I haven’t gotten into the Beta, so I haven’t played it, but I’m curious, is the game designed so you can’t do anything without walking, or is it so that you can creep along at a snail’s pace without walking but to actually make real progress you have to walk?

It seems to me like you could use the psychology of a Pay To Win / Microtransactions game to motivate people but by using walking instead of money.

Does GPS really require an internet connection? I know it uses the radio, which kills the battery, but AFAIK you can get GPS without Internet access. For example, I’ve downloaded offline areas for Google Maps and have tracked my location that way, while traveling in countries where I didn’t have a SIM allowing me to access the Internet.

Fundamentally DEI is about good teamwork. You want a diverse team of people with different backgrounds and experiences.

Almost all VPs are DEI hires.

Trump chose Pence because he had government experience and his religious conservatism helped reassure the GOP’s religious voters.

Biden choosing Harris:

Biden committed to selecting a woman as his running mate … He noted that his selection would likely be younger than he is

Obama choosing Biden:

Obama recalled that he and his advisers Axelrod and Plouffe wondered if voters would accept a ticket of “two relatively young, inexperienced, and liberal civil rights attorneys” and ultimately Obama felt the contrast between him and Biden was a strength, and that Biden being older than Obama would reassure those voters who were concerned that Obama was too young to be president

https://en.wikipedia.org/wiki/2008_Democratic_Party_vice_presidential_candidate_selection

Bush Jr. choosing Cheney

By picking Cheney, Bush had a running mate who had years of experience as well as an extensive foreign policy expertise.

https://en.wikipedia.org/wiki/2000_Republican_Party_vice_presidential_candidate_selection

Clinton was an exception:

In making the selection, Clinton emphasized Gore’s experience with foreign policy and environmental issues.[1] Clinton’s choice of a fellow young southern centrist defied conventional wisdom

https://en.wikipedia.org/wiki/1992_Democratic_Party_vice_presidential_candidate_selection

This pattern goes through the years. If the presidential candidate is a northerner, they often pick a southerner. If he’s old, he chooses a younger VP candidate. If he’s from a wealthy background, he chooses someone who has a more humble background. If he lacks political experience, he chooses someone who has it. If he lacks international experience, he chooses someone who has it.

All VPs are DEI hires, at least to some extent. It’s accepted that if the two candidates are too similar that the ticket will fail.

What about the Nazi-sponsored English motor-racing legend, Dick Seaman.

Something like this would make for an amazing Olympics-related TV series. Take 2 well known recently retired athletes, one male and one female. Say something like Roger Federer and Megan Rapinoe. Have them spend a few hours with an Olympic Athelete trying to learn their sport, and then show off what they can do for the camera.

You’d know just how coordinated and talented these athletes are, and then you’d see just how awful they were at these sports. It would really make you appreciate the skill of the Olympians.

And there’s a better reason for wanting to delay definition updates: this outage.

It would definitely accomplish something. It’s not clear if that would necessarily be a positive thing, but it would definitely be a pivotal moment in history. Even just the attempted assassination might eventually be seen as pivotal.

The right-wing is full of so many contradictory forces, and right now it’s only Trump’s cult of personality that’s holding them together. It seems very likely to me that post-Trump they’re going to fracture. If Trump becomes dictator he’ll probably groom Don Jr. to succeed him, and he’ll eliminate anybody who might question that decision. But, right now, Jr. is not in a position to take over from dad, so if Trump were no longer in the picture, there would be a power vacuum and massive infighting.

But, if the assassination attempt had succeeded, there would probably have been a lot more political killings, and just violence in general.

All EVs come with Level 1 chargers that plug in to your standard house outlet.

Sure, but if you use those it takes a very long time to charge. Like, from empty it can take 40+ hours to charge a battery EV from empty to 80%. If you’re using your car to commute and your commute is anywhere near the max range of your car, that isn’t a viable option.

