- 6 hours
I partially agree with the idea that AI is the same as industrial revolution. Yes, AI is just as revolutionary as steam engine.
Except we are currently at the stage steam engine was in ancient Greece and Roman Empire - a curiosity too expensive to use for value it provides and too crude to provide anything better.
Luckily ancient times didn’t have billionaires playing speculative gambling on economy by trying to push any new thing as something revolutionizing right now.
Let the AI flop now, wait a few centuries and it will return better.
- 7 hours
This is inaccurate. What’s currently happening is that the AI companies are throwing away money because they’re riding the bubble.
What they pretend they’ll be able to sell is a replacement employee. Of course we’ve seen that in most fields, AI is not nearly good enough to replace us, and probably in most fields it never will be.
What you’re pointing out is one way to generate more consumption of AI, and that would be monetizable if AI were actually making money on queries. But right now it’s losing money on those queries.
- 22 hours
No, that’s google. A leaked memo indicates that they deliberately made search worse so people would have to search twice and see twice the number of ads
AI companies lose money every time someone uses it. Even those that charge per-token
- thermal_shock@lemmy.worldEnglish17 hours
I refuse to use a computer or browser without ublock origin. No fucking way I’m accepting that poisonous shit they call ads.
- thermal_shock@lemmy.worldEnglish13 hours
I’ve been on Firefox for the last year where it does work. I don’t use chrome anymore.
I also host a pihole at home blocking stuff too.
Stands works well though, saw a client using it recently
- MangoCats@feddit.itEnglish12 hours
Pretty amazing what PiHole reveals. My work laptop constantly chatters with sites all over the world, it’s the biggest source of DNS lookup in my home network while it’s on, and it’s competing with six people browsing the web for their various purposes…
- thermal_shock@lemmy.worldEnglish12 hours
Oh yeah, very eye-opening the amount of data it’s sending/receiving. Looking into gateways with VPN built in.
This is the entire business model for most if not all of tech really
- [deleted]@piefed.worldEnglish1 day
If it worked they could be profitable already instead of continuing to burn billions of investment dollars.
- 1 day
They’re burning billions because they’re trying to rush ahead of the competition in capabilities. No matter how good LLMs get, that is not their goal. They’re trying to reach AGI and there are no second places in that race.
- 8 hours
I think they know AGI is far off, and are setting a more medium term goal. I think they are just trying to corner the new LLM market that might emerge. Even if the bubble pops and 3/4 go bust, they hope to be the one that survives and gets to be the quasi-monopoly in that market for the next decade.
- 6 hours
Nobody “knows” that AGI is far off just like nobody knows that it’s near either. That is not an established fact. Those are both just popular narratives in their respective camps. We could get there tomorrow or it could take usanother 400 years. Both are plausible outcomes.
- [deleted]@piefed.worldEnglish24 hours
Thinking that LLMs will ever become AGI is fucking hysterical, and that is what the shills keep saying is going to happen. They are trying to turn lead into gold using a stove and a skillet.
No money they have dumped into LLMs is going to contribute to something that could achieve AGI. They are running the wrong race.
- [deleted]@piefed.worldEnglish23 hours
Altman and the other shills claim that.
Are you an AI shill?
If not, then I didn’t say you claimed that.
- 23 hours
Then I don’t see how your response in any way relates to what I said.
Whether you think they’ll ever get there or not is completely irrelevant. That is still what they’re after and the reason for the massive upfront investments.
- MangoCats@feddit.itEnglish13 hours
There is no AGI. Whatever they reach, there will always be a next level.
In the 1980s if you said you could make a computer play Go better than the best human masters, there are those who would have said “that would be true artificial intelligence.” Then it happened, and the world moved on.
There was a time when a computer understanding speech, translating languages, would have been considered true artificial intelligence, then we got there, and the world moved on.
LLMs have solved a mathematical question left open with prize money for decades unsolved by humans (just one, really, that I know of, so far…), but that’s not AGI yet…
Many forms of “the Turing Test” are being passed by LLMs tested against the majority of the general population now, but apparently that’s not AGI yet…
AGI will continue to be a moving goalpost, as it should be. It’s not a finish line, it’s a journey. Even when automated systems are building themselves from raw material inputs, designing and building their own infrastructure, power plants, communications, and continuously improving their own designs, there will be those who still design new tests for “AGI” that they don’t pass, yet.
- 9 hours
AI and AGI are not synonymous terms. We’ve had AI since 1956. General intelligence means human-level intelligence. When an AI system can do any task as well as or better than humans can, it’s by definition generally intelligent.
We’re not changing the definitions. People thought that chess is so hard that once an AI system can play chess it has to be as intelligent as humans. That just turned out to be a false assumption. A system can be superhuman at playing chess but that ability doesn’t need to translate to any other field.
- 1 day
That is just utter bullshit. Hallucinations are a by-product of how LLMs work under the hood, not an intentional design choice. An AI that doesn’t make mistakes would be orders of magnitude more profitable.
- MangoCats@feddit.itEnglish13 hours
Mistakes are part of the human process… an automaton which produces only one solution for a problem is easily stuck, trapped, dead-ended. Building imperfect solution candidates and improving them until they are acceptable is how humans have designed things since forever. There are no perfect answers to the questions that matter.
- [deleted]@piefed.worldEnglish24 hours
The prevalence of hallucination in LLMs is a design choice. It is a result of raising the ‘temperature’ which is just fancy speak for randomization so it doesn’t spit out the same text for the same question over and over to make it look like it has nuance and whatever.
