Experts list various problems, including the fact that AI is vulnerable to failure due to unforeseen problems, including problems with data (too sparse, too noisy, too many outliers, etc.). It also doesn’t learn as well from experience as humans do.
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As we struggle with the COVID-19 crisis, many are beginning to ask hard questions about how our system works, its strengths, weaknesses, and vulnerabilities. One vulnerability might be too heavy reliance on a single source for data modeling and predictions. Considering all the uses to which AI may be put in health care, getting our guidance exclusively from the Institute for Health and Metric Evaluation for modeling is reckless.
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I understand the panic many business leaders experience as they try to stay solvent while customers evaporate. Panic, however, is a poor teacher: AI-based automation will not only not solve all their problems, it may very well add to them. AI is not a magic box into which we can stuff them and make them disappear.
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Big social media companies have long wanted to replace human content moderators with AI. COVID-19 quarantines have only intensified that discussion. But AI is far, far from ready to successfully moderate content in an age of where virtual monopolies make single point failure a frequent risk.
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Building an AI entails moving parts of our intelligence into a machine. We can do that with rules, (simplified) virtual worlds, statistical learning… We’ll likely create other means as well. But, as long as “no one is home”—that is, the machines lack minds—gaps will remain and those gaps, without human oversight, can put us at risk.
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Not only was Jean-François Bonnefon’s paper rejected by conventional anti-plagiarism software but the rejection didn’t make any sense. Bonnefon, research director at Toulouse School of Economics, was informed of “a high level of textual overlap with previous literature” (plagiarism) when he was citing scientists’ affiliations, standard descriptions, and papers cited by other—information he was obliged to cite accurately, according to a standard format. “It would have taken two [minutes] for a human to realise the bot was acting up,” he wrote on Twitter. “But there is obviously no human in the loop here. We’re letting bots make autonomous decisions to reject scientific papers.” Reaction to the post by Dr Bonnefon, who is currently a visiting scientist at the Massachusetts Institute Read More ›
Media often co-operate with researchers’ inflated claims about machine learning’s powers of discovery. An ingenious “creative” approach to accuracy enables the misrepresentation, says data analyst Eric Siegel.
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AI can certainly help scientists. But to understand why AI can’t do science on its own, we should take a look at the NP-Hard Problem in computer science. The “Hard” is in the name of the problem for a reason…
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Machine learning is not at all like human learning. For example, machine learning frequently requires millions of examples. Humans learn from a few examples.
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AI can speed up translation of ancient documents where only a few scholars know the language. Whether it can help with mysterious unknown languages like Minoan A is another question.
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Irish playwright John Waters warns of a time when we might have to grant moral discretion to computer algorithms, just as Christians now grant to the all-knowing but often inscrutable decrees of God. Not likely.
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One new issue that the cloud creates is that programmers are more often required to be “full stack” developers,” Jonathan Bartlett explains. “Unfortunately, most programmers coming out of college have little to no system administration experience. That’s why this book is based on the ‘full stack’ concept, showing how system administration and programming relate to each other.”
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Contrary to traditional Fisherian hypothesis testing, it is possible to create models after viewing the data and still quantify the generality of the model.
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We want our calculation to demonstrate the notion that if we have high accuracy and a small model, then we have high confidence of generalizing. Intuitively, then, we add the model size to the accuracy and subtract this quantity from the entropy of having absolutely no information about the problem.
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Entropy is a concept in information theory that characterizes the number of choices available when a probability distribution is involved. Understanding how it works helps us make better guesses.
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This approach is contrary to Fisher's method where we formulate all theories before encountering the data. We came up with the model after we saw the cards we drew.
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By all means, let’s build machines that enhance our abilities. But let’s not forget that the really amazing thing is not the tool, but the tool builder.
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Robot error turned out to be a bigger problem than human error.
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When we get to the end of the list, we will see that it is like beads connected by a string—revealing the most dangerous threat.
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Some of the recent conflicts around algorithms and ethnicity are flubs that social media entrepreneurs will regret. Others may endanger life.
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