Demand for non-equity trading algorithms serving institutional asset managers and retail investors is expanding the prevalence of artificial intelligence in the world's financial markets. A recent report by Thomson Reuters estimates that algorithmic trading systems now handle 75 per cent of the volume of global trades worldwide and this figure is predicted, by those in the industry, to grow steadily. Firstly, while the institutional market has enjoyed a large variety of "algos" serving the equity markets to date, other areas such as futures are still witnessing huge product demand and innovation as a result. Secondly, regulations affecting the institutional investment market, such as the European Union Markets in Financial Instruments Directive II or MiFID II, are pushing for greater automation of trades in some asset classes which traditionally were not executed electronically. The fixed income market is a prime example and negotiations between industry groups are ongoing as to how practical a fully automated fixed income could really be, given the magnitude of the required shift from telephone to electronic trading.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

We already know that artificial intelligence systems can work in law firms and beat the world champion at a game of Go. Now it turns out that AI can write some pretty good pop songs, too. Researchers at Sony have been working on AI-generated music for years, and has previously used AI to create impressive jazz tracks. But this is the first time the Sony CSL Research Laboratory has released pop music composed by AI, and the results are impressive. The first song, "Daddy's Car," is a catchy, sunny tune reminiscent of The Beatles.

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Chris Messina Medium "It's a rare moment when it becomes clear that a technological revolution is upon us, and I believe we're in the midst of one such transition right now. Even if you haven't realized it yet, bots are everywhere... With proper forethought and consideration, bots present a new, unpolluted opportunity to build lasting relationships with people." ROBOTICS: Do No Harm, Don't Discriminate: Official Guidance Issued on Robot Ethics Hannah Devlin The Guardian "The BSI document begins with some broad ethical principles: 'Robots should not be designed solely or primarily to kill or harm humans; humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.'...The code suggests designers should aim for transparency, but scientists say this could prove tricky in practice. 'The problem with AI systems right now, especially these deep learning systems, is that it's impossible to know why they make the decisions they do,' said Winfield."

I recently consulted with the US Navy on all things "transhuman." In those conversations about how science and technology can help the human race evolve beyond its natural limits, it was clear that military is keen on replacing human soldiers with both fighting and peacekeeping machines so American military lives never have to come under fire or be in harm's way. However, it's the peacekeeping technology that is particularly interesting for many civilians. While you wouldn't want an armed Terminator in your home, you might like a robot that travels with you and offers personal protection, like a bodyguard. In a survey by Travelzoo of 6,000 participants, nearly 80 percent of people said they expect robots to be a significant part of their lives by 2020 -- and that those robots might even join them on holidays.

Fifty years ago, the first molecular dynamics papers allowed scientists to exhaustively simulate systems with a few dozen atoms for picoseconds. Today, due to tremendous gains in computational capability from Moore's law, and due to significant gains in algorithmic sophisticiation from fifty years of research, modern scientists can simulate systems with hundreds of thousands of atoms for milliseconds at a time. Put another way, scientists today can study systems tens of thousands of times larger, for billion of times longer than they could fifty years go. The effective reach of physical simulation techniques has expanded handleable computational complexity ten-trillion fold. The scope of this achievement should not be underestimated; the advent of these techniques along with the maturation of deep-learning has permitted a host of start-ups (1, 2, 3, etc) to investigate diseases using tools that were hitherto unimaginable. The dramatic progress of computational methods suggests that one day scientists should be able to exhaustively understand complete human cells.

Stanford student Joshua Browder just launched a new "chatbot" service on his website DoNotPay.co.uk that's designed to help tenants force their negligent landlords to fix problems like leaky taps or moldy walls. The new online service launched Friday in the U.K. and follows the highly publicized success of Browder's online parking ticket appeal service, which started in the U.K. but recently expanded to New York and Seattle. Browder estimates he has saved drivers about 5 million through successfully appealing 180,000 parking tickets. CNNMoney was given exclusive access to the new landlord service before the launch. It's not a glamorous website and it's got a few kinks, but it generally works as promised.

Watch out IBM Watson, Google has its own kickass'Show and Tell' AI and it's getting pretty damn good at depicting what it sees in photos – and now everyone can use it. Today, the tech giant announced it's open-sourcing its automatic image-captioning algorithm as a model in TensorFlow for everyone to use. This means anyone can now train the algorithm to recognize various objects in photos with up to 93.9 percent accuracy – a significant improvement to the 89.6 percent that the company touted when the project initially launched back in 2014. Training'Show and Tell' requires feeding it hundreds of thousands of human-captioned images that the machine then uses and re-uses when "presented with scenes similar to what it's seen before." To learn smoother, Google's impressive AI also uses a new vision component that allows for faster training and more detailed captions as it's gotten much better at telling objects apart from each other.

In this special guest feature, Alexander Khaytin, COO for Yandex Data Factory, explains how businesses can introduce "data democracy" and systematic testing and how agility can be introduced into even the most inflexible of organizations, overcoming the barriers prohibiting machine learning adoption and benefit. As Chief Operating Officer at YDF, Alexander oversees projects from concept to completion, alongside contributing to YDF's partnership, sales and technology strategies. Prior to joining Yandex in 2014, Alexander spent over a decade providing consulting and strategic analysis services for businesses in telecom, construction, energy, retail and finance industries. As a Partner at a system integrator, Korus Consulting, from 2011 to 2014, he ran projects for some of Russia's leading brands, including state-owned hi-tech corporation Rostech, Moscow City Telephone Network, mobile broadband services provider and smartphone manufacturer Yota, Bank Saint Petersburg and Russia's largest e-payment system Yandex.Money. Machine learning has come to play an important role for businesses looking to transform the way they work.