redmoskito

I don't understand. From my brief reading it looks like measured outcomes did improve significantly (see diabetes, depression, cholesterol). Am I misreading this?

*Freezer and electricity are available to 97% of _all_ American (households). One or more cars is available to 73% of _all_ American (households).

Respectfully, it believe you've misread that census report. 3% of _all_ American households lack a complete kitchen, not 3% of poor Americans.

That is a much more striking statistic, and there's reason to believe it may be higher, since marginalized Americans are both the most likely to be suffering lack of basic resources and the most likely to be unrepresented by the US census. Further, this study only measured households, and ignores the homeless and incarcerated, which represent another 2% of the population.

Considering that 10-15% of adults in the US live in poverty, your reference suggests that anywhere between 30% and 50% live without a complete kitchen.

As long as we're admiring simple solutions that work on a dataset of size one, and as long as we're allowing a human in the loop, you might like this algorithm:

    return (150, 200);

Obviously I'm being pedantic, and I mean no disrespect, but I have a bone to pick with so-called "computer vision" algorithms that are little more than simple image processing. In this case, the time spent implementing and tuning the algorithm exceeds the time it would take to solve the task manually (e.g. pay a second-grader to do it). And as others have observed, it isn't obvious that this algorithm would generalize to other images, in which case no time is saved over the manual approach.

It is tempting to dismiss sophisticated techniques because (a) they are hard to understand and (b) the task seems so easy to our own brain-equipped vision systems. But the fact is, most interesting computer vision problems (including this one) require sophisticated representations to achieve robustness and generality. In other words, any good solution will need an answer to the question "What is a Waldo?" that is better than "a 50x50 patch of pixels with red and white stripes".

Posession is nine-tenths of the law.

To a computer vision researcher, 15.8% on 20k categories is phenomenal.

I find both of your comments extremely condescending, both toward saalweachter and the authors of this article.

1. The fact that Claude Shannon succeeded in training a chess system has virtually no impact on sallweachter's claim that many AI results were overstated.

2. Certainly the press overstated them, which supports saalweachter's premise rather than weakening it. Even if the _implied_ claim was that _researchers_ overstated results, your argument does nothing to weaken this claim.

3. Frito Lay solved a problem several orders of magnitude easier that of face recognition in natural images, which is still very much an open problem in computer vision.

4. Similar to 1., the Frito Lay example contributes nothing to your goal of weakening saalweachter's claim that this is valuable research--a claim which is exceedingly innocuous.

I understand that you've probably got a bone to pick against the many AI naysayers and saalweachter's comments conjured a few common misrepresentations (i.e. (a) that the "AI revolution" burnt-out because it's researchers were somehow naive and (b) that neural networks are something new invented by computer vision researchers). You'd be justified in arguing against these claims, and I'm sure your father (respected AI researcher of the same name) would make them too, if saalweachter had tried to make them (which he didn't). But even if you were justified in making the argument, I would expect a less condescending one that made better use of evidence than the argument you've made here.