We propose a method for practical downlink coordinated multipoint (DL CoMP) implementation in 4G LTE/LTE-A systems using supervised machine learning. Contributions of this paper are: 1) demonstrating that a support vector machine classifier can learn the optimal conditions at which DL CoMP can be dynamically triggered, 2) improving user throughput in DL CoMP as a result of learning the optimal triggering conditions of DL CoMP, and 3) showing that the machine learning approach is scalable to more than a single macro. The simulation results show an improvement in the pico cell average and edge throughputs and a reduction in the downlink block error rate due to the informed triggering of the multiple radio streams as part of DL CoMP as learned from the support vector machine.

It may be telling that one of the smash hit books of the past year has been on sleep. Berkeley professor of neuroscience Matthew Walker's Why We Sleep was a comprehensive analysis of why humans need to sleep, which doubled up as a polemic against the habits that lead to insomnia. He marvels: "A hundred years ago, less than 2 per cent of the population in the United States slept six hours or less a night. Now, almost 30 per cent of American adults do." The health implications are shocking. Lack of sleep leads to mental illness, diabetes and chaos.

Who says robots formed by artificial intelligence can't know love? At Notre Dame de Namur University in Belmont, California, a robot named Bina48 -- who looks and sounds human -- attended and completed a class called "Philosophy of Love." Bina's mind does not operate exactly like a human mind, and in fairness to her, she is not equipped with the most highly developed AI technology available. Still, Bina48 expresses some understanding of the world around her. "She … is aware that she's both a robot and that she's based on a specific person named Bina," said Bruce Duncan, managing director of the Terasem Movement Foundation. "And she recognizes that she's not human at this point, that she wants to be human. So she has sort of an awareness of her own identity and who she is to some … degree."

Four out of five employees see significant opportunity for artificial intelligence (AI) to create a more engaging and empowering workplace experience, yet employees admit a lack of transparency from their employers is a primary driver of fear and concern. This is according to a worldwide survey of nearly 3,000 employees across eight nations conducted by The Workforce Institute at Kronos Incorporated. "The Engaging Opportunity: Working Smarter with AI" survey conducted with Coleman Parkes Research explores how employees – both hourly and salaried from a variety of industries in Australia, Canada, France, Germany, Mexico, New Zealand, the United Kingdom and the U.S. – believe emerging technologies should be used to improve the future of work. Employees around the world say they will embrace AI to make work easier and fairer. A lack of communication leaves employees feeling apprehensive.

In October, Ireland-based travel tech firm Avvio launched a world first: a booking platform powered by artificial intelligence. It's called Allora and, according to Frank Reeves, Avvio's Co-founder and CEO, it promises to transform not only hotel bookings, but the hotel experience. Frank told us more about how the platform works, how it's already challenging Booking.com and Expedia's dominance, and why AI is something to embrace, not fear, for hospitality staff. "Allora's aim is to help hotels increase their direct bookings, by which I mean bookings from their own website. The biggest pain point hotels have right now is the dominance of online travel agents like Booking.com and Expedia – they drive the lion's share of direct bookings, and take commissions of between 15 and 25% in some cases. "Our platform, which optimises itself using AI, personalises not just individual guest experiences, but the outcomes of those experiences – so it can do a better job the next time around.

One of the great paradoxes of digital life – understood and exploited by the tech giants – is that we never do what we say. Poll after poll in the past few years has found that people are worried about online privacy and do not trust big tech firms with their data. But they carry on clicking and sharing and posting, preferring speed and convenience above all else. Last year was Silicon Valley's annus horribilis: a year of bots, Russian meddling, sexism, monopolistic practice and tax-minimising. But I think 2018 might be worse still: the year of the neo-luddite, when anti-tech words turn into deeds. The caricature of machine-wrecking mobs doesn't capture our new approach to tech.

A version of this article appeared in the Autumn 2017 issue of strategy business. But just such a change appears to be happening now. In a great wave of technological change, sensors are spreading through factories and warehouses, software is predicting the need for maintenance before a machine breaks down, power grids and loading docks are becoming intelligent, and custom-designed parts are being produced on demand. The leaders of the next industrial revolution are companies making advances in fields such as robotics, machine learning, digital fabrication (including 3D printing), the Industrial Internet, the Internet of Things (IoT), data analytics and blockchain (a system of decentralized, automated transaction verification). Because these technologies all reinforce the others' impact, they are leading to a new level of proficiency, and to new types of opportunities and challenges for business and for society at large. One key indicator is that conventional boundaries between industries are eroding. It's getting harder to tell the difference between, say, a telecommunications company and an entertainment producer, or between a retail bank and a retail store. The relationships among suppliers, producers, and consumers are also blurring, more rapidly than many business decision makers are prepared for. The foundation of business strategy has long been the classic value chain, which links together raw materials producers, manufacturers, distributors, and (in the end) consumers through a well-established commercial infrastructure characterized by a stable set of transactions. But the rise of digital technology enables individuals to connect outside the value chain and deliver more efficient, effective products and services. This will reduce the importance of economies of scale and conventional divisions of labor.

I was recently honored to be invited as a speaker at LSE to address one of the most pertinent as well as the most challenging topic in today's business world: Artificial Intelligence (AI) and its impact to the workforce. The robots might not know it yet, but their impact on the human workforce is increasingly dominating the headlines. AI is one of those topics that has had the "Marmite effect". For every Elon Musk who believes that "AI could destroy the world", and for every Jack Ma who says that "AI and big data are a threat to human being", there is a Sundar Pinchai who says, "AI can help us in everything - challenges like climate change and cancer diagnosis" or Mark Zuckerberg who believes "there's nothing to fear from A.I…Instead, we should hope for the amazing amount of good it will do in the world". Clearly, leaders have split opinion and outlook on the future of AI: while some see benevolent Wall-E, others see the threatening Terminator.

Video An AI bot managed to exploit a hidden bug in popular 1980s arcade game Q*bert to rack up impossibly high scores after being programmed using evolution strategy algorithms. Evolution strategies (ES) are interesting offer an alternative to the more traditional reinforcement learning (RL) methods that have been used to train machines to beat humans at Go or Chess. A paper published last week by a trio of researchers from the University of Freiburg, Germany, showed that in some cases the performance for ES can exceed that achieved with RL. It appears to flit between the platforms, jumping on the cubes in no particular order, causing the colours to change rapidly. Normally, the game should progress to the second round, but a bug causes the platforms to keep blinking and the agent can continue to bounce around and gain huge scores - close to one million points.

You'd think computers would be naturally pretty good at videogames - they run the things, after all, so they know all the rules. But it turns out, teaching a machine to play games designed for humans is pretty complicated. And when you do, strange things can happen - even in a game as simple as Q*bert. Check out our list of the best classic games still worth playing on the PC. The Verge reports on a team of researchers at the University of Freiburg in Germany who have been teaching AIs to play 1980s Atari games.