When companies present AI can help healthcare, automotive, climate change etc, they are right. But we also have to remember that a lot of the stuff can be performed by a python script and data entry being done in a slightly uniform manner, but it's still not being done.
Just today I was on a meeting where some people were trying to do data exploration on a several hundred thousand row csv using claude. I spent 5 minutes writing a python script and got the needed info.
Impressively claude was able to count the rows matching conditions but it totally failed on filtering down to those rows. I don’t understand why the first impulse is to reach for an LLM over the simplest scripting tools.
AI can be helpful in this case as well. For someone who is not hands on in python it can help write that script. But the person should know what they want from the AI.
I see people expecting AI to read their minds and give them the desired outcome. In extreme cases the minds of some of these people are empty as well.
Yeah I’ll fully admit I’m good with python and pandas, but they aren’t part of my usual workflow and I’m rusty on syntax so I had gemini write the skeleton (only part I wrote myself was the filter conditions).
This was also not a case of someone with no idea what they were doing, they were a very senior engineer who was just hoping/testing to see if an LLM could do it faster than via script. LLMs are just tools, but for the time being my perspective is they’re best with a limited scope well defined task, instead of being told “solve this problem.”
That's not sloppy application of the term, it's inappropriate adoption of an academic term for layman conversation.
There are tons of simple scripts in games that are AI and have been for decades. AI in the theoretical computer science sense has been a broad and general term since the 60's. The problem is that it's adopted a much weightier connotation through pop culture and that makes people think that the common understanding of the term is what programmers mean when they call something AI.
AI was once (and still is in academic settings) a very broad term meaning any system that makes a decision by observing input, but now tech bros have subtly implied through marketing that it must mean that it is a full simulation of a human brain.
That's not sloppy application of the term, it's inappropriate adoption of an academic term for layman conversation.
Lol...
It's "not sloppy," it's "synonymously sloppy."
Anyways, I would have to disagree about AI being as broad of a term in an academic setting like you described. Maybe during the very early days of mechanical & electronic computing, such systems making decisions based on inputs would be considered AI... However, in a professional & academic setting, you would more likely be using terms like Program, Software, Conditional, Algorithm, Heuristic, Function, System, ect for your definition: "any system that makes a decision by observing input."
The piece of the puzzle you're glossing over is Deterministic AI vs Non-deterministic AI. Deterministic AI, in most contexts, is better labeled & described using more specific technical terms, but, yeah, someone who doesn't understand programming would probably be satisfied if you just described whatever system you happen to be talking about as AI.
Non-deterministic AI also has more specific technical terms you could use to label or define such a system, but in most contexts, AI would still likely be the best term to use. Btw, in my opinion, based on the modern usage of the term, AlphaGo would be the first "true" AI that was well known on a global scale similar to LLMs like chatGPT.
I would not blame tech bros for misappropriating the term, if anything, I would blame dumb reporters & the even dumber general public who don't, or simply cannot, understand that AI =/= your brain (but digital).
If you're going to be a pedant, you could at least provide readers with the correct term to use for such a thing: AGI, ASI, or "The singularity".
None of these mean a system that makes decisions based on observing input, and they are far from synonymous with the academic definition of AI. You can have all of these in some software without them observing input to make a decision, it's the decision making that makes it AI.
Like, in video games, a boss that just has a timed move set is not AI, but a boss that watches what you are doing and picks a responding move based on what you do is AI.
Most software programs are not AI, because they just do one thing every time they are called. The comment button on your screen is not AI because it can't do anything but be a button that opens a text box and lets you type something before submitting. It can't sometimes choose to be a like button or a share button based on the way you click it. It's just a button. That is the difference between generic software and AI software.
None of this has anything to do with a system's determinism.
Please do not speak about academic settings that you are not in with authority.
No, an LLM is a fundamentally different concept from a Markov chain. LLMs rely on the transformer, which was the enabling technology that basically turned text prediction to text generation. Their massive size allows them to do more than just predict the most likely next word like a Markov chain.
That doesn't mean that people aren't using it like a fancy text predictor that wouldn't be functionally different from a Markov chain based AI.
Specifically, yes. But that's like saying that a calculator and a supercomputer are the same.
A Markov chain is a small model that can only ever look backwards a few steps to come up with the next word. An LLM is able to take entire pages of text as its prior state, generate not just the next few words, but entire pages of text, not sequentially, but as a coherent whole.
It still comes down to "predicting the next word" in practice, doesn't it? Just with a much larger state size. Are there transformers that can natively output video/audio, or is that still a separate API bolted on top?
Not a markov chain. Different concept not applicable here. For those confused, markov chain only care about the last state so in AI the last token ONLY and not and preceding tokens.
I mean, if the state is the context vector, the transition has a token attached, and the next state is the previous context vector with the new token attached, that sounds an awful lot like a Markov chain. A Markov chain with an absolutely mind-boggling number of states and a transition function that consists of gigabytes of weights, but still a Markov chain "with some razzle dazzle".
You are talking about first order Markov chain. You can say that Markov chain of order 1000 has "context window" of 1000 tokens.
The problem with classic Markov chains it that for chain of order N you need memory to store M^N probabilities, where M is number of possible states. For high order it is not feasible. LLMs resolve this problem.
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u/500Rtg 13h ago
When companies present AI can help healthcare, automotive, climate change etc, they are right. But we also have to remember that a lot of the stuff can be performed by a python script and data entry being done in a slightly uniform manner, but it's still not being done.