The likely near future creation of artificial superintelligence carries significant risks to humanity. These risks are difficult to conceptualise and quantify, but malicious use of existing artificial intelligence by criminals and state actors is already occurring and poses risks to digital security, physical security and integrity of political systems. These risks will increase as artificial intelligence moves closer to superintelligence. While there is little research on risk management tools used in artificial intelligence development, the current global standard for risk management, ISO 31000:2018, is likely used extensively by developers of artificial intelligence technologies. This paper argues that risk management has a common set of vulnerabilities when applied to artificial superintelligence which cannot be resolved within the existing framework and alternative approaches must be developed.

Algorithmic trading relies on computer programs that execute algorithms to automate some, or all, elements of a trading strategy. Algorithms are a sequence of steps or rules to achieve a goal and can take many forms. In the case of machine learning (ML), algorithms pursue the objective of learning other algorithms, namely rules, to achieve a target based on data, such as minimizing a prediction error. In this article, we have a look at use cases of ML and how it is used in algorithmic trading strategies. These algorithms encode various activities of a portfolio manager who observes market transactions and analyzes relevant data to decide on placing buy or sell orders.

The robo analysts are here. Having demonstrated their analytical rigor and objectivity, are they set to overtake robo advisors in the race to transform wealth management? Today, the wealth management industry finds itself on the brink of a chasm. In the same way online trading disrupted the distribution of investment advice, big data analytics and machine learning will disrupt how financial advice and the research behind it is created. The way firms respond to the new technologies transforming how research is performed and investment advice is applied will determine their future success or failure.

The robot revolution isn't about to take over stock-picking duties at BlackRock. That's at least according to Larry Fink, BlackRock Inc.'s chairman and chief executive officer of the world's largest money manager, in reference to a recent move by the group to put more emphasis on computer models over human managers. BlackRock said last week that it will increase its quantitative-analysis division at the expense of traditional active management, as part of an overhaul of active management, causing some jitters in the stock-picking community. Fink defended that move in an interview with CNBC Thursday morning, saying it represented a small part of the group's $5.1 trillion business, and was in line with a two-year plan, to deal with the "democratizing of information," which is so abundant. Meaning, there is an abundant source of information to be sifted through out there for asset managers.