It's day one at the Singularity University: the opening address has just been delivered by a hologram. Craig Venter, who was one of the first scientists to sequence the human genome and created the first synthetic life form, is up next. And later, we will see two people, paralysed from the waist down, use robotic exoskeletons to rise up and walk. But first, the co-founder of the Singularity University, Peter Diamandis, gives us our instructions for the day. Your task, he says, is to pick one of the "grand challenges of humanity" – the lack of clean drinking water, say. And then come up with an idea that "can positively impact the lives of a billion people". Some of us haven't even had coffee yet. There's about 50 of us present and the room has been divided up into tables, one for education, another for poverty, another for water, and I'm not sure where I should sit. Diane Murphy, the university's PR executive, hesitates for a moment and then directs me over to the table marked "food". "Tell you what," she says.

Reinforcement Learning, a key Google DeepMind algorithm, could overhaul news recommendation engines and greatly improve users stickiness. After beating a Go Grand Master, the algorithm could become the engine of choice for true personalization. My interest for DeepMind goes back to its acquisition by Google, in January 2014, for about half a billion dollars. Later in California, I had conversations with Artificial Intelligence and deep learning experts; they said Google had in fact captured about half of the world's best A.I. minds, snatching several years of Stanford A.I. classes, and paying top dollar for talent. Acquiring London startup Deep Mind was a key move in a strategy aimed at cornering the A.I. field.

Detecting anomalous behavior remains one of security's most impactful data science challenges. Most approaches rely on signature-based techniques, which are reactionary in nature and fail to predict new patterns of malicious behavior and modern adversarial techniques. Instead, as a key component of research in Intrusion Detection, I'll focus on command line anomaly detection using a machine-learning based approach. A model based on command line history can potentially detect a range of anomalous behavior, including intruders using stolen credentials and insider threats. Command lines contain a wealth of information and serve as a valid proxy for user intent.

Humans have a terrible track record of predicting financial crises in time to fend them off. Some computer scientists think that algorithms might help. Given the right information, some crises can be foreseen. In "The Big Short", Michael Lewis told the story of the scattered few who saw the imbalance growing in the mortgage market and profited as a result. Over decades, academic research has shown that many banking crises come with early warning signals, such as rapidly increasing debt and leverage.

As Artificial Intelligence (AI) is fast moving beyond the realms of science fiction and entering the workplace, and this will have implications for the corporate IT infrastructure of the future. Facebook's announcement that it will enable businesses to deliver automated customer support, online shopping guidance, content and interactive experiences to its users through ChatBots is just the beginning. Whilst some Bots, like the Microsoft's recent Tay experiment, aren't foolproof yet, Messenger Bots represent the new and acceptable face of Artificial Intelligence. ChatBots use deep learning and neural networks to allow them to learn from data sets in the same way a human brain does. Whilst still in development phase, they are fast becoming increasingly popular with developers and platforms.

Word has spread that artificial intelligence (AI) is the next big thing in business. But don't be fooled; in reality, it's so much more than that. Take a look at how it's set to overhaul your organisation.

Knowing a cyberattack's going to occur before it actually happens is very useful--but it's tricky to achieve in practice. Now MIT's built an artificial intelligence system that can predict attacks 85 percent of the time. Cyberattack spotters work in two main ways. Some are AIs that simply look out for anomalies in internet traffic. They work, but often throw up false positives--warnings about a threat when actually nothing's wrong. Other software systems are built on rules developed by humans, but it's hard to create systems like that which catches every attack.

The notion of Artificial Intelligence has been around for a while. Yet, unlike other prominent technological innovations such as electric cars or the processor speed, its progress has not been linear. In fact, as far as industrial impact is concerned, there were times when allegedly there was no progress at all. The widespread fascination with AI started several generations ago, in 80-s of the last century. This is when a pioneering work of Noam Chomsky on computational grammar led to a belief that human language capabilities in particular, and human intelligence in general, can be straightforwardly algorithmized.

Existing threat-detection systems broadly fall into two categories: a software bot that can detect patterns and human analysis. AI2's gimmick is that it mashes together a handful of different machine learning tools and asks its flesh-and-blood counterparts for help. When it thinks it's found a pattern amongst the noise of data, it offers it up to a person for a second opinion. After a short period of time, AI2 will learn from its errors and what the human experts are telling it. As Arnaldo says, "it continuously generates new models that it can refine in as little as two hours."

Moves to tighten rules on drones have been promised by the government after Labour and pilots unions called for urgent action, including a possible register of drone users and "geo-fencing" of airports, after a British Airways plane was struck on its descent into Heathrow. Sunday's incident is believed to be the first such collision between a passenger plane and a drone, after a series of near misses that led pilots to warn that a strike could be disastrous. The Air Accidents Investigations Branch said it would launch an inquiry. The Department for Transport (DfT) said it would hold a public consultation before a government strategy is published later this year. Labour has accused ministers of dragging their feet after aviation authorities confirmed a number of potentially serious incidents in 2015, including 23 near misses between aircraft and drones in six months investigated by the UK Airprox Board.