Appropriately regulating artificial intelligence is an increasingly urgent policy challenge. Legislatures and regulators lack the specialized knowledge required to best translate public demands into legal requirements. Overreliance on industry self-regulation fails to hold producers and users of AI systems accountable to democratic demands. Regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a private regulator, are proposed. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation. Regulatory market could enable governments to establish policy priorities for the regulation of AI, whilst relying on market forces and industry R&D efforts to pioneer the methods of regulation that best achieve policymakers' stated objectives.

Artificial intelligence has the power to change the world -- and it, as it advances from useful to essential, then from essential to mandatory and finally from mandatory to directive. "Machines will tell us what to do," warns entrepreneur and author Bill Bishop. Gillian K. Hadfield, the director of the Schwartz Reisman Institute for Technology and Society, agrees AI will present new problems but is confident society will be bold and come up with new ideas for regulating AI. To paraphrase Tolstoy, all nice, helpful robots are alike, but all dangerous robots are dangerous in their own unique way. In this debate, I will show that we need to fear the dangerous robots (AIs), not by acting like paranoid Luddites, but to ensure we fear AIs enough to pay attention to potential threats, and to take proactive steps to mitigate them.

Today's customers demand more options, more creative solutions, greater flexibility and faster responses from banks and other financial institutions. Survival and success for financial institutions in this new world requires that they operate with intelligence, agility and speed to keep up with evolving customer preferences and technologies. Consequently, more and more customer interactions and financial transactions are going digital as online and mobile payments, customer on-boarding and account opening are on the rise. Yet, while digital interfaces present an opening for innovative business services, they also yield new challenges, such as pressure on back office operations or increased regulatory scrutiny. Largely automated interactions generate more data to analyse, demand higher volumes of sample testing, and expand the compliance burden. To create a flawless customer experience, the back office has to keep up as well.

Stockbrokerage might be viewed by investors as a traditionally human-based service allowing them to buy and sell equities. When looking at the shift in how stock brokerage is different today compared to the early 2000s, the largest change seems to be in software-based automation. Put simply, a lot of what was being done by humans (such as executing trades, giving advice to investors, discretionary trading) can now be done through software.

Up until recently, compliance has mainly relied on people. And as a result of the significant increase in regulatory reporting requirements for financial institutions over the last decade, demand for compliance professionals has surged. Companies have had no choice but to hire more and more compliance staff, in an effort to tackle the growing regulatory burden. However, over the last few years, technology has begun to play a much larger role within compliance. Financial institutions and regulators have realised that by harnessing the power of technology, and more specifically, the power of artificial intelligence (AI) and machine learning (ML), a considerable proportion of the compliance function can actually be automated, reducing the burden on institutions and compliance professionals.

London-based CUBE, was founded in 2011 and claims to offer a RegTech platform that can help businesses cut regulatory costs and minimize risk of non-compliance. The 94-employee company claims their platform can assist in predicting compliance risk, automating AML, Know Your Customer (KYC) and Cyber/information security processes. CUBE states that the platform uses machine learning to help enterprises to automatically keep track of global regulatory data and prompt alerts by detecting regulatory changes that pose a compliance risk. The company claims it has created a regulatory'data lake' that covers the regulations for financial services organizations across the globe. We, however, could find no evidence of how extensive their database is.

London-based CUBE, was founded in 2011 and claims to offer a RegTech platform that can help businesses cut regulatory costs and minimize risk of non-compliance. The 94-employee company claims their platform can assist in predicting compliance risk, automating AML, Know Your Customer (KYC) and Cyber/information security processes. CUBE states that the platform uses machine learning to help enterprises to automatically keep track of global regulatory data and prompt alerts by detecting regulatory changes that pose a compliance risk. The company claims it has created a regulatory'data lake' that covers the regulations for financial services organizations across the globe. We, however, could find no evidence of how extensive their database is.

John Rampton is founder of online invoicing company Due. By now everyone has heard of Google's RankBrain, the new artificial intelligence machine learning algorithm that is supposed to be the latest and greatest from Mountain View, Calif. What many of you might not realize, however, is just how fast the SEO industry is changing because of it. In this article, I'll take you through some clear examples of how some of the old rules of SEO no longer apply, and what steps you can take to stay ahead of the curve in order to continue to provide successful SEO campaigns for your businesses. So what is artificial intelligence? Artificial Narrow Intelligence (ANI): This is like AI for one particular thing (e.g.