4/18/2023 0 Comments Sneak peek or sneak peakFor example, HireVue claims to predict future job performance using questions such as “Is your desk busy or minimal?” to infer applicants’ personalities. Yet, many companies and developers claim that their products can predict the future. Small changes in a person’s life, such as one visit to the emergency room, could have compounding effects on their future, for instance, due to large medical bills. There are many reasons why predicting the future is hard : peoples’ lives could face sudden shocks, such as getting laid off or winning a lottery, that no model can predict. None of the models could predict future outcomes very well, reminiscent of COMPAS. They were massively disappointed to find that the latest AI techniques barely performed better than simple linear regression and just four pieces of information about the children, such as race and mother’s education level. Limitations of AI are amplified when there are no clear rules, when collecting additional data is hard or impossible, and when reasonable people can disagree about the right answer. Again, the lack of ambiguity and the abundance of data allow AI to get better at playing Go.īut the progress that’s been made in face recognition or Go-playing does not transfer to other domains. Similarly, the game Go has clear rules, and we can generate as much data as we want by just letting the machine play against itself. Though there have been some notable failures of face recognition, it continues to get much more accurate (and that’s exactly why we should worry ). So, given enough data and computational resources, it will learn the patterns that distinguish one face from another. For instance, when training a face recognition model, the model uses labels that tell it whether any two photos represent the same person or not. What do these applications have in common?ĪI has made massive progress in applications where there is little uncertainty or ambiguity. None of these was possible a decade or two ago. Machines have trounced the world champion at games such as Go. Our phones have apps that can instantly recognize which song is playing in the background or transcribe our speech fairly accurately. It is also essential for policymakers, journalists, and many others. AI is everywhere today, so this is essential knowledge if you ever make decisions about AI-powered products or services, whether at work or in your personal life. We explore what makes AI click, what makes certain problems resistant to AI, and how to tell the difference. So how can we understand which AI systems can work and which ones cannot? Others, such as COMPAS, do not work-and perhaps never will. Some of them have made massive progress, such as DALL-E 2. Today, it is used to refer to a large number of related but different applications. Why is AI so good at creating images from text, and yet so bad at predicting who will commit a crime?ĪI is an umbrella term. In fact, COMPAS was no better than using just two pieces of information to predict if someone would commit a crime: their age and the number of prior offenses!īoth DALL-E 2 and COMPAS work by learning patterns from data, and can be thought of as AI. More worryingly, researchers found that COMPAS, which used hundreds of data points about a person to make a decision, was not very effective at predicting the future at all-it was as good as leaving the decisions up to people with no background in criminal justice.
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