![]() We make technology more complex until it becomes too annoying or unsafe to use. Yet ask anybody who used a computer or car 50 years ago how they actually worked, and you will note that they were both less capable and less reliable. We do notice when the Zoom video lags, the AI program answers wrong or the computer crashes. For example, we do not notice how lost packets of data on the internet are constantly being found behind the scenes, how error-correcting codes remove noise or how fuses and redundancy make appliances safe.īut when we pile on level after level of complexity, it looks very unreliable. Much of our technology is amazingly reliable. The model was wrong, but it presented the results in such a compelling way that they looked reliable. The first of a new breed of oil platform (Sleipnir A) sank because engineers trusted the software calculation of the forces acting on it. This is bad news when the autopilot fails, and the pilot has less experience to go on to rectify the situation. Airline pilots have fewer true flying hours today than in the past due to the amazing efficiency of autopilot systems. When technology works well, we often trust it too much. But we don't care enough about less important situations, making the design there far less idiot-proof. Here, careful design renders mistakes hard to make. To launch missiles, two people need to turn keys simultaneously across a room. Microwave ovens turn off the radiation when the door is opened. A "dead man's switch" stops a machine if the operator becomes incapacitated. Cutting machines require you to hold down buttons, keeping your hands away from the blades. Where does technology stop mistakes? Idiot-proofing works. This is difficult to find in a human advisor, and even more so in an AI. ![]() They have about the same level of caution as we do, and we know they know what we want. This is a fundamental problem and applies just as much to any advisor: the cautious advisor will cry wolf too often, the optimistic advisor will miss risks.Ī good advisor is somebody we trust. Tuning its sensitivity down means increasing the risk of not getting a warning when it is needed. That means it will warn us about things we do not care about, possibly in an annoying way. And it was a sheer coincidence that there were tulips on the table-no reason to avoid them. But it was never the dining that was the problem. But if it is good at noticing possible similarities (after all, it could search all of the internet and all our personal data) it will also often notice false ones.įor failed dates, it may note that they all involved dinner. Remembering involves spotting similarities and bringing things to mind.Īn artificial intelligence (AI) also needs to be able to spontaneously bring similarities to our mind-often unwelcome similarities. But remembering is not the same thing as retrieving a file from a known location or date. Storing information is useful when it can be retrieved well. That ought to make learning and remembering easy, right? Or we are biased to misinterpret what is going on.īut surely technology can help us? We can now store information outside of our brains, and use computers to retrieve it. Perhaps the situation is too complex or too time-consuming to think about. That said, we also make mistakes when we cannot efficiently deduce what is going to happen. In the second, we fail to encode information when it is available. In the first case, we fail to remember personal or historical information. ![]() That means that the next time a similar situation comes around, we do not see the similarity-and repeat the mistake.īoth reveal problems with information. Instead of determining why a decision was wrong and how to avoid it ever happening again, we often try to ignore the embarrassing turn of events. We are also bad at learning when things go wrong. Napoleon ought to have noticed the similarities between his march on Moscow and the Swedish king Charles XII's failed attempt to do likewise roughly a century before him. One issue is forgetfulness and " myopia": we do not see how past events are relevant to current ones, overlooking the unfolding pattern.
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