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The heads of more than 100 of the world\u2019s top artificial intelligence companies are very alarmed about the development of \u201ckiller robots\u201d. In an open letter<\/a> to the UN, these business leaders \u2013 including Tesla\u2019s Elon Musk and the founders of Google\u2019s DeepMind AI firm \u2013 warned that autonomous weapon technology could be misused by terrorists and despots or hacked to perform in undesirable ways.<\/p>\n But the real threat is much bigger \u2013 and not just from human misconduct but from the machines themselves. The research into complex systems shows how behaviour can emerge that is much more unpredictable than the sum of individual actions. On one level this means human societies can behave very differently to what you might expect just looking at individual behaviour. But it can also apply to technology. Even ecosystems of relatively simple AI programs \u2013 what we call stupid, good bots \u2013 can surprise us, and even when the individual bots are behaving well.<\/p>\n The individual elements that make up complex systems, such as economic markets or global weather, tend not to interact in a simple linear way. This make these systems very hard to model and understand. For example, even after many years of climatology, it\u2019s still impossible to make long-term weather predictions. These systems are often very sensitive to small changes and can experience explosive feedback loops. It is also very difficult to know the precise state of such a system at any one time. All these things make these systems intrinsically unpredictable.<\/p>\n All these principles apply to large groups of individuals acting in their own way, whether that\u2019s human societies or groups of AI bots. My colleagues and I recently studied<\/a> one type of a complex system that featured good bots used to automatically edit Wikipedia articles. These different bots are designed and exploited by Wikipedia\u2019s trusted human editors and their underlying software is open-source and available for anyone to study. Individually, they all have a common goal of improving the encyclopaedia. Yet their collective behaviour turns out to be surprisingly inefficient.<\/p>\n These Wikipedia bots work based on well-established rules and conventions, but because the website doesn\u2019t have a central management system there is no effective coordination between the people running different bots. As a result, we found pairs of bots that have been undoing each other\u2019s edits for several years without anyone noticing. And of course, because these bots lack any cognition, they didn\u2019t notice it either.<\/p>\n The bots are designed to speed up the editing process. But slight differences in the design of the bots or between people who use them can lead to a massive waste of resources in an ongoing \u201cedit war\u201d that would have been resolved much quicker with human editors.<\/p>\n We also found that the bots behaved differently in different language editions of Wikipedia. The rules are more or less the same, the goals are identical, the technology is similar. But in German Wikipedia, the collaboration between bots is much more efficient and productive compared to, for example, Portuguese Wikipedia. This can only be explained by the differences between the human editors who run these bots in different environments.<\/p>\n Wikipedia bots have very little autonomy and the system already operates very differently to the goals of individual bots. But the Wikimedia Foundation is planning to use<\/a> AI that will give more autonomy to the bots. That will likely lead to even more unexpected behaviour.<\/p>\n Another example is what can happen when two bots designed to speak to humans interact with each other. We\u2019re no longer surprised by the answers given by artificial personal assistants such as the iPhone\u2019s Siri. But put several of these kind of chatbots together and they can quickly start acting in surprising ways, arguing and even insulting each other.Exponential confusion<\/strong><\/h4>\n
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