikingly smart--and useful. Working at Bell Labs, he made software that roughly simulated neurons and learned to read handwritten text by looking at many different examples. Bell Labs' corporate parent, AT&T, used it to sell the first machines capable of reading the handwriting on checks and written forms. To LeCun and a few fellow believers in artificial neural networks, it seemed to mark the beginning of an era in which machines could learn many other skills previously limited to humans. "This whole project kind of disappeared on the day of its biggest success," says LeCun. On the same day he celebrated the launch of bank machines that could read thousands of checks per hour, AT&T announced it was splitting into three companies dedicated to different markets in communications and computing. LeCun became head of research at a slimmer AT&T and was directed to work on other things; in 2002 he would leave AT&T, soon to become a professor at New York University.

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In the last few years, the world of artificial intelligence has mainly been dominated by large internet companies with huge computing infrastructures like Google and Facebook, or research universities like MIT or Stanford. The non-profit research firm is backed by heavy hitters like co-chairs Elon Musk (of SpaceX and Tesla fame), Y Combinator's Sam Altman, as well as investor Peter Thiel (who worked with Musk at PayPal). They claim to have garnered a billion dollars in private funding, from people like Thiel and Amazon Web Services. "We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as is possible safely," OpenAI writes in its first blog post, published just a few moments ago. The goal? Make the scope of A.I less narrow.