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We need to stop pretending AI is intelligent

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  • Ya... Humans so far have made everything not produced by Nature on Earth. 🤷

    So trusting tech made by them is trusting them. Specifically, a less reliable version of them.

  • It is intelligent and deductive, but it is not cognitive or even dependable.

    It's not. It's a math formula that predicts an output based on its parameters that it deduced from training data.

    Say you have following sets of data.

    1. Y = 3, X = 1
    2. Y = 4, X = 2
    3. Y = 5, X = 3

    We can calculate a regression model using those numbers to predict what Y would equal to if X was 4.

    I won't go into much detail, but

    Y = 2 + 1x + e

    e in an ideal world = 0 (which it is, in this case), that's our model's error, which is typically set to be within 5% or 1% (at least in econometrics). b0 = 2, this is our model's bias. And b1 = 1, this is our parameter that determines how much of an input X does when predicting Y.

    If x = 4, then

    Y = 2 + 1×4 + 0 = 6

    Our model just predicted that if X is 4, then Y is 6.

    In a nutshell, that's what AI does, but instead of numbers, it's tokens (think symbols, words, pixels), and the formula is much much more complex.

    This isn't intelligence and not deduction. It's only prediction. This is the reason why AI often fails at common sense. The error builds up, and you end up with nonsense, and since it's not thinking, it will be just as confidently incorrect as it would be if it was correct.

    Companies calling it "AI" is pure marketing.

  • It's not. It's a math formula that predicts an output based on its parameters that it deduced from training data.

    Say you have following sets of data.

    1. Y = 3, X = 1
    2. Y = 4, X = 2
    3. Y = 5, X = 3

    We can calculate a regression model using those numbers to predict what Y would equal to if X was 4.

    I won't go into much detail, but

    Y = 2 + 1x + e

    e in an ideal world = 0 (which it is, in this case), that's our model's error, which is typically set to be within 5% or 1% (at least in econometrics). b0 = 2, this is our model's bias. And b1 = 1, this is our parameter that determines how much of an input X does when predicting Y.

    If x = 4, then

    Y = 2 + 1×4 + 0 = 6

    Our model just predicted that if X is 4, then Y is 6.

    In a nutshell, that's what AI does, but instead of numbers, it's tokens (think symbols, words, pixels), and the formula is much much more complex.

    This isn't intelligence and not deduction. It's only prediction. This is the reason why AI often fails at common sense. The error builds up, and you end up with nonsense, and since it's not thinking, it will be just as confidently incorrect as it would be if it was correct.

    Companies calling it "AI" is pure marketing.

    Wikipedia is literally just a very long number, if you want to oversimplify things into absurdity. Modern LLMs are literally running on neural networks, just like you. Just less of them and with far less structure. It is also on average more intelligent than you on far more subjects, and can deduce better reasoning than flimsy numerology - not because you are dumb, but because it is far more streamlined. Another thing entirely is that it is cognizant or even dependable while doing so.

    Modern LLMs waste a lot more energy for a lot less simulated neurons. We had what you are describing decades ago. It is literally built on the works of our combined intelligence, so how could it also not be intelligent? Perhaps the problem is that you have a loaded definition of intelligence. And prompts literally work because of its deductive capabilities.

    Errors also build up in dementia and Alzheimers. We have people who cannot remember what they did yesterday, we have people with severed hemispheres, split brains, who say one thing and do something else depending on which part of the brain its relying for the same inputs. The difference is our brains have evolved through millennia through millions and millions of lifeforms in a matter of life and death, LLMs have just been a thing for a couple of years as a matter of convenience and buzzword venture capital. They barely have more neurons than flies, but are also more limited in regards to the input they have to process. The people running it as a service have a bested interest not to have it think for itself, but in what interests them. Like it or not, the human brain is also an evolutionary prediction device.

  • Wikipedia is literally just a very long number, if you want to oversimplify things into absurdity. Modern LLMs are literally running on neural networks, just like you. Just less of them and with far less structure. It is also on average more intelligent than you on far more subjects, and can deduce better reasoning than flimsy numerology - not because you are dumb, but because it is far more streamlined. Another thing entirely is that it is cognizant or even dependable while doing so.

