142 karma · joined May 13, 2022
You can have both guaranteed delivery and no downtime if your whole system is so deterministic that anything that normally would result in blocking just will not, cannot happen. In other words it should be a hard real-time system that is formally verified top to bottom, down to the last transistor. Does anyone actually do that? Verify the program and the hardware to prove that it will never run out of memory for logs and such?
Continuing this thought, logs are probably generated endlessly, so either whoever wants them has to also guarantee that that they are processedand disposed of right after being logged... or there is a finite ammount of log messages that can be stored (arbitrary number like 10 000) but the user (of logs) has to guarantee that they will take the "mail" out of the box sooner than it overfills (at some predictable, deterministic rate). So really that means even if OUR system is mathematically perfect, we're just making the downtime someone elses problem - namely, the consumer of the infinite logs.
That, or we guarantee that the final resources of our self-contained, verified system will last longer than the finite shelf life of the system as a whole (like maybe 5 years for another arbitrary number)
It's a huge money drain, but fun even if it doesn't ultimately get you anywhere ;)
Code points are Unicode's closest equivalent to "single graphical character" and everything you've been doing when iterating ASCII strings can be done using code points. It's also the closest thing to "a single key-stroke" and "how much text should disappear when I press backspace once".
The linked article suggests basically doing nothing because a few edge cases exist.
So it's possible, in theory, that an AI-in-a-box could do the hardest parts of science aside from experiments. Whether modern LLMs are any good at it is a different question.
On the topic of the theme and colors, I think the trick for keeping elements distinct despite low contrast, is to have fewer "levels" of elements. Modern "flat" designs keep very low contrast between the actual page background and any organization elements (containers, panels etc) and only give high contrast to the "main" elements, such as the primary text content and whatever element is currently selected. For example, on a page with a "download" button it's a good idea to give that one button a high contrast scheme, instead of all buttons on the page. Contrast can be done between light and dark, between gray and color, or between color and a different color.
Another thought I had about light/dark theme, is that dark theme is often modeled after a colorful console terminal, where technicolor colors are used to draw attention to different types of messages, or as blinking LEDs on a device front panel, so that gives extremely high contrast between the (basically invisible) bg and the object. Light mode, otoh, is often modeled after a book or a newspaper, or some other physical medium, where there is one basic color for text and the background does the heavy lifting - bright for bulk text, colored for special notices or announements, pale for uninteresting stuff, etc.
OpenAI is working with a vanilla GPT architecture which lacks the machinery to write things down and read them later. There are other architetures that can (Retrieval-augmented GPT) but those are not yet production-ready.
The current version of ChatGPT is limited to a working memory of 3000 tokens - while this could be persisted as a session, the AI would still forget everything a few paragraphs prior. Increasing this limit requires re-teaining the entire model from scratch, and it takes exponentially more time the larger your context is.
Humans are inherently irrational. We try to emulate rational behavior because a logic gives us certainity and lets us feel secure about the outcomes, but we are physically incapable of acting in a logical way for several reasons. We don't have enough data to make a fully justified decision in every situation, and if we did, it would still cost too much energy for our meat brains to actually compute those decisions.
So we approximate rational behavior, making statistical guesses. With enough data (experience), those guesses are good. Other times, your guesses are bad. Sometimes you have barely any data at all and are forced to choose between several options that all carry risks, so you go with your instinct. Is it rational? No, but it might have been the least bad choice, as in real-world, real-time situations, failure to make any choice whatsoever is itself a choice, and usually a bad one.
ps: I come from the AI field so I have no idea how any of this relates to economics.
If they can make the flip-phone variant hardy enough to play football with I'll definitely buy it. But chances are, my S41 will still be working by then ;)