4 ms·

ChatGPT does surprisingly well at this task. My question was very biased towards it knowing the answer because I asked it for examples of mid-80s Japanese ICs w

ChatGPT does surprisingly well at this task. My question was very biased towards it knowing the answer because I asked it for examples of mid-80s Japanese ICs when google failed me, but it definitely knows a ton of model numbers for real parts from the era. Give it a shot with a real-world example and report back

amrocha 4y ago
The point is that you can't trust it because language models are bullshitters. What's the point of a search you have to verify through another search afterwards.
- masfuerte 4y ago
  As long as it's not wrong too often, a black box that coughs up answers which are hard to find but easy to verify is very useful.
  - nico 4y ago
    Great way to put it!
  - amrocha 4y ago
    I can see that, and I think that's a great way to use LLMs right now. But I worry that people will start taking their word at face value and not verify. Not to mention the areas that are difficult to verify.
    - fauigerzigerk 4y ago
      People will also start to publish these false and unverified claims. We will then find the resulting articles and comments when we try to verify what the LLM is telling us. This is nothing new in principle but the size of the problem will most certainly grow.
- TeMPOraL 4y ago
  > What's the point of a search you have to verify through another search afterwards. Related terms. Even though an answer generated by an LLM is most likely wrong, and definitely can't be taken at face value, the words and phrases used in that answer can be exactly what you need to create a search query that you wouldn't be able to otherwise.
  - galaxyLogic 4y ago
    That's a good use-case for LLMs. Find related terms. But I wonder couldn't google add such a function without AI. Some kind of Thesaurus.
    - TeMPOraL 4y ago
      LLMs are a kind of thesaurus, in a way - but instead of just words, they also handle phrases, sentences and whole paragraphs. The conversational interface is just a gimmick on top. Also, thesaurus isn't a good tool for exploring an unknown problem domain. It gives synonyms, not related terms. LLMs let you input a layman description of your problem, and get an answer that's using correct domain terms and phrases (even if using them incorrectly). I imagine Google will add such a function. They probably tried already - I've heard that current search is already powered by ML models to a degree.
- prox 4y ago
  Currently I use you.com , because it adds sources after most queries.
codebje 4y ago
I have been on and off writing an emulator for the eZ80 CPU. This CPU has two addressing modes, Z80 mode in which it's effectively an 8-bit processor with a 16-bit address space, and ADL (Address Data Long) mode in which it has a 24-bit address space. There is a CPU control bit 'MADL' (Mixed ADL) for mixed-mode applications; when set, using certain prefixed opcodes will switch modes during call and return. A Z80 routine can call outside of its 16-bit address space with 'CALL.IL'. This pushes the 16-bit return address onto the 16-bit stack, switches to 24-bit mode, and pushes the magic 'return to 16-bit mode' number to the 24-bit stack. However, it's not clear from the manuals or datasheets what happens if you use the prefixed 'CALL.IL' opcode sequence when the 'MADL' bit is reset. I asked ChatGPT, because this is something that Google searching hasn't yielded answers for. It had this to say: "The MADL bit (short for Memory Access During Interrupts Low) is a flag in the Interrupt Control Register that determines whether or not interrupt service routines (ISRs) can access low memory (addresses 0000h-3FFFh) during interrupts." Plus some more stuff building on that, on CALL.IL being about ISRs, and about low memory. All of it is completely, fundamentally wrong. I did a handful of rounds of trying to steer it to a more correct answer but it continued to get additional basic facts wrong and would lean back to earlier incorrect facts as others conflicted with its answers. I asked it another question I have, this time about the UART on the CPU. There is a Receive Buffer Register (UARTx_RBR) that contains the head of the receive FIFO. The documentation does not make it clear what is in the RBR if the FIFO is empty, so I asked ChatGPT. It told me a very plausible answer, the one I suspect myself, which is that it'll keep returning the same value until new data is available. But then it went on to tell me this is called receiver overrun, and described how an overrun occurs, including noting that it happens when the FIFO is full. And we went round in circles on this for a little while. ChatGPT is a major step forwards in our post-truth existence: its answers are an amalgam of the most frequently repeated views on a topic, not those with stronger reasoning or more effective evidence to support. If there is little or no source data on a topic (as would be the case with my very specific questions on a rarely used processor) LLMs are (presently?) unable to detect that they are responding to a topic with limited contextual information and tailor their responses accordingly, and instead confidently provide utter nonsense. I trust ChatGPT to do things that LLMs are good at, though: if I give it some bullet points and some style guidance it can give me written paragraphs. If I ask it to rephrase a well known song in the style of some modern artist I'll get something back that's pretty plausible. It can give me some starting points for learning more about some well known topic, even. I would definitely not trust it _at all_ to give me something factual like part numbers of uncommon ICs, because LLMs cannot distinguish between fact and fiction, not in what they ingest, and not in what they produce.