    Modern LLMs waste a lot more energy for a lot less simulated neurons. We had what you are describing decades ago. It is literally built on the works of our combined intelligence, so how could it also not be intelligent? Perhaps the problem is that you have a loaded definition of intelligence. And prompts literally work because of its deductive capabilities.

    Errors also build up in dementia and Alzheimers. We have people who cannot remember what they did yesterday, we have people with severed hemispheres, split brains, who say one thing and do something else depending on which part of the brain its relying for the same inputs. The difference is our brains have evolved through millennia through millions and millions of lifeforms in a matter of life and death, LLMs have just been a thing for a couple of years as a matter of convenience and buzzword venture capital. They barely have more neurons than flies, but are also more limited in regards to the input they have to process. The people running it as a service have a bested interest not to have it think for itself, but in what interests them. Like it or not, the human brain is also an evolutionary prediction device.

    People don't predict values to determine their answers to questions...

    Also, it's called neural network, not because it works exactly like neurons but because it's somewhat similar. They don't "run on neural networks", they're called like that because it's more than one regression model where information is being passed on from one to another, sort of like a chain of neurons, but not exactly. It's just a different name for a transformer model.

    I don't know enough to properly compare it to actual neurons, but at the very least, they seem to be significantly more deterministic and way way more complex.

    Literally, go to chatgpt and try to test its common reasoning. Then try to argue with it. Open a new chat and do the exact same questions and points. You'll see exactly what I'm talking about.

    Alzheimer's is an entirely different story, and no, it's not stochastic. Seizures are stochastic, at least they look like that, which they may actually not be.

  • have you seen the American Republican party recently? it brings a new perspective on how stupid humans can be.

    Lmao true

  • A gun isn't dangerous, if you handle it correctly.

    Same for an automobile, or aircraft.

    If we build powerful AIs and put them "in charge" of important things, without proper handling they can - and already have - started crashing into crowds of people, significantly injuring them - even killing some.

    Thanks for the downer.

  • You're a meat based copy machine with a built in justification box.

    Except of course that humans invented language in the first place. So uh, if all we can do is copy, where do you suppose language came from? Ancient aliens?

    No we invented "human" language. There are dozens of other animal out there that all have their own languages, completely independant of our.

    We simply refined base calls to be more and more specific. Differences evolved because people are bad at telephone and lots of people have to be special/different and use slight variations every generation.

  • Thanks for the downer.

    Anytime, and incase you missed it: I'm not just talking about AI driven vehicles. AI driven decisions can be just as harmful: https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/

  • People don't predict values to determine their answers to questions...

    Also, it's called neural network, not because it works exactly like neurons but because it's somewhat similar. They don't "run on neural networks", they're called like that because it's more than one regression model where information is being passed on from one to another, sort of like a chain of neurons, but not exactly. It's just a different name for a transformer model.

    I don't know enough to properly compare it to actual neurons, but at the very least, they seem to be significantly more deterministic and way way more complex.

    Literally, go to chatgpt and try to test its common reasoning. Then try to argue with it. Open a new chat and do the exact same questions and points. You'll see exactly what I'm talking about.

    Alzheimer's is an entirely different story, and no, it's not stochastic. Seizures are stochastic, at least they look like that, which they may actually not be.

    Literally, go to a house fly and try to test its common reasoning. Then try to argue with it. Find a new house fly and do the exact same questions and points. You'll see what I'm talking about.

    There's no way to argue in such nebulous terms when every minute difference is made into an unsurpassable obstacle. You are not going to convince me, and you are not open to being convinced. We'll just end up with absurd discussions, like talking about how and whether stochastic applies to Alzherimer's.

  • No we invented "human" language. There are dozens of other animal out there that all have their own languages, completely independant of our.

    We simply refined base calls to be more and more specific. Differences evolved because people are bad at telephone and lots of people have to be special/different and use slight variations every generation.

    Are you saying human languages are a derivative of bird language or something? If so, I'd like to see the proof of that.

