There are other ways to lower the amount of plastic in you. If you donate your blood you can measurably lower your pfas levels. Really just removing blood which carries plastic through your whole body will also lower your concentration of plastics. Because plastic is in the water, make sure you drink filtered water. They do make filters that will catch micro plastics and some will advertise it. If you want to keep your levels lower avoid hydrophobic coatings that sit next to food for extended periods of time and definitely don't heat that food next to a hydrophobic coating. Think microwaving food in a container with coatings that'll leach into the food. So bags of popcorn should be avoided like the plague, unfortunately.

Source: Veritasium, skip to at least 50:15, but honestly I'd recommend watching the whole thing https://youtu.be/SC2eSujzrUY.

62% of Americans are living paycheck to paycheck. Perhaps saying most Americans are struggling is doomerism, but what percentage living paycheck to paycheck no longer counts as doomerism and is just a harsh truth? 75%? 90%? Do you think the number of people living paycheck to paycheck is increasing or decreasing this year?

https://econofact.org/factbrief/is-there-a-consensus-that-a-majority-of-americans-are-living-paycheck-to-paycheck#%3A%7E%3Atext=While+one+survey+by+LendingClub%2Cspending%3B+62%25+answered+yes.

In a capitalist society, what is good or best is irrelevant. All that matters is if it makes money. AI makes no money. The $200 and $300/month plans put in rate limits because at those prices they're losing too much money. Lets say the beak-even cost for a single request is somewhere between $1-$5 depending on the request just for the electricity, and people can barely afford food, housing, and transportation as it is. What is the business model for these LLMs going to be? A person could get a coffee today, or send a single request to an LLM? Now start thinking that they'll need newer gpus next year. And the year after that. And after that. And the data center will need maintenance. They're paying literally millions of dollars to individual programmers.

Maybe there is a niche market for mega corporations like Google who can afford to spend thousands of dollars a day on LLMs, but most companies won't be able to afford these tools. Then there is the problem where if the company can afford these tools, do they even need them?

The only business model that makes sense to me is the one like BMW uses for their car seat warmers. BMW requires you to pay a monthly subscription to use the seat warmers in their cars. LLM makers could charge a monthly subscription to run a micro model on your own device. That free assistant in your Google phone would then be pay walled. That way businesses don't need to carry the cost of the electricity, but the LLM is going to be fairly low functioning compared to what we get for free today. But the business model could work. As long as people don't install a free version.

I don't buy the idea that "LLMs are good so they are going to be a success". Not as long as investors want to make money on their investments.

Holy crap, has anyone ever attempted to create an "AI fork bomb"? Go to one of these agent bots, and tell it to create accounts with the other agent code bots. These new accounts will all be told to create accounts on all the other code bots services. And do this recursively forever. So the flow would be 1 bot makes lets say 5 bots. Each of those 5 bots make 5 more bots. And each of those 5 bots make 5 more. So the total number of running bots becomes like 1 * 5 * 5 * 5 * 5...

Obligatory, this is purely hypothetical, and you should never do this for legal reasons.

If the LLM could reason, shouldn't it be able to say "my token training prevents me from understanding the question as asked. I don't know how many 'r's there are in Strawberry, and I don't have a means of finding that answer"? Or at least something similar right? If I asked you what some word in a language you didn't know, you should be able to say "I don't know that word or language". You may be able to give me all sorts of reasons why you don't know it, and that's all fine. But you would be aware that you don't know and would be able to say "I don't know".

If I understand you correctly, you're saying the LLM gets it wrong because it doesn't know or understand that words are built from letters because all it knows are tokens. I'm saying that's fine, but it should be able to reason that it doesn't know the answer, and say that. I assert that it doesn't know that it doesn't know what letters are, because it is incapable of coming to that judgement about its own knowledge and limitations.

Being able to say what you know and what you don't know are critical to being able to solve logic problems. Knowing which information is missing and can be derived from known things, and which cannot be derived is key to problem solving based on reason. I still assert that LLMs cannot reason.

I don't think the current common implementation of AI systems are "thinking" and I'll base my argument on Oxford's definitions of words. Thinking is defined as "the process of using one's mind to consider or reason about something". I'll ignore the word "mind" and focus on the word "reason". I don't think what AIs are doing counts as reasoning as defined by Oxford. Let's go to that definition: "the power of the mind to think, understand, and form judgments by a process of logic". I take issue with the assertion that they form judgments. For completeness, but I don't think it's definition is particularly relevant here, a judgment is: "the ability to make considered decisions or come to sensible conclusions".

I think when you ask an LLM how many 'r's there are in Strawberry and questions along this line you can see they can't form judgments. These basic but obscure questions are where you see that the ability to form judgements isn't there. I would also add that if you "form judgments" you probably don't need to be reminded you formed a judgment immediately after forming one. Like if I ask an LLM a question, and it provides an answer, I can convince it that it was wrong whether or not I'm making junk up or not. I can tell it it made a mistake and it will blindly change it's answer whether it made a mistake or not. That also doesn't feel like it's able to reason or make judgments.

This is where all the hype falls flat for me. It feels like sometimes it looks like a concrete wall, but occasionally that concrete wall is made of wet paper. You can see how impressive the tool is and how paper thin it is at the same time. It's cool, it's useful, it's fake, and that's ok. Just be aware of what the tool is.

Reddit cfb was fun, but I'm not going back this season. I'll try to post some here some once the season starts. Particularly whenever the gators lose.