Hopefully it won’t be, but charging an electric car is still not a standard thing for apartment buildings to offer tenants. So, for the moment, that’s a major reason for renters to not take the plunge.

it’s super convenient to never go to a gas station again, and to wake up to a full tank

But, to make that possible, you basically have to have a “gas station” at home. If you own your own house you can modify it to install a charging spot. If you rent, you might not have that option.

I don’t have a parking space at my apartment

A way to charge it at home is also a major issue for anybody who lives in an apartment.

I mean alledgedly ChatGPT passed the “bar-exam” in 2023. Which I find ridiculous considering my experiences with ChatGPT and the accuracy and usefulness I get out of it which isn’t that great at all

Exactly. If it passed the bar exam it’s because the correct solutions to the bar exam were in the training data.

The other side can immediately tell that somebody has made an imitation without understanding the concept.

No, they can’t. Just like people today think ChatGPT is intelligent despite it just being a fancy autocomplete. When it gets something obviously wrong they say those are “hallucinations”, but they don’t say they’re “hallucinations” when it happens to get things right, even though the process that produced those answers is identical. It’s just generating tokens that have a high likelihood of being the next word.

People are also fooled by parrots all the time. That doesn’t mean a parrot understands what it’s saying, it just means that people are prone to believe something is intelligent even if there’s nothing there.

ChatGPT refuses to tell illegal things, NSFW things, also medical advice and a bunch of other things

Sure, in theory. In practice people keep getting a way around those blocks. The reason it’s so easy to bypass them is that ChatGPT has no understanding of anything. That means it can’t be taught concepts, it has to be taught specific rules, and people can always find a loophole to exploit. Yes, after spending hundreds of millions of dollars on contractors in low-wage countries they think they’re getting better at blocking those off, but people keep finding new ways of exploiting a vulnerability.

Yeah, that’s basically the idea I was expressing.

Except, the original idea is about “Understanding Chinese”, which is a bit vague. You could argue that right now the best translation programs “understand chinese”, at least enough to translate between Chinese and English. That is, they understand the rules of Chinese when it comes to subjects, verbs, objects, adverbs, adjectives, etc.

The question is now whether they understand the concepts they’re translating.

Like, imagine the Chinese government wanted to modify the program so that it was forbidden to talk about subjects that the Chinese government considered off-limits. I don’t think any current LLM could do that, because doing that requires understanding concepts. Sure, you could ban key words, but as attempts at Chinese censorship have shown over the years, people work around word bans all the time.

That doesn’t mean that some future system won’t be able to understand concepts. It may have an LLM grafted onto it as a way to communicate with people. But, the LLM isn’t the part of the system that thinks about concepts. It’s the part of the system that generates plausible language. The concept-thinking part would be the part that did some prompt-engineering for the LLM so that the text the LLM generated matched the ideas it was trying to express.

The “learning” in a LLM is statistical information on sequences of words. There’s no learning of concepts or generalization.

And what do you think language and words are for? To transport information.

Yes, and humans used words for that and wrote it all down. Then a LLM came along, was force-fed all those words, and was able to imitate that by using big enough data sets. It’s like a parrot imitating the sound of someone’s voice. It can do it convincingly, but it has no concept of the content it’s using.

How do you learn as a human when not from words?

The words are merely the context for the learning for a human. If someone says “Don’t touch the stove, it’s hot” the important context is the stove, the pain of touching it, etc. If you feed an LLM 1000 scenarios involving the phrase “Don’t touch the stove, it’s hot”, it may be able to create unique dialogues containing those words, but it doesn’t actually understand pain or heat.

We record knowledge in books, can talk about abstract concepts

Yes, and those books are only useful for someone who has a lifetime of experience to be able to understand the concepts in the books. An LLM has no context, it can merely generate plausible books.

Think of it this way. Say there’s a culture where instead of the written word, people wrote down history by weaving fabrics. When there was a death they’d make a certain pattern, when there was a war they’d use another pattern. A new birth would be shown with yet another pattern. A good harvest is yet another one, and so-on.

