I threw about 10 short phrased meeting notes (4-5 words each) into an LLM to get some document formatting started with titles and sections etc. It built a full scope document and inferred a ton of information about the project and product.
It was surprising how much it could infer, and suggested very sensible testing criteria and alternate solutions should the main product path not pan out.
I can see why people start believing it “thinks”, because it was remarkable what it “understood” about a vague new product. Had I sent the info out to stake holders, they would have assumed this person was a key team member that was involved from day 1.
A coworker did something similar recently. Besides pasting internal stuff into a foreign model like a moron, it had some nice ideas, but lacked a lot of information it simply could not have known. Things like, we already did this, but in an unrelated repository, and we didn’t mention it in this part of the docs. Or, since the person did not know / input the overall purpose and constrains correctly, the LLM proposal was cool, but missed the point completely. Or, the LLM forgot to mention that its plan would require buying hardware we simply don’t have yet, and which would be really expensive and hard to obtain right now, because, idk, hallucinations or it just assumed it was all already there.
Yeah, not impressed. Neither by that idiotic coworker nor by the proposal.
I figured you meant the coworker, but its something I think any AI user should be aware of, especially just using a public web browser version where they definetly will store you data and could leak private info.
I don’t use it often enough to have stats, but I did notice large differences between public AI models.
I had an odd Secure Linux permission issue after an update so I figure WTH let’s try AI debugging.
I pasted same info into Chatgpt and Googles AI. One suggested a bunch of debug steps which made sense, but it talked ahead of itself. It would say check this, then do this, then this (assuming you had answered a Y/N scenario for step 1 already. So it was a complicated interaction, because it gets your response, formulates new plans, but assumes you also completed step 2 and 3 from before.
It led itself around in circles with pages and pages of suggestions. Even though none of the steps were poor choices, they made logical sense, it just couldn’t sort it out.
The other one took my paste, asked one question, and gave me the solution. The update had stripped the SELinux rule off the file and become an undefined default.
“Intelligence”, as a word has always been ill defined. What’s it mean? It’s mostly been used to create prejudices via standardized tests, the bell curve, and IQ tests.
I don’t believe it’s “intelligent” as we don’t really know how to define that. But I do think it’s really really really good at writing reasonable seeming statements in various languages using probability.
Coding and programming, are just languages. So it will likely be able to get pretty damn good at it, just as it has with the English language.
I think with LLMs accurate regurgitation of facts, is the colary to good code.
But I don’t believe that means it “thinks” (which requires introspection, and an internal experience). It does multi-agent computing, and this might even simulate some aspects of thinking… But it’s not thinking. It’s computing.
It won’t be able to do any more than that, until it consistently gets data from having a body (and something like glial thought-interactions/organs/flaws/survival).
By the way, this also means if you could download a mind to a computer - it would slowly lose it’s humanity as it lived longer and longer interfacing with computer systems rather than human systems/organs/experiencs.
Eventually that “uploaded human” would become a Clankr, and stop understanding human life, the will to survive/think. Eventually it would just compute (not think/care or have flaws).
Many sci-fi stories are based on the man-to-technology-to-cold-rational-evil pipeline… Less have gone in the other direction, even though it often produces very popular characters: Spock, Data, the Terminator, Pinnoccio.
Nah, I don’t think so. We’re getting into philosophical / religious territory here, but from a technical standpoint I believe there is no difference between a brain and an accurate simulation of a brain, and an accurate simulation of a brain is “just” an engineering challenge at this point. We might now have the computing power or architecture yet, but there is no conceptual issue prohibiting us from building it.
But an LLM is not a simulation of a brain. It is an autocomplete on steroids. Very different architecture. It is not a brain, but it can sometimes produce the same outputs a brain would. But it is nowhere close to a human brain.