Go Vols

As a foster parent, we get trained on how kids frequently get trafficked and the number one place is anywhere parents feel their kids are safe and don't need close supervision. So anything kid centric like Disney World or family centric like a church are prime targets for predators. Roblox is a kid centric place where parents don't closely watch their kids.

Roblox is a big enough company and has been around long enough that they should be doing something. They should be doing something because they definitely know this happens at this point. If you believe everything they claim on their website is true: https://corp.roblox.com/resource/child-safety, they are doing something. As far as I can tell, there isn't a report or any way to validate they are actually doing anything. You just have to trust that the publicly traded company is investing in a department that doesn't directly generate profits for its stock holders. You have to trust this company is not giving in to pressure each quarter to increase profits and decrease costs around this function of their business.

I push back on the idea that if something is designed for kids it doesn't need to be safe for kids. Roblox has designed something for kids, they should do something to make it safe for kids, and parents should watch their kids.

Billy Napier wears jorts!

This is a strong argument. One of my main complaints with modern large companies is the need to operate for short term gains long term losses, so point number 3 sounds amazing to me. Does this mean Intel would no longer be a publicly traded company, but a US Government owned company, something similar to the USPS?

I would defend Tennessee at least partially. Tennessee is home to the Oak Ridge National Laboratory which houses the world's second largest supercomputer (Frontier). Scientists from across the world to come work there every year. These scientists are woefully out numbered by everyone else like you said, but some of the brightest minds are in fact in Tennessee.

If you think about it, this was perhaps the most humane way to conduct war. No humans were harmed in this attack, and the ability to harm humans was severely degraded. You had drones smash into unmanned airplanes. Nothing but money and hardware was lost. This is the utopian version of war if such a thing could ever exist. One country removes another country's ability to harm humans with nobody getting hurt and everyone gets to go home.

This is all new to me, so I'll have to do a bit of homework on this. Thanks for the detailed and linked reply!

Awesome, I'll give these a spin and see how it goes. Much appreciated!

I'm uninformed on this topic, perhaps you or someone else can teach me a bit more on this. What would the argument be for bailing them out, and what would be the argument for letting them fail? Without any knowledge of the consequences of either, I feel like letting the business fail is what we should do. We let businesses fail all the time, especially small ones. Why should we bail out this business when we let other fail all the time?

It feels like the core concern is letting that many people all lose their job at the same time would be particularly challenging issue for the people affected. But these numbers are far less than the number that have been laid off recently by other companies. The government didn't step in to help those people or companies performing massive layoffs, why bailout this company? I don't know, but would like to hear arguments for both

Very cool thanks for the informative answers

Is the argument that LLMs are thinking because they make guesses when they don't know things combined with no provided quantity or quality to describe thinking?

If so, I would suggest that the word "guessing" is doing a lot of heavy lifting here. The real question would be "is statistics guessing"? I would say guessing and statistics are not the same thing, and Oxford would agree. An LLM just grabs tokens based on training data on what word or token most likely comes next, it will just be using what the statistically most likely next token or word is. I don't think grabbing the highest likely next token counts as guessing. That feels very algorithmic and statistical to me. It is also possible I'm missing the argument still.

I don't think you can disconnect how an LLM was trained from how it operates. If you train an LLM to use trigonometry to solve addition problems, I think you will find the LLM will do trigonometry to solve addition problems. If you train an LLM in only Russian, it will speak Russian. I would suggest that regardless of what you train it on it will choose the statistically most likely next token based on its training data.

I would also suggest we don't know the exact training data being used on most LLMs, so as outsiders we can't say one way or another on how the LLM is being trained to do anything. We can try to extrapolate from posts like the one that you linked to how the LLM was trained though. In general if that is how the LLM is coming to its next token, then the training data must be really heavily weighted in that manner.

My apologies, I was too vague. I'm saying "thinking" by definition is not "statistics". Where Monkeys, birds, and human babies all "think", LLMs use algorithms and "statistics". I also think that "statistics" not meaning the same thing that "thinking" is a valid argument. I would go farther and say it's important that words have meaning. That is what I was attempting to convey. I'm happy to clear up anything I was unclear about.

I would point out I think you might be overly confident in the manner in which it was trained addition. I'm open to being wrong here, but when you say "It was not trained to do trigonometry to solve addition problem", that suggests to me either you know how it was trained, or are making assumptions about how it was trained. I would suggest unless you work at one of these companies, you probably are not privy to their training data. This is not an accusation, I think that is probably a trade secret at this point. And if the idea that there would be nobody training an LLM to do addition in this manner, I invite you to glance the Wikipedia article on addition. Really glance at literally any math topic on Wikipedia. I didn't notice any trigonometry in this entry but I did find the discussion around finding the limits of logarithmic equations in the "Related Operations" section: https://en.m.wikipedia.org/wiki/Addition. They also cite convolution as another way to add in which they jump straight to calculus: https://en.m.wikipedia.org/wiki/Convolution.

This is all to say, I would suggest that we don't know how they're training LLMs. We don't know what that training data is or how it is being used exactly. What we do know is that LLMs work on tokens and weights. The weights and statistical relevance to each of the other tokens depends on the training data, which we don't have access to.

I know this is not the point, but up until this point I've been fairly pedantic and tried to use the correct terminology, so I would point out that technically LLMs have "tensors" not "neurons". I get that tensors are designed to behave like neurons, and this is just me being pedantic. I know what you mean when you say neurons, just wanted to clarify and be consistent. No shade intended.