There is no doubt the world is moving through a “digital age paradigm shift”, and the next step is the much-vaunted artificial intelligence. The signs are all around us: Mr. Globaloney of finance crapitalism (as we like to call it here) has for decades been executing commodities, securities, and equities trades with computer algorithms, and now wants to role out a cashless world with digital “currencies”, linking them to social credit systems and other draconian measures, like “vaccine passports”.  The result will  of course be a one-way mirror behind which Mr. Globaloney hides his own corruption. Additionally, we’ve seen article after article of a “transhumanist” stripe of how Mr. Globaloney wants to merge man and machine. Just last week I blogged about the US Army’s new “virtual reality” headset to enable soldiers to see better and to make tactical decisions better.

The only problem, as I pointed out in that blog, was that the headset contract had been awarded to Baal Gates’ Microsoft, which doesn’t bode well for the tactical situation of the future: “Please suspend your firefight while Windows completes your update. This will take just a few minutes. We apologize for any inconvenience to your platoon or your enemy.”

Beyond this, I’ve tried to sound the warning about this reliance on such systems by pointing out that no cyber systems are ever totally secure, that major powers have their own cyber warfare departments in their militaries, and that computer trading on markets only divorces them more and more from actual human risk assessment, as the pricing mechanism more and more reflects the aggregate “decisions” of algorithms.

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But with the move to Artificial Intelligence, a new danger looms: what if the foundational principles of Artificial Intelligence are themselves ill-founded? That’s the question addressed in the following article from Wired magazine by author Will Knight, that was passed along by L.G.L.R., and it’s an article well-worth pondering in its entirety, beyond the snippets we quote here:

The Foundations of AI Are Riddled With Errors

Ponder the following observation in connection with last week’s blog about the US Army’s new virtual reality headset:

The current boom in artificial intelligence can be traced back to 2012 and a breakthrough during a competition built around ImageNet, a set of 14 million labeled images.

In the competition, a method called deep learning, which involves feeding examples to a giant simulated neural network, proved dramatically better at identifying objects in images than other approaches. That kick-started interest in using AI to solve different problems.

But research revealed this week shows that ImageNet and nine other key AI data sets contain many errors. Researchers at MIT compared how an AI algorithm trained on the data interprets an image with the label that was applied to it. If, for instance, an algorithm decides that an image is 70 percent likely to be a cat but the label says “spoon,” then it’s likely that the image is wrongly labeled and actually shows a cat. To check, where the algorithm and the label disagreed, researchers showed the image to more people.

But why the mistaken labeling to begin with? This is where it gets “fun,” if it weren’t for the fact that under certain circumstances, like the US Army’s headset, or a self-driving automobile, people’s lives were not at risk.  It seems that image recognition is based on massive statistical databases of people’s responses to ambiguous images:

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