I keep watching for the AI bubble to burst.
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@VeeRat Have you seen the Big Short? The bit where the guy keeps expecting everything to fall, but a bunch of rich assholes keep propping it up while they raid the piggy bank?
the same guy who the big short was based on has already shorted on ai companies
"AI valuation concerns took hold in the US as well after it was revealed the trader who inspired The Big Short has bet $1.1bn (£840m) on a fall in prices for AI-related stocks Nvidia and Palantir."
Trader who inspired The Big Short bets against AI as tech shares fall
https://www.bbc.co.uk/news/articles/c867vyn2evlo -
I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat Same as me.
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@VeeRat
> When are they going to cut their losses?They can't. It is easy to fool a man, getting them to ack they were fooled is next to impossible.
https://www.groundbrkr.com/p/the-second-derivative-why-no-one
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the same guy who the big short was based on has already shorted on ai companies
"AI valuation concerns took hold in the US as well after it was revealed the trader who inspired The Big Short has bet $1.1bn (£840m) on a fall in prices for AI-related stocks Nvidia and Palantir."
Trader who inspired The Big Short bets against AI as tech shares fall
https://www.bbc.co.uk/news/articles/c867vyn2evlo@Captain_Jack_Sparrow @VeeRat Seems like he has worked on his timing. I'll guess he wins on this one. It feels like we are at that time.
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@VeeRat sounds like you are trying to solve the wrong problem. different outputs would not necessarily be bad for certain use-cases. let multiple people analyse or summarise the same document document, or even have the same person do it multiple times, and you will also get different outputs. what you are doing wrong is that you are trying to use something that is a tool (in a weird sense) to replace something that can only come from an intelligent designer. your keyboard autocomplete will never write a novel for you.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
Yeah, they're the behavior of a non deterministic toy not a tool.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat
Sunk cost fallacy on their part? -
Are you tracked by token use?
The best answer I've heard for this is to set it up on a pointless loop and go do your work the old way.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat I mean... give the presentation with this conclusion?
"I tried to use it, it is too unreliable, anything I try to do with it could be better done with just an old fashioned program, it would be better to cut our losses and not waste more money on this." is a perfectly reasonable conclusion. -
I couldn't even get AI to make me a compound interest calculator. It did it incorrectly. I have no idea what anyone uses this "tool" for.
I do use a dictation tool which is an LLM (local) and it works well. That is the only use case I have found so far for me.
@jmcrookston @VeeRat To be fair there are many people who could tell you about things they have used LLMs successfully for. But those are generally things where there isn't one correct answer, or at least where it's really easy to check whether an answer is correct or not (so the LLM can keep trying until it gets it right, or at least you can easily tell whether it was right).
It is definitely true that much of what people try to use LLMs for, they are not good at. And they're particularly bad at math and similar things that require manipulation of abstract concepts, i.e. actual understanding.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat Agree with all this. i have used a generative ai chatbot (Google Gemini) to create caricatures based on actual photos. With every iteration, the chatbot seemingly ignores some or all of the instructions in my prompt, which means unacceptable output is generated repeatedly. It is completely unusable for serious / non-serious work.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat
I work in healthcare. We need our processes to be deterministic. I can't have an "agent" producing unpredictable, random outputs. -
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@jmcrookston @VeeRat To be fair there are many people who could tell you about things they have used LLMs successfully for. But those are generally things where there isn't one correct answer, or at least where it's really easy to check whether an answer is correct or not (so the LLM can keep trying until it gets it right, or at least you can easily tell whether it was right).
It is definitely true that much of what people try to use LLMs for, they are not good at. And they're particularly bad at math and similar things that require manipulation of abstract concepts, i.e. actual understanding.
Any examples? I would presume there must be some uses for them, but I've not heard a word about those uses. Perhaps they're very niche and so don't tend to hit general media
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat this is the thing. Exactly this. The non-deterministic behaviour makes it pretty useless for many many things. If we want more business-automation? We can do that, we have tools adapting to many systems, and we can figure out some logic to get stuff processed. But most of the times LLMs do not need to be involved for that.
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@VeeRat make the AI once generate code with does the job. Then run the code. The code always does the same thing as good or bad than yesterday. Don't ask the AI to come up with code to do the same task than yesterday. Sell it as "a nice method to use less tokens for the similar result". Get a raise for optimizing the bullshit machine.
@assimilateborg
@VeeRat
This.
Generate scripts using AI that are reusable and consistent. It's good at that, and you only have to review once. -
@jmcrookston @diazona @VeeRat
Sadly, the one that comes up again and again: Reading and writing email.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
In general, machine learning is good for situations where the value of a correct answer is significantly higher than the cost of an incorrect one. Prefetching is my favourite example (not least because that’s what I used it for in my PhD): if you prefetch the right data, the processor avoids a long idle time. If you prefetch the wrong data, you discard it and you’re not in a worse position than if you hadn’t used it.
Machine translation is a great case study for this: for any given situation, is an incorrect translation worse than no translation? For translating a toot, the worst-case cost is that I am briefly confused and move on. I don’t have time to learn every human language and I don’t have the money to pay someone to translate every toot I see, so machine translation is better than nothing. For translating a user manual or a contract, the worst-case cost may be someone dies or I am exposed to unbounded legal liability (or both), so machine translation is worse than nothing.
The corollary is that machine learning is also useful if the cost of generating an optimal solution is high and it’s possible to mechanically generated solutions. Stochastically generating plausible solution-shapes things then checking if they actually are solutions and picking the one that works best with a cheap-to-run machine-learning system providing the inputs is great. Vulnerability discovery with guided fuzzing works like this, for example.
The big issue with LLMs is that, generally, text is something we use for high-stakes problems. The set of things where the desired output is a pile of text, where the cost of incorrect text is low or the cost of checking the text is low, is very small. This includes programming: if you have a sufficiently detailed spec that you can mechanically check whether an implementation satisfies it, you almost certainly have something that can be used to synthesise a program already.
Some cases, such as searching documentation, are right on the cusp. Is it better to have a machine that answers a question about a system but gives you a right answer 60% of the time and plausible nonsense 40% of the time than nothing? What if it’s 90%? At some point, the Paradox of Automation kicks in: system efficiency does not correlate with component efficiency. You get a significant dip when the system is good enough that people stop checking the answers but it’s still producing a lot of mistakes, only now those make it further along the process.
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I keep watching for the AI bubble to burst. It's been so expensive for so little returns.
At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not.
Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it.
Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed.
How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that.
I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it.
The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses?
@VeeRat yes 'we' are traced on AI usage supposed to be the clue for everything. Company pay company wants return
Nothing is free in this brave new world