amrocha 4y ago

The point is that you can't trust it because language models are bullshitters. What's the point of a search you have to verify through another search afterwards.

masfuerte 4y ago
As long as it's not wrong too often, a black box that coughs up answers which are hard to find but easy to verify is very useful.
- nico 4y ago
  Great way to put it!
- amrocha 4y ago
  I can see that, and I think that's a great way to use LLMs right now. But I worry that people will start taking their word at face value and not verify. Not to mention the areas that are difficult to verify.
  - fauigerzigerk 4y ago
    People will also start to publish these false and unverified claims. We will then find the resulting articles and comments when we try to verify what the LLM is telling us. This is nothing new in principle but the size of the problem will most certainly grow.
TeMPOraL 4y ago
> What's the point of a search you have to verify through another search afterwards. Related terms. Even though an answer generated by an LLM is most likely wrong, and definitely can't be taken at face value, the words and phrases used in that answer can be exactly what you need to create a search query that you wouldn't be able to otherwise.
- galaxyLogic 4y ago
  That's a good use-case for LLMs. Find related terms. But I wonder couldn't google add such a function without AI. Some kind of Thesaurus.
  - TeMPOraL 4y ago
    LLMs are a kind of thesaurus, in a way - but instead of just words, they also handle phrases, sentences and whole paragraphs. The conversational interface is just a gimmick on top. Also, thesaurus isn't a good tool for exploring an unknown problem domain. It gives synonyms, not related terms. LLMs let you input a layman description of your problem, and get an answer that's using correct domain terms and phrases (even if using them incorrectly). I imagine Google will add such a function. They probably tried already - I've heard that current search is already powered by ML models to a degree.
prox 4y ago
Currently I use you.com , because it adds sources after most queries.

codebje 4y ago

I have been on and off writing an emulator for the eZ80 CPU. This CPU has two addressing modes, Z80 mode in which it's effectively an 8-bit processor with a 16-bit address space, and ADL (Address Data Long) mode in which it has a 24-bit address space. There is a CPU control bit 'MADL' (Mixed ADL) for mixed-mode applications; when set, using certain prefixed opcodes will switch modes during call and return. A Z80 routine can call outside of its 16-bit address space with 'CALL.IL'. This pushes the 16-bit return address onto the 16-bit stack, switches to 24-bit mode, and pushes the magic 'return to 16-bit mode' number to the 24-bit stack. However, it's not clear from the manuals or datasheets what happens if you use the prefixed 'CALL.IL' opcode sequence when the 'MADL' bit is reset. I asked ChatGPT, because this is something that Google searching hasn't yielded answers for. It had this to say: "The MADL bit (short for Memory Access During Interrupts Low) is a flag in the Interrupt Control Register that determines whether or not interrupt service routines (ISRs) can access low memory (addresses 0000h-3FFFh) during interrupts." Plus some more stuff building on that, on CALL.IL being about ISRs, and about low memory. All of it is completely, fundamentally wrong. I did a handful of rounds of trying to steer it to a more correct answer but it continued to get additional basic facts wrong and would lean back to earlier incorrect facts as others conflicted with its answers. I asked it another question I have, this time about the UART on the CPU. There is a Receive Buffer Register (UARTx_RBR) that contains the head of the receive FIFO. The documentation does not make it clear what is in the RBR if the FIFO is empty, so I asked ChatGPT. It told me a very plausible answer, the one I suspect myself, which is that it'll keep returning the same value until new data is available. But then it went on to tell me this is called receiver overrun, and described how an overrun occurs, including noting that it happens when the FIFO is full. And we went round in circles on this for a little while. ChatGPT is a major step forwards in our post-truth existence: its answers are an amalgam of the most frequently repeated views on a topic, not those with stronger reasoning or more effective evidence to support. If there is little or no source data on a topic (as would be the case with my very specific questions on a rarely used processor) LLMs are (presently?) unable to detect that they are responding to a topic with limited contextual information and tailor their responses accordingly, and instead confidently provide utter nonsense. I trust ChatGPT to do things that LLMs are good at, though: if I give it some bullet points and some style guidance it can give me written paragraphs. If I ask it to rephrase a well known song in the style of some modern artist I'll get something back that's pretty plausible. It can give me some starting points for learning more about some well known topic, even. I would definitely not trust it _at all_ to give me something factual like part numbers of uncommon ICs, because LLMs cannot distinguish between fact and fiction, not in what they ingest, and not in what they produce.