I threw about 10 short phrased meeting notes (4-5 words each) into an LLM to get some document formatting started with titles and sections etc. It built a full scope document and inferred a ton of information about the project and product. It was surprising how much it could infer, and suggested very sensible testing criteria and alternate solutions should the main product path not pan out.
I can see why people start believing it “thinks”, because it was remarkable what it “understood” about a vague new product. Had I sent the info out to stake holders, they would have assumed this person was a key team member that was involved from day 1.
A coworker did something similar recently. Besides pasting internal stuff into a foreign model like a moron, it had some nice ideas, but lacked a lot of information it simply could not have known. Things like, we already did this, but in an unrelated repository, and we didn’t mention it in this part of the docs. Or, since the person did not know / input the overall purpose and constrains correctly, the LLM proposal was cool, but missed the point completely. Or, the LLM forgot to mention that its plan would require buying hardware we simply don’t have yet, and which would be really expensive and hard to obtain right now, because, idk, hallucinations or it just assumed it was all already there.
Yeah, not impressed. Neither by that idiotic coworker nor by the proposal.
Well i was careful and used a private LLM, and didn’t include any customer specific info just in case.
That “moron” was not directed at you, but only at my coworker. I just wanted to point out that it seems to be a bit hit-or-miss sometimes :)
Apparently you write good meeting notes.
I figured you meant the coworker, but its something I think any AI user should be aware of, especially just using a public web browser version where they definetly will store you data and could leak private info.
I don’t use it often enough to have stats, but I did notice large differences between public AI models.
I had an odd Secure Linux permission issue after an update so I figure WTH let’s try AI debugging.
I pasted same info into Chatgpt and Googles AI. One suggested a bunch of debug steps which made sense, but it talked ahead of itself. It would say check this, then do this, then this (assuming you had answered a Y/N scenario for step 1 already. So it was a complicated interaction, because it gets your response, formulates new plans, but assumes you also completed step 2 and 3 from before. It led itself around in circles with pages and pages of suggestions. Even though none of the steps were poor choices, they made logical sense, it just couldn’t sort it out.
The other one took my paste, asked one question, and gave me the solution. The update had stripped the SELinux rule off the file and become an undefined default.
“Intelligence”, as a word has always been ill defined. What’s it mean? It’s mostly been used to create prejudices via standardized tests, the bell curve, and IQ tests.
I don’t believe it’s “intelligent” as we don’t really know how to define that. But I do think it’s really really really good at writing reasonable seeming statements in various languages using probability.
Coding and programming, are just languages. So it will likely be able to get pretty damn good at it, just as it has with the English language.
I think with LLMs accurate regurgitation of facts, is the colary to good code.
But I don’t believe that means it “thinks” (which requires introspection, and an internal experience). It does multi-agent computing, and this might even simulate some aspects of thinking… But it’s not thinking. It’s computing.
It won’t be able to do any more than that, until it consistently gets data from having a body (and something like glial thought-interactions/organs/flaws/survival).
By the way, this also means if you could download a mind to a computer - it would slowly lose it’s humanity as it lived longer and longer interfacing with computer systems rather than human systems/organs/experiencs.
Eventually that “uploaded human” would become a Clankr, and stop understanding human life, the will to survive/think. Eventually it would just compute (not think/care or have flaws).
Many sci-fi stories are based on the man-to-technology-to-cold-rational-evil pipeline… Less have gone in the other direction, even though it often produces very popular characters: Spock, Data, the Terminator, Pinnoccio.
Nah, I don’t think so. We’re getting into philosophical / religious territory here, but from a technical standpoint I believe there is no difference between a brain and an accurate simulation of a brain, and an accurate simulation of a brain is “just” an engineering challenge at this point. We might now have the computing power or architecture yet, but there is no conceptual issue prohibiting us from building it.
But an LLM is not a simulation of a brain. It is an autocomplete on steroids. Very different architecture. It is not a brain, but it can sometimes produce the same outputs a brain would. But it is nowhere close to a human brain.