Thread

1. drtgh · 1789758284 · focus · HN ↗

   > relatively poorly understood technology

   Poorly understood? how convenient...

   LLMs are vectorial databases with losses that index statistically filled data, which uses a text interface to query such statistically filled data. The output is a string concatenation (statistically concatenated bit by bit).

   When the LLMs are queried (prompted), you can get random mixed data as output, ERRORS, due to undesired indexes getting closer at one point while the string was being concatenated for the output, what affects the rest of the indexed content that will be concatenated.

   It is intrinsic to this tech. The larger the context, the greater the probability of get mixed data. And if the provider lowers the precision of those indexes -in order to decrease hardware resources and energy consumption- such probability increases to the point where those errors are granted.

   Even knowing that the queries can return wrong/mixed data in the responses, errors, the companies developing this, decided to introduce a new product, that connects such LLMs outputs to the command console, latter connected to internet, raw 'eval' running commands from such outputs witch obviously can contain whatever mixed random. Then we started to hear "oh, it deleted my directory", etc, and it seems the next one will be "a missile killed my wife", because it is a text concatenation engine with errors.

   To name it "hallucination" is an euphemism... those are errors, and they are granted to happen at one moment. If they do not know this, then they ate too much marketing without doing their job, or it was a convenient contract for the pocket$ of someone.

   1. theptip · 1789760419 · focus · HN ↗

      > LLMs are vectorial databases

      You use a bunch of technical-sounding words here to make it sound like you understand. But to be clear, nobody understands why the evolved weights of a NN make the decisions that they do.

      Almost nothing is understood about the actual representations used for nontrivial concepts, decision algorithms, etc.

      If you look at the field of mechanistic interpretability, compared to “GOFAI” like learned decision trees, an LLM is completely opaque.

      1. semiquaver · 1789762968 · focus · HN ↗

         I’m shocked how many otherwise well-informed people don’t understand or agree with this very fundamental fact of just how little we actually understand about why LLMs work as well as they do. They figure “it’s science, of course there’s math and theory behind it.”

         AI research is almost as purely empirical as the gradient descent loops its practitioners use to optimize their models. “Why” anything at all works is barely an afterthought.

         1. bigyabai · 1789763371 · focus · HN ↗

            > why LLMs work as well as they do.

            That's a very different claim from being "poorly understood" though. The emergent properties of any system with billions of parameters is hard to understand completely, that's the fault of data science more than computer science or even mathematics.

            1. semiquaver · 1789763816 · focus · HN ↗

               I think ”poorly understood” is accurate. Understanding has levels. How brains think is also poorly understood.

               1. bigyabai · 1789764967 · focus · HN ↗

                  I disagree, because you can represent the constituent parts of any AI model as code and data. We can reliably build AI with this knowledge, but not brains.

                  Understanding does have layers, and that's why "poorly understood" is a meaningless goalpost. A book can be well understood without researching the gematria behind character's the names when you write them in reverse. An LLM can be well-understood even if you don't comprehensively test each quantization for miraculous unexpected behavior at the FFN level.

                  1. semiquaver · 1789828750 · focus · HN ↗

                     It’s likely that at some point we will also be able to represent a scan of the human brain digitally as code and data. Assuming we don’t separately make a huge number of leaps in neuroscience which are far from assured, it will be very likely that we can get the appearance of an operational human brain simulated digitally well before we gain much if any understanding of why it does what it does.

                     All that is to say, being able to build something is not not not the same thing as understanding it.

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