- cross-posted to:
- technology@lemmy.zip
- cross-posted to:
- technology@lemmy.zip
- Rabbit R1 AI box is actually an Android app in a limited $200 box, running on AOSP without Google Play.
- Rabbit Inc. is unhappy about details of its tech stack being public, threatening action against unauthorized emulators.
- AOSP is a logical choice for mobile hardware as it provides essential functionalities without the need for Google Play.
Why are there AI boxes popping up everywhere? They are useless. How many times do we need to repeat that LLMs are trained to give convincing answers but not correct ones. I’ve gained nothing from asking this glorified e-waste something, pulling out my phone and verifying it.
What I don’t get is why anyone would like to buy a new gadget for some AI features. Just develop a nice app and let people run it on their phones.
The answer is “marketing”
They have pushed AI so hard in the last couple of years they have convinced many that we are 1 year away from Terminator travelling back in time to prevent the apocalypse
s/Crypto/AI/
Because money, both from tech hungry but not very savvy consumers, and the inevitable advertisers that will pay for the opportunity for their names to be ejected from these boxes as part of a perfectly natural conversation.
I have now heard of my first “ai box”. I’m on Lemmy most days. Not sure how it’s an epidemic…
I haven’t seen much of them here, but I use other media too. E.g, not long ago there was a lot of coverage about the “Humane AI Pin”, which was utter garbage and even more expensive.
It’s not black or white.
Of couse AI hallucinates, but not all that an LLM produces is garbage.
Don’t expect a “living” Wikipedia or Google, but, it sure can help with things like coding or translating.
I don’t necessarily disagree. You can certainly use LLMs and achieve something in less time than without it. Numerous people here are speaking about coding and while I had no success with them, it can work with more popular languages. The thing is, these people use LLMs as a tool in their process. They verify the results (or the compiler does it for them). That’s not what this product is. It’s a standalone device which you talk to. It’s supposed to replace pulling out your phone to answer a question.
I quite like kagis universal summarizer, for example. It let’s me know if a long ass YouTube video is worth watching
I use LLMs as a starting point to research new subjects.
The google/ddg search quality is hot garbage, so LLM at least gives me the terminology to be more precise in my searchs.
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There is s fuck ton on money laundering coming from China nowadays and they invest millions in any tech-bro stupid idea to dump their illegal cash.
I think it’s a delayed development reaction to Amazon Alexa from 4 years ago. Alexa came out, voice assistants were everywhere. Someone wanted to cash in on the hype but consumer product development takes a really long time.
So product is finally finished (mobile Alexa) and they label it AI to hype it as well as make it work without the hard work of parsing wikipedia for good answers.
Alexa and Google home came out nearly a decade ago
Alexa is a fundamentally different architecture from the LLMs of today. There is no way that anyone with even a basic understanding of modern computing would say something like this.
Which is why I explicitly said they used AI (LLM) instead of the harder to implement but more accurate Alexa method.
Maybe actually read the entire post before being an ass.
I just started diving into the space from a localized point yesterday. And I can say that there are definitely problems with garbage spewing, but some of these models are getting really really good at really specific things.
A biomedical model I saw seemed lauded for it’s consistency in pulling relevant data from medical notes for the sake of patient care instructions, important risk factors, fall risk level etc.
So although I agree they’re still giving well phrased garbage for big general cases (and GPT4 seems to be much more ‘savvy’), the specific use cases are getting much better and I’m stoked to see how that continues.
I just used ChatGPT to write a 500-line Python application that syncs IP addresses from asset management tools to our vulnerability management stack. This took about 4 hours using AutoGen Studio. The code just passed QA and is moving into production next week.
https://github.com/blainemartin/R7_Shodan_Cloudflare_IP_Sync_Tool
Tell me again how LLMs are useless?
To be honest… that doesn’t sound like a heavy lift at all.
Dream of tech bosses everywhere. Pay an intermediate dev for average level senior output.
Intermediate? Nah, junior. They’re cheaper after all.
But senior devs do a lot more than output code. Sometimes - like Bill Atkinson’s famous -2000 line change to Quickdraw - their jobs involve a lot of complex logic and very little actual code output.
It’s a shortcut for experience, but you lose a lot of the tools you get with experience. If I were early in my career I’d be very hesitant relying on it as its a fragile ecosystem right now that might disappear, in the same way that you want to avoid tying your skills to a single companies product. In my workflow it slows me down because the answers I get are often average or wrong, it’s never “I’d never thought of doing it that way!” levels of amazing.