If it was consistent they would be able to reduce incorrect results, but they want it to look like a human response.
- MangoCats@feddit.itEnglish13 hours
It’s not just “looking like a human response” it’s also functioning like a human response. The randomness of results enables iterative soltions that make forward progress instead of getting stuck.
There are a vast set of problems which don’t have a single perfect “correct” answer where all others are wrong, there are just collections of “answers” which - when taken as a set - work together to form a working solution. You may have 100 questions to answer, and how you answer the first 10 will affect what does or does not work for the next 10, and the next, down the line, and you may find when you get to the last set of 10 that you can’t get to the end solution unless you revise some of the answers that you previously gave - answers that looked resonable until you built the next 80% of the product…
Life isn’t school - there aren’t 10 question quizzes with pick one of 4 multiple choice possible answers where you can get a perfect score just by answering each question correctly one at a time. Real-life school is being in charge of class assignments for 1000 students, chosing which 25 students go in each room with each teacher. What classes do they get, what combinations of students should be kept together, kept apart, grouped with which teachers… they aren’t impossible problems, but they are impossible to optimize for all possible considerations. Tradeoffs have to be made.
- [deleted]@piefed.worldEnglish11 hours
Tradeoffs like not understanding time and always giving an answer even when there isn’t one.
- 19 hours
Can you provide sources to “they want it to look like a human response?”
I have not read about that before.
- [deleted]@piefed.worldEnglish16 hours
The whole idea of LLMs is to replicate human language, as in have LLM output replicate language spoken by humans.
Here’s something about improving it: Enhancing Human-Like Responses in Large Language Models
Here’s a big thing about temperature: https://www.ibm.com/think/topics/llm-temperature
- morto@piefed.socialEnglish22 hours
Also, when you get used to relying on ai, you lose the practice and forget part fo your knowledge and skills. So, if you try to stop using ai, you will first have a steep decrease in productivity as you have to resharp your skills and remember a lot of stuff, and that creates a barrier preventing people from going out
- MangoCats@feddit.itEnglish13 hours
Jesus, people, AI has barely been usable for a year - do you all forget your years of study and practice if the coffee breaks are too long, too?
- morto@piefed.socialEnglish13 hours
Many people will have already forgotten what they studied in the first semesters when they finish the course lol
WIthout constant practice, most people’s skills get rusty in a faster rate than we can imagine
- MangoCats@feddit.itEnglish12 hours
In the 1980s I worked at a factory where the joke was “why are coffee breaks only 15 minutes long?” “Because when we give 'em 30 minutes for lunch we have to retrain them before they start work again after lunch.”
And when we’re done fixing, we’re unnecessary and have time to eat the rich \o/
One can dream … why not replace billionaires with AI too?
- MangoCats@feddit.itEnglish13 hours
The thing about money - the only thing money is actually good for is: manipulating the behavior of humans.
You can’t eat it, but you can get people to give you food in exchange for it. Money doesn’t grow food, but it does get people to do the work of growing and harvesting and shipping and preparing food…
Money doesn’t build your house, but it does get people to build a house for you. It doesn’t make electricity, but it does get people to build and operate electrical generation systems…
So, what are Billionaires? They’re actually just power-centers of people manipulation by money.
- realitista@lemmus.orgEnglish1 day
Unless you pay more for the better model, then it makes slightly less mistakes.
- MangoCats@feddit.itEnglish13 hours
The “better models” have been interesting to watch progress over the past year. I’d say the free to use models today are better than the best that were available a year ago. The ones with bigger context windows use more resources, and sometimes can give better results, often not. In LLMs, management of what is, and is not, in the context window seems to be the key to the kinds of results you get, and it feels like they have been “learning” to self-manage their context windows quite a bit better over the past 12 months.
- realitista@lemmus.orgEnglish8 hours
I agree. Over time I have learned to be a lot more careful with the context window and periodically start over to keep it small. This was one of the reasons I left the free ChatGPT, it seemed to have a very small context window and was not graceful at all about going outside it. Gemini free tier was a lot more graceful about this. I think the advantage of the paid tiers is simply that they will try to manage for longer and report to you how big your context window has gotten. So you have more time and you know when to start thinking about starting from scratch again.
- 1 day
and then fix the new mistakes it made while trying to fix the old ones and
I use local AI models to improve my process. I pay zero, and they do a pretty good job of taking the grunt work out of my tasks
- 22 hours
I use AI for first drafts of work frequently. I’m also in the process of building a chief of staff agent, which it pretty cool. I pay $30/month. Not bad.
- MangoCats@feddit.itEnglish13 hours
I use AI to review proposed final drafts a lot. It finds all kinds of nuance (and problems) in minutes that would take me hours to find without it.
It’s not that. It’s the idea that by being right just a bit more times than wrong, statistically this would bring out good results if you try it enough times. If that doesn’t happen, it’s really your fault 😀.
- MangoCats@feddit.itEnglish12 hours
This is the power of specifications. With a specification, the LLM can create a test to ensure what it creates meets the specification. Then the agent can iterate on solution after solution until its proposed solution meets all your specifications. Then you get to discover how many holes you left open in your specifications.
Another thing AI agents based on LLMs aren’t half bad at: reviewing specifications for completeness and readiness for implementation. They’ll go ahead and fill in the blanks for you if you ask them to, but if you ask them instead to point out the holes then you can decide what should be done instead of “yeah, just make mine like all the other ones you have seen in your training.”
