Here’s the headline: “AI can predict when someone will die with unsettling accuracy: This isn’t the first time experts have harnessed AI’s predictive power for healthcare.”

Unsettling accuracy? Is accuracy unsettling? Has AI progressed so far that all you have to do is step on an AI scale and the AI computer spits out an unsettlingly accurate AI prediction of the AI end of your AI life? AI AI AI AI AI? AI!

I’ve said it many times, but the marketing firm hired by computer scientists has more than earned its money. Science fiction in its heyday had nothing on these guys. Neural nets! Why, those are universal approximators! Genetic algorithms! Genes in the machine. Machine learning! Deep learning! Like, that’s deep, man. Artificial intelligence! Better than the real thing!

What has statistics got? Statistically significant? No, that’s dead. Thank God. Uniformly most powerful test? Unbiased estimator? Auto-regressive? Dull isn’t in it. You won’t buy any headlines talking about mu-hat.

What’s the difference between statistics and AI? Besides the overblown hype, that is? One thing: a focus on the results. That’s the reason AI is landing every punch, and why statistics is reeling. Statistical models focus on fictional non-existent hidden unobservable parameters, whereas AI tries to get the model to make good predictions of reality.

Now AI is nothing but statistical modeling appended with a bunch of if-then statements. Only this, and nothing more. Computers do not know as we know; they do not grasp universals or understand cause. They don’t even know what inputs to ask for to predict the outputs of interest. We have to tell them. Just as we do in statistics.

The reason AI models beat statistical ones is because AI models are tuned to making good predictions, whereas statistical models are usually tuned to things like wee p-values or parameter estimates. Ladies and gentlemen, parameters are of no interest to man or beast. The focus on them has forced, in a way, a linearity culture, whereas if we can’t write down the model in pleasing parameterized form, we’re not interested. Besides, we need that form to do the limit math of statistics of estimators of these parameters so that we can get p-values, which do not mean what anybody thinks they do.

AI scoffs at parameters and says, how can I create a mathematical function, however complex, of these input measures so that skillful, but not over-fit, predictions of the output measure are good?

That, and its understanding, or its attempts at understanding, cause. We’ve discussed many times, and it’s still true, that you can’t get cause from a probability model. Cause is in the mind, not the data. We need to be part of the modeling process. And so on. AI, though it’s at the beginning of all this, tries to get this right. I’ll have a paper tomorrow on this. Stay tuned!

I say AI will never make it. Computers, being machines, aren’t intellects; they are not rational creatures like we are. Intellect is needed to extract universals from individual cases, and computers can never do that—unless we have first programmed them with the answer, of course.

That is to say, strong AI is not possible. Others disagree. To them I say, don’t wait up.

We can’t discount the over-blownness of the comparison. Reporters love AI, and nearly all cherish the brain-as-computer metaphor, so we’ll apt to see intellect where it is not. Plus hype sells. Who knew?

It’s not all hype, of course. AI is better, in general, at making predictions. But headlines like the one above are ridiculous.

When all the number crunching was done, the deep-learning algorithm delivered the most accurate predictions, correctly identifying 76 percent of subjects who died during the study period. By comparison, the random forest model correctly predicted about 64 percent of premature deaths, while the Cox model identified only about 44 percent.

These are not unsettling rates. The “deep learning” is AI, the “random forest” is “machine learning” (if you like, a technique invented by a statistician), and “Cox model” is regression, more or less. I didn’t look at the details of how the regression picked its variables, but if it’s anything like “stepwise”, the method was doomed.

We always have to be suspicious about the nature of the predictions, too. These should be on observations never before see or used in any way. They should not be part of a “validation set”, because everybody cheats and tweaks their models to do well on the validation set, which, as should be clear, turns the validation set into an extension of the training set.

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