The reason for the continued existence of HR is in the name: humans are fickle and complicated characters who don't fit into nicely defined boxes, unlike computers. As far as we might get with AI and machine learning, there is no substitute for emotional intelligence, and many of an HR manager's processes are too nuanced to be reduced to a binary solution. However, machine learning will significantly increase the efficiency of the HR manager. To illustrate the progress of machine learning, Facebook can now define someone's personality better than their spouse, based on just 300 'likes'. While an HR manager would not be advised to use this method, an unstructured machine learning application could quickly build up a profile of strengths and weaknesses of employees.

Grim news – Silicon Valley is coming to the UK. Google is planning to build a gigantic £600m "landscraper" in King's Cross, central London. It plans to "bring our London Googlers together" with a 25-metre swimming pool, massage rooms, a basketball court, nap pods and a 200-metre "trim trail", which loops around a rooftop meadow, to encourage "the Google culture of walking meetings", as the Guardian's architecture and design critic, Oliver Wainwright, put it. This is where I begin to feel queasy. Google has its own "culture", which its 7,000 London employees will presumably have to embrace cheerily while they work, jog, snooze, eat, play and eventually live on this monster "campus".

Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. The popularity of smartphones, wearables and social media platforms has led to an explosion in the amount of data being recorded and AI is the only way to make use of it. With the surge of digital disruption in the financial services, the industry has led to hundreds of emerging startups bringing new ways for people to bank, which is causing traditional methods to undergo an innovation overhaul to integrate new technological advancements in order to compete. To celebrate London's 3rd Annual (15-22 July), we spoke to experts in the field to find out how and why, and, most importantly, what we can expect in the future. What have been the leading factors enabling recent advancements and uptake of deep learning? Jan: Astonishing increases in computing power and data availability in recent years have been the main benefactors of deep learning technology.

Jan: Astonishing increases in computing power and data availability in recent years have been the main benefactors of deep learning technology. Hitoshi: Some of the easily understandable applications, such as image recognition, video captioning and beating the world champion of Go, are pushing people hard to be excited. From a technical perspective, the generality and high accuracy that deep learning has is the main motivation for using it instead of other machine learning methods. In our case, for example, our AI engine learns how traders trade from the technical chart, no matter what kind of strategy or what kind of indicators they use. Alesis: The computational power and tools to utilize that power has definitely enabled the recent advancements in Deep Learning.

Ryan Brady is a digital strategist and social media manager for Digital Response Marketing Group. "Enjoy your com-FORT-able stay," says a robot front-desk clerk at Japan's Robot Hotel. Do you thank the robot for its awkward salutation? Or maybe you hesitate for a moment before shuffling off in silence. If our digital screens are separating us from human interaction, you better believe AI will further tear that tenuous social fabric.