Machine learning today represents the single biggest investment area at AHL, chief executive Sandy Rattray has told the Financial Times, revealing that Man Group's 19bn-plus, quantitative hedge fund would increasingly adopt the technique for trading across its various vehicles. AHL, which began researching the artificial intelligence niche in 2011 and started deploying the same in trading three years ago, has seen "encouraging" results from the experiment, Rattray said.

Network television is at risk of getting caught in a vicious cycle. As the audience fragments in a million different directions, smaller subsets of that audience see promos for new shows. Then, as new shows draw smaller crowds, even fewer viewers see promos for other programs. The reach of television networks (the total number of viewers who watch for a minute or more once a day) is down a daunting 12 percent in one year. Yet a six percent larger audience has seen the promos for MTV's Viacom networks--even though they're using fewer spots.

I recently enjoyed the series of TV programmes on Sky News and the BBC profiling developments in Artificial Intelligence (AI) and robotics. But while the subject has always fascinated me, what really struck me was the underlying theme of fear that pervaded both programmes: the fear of what advances in AI might mean for the job security and the future of humans. There seems to be a war-like philosophy – if we develop them, they will overtake us, putting our very existence in danger. Robots will take over the world, and we will lose control. Discussions and research about the use of robots can be dated back to the first millennium, but the conversation today is indeed a world away from the musings of days gone by.

In professional cycling, it's well known that a pack of 40 or 50 riders can ride faster and more efficiently than a single rider or small group. As such, you'll often see cycling teams with different goals in a race work together to chase down a breakaway before the finish line. This analogy is one way to think about collaborative multi-agent intelligent systems, which are poised to change the technology landscape for individuals, businesses, and governments, says Dr. Mehdi Dastani, a computer scientist at Utrecht University. The proliferation of these multi-agent systems could lead to significant systemic changes across society in the next decade. "Multi-agent systems are basically a kind of distributed system with sets of software. A set can be very large. They are autonomous, they make their own decisions, they can perceive their environment, "Dastani said.

Sébastien Bratières is a PHD candidate in Engineering at the University of Cambridge, where he researches probabilistic machine learning in the Bayesian methods. His background is in natural language processing and speech recognition. Currently he works for Dawin, a company making speech recognition interfaces.

At the NEXT2016 Google cloud platform user conference in San Francisco held this Wednesday, the internet search tech giant commenced a new machine learning platform for developers. The name of this new service called Google Cloud Machine Learning, and will be initially available in a limited preview edition. As the company chairman Eric Schmidt said that Google believes machine learning is "what's next.". With this new cloud platform, Google will make it easier for developers to apply some of the machine learning tactics used by the company to power features like Smart Reply in Inbox services. "Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate and Inbox (Smart Reply)," the company says.

Click to learn more about Yefim (Jeff) Zhuk. Collaboration between people and groups seems to be a thing with a positive sign, although we know how difficult this can be. Distributed knowledge and process systems [9] "allows involved parties, people and companies, negotiate multiple forms of collaboration online while sharing data and services." Collaborative security of service groups is different from a single service security. Simultaneous activity of many services, working on a common task, requires collaborative decision making.

Once the three-billion-letter-long human genome was sequenced, we rushed into a new'omics' era of biological research. Scientists are now racing to sequence the genomes (all the genes) or proteomes (all the proteins) of various organisms – and in the process are compiling massive amounts of data. For instance, a scientist can use'omics' tools such as DNA sequencing to tease out which human genes are affected in a viral flu infection. But because the human genome has at least 25,000 genes in total, the number of genes altered even under such a simple scenario could potentially be in the thousands. Although sequencing and identifying genes and proteins gives them a name and a place, it doesn't tell us what they do.

They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Apple's new robot, Liam, is designed to disassemble iPhones for recycling purposes. Meet Liam, an Apple robot designed to take apart 1.2 million iPhones a year. Mashable reporter Samantha Murphy Kelly got a first look at the robot at Apple's headquarters. It has 29 arms and it was an Apple secret for three years. "Liam is programmed to carefully disassemble the many pieces of returned iPhones, such as SIM card trays, screws, batteries and cameras, by removing components bit by bit so they'll all be easier to recycle. Traditional tech recycling methods involve a shredder with magnets that makes it hard to separate parts in a pure way (you'll often get scrap materials commingled with other pieces)."