This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.

Forget Privacy: You're Terrible at Targeting Anyway

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Since the generative AIs have been trained on the entirety of human work — most of it mediocre — it produces “wisdom of the crowd”-like results. They may hit the mark but only because they are average.

Kevin Kelly • Interview: Kevin Kelly, Editor, Author, and Futurist

and added

In little more than a decade, machine learning has moved from a highly specialized technique to something that almost anyone with data and computational power can do. That is to be welcomed — yet it remains essential that the industry can navigate both the proliferation of tools and frameworks in the space and the ethical issues that are becoming

Thoughtworks • Technology Radar | An opinionated guide to technology frontiers | Thoughtworks

Tom So added

There is a broad assumption underlying many machine-learning models that the model itself will not change the reality it’s modeling. In almost all cases, this is false.

Brian Christian • The Alignment Problem

A couple months ago, Oxford and Cambridge researchers illustrated the risk of homogeneity in a study of AI Generated content in Nature magazine. The risk increases as AI gets trained not only on human-created content, but on other AI-generated content.

As an example, the researchers studied an AI model trained on images of different breeds of dogs.

tomfishburne • AI-Generated Homogeneity

Daniel Santos added

The essential lesson here is that, when datasets are large enough, the knowledge encapsulated in all that data will often trump the efforts of even the best programmers.

Martin Ford • Rise of the Robots: Technology and the Threat of a Jobless Future

Hiring algorithms are but one example of a larger set of technologies which promise to disclose some deeper truth about the self or the world that would be otherwise unnoticed. Similar tools are deployed in the realms of finance, criminal justice, and health care among others. The underlying assumption, occasionally warranted, is that analyzing cop... See more

L. M. Sacasas • The Uncanny Gaze of the Machine

Alex Wittenberg added

A machine-learning model, trained by data, “is by definition a tool to predict the future, given that it looks like the past. . . . That’s why it’s fundamentally the wrong tool for a lot of domains, where you’re trying to design interventions and mechanisms to change the world.”

Brian Christian • The Alignment Problem

If there is one myth regarding computer technology that ought to be swept into the dustbin it is the pervasive believe that computers can do only what they are specifically programmed to do. As we’ve seen, machine learning algorithms routinely churn through data, revealing statistical relationships and, in essence, writing their own programs on the