Earlier quoted context omitted.
>> The world is a hugely better place with our 8 billion people than it was when there were 50 million people kind of like living in caves and whatever. There is a theory that hunter-gatherers were much more happier compared to us because they were more in tune with the natural environment, had fewer sources of stress, and were more connected to their community than modern humans. https://www.npr.org/sections/goatsan…
Idk... I'm very grateful for modern dentistry...
John Carmack’s ‘Different Path’ to Artificial General Intelligence
471–480 of 506 posts
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#472Earlier quoted context omitted.
productivity by who and for who ? AI will belong to a small number of people, and I will not be part of them
When do you expect that this will change? It certainly isn’t the case now. Rich folks don’t broadly have access to significantly better AI today. This seems to be a common misconception - the rich and powerful have access to far more advanced technology than the average person. The economics just don’t support it. Let’s say Bezos wanted a better computer chip, just for himself. Ok, fine, try to start a company, hire…
To continue on your example, Besos could probably buy OpenAI.
I don't see that as threatening by itself, but more a continuation of a particular class of people owning the means of productions.
Props to Stable Diffusion and I hope to see more of those type of AI, as opposed to fancy black box at the other side of an API.
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#473Earlier quoted context omitted.
Idk... I'm very grateful for modern dentistry...
I have read that North American indigenous people were known for having great teeth. Here is one random citation I found searching for indigenous teeth: https://drscottgraves.com/the-secrets-to-healthy-teeth-from-...
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#474This paragraph took me aback: But if I just look at it and say, if 10 years from now, we have ‘universal remote employees’ that are artificial general intelligences, run on clouds, and people can just dial up and say, ‘I want five Franks today and 10 Amys, and we’re going to deploy them on these jobs,’ and you could just spin up like you can cloud-access computing resources, if you could cloud-access essentially arti…
Why waste time figuring out I need five Franks and 10 Amys? I'll just dial up a one of me and head home early.
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#475Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#476Earlier quoted context omitted.
You seem to have missed the part about having a qualified human mentor guiding you through so that content. It will be hard to impossible to build career as a teacher with all that free content as a competitor, unless you're an extremely talented teacher who can sell your services to the wealthy.
Even now ChatGPT is pretty close to being able to tutor someone in a subject. You could build a product around it that would work off of a lesson plan, ask the student questions and determine if they answered correctly or not and be able to identify what part of their knowledge was missing and then create a lesson based around explaining that. It would need to have some capabilities that ChatGPT doesn't have by itsel…
What you describe is the "learning equivalent to personalized ads" that I was talking about as the only option available to poor people.
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#477Earlier quoted context omitted.
Because accounting is not the same thing as book keeping. Book keeping can, and in fact is, partially automated. Accounting however, is not just about data entry and doing sums, things which frequently are automated, but also about designing the books for a given organization. Every company is different in how it does business so every accounting system is a bespoke solution. There are a lot of rules and judgement ca…
>> Every company is different in how it does business so every accounting system is a bespoke solution Who benefits from these bespoke solutions? Can you give a example of how one company would do its books vs another and why it would be beneficial? >> accountants don't just track the numbers, they also validate them What information do they use to validate numbers? Why is it not possible for today's AI to do it?
For a more concrete example, I'll tell you about something I have some experience with, commission systems. Commissions seems like it would be something that was straightforward to calculate but it's tied to business strategy and that's different for every company. Most companies for example will want to compute commissions on posted invoices, which makes the process much simpler because posted invoices are immutable, but I once built a commission calculator for a company years ago that often had a long gap (months) between a booking and when they could invoice the client, so they wanted to calculate commissions from bookings but only pay them when invoiced. Because bookings were mutable, and there were legitimate reasons to change a booking before you invoiced it, that, combined with a lot of fiddly rules about which products were compensated at which rates and when, meant that there was a lot of "churn" in the compensation numbers for sales reps from day to day; they're actual payment might differ from what they thought they earned. That was a problem that the company dealt with, the tradeoff being that they could show earnings numbers to the sales reps much more quickly and incentivize them to follow up with the clients on projects so that they could eventually be paid.