You used the right tool for the job, saved you from hours of work. General AI is still a very long ways off and people expecting the current models to behave like one are foolish.
Are they useless? For writing code, no. Most other tasks yes, or worse as they will be confiently wrong about what you ask them.
I think the reason they’re useful for writing code is that there’s a third party - the parser or compiler - that checks their work. I’ve used LLMs to write code as well, and it didn’t always get me something that worked but I was easily able to catch the error.
Only if you believe most Lemmy commenters. They are convinced you can only use them to write highly shitty and broken code and nothing else.
This is my expirence with LLMs, I have gotten it to write me code that can at best be used as a scaffold. I personally do not find much use for them as you functionally have to proofread everything they do. All it does change the work load from a creative process to a review process.
I don’t agree. Just a couple of days ago I went to write a function to do something sort of confusing to think about. By the name of the function, copilot suggested the entire contents of the function and it worked fine. I consider this removing a bit of drudgery from my day, as this function was a small part of the problem I needed to solve. It actually allowed me to stay more focused on the bigger picture, which I consider the creative part. If I were a painter and my brush suddenly did certain techniques better, I’d feel more able to be creative, not less.
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But we never have proofs that it gives good code, that’s convenient…
So you want me to go into one of my codebases, remember what came from copilot and then paste it here? Lol no
Of course you can’t.
You already forgot, that’s convenient, again.
Yeah you post your employer first, dumbass
All you want is something to belittle
You say it’s magical but never post proof. That’s all I need to think it’s shit. No need to debate about it for hours. Come back when you entice us with something instead of the billion REST APIs that are useless but seem to give a hard on to all the AI bros out there.
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Who’s going to tell them that “QA” just ran the code through the same AI model and it came back “Looks Good”.
:-)
I don’t think LLMs are useless, but I do think little SoC boxes running a single application that will vaguely improve your life with loosely defined AI features are useless.
The code is bad and I would not approve this. I don’t know how you think it’s a good example for LLMs.
The code looks like any other Python code out there.
We’re doomed then because I would reject that in a MR for being unprofessional and full of bugs.
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In one of those weird return None combination. Also I don’t get why it insists on using try catch all the time. Last but not least, it should have been one script only with sub commands using argparse, that way you could refactor most of the code.
Also weird license, overly complicated code, not handling HTTPS properly, passwords in ENV variables, not handling errors, a strange retry mechanism (copy pasted I guess).
It’s like a bad hack written in a hurry, or something a junior would write. Something that should never be used in production. My other gripe is that OP didn’t learn anything and wasted his time. Next time he’ll do that again and won’t improve. It’s good if he’s doing that alone, but in a company I would have to fix all this and it’s really annoying.
It’s no sense trying to explain to people like this. Their eyes glaze over when they hear Autogen, agents, Crew ai, RAG, Opus… To them, generative AI is nothing more than the free version of chatgpt from a year ago, they’ve not kept up with the advancements, so they argue from a point in the distant past. The future will be hitting them upside the head soon enough and they will be the ones complaining that nobody told them what was comming.
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Downvotes by a few uneducated people mean nothing. The tools are already there. You are free to use them and think about this for yourself. I’m not even talking about what will be here in the future. There is some really great stuff right now. Even if doing some very simple setup is too daunting for you, you can just watch people on youtube doing it to see what is available. People in this thread have literally already told you what to type into your search box.
In the early 90s, people exactly like you would go on and on about how stupid the computerbros were for thinking anyone would ever use this new stupid “intertnet” thing. You do you, it is totally fine if you think because a handful of uneducated, vocal people on the internet agree with you that technology has mysteriously frozen for the first time in history, then you must all be right.
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They aren’t trying to have a conversation, they’re trying to convince themselves that the things they don’t understand are bad and they’re making the right choice by not using it. They’ll be the boomers that needed millennials to send emails for them. Been through that so I just pretend I don’t understand AI. I feel bad for the zoomers and genas that will be running AI and futilely trying to explain how easy it is. Its been a solid 150 years of extremely rapid invention and innovation of disruptive technology. But THIS is the one that actually won’t be disruptive.