Thousands of rugs from that culture are shipped to some guy in Europe, and he spends years studying them. He sees that pattern X often follows pattern Y, and that pattern Z only ever seems to appear following patterns R, S and T. After a while, he makes a fabric, and it’s shipped back to the people who originally made the weaves. They read a story of a great battle followed by lots of deaths, but surprisingly there followed great new births and years of great harvests. They figure that this stranger must understand how their system of recording events works. In reality, all it was was an imitation of the art he saw with no understanding of the meaning at all.

That’s what’s happening with LLMs, but some people are dumb enough to believe there’s intention hidden in there.

That is to force it to form models about concepts.

It can’t make models about concepts. It can only make models about what words tend to follow other words. It has no understanding of the underlying concepts.

You can see that by asking them to apply their knowledge to something they haven’t seen before

That can’t happen because they don’t have knowledge, they only have sequences of words.

For example a cat is closer related to a dog than to a tractor.

The only way ML models “understand” that is in terms of words or pixels. When they’re generating text related to cats, the words they’re generating are closer to the words related to dogs than the words related to tractors. When dealing with images, it’s the same basic idea. But, there’s no understanding there. They don’t get that cats and dogs are related.

This is fundamentally different from how human minds work, where a baby learns that cats and dogs are similar before ever having a name for either of them.

Yeah. This is related to supernatural beliefs. If the grass moves it might just be a gust of wind, or it might be a snake. Even if snakes are rare, it’s better to be safe than sorry. But, that eventually leads to assuming that the drought is the result of an angry god, and not just some random natural phenomenon.

So, brains are hard-wired to look for causes, even inventing supernatural causes, because it helps avoid snakes.

The construction workers also don’t have a “desire” (so to speak) to connect the cities. It’s just that their boss told them to do so.

But, the construction workers aren’t the ones who designed the road. They’re just building some small part of it. In the LLM case that might be like an editor who is supposed to go over the text to verify the punctuation is correct, but nothing else. But, the LLM is the author of the entire text. So, it’s not like a construction worker building some tiny section of a road, it’s like the civil engineer who designed the entire highway.

Somehow making them want to predict the next token makes them learn a bit of maths and concepts about the world

No, it doesn’t. They learn nothing. They’re simply able to generate text that looks like the text generated by people who do know math. They certainly don’t know any concepts. You can see that by how badly they fail when you ask them to do simple calculations. They quickly start generating text that looks like it contains fundamental mistakes, because they’re not actually doing math or anything, they’re just generating plausible next words.

The “intelligence”, the ability to anwer questions and do something alike “reasoning” emerges in the process.

No, there’s no intelligence, no reasoning. The can fool humans into thinking there’s intelligence there, but that’s like a scarecrow convincing a crow that there’s a human or human-like creature out in the field.

But we as humans might be machines, too

We are meat machines, but we’re meat machines that evolved to reproduce. That means a need / desire to get food, shelter, and eventually mate. Those drives hook up to the brain to enable long and short term planning to achieve those goals. We don’t generate language its own sake, but instead in pursuit of a goal. An LLM doesn’t have that. It merely generates plausible words. There’s no underlying drive. It’s more a scarecrow than a human.

The annoying thing is that “I’ll go to work tomorrow” and “I’m going to go to work tomorrow” have subtly different meanings to English speakers, but good luck trying to come up with a rule to explain the difference to someone learning the language.

The spoken language has more vowel sounds than the alphabet has vowels, even when Danish has added three extra (æ, ø and å).

That’s one thing I think English at least did right. Other languages added extra letters and/or diacritics to try to capture all the variations on vowel sounds. But, in most languages there are far more vowel sounds than there are vowels. So, don’t make things unnecessarily complicated by adding extra letters. The one language that seems to do it basically right is Spanish, where there are only about 5 vowel sounds and they use the accent character not to show that a letter is pronounced differently, but just to cue you in on which syllable is accented when it might not be the one you expect. (With a few minor annoying variations, like el and él).