I remember another commissions situation where there was a company that sold a lot projects with labor involved. They were able to track the amount of labor done per project, but they compensated the sales reps by line item in the invoices, and the projects didn't necessarily map to the line items. This meant that even though the commissions were supposed be computed from the GP, there wasn't necessarily a way to calculate the labor cost in a way that usable for commissions so the company had to resort to a flat estimate. This was a problem because the actual profitability of a project didn't necessarily factor into the reps' compensation. Different companies that had a different business model, different strategy, or just different overall approach would not have had this problem, but they might have had other problems to deal with created by their different strategies. This company could have solved this problem, but they would have had to renegotiate comp plans with their sales reps.
There are off the shelf tools available for automatically calculating commissions, but even the most opinionated of them are essentially glorified scripting platforms that let you specify a formula to calculate a commission, and they don't all have the flexibility that manager might want if they wanted to changed their compensation strategy. And this is only one tiny corner of accounting practice.
Basically, when it comes to arithmetic very few accountants are out there manually summing up credits and debits. In large companies, the arithmetic has been automated since the 70s; that's largely what those old mainframes are still doing. But every company has a different compensation plan, different org structure, different product, different supply chain, different legal status, different reporting requirements, etc, etc, and that requires things to be done differently.
> What information do they use to validate numbers? Why is it not possible for today's AI to do it?
For an example, they would need to cross check with a sales rep and an engineer to makes sure that the engineer had not turned on a service for the customer that the sales rep had not sold. If that happened, they would have to figure out how to account for the cost. Given that the SOPs were written in plain English, I suppose it's possible that an AI might be trained to notice the discrepancy, but if you could do that, you could just as easily replace the engineer. And that didn't account for situations where the engineer might have had an excuse or good reason for deviating from the SOP that would only come to light by actually talking to them.
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#478Earlier quoted context omitted.
> The fact that you don't understand how GPT models language does not make it less of a model. It did learn the syntax, you are just incapable to grasp the formula it represents. The whole point of science is understanding and LLMs don't provide understanding of how human language works.
This is a pseudophilosophical mumbo-jumbo. It does not really address the comment you replied to, because it does not contradict any of the following statements (from which my original point trivially follows): 1. Chomsky claimed syntax can't be modeled statistically. 2. GPT is a nearly perfect statistical model of syntax.
Chomky's point is that there is a lot of evidence that humans don't use a statistical process to produce language and these statistical "models" don't tell you anything about the human language faculty.
Whether your 1 & 2 are meaningful depend on how you define "model" which is the real issue at hand: Do you want to understand something (science) --- in which case the model should explain something --- or do you want a useful tool (engineering) --- in which case it can essentially be a black box.
I don't know why you care to argue about this though; my impression is that you don't really care about how human's do language so why does it matter to you?
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#479Earlier quoted context omitted.
They also happily murdered unaffiliated tribes just because. There are tons of trade offs.
> They also happily murdered unaffiliated tribes just because. We still do it.
Re: John Carmack’s ‘Different Path’ to Artificial General Intelligence
#480Earlier quoted context omitted.
This is a pseudophilosophical mumbo-jumbo. It does not really address the comment you replied to, because it does not contradict any of the following statements (from which my original point trivially follows): 1. Chomsky claimed syntax can't be modeled statistically. 2. GPT is a nearly perfect statistical model of syntax.
The point is very basic: These "models" don't tell you anything about the human language faculty. They can be useful tools but don't serve science. Chomky's point is that there is a lot of evidence that humans don't use a statistical process to produce language and these statistical "models" don't tell you anything about the human language faculty. Whether your 1 & 2 are meaningful depend on how you define "model" wh…
Re: meaningfulness. Your scientific vs engineering model distinction is not how "scientific model" is defined. It includes both. The existence of the model itself does explain something, specifically, that statistics can model language. That alone is explanatory power, so the claim that it doesn't explain anything is a lie. Therefore it is both an "engineering" model (because it can predict syntax) and scientific (because it demonstrates statistical approach to language has predictive powers in scientific sense).