Cheddar saltine crackers. Premium made them, but I think it was only for a year or two.

I’d rather focus on ripping cars out of cities, promoting mixed use zoning areas, removing regulations on food service (which is the reason small American food vendors need food trucks, instead of “street food” like the rest of the world.

The disjointed, car based, child hating society we have is a big problem.

What we have done is invented massive, automatic, no holds barred pattern recognition machines. LLMs use detected patterns in text to respond to questions. Image recognition is pattern recognition, with some of those patterns named things (like “cat”, or “book”). Image generation is a little different, but basically just flips the image recognition on its head, and edits images to look more like the patterns that it was taught to recognize.

This can all do some cool stuff. There are some very helpful outcomes. It’s also (automatically, ruthlessly, and unknowingly) internalizing biases, preferences, attitudes and behaviors from the billion plus humans on the internet, and perpetuating them in all sorts of ways, some of which we don’t even know to look for.

This makes its potential applications in medicine rather terrifying. Do thousands of doctors all think women are lying about their symptoms? Well, now your AI does too. Do thousands of doctors suggest more expensive treatments for some groups, and less expensive for others? AI can find that pattern.

This is also true in law (I know there’s supposed to be no systemic bias in our court systems, but AI can find those patterns, too), engineering (any guesses how human engineers change their safety practices based on the area a bridge or dam will be installed in? AI will find out for us), etc, etc.

The thing that makes AI bad for some use cases is that it never knows which patterns it is supposed to find, and which ones it isn’t supposed to find. Until we have better tools to tell it not to notice some of these things, and to scrub away a lot of the randomness that’s left behind inside popular models, there’s severe constraints on what it should be doing.

That estimate is based on assuming that the ratio of matter to light output is the same between galaxies 10 billion years apart in age. The high light output of these young galaxies could also be supermassive stars that burn out very quickly, larger stars typically forming faster than smaller stars, or many other things.

Blindly assuming a linear relationship between two things, then extrapolating is how you get the Windows loading bar circa 2000.

Separately, but just as big a potential issue, the data itself may be incorrect. Previous galaxies measured at extreme redshift values were remeasured, and found to have less extreme values. This can be as simple as there aren’t that many photons from these galaxies reaching us, so a short measurement period might not be enough to get an accurate picture.

As the President and CEO of a fortune 500, and a neurosurgeon that does rocket surgery as a side project, there are many people relying on me to be an upstanding member of the community at all times.

In reality, most of the ways I misrepresent myself are to obscure my identity, and mostly it’s by leaving things out.

They’ve finally found their silicon mates.

These are 6th graders. There’s no “top” competition for 6th graders at all, which is why this is a region specific league, and most of the sports are divided into boys and girls divisions at those ages.