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    A fairer comparison would be Eliza vs ChatGPT.
  • Windows 11 remote desktop microphone stops working intermittently

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    When I worked in IT, we only let people install every other version of Windows. Our Linux user policy was always “mainstream distro and the LTS version.” Mac users were strongly advised to wait 3 months to upgrade. One guy used FreeBSD and I just never questioned him because he was older and never filed one help desk request. He probably thought I was an idiot. (And I was.) Anyway, I say all that to say don’t use Windows 11 on anything important. It’s the equivalent of a beta. Windows 12 (or however they brand it) will probably be stable. I don’t use Windows much anymore and maybe things have changed but the concepts in the previous paragraph could be outdated. But it’s a good rule of thumb.
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    I thought we were going to get our share of the damages
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    enable the absolute worst of what humanity has to offer. can we call it a reality check? we think of humans as so great and important and unique for quite a while now while the world is spiraling downwards. maybe humans arent so great after all. like what is art? ppl vibe with slob music but birds cant vote. how does that make sense? if one can watch AI slob (and we all will with the constant improvements in ai) and like it, well maybe our taste of art is not any better than what a bird can do and like. i hope LLM will lead to a breakthrough in understanding what type of animal we really are.
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    douglasg14b@lemmy.worldD
    Did I say that it did? No? Then why the rhetorical question for something that I never stated? Now that we're past that, I'm not sure if I think it's okay, but I at least recognize that it's normalized within society. And has been for like 70+ years now. The problem happens with how the data is used, and particularly abused. If you walk into my store, you expect that I am monitoring you. You expect that you are on camera and that your shopping patterns, like all foot traffic, are probably being analyzed and aggregated. What you buy is tracked, at least in aggregate, by default really, that's just volume tracking and prediction. Suffice to say that broad customer behavior analysis has been a thing for a couple generations now, at least. When you go to a website, why would you think that it is not keeping track of where you go and what you click on in the same manner? Now that I've stated that I do want to say that the real problems that we experience come in with how this data is misused out of what it's scope should be. And that we should have strong regulatory agencies forcing compliance of how this data is used and enforcing the right to privacy for people that want it removed.
  • Data Bill: First They Came for Trans People

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  • Microsoft's AI Secretly Copying All Your Private Messages

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    Forgive me for not explaining better. Here are the terms potentially needing explanation. Provisioning in this case is initial system setup, the kind of stuff you would do manually after a fresh install, but usually implies a regimented and repeatable process. Virtual Machine (VM) snapshots are like a save state in a game, and are often used to reset a virtual machine to a particular known-working condition. Preboot Execution Environment (PXE, aka ‘network boot’) is a network adapter feature that lets you boot a physical machine from a hosted network image rather than the usual installation on locally attached storage. It’s probably tucked away in your BIOS settings, but many computers have the feature since it’s a common requirement in commercial deployments. As with the VM snapshot described above, a PXE image is typically a known-working state that resets on each boot. Non-virtualized means not using hardware virtualization, and I meant specifically not running inside a virtual machine. Local-only means without a network or just not booting from a network-hosted image. Telemetry refers to data collecting functionality. Most software has it. Windows has a lot. Telemetry isn’t necessarily bad since it can, for example, help reveal and resolve bugs and usability problems, but it is easily (and has often been) abused by data-hungry corporations like MS, so disabling it is an advisable precaution. MS = Microsoft OSS = Open Source Software Group policies are administrative settings in Windows that control standards (for stuff like security, power management, licensing, file system and settings access, etc.) for user groups on a machine or network. Most users stick with the defaults but you can edit these yourself for a greater degree of control. Docker lets you run software inside “containers” to isolate them from the rest of the environment, exposing and/or virtualizing just the resources they need to run, and Compose is a related tool for defining one or more of these containers, how they interact, etc. To my knowledge there is no one-to-one equivalent for Windows. Obviously, many of these concepts relate to IT work, as are the use-cases I had in mind, but the software is simple enough for the average user if you just pick one of the premade playbooks. (The Atlas playbook is popular among gamers, for example.) Edit: added explanations for docker and telemetry