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Tell me about how when you used Llama 3 with Autogen locally, and how in the world you managed to pay a large company to use disproportionate amounts of energy for it. You clearly have no idea what is going on on the edge of this tech. You think that because you made an openai account that now you know everything that’s going on. You sound like an AOL user in the 90 that thinks the internet has no real use.
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You’re just saying that you will only taste free garbage wine, and nobody can convince you that expensive wine could ever taste good. That’s fine, you’ll just be surprised when the good wine gets cheap enough for you to afford or free. Your unwillingness to taste it has nothing to do with what already exists. In this case, it’s especially naive since you could just go watch videos of people using actually good wine.
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Please show me good code done with AI. I’m waiting.
Wonderfully said, this is a very good point.
The best convincing answer is the correct one. The correlation of AI answers with correct answers is fairly high. Numerous test show that. The models also significantly improved (especially paid versions) since introduction just 2 years ago.
Of course it does not mean that it could be trusted as much as Wikipedia, but it is probably better source than Facebook.
“Fairly high” is still useless (and doesn’t actually quantify anything, depending on context both 1% and 99% could be ‘fairly high’). As long as these models just hallucinate things, I need to double-check. Which is what I would have done without one of these things anyway.
1% correct is never “fairly high” wtf
Also if you want a computer that you don’t have to double check, you literally are expecting software to embody the concept of God. This is fucking stupid.
It’s all about context. Asking a bunch of 4 year olds questions about trigonometry, 1% of answers being correct would be fairly high. ‘Fairly high’ basically only means ‘as high as expected’ or ‘higher than expected’.
Hence, it is useless. If I cannot expect it to be more or less always correct, I can skip using it and just look stuff up myself.
Obviously the only contexts that would apply here are ones where you expect a correct answer. Why would we be evaluating a software that claims to be helpful against 4 year old asked to do calculus? I have to question your ability to reason for insinuating this.
So confirmed. God or nothing. Why don’t you go back to quills? Computers cannot read your mind and write this message automatically, hence they are useless
That’s the whole point, I don’t expect correct answers. Neither from a 4 year old nor from a probabilistic language model.
And you don’t expect a correct answer because it isn’t 100% of the time. Some lemmings are basically just clones of Sheldon Cooper
I don’t expect a correct answer because I’ve used these models quite a lot last year. At least half the answers were hallucinated. And it’s still a common complaint about this product as well if you look at actual reviews (e.g., pretty sure Marques Brownlee mentions it).
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Hallucinations are largely dealt with if you use agents. It won’t be long until it gets packaged well enough that anyone can just use it. For now, it takes a little bit of effort to get a decent setup.
An LLM has never generated a correct answer to any of my queries.
That seems unlikely, unless “any” means two.
Perhaps the problem is that I never bothered to ask anything trivial enough, but you’d think that two rhyming words starting with 'L" would be simple.
“AI” is a really dumb term for what we’re all using currently. General LLMs are not intelligent, it’s assigning priorities to tokens (words) in a database, based on what tokens were provided before it, to compare and guess the next most logical word and phrase, really really fast. Informed guesses, sure, but there’s not enough parameters to consider all the factors required to identify a rhyme.
That said, honestly I’m struggling to come up with 2 rhyming L words? Lol even rhymebrain is failing me. I’m curious what you went with.
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Ok, by asking you mean that you find somewhere questions that someone identified as being answered wrongly by LLM, and asking yourself.
Nope.
I’ve asked GPT4 to write specific Python programs, and more often than not it does a good job. And if the program is incorrect I can tell it about the error and it will often manage to fix it for me.
I don’t believe you
OK
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I only “believe the hype” because a good developer friend of mine suggested I try copilot so I did and was impressed. It’s an amazing technical achievement that helps me get my job done. It’s useful every single day I use it. Does it do my job for me? No of fucking course not, I’m not a moron who expected that to begin with. It speeds up small portions of tasks and if I don’t understand or agree with its solution, it’s insanely easy not to use it.
People online mad about something new is all this is. There are valid concerns about this kind of tech, but I rarely see that. Ignorance on the topic prevails. Anyone calling ai “useless” in a blanket statement is necessarily ignorant and doesn’t really deserve my time except to catch a quick insult for being the ignorant fool they have revealed themselves to be.
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You would need to be pulling some trickery on Microsoft to get access to copilot for more than a single 30 day trial so I’m skeptical you’ve actually used it. Sounds like you’re using other products which may be much worse. It also sounds like you work in a conservative shop. Good luck with that
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I think Meta hates your answer