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> The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this worl
by mike741 3y ago
> The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become.
Increasing training data doesn't increase consistency. Each data point acts as a potential new axiom, and each axiom decreases consistency. GPT-4 is trained to satisfy humans, and humans are wildly inconsistent. Even if humans were perfectly consistent, attempting to satisfy multiple different humans simultaneously results in inconsistency. Additionally, even if GPT-4 were perfectly complete and consistent it still wouldn't have reached this state autonomously. So the difference between GPT-4 and intelligence, by my definition, is night and day.
> FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's.
Floating points are digital 1's and 0's. Adding more digits is never going to make something analog.
> The values are imperfectly calculated (limited accuracy) as computer math always is.
Agreed.
>There is also a large element of pure randomness to the output of any of these LLMs.
Strongly disagree. There isn't a single element of randomness during the training stage. We know the exact architecture of the neural net, we know the exact data it was trained on, and we know the exact beam selection algorithms used to synthesize outputs. Every single step can be simulated, traced, and recreated to achieve the exact same results. The number of steps involved might overwhelm us, but that doesn't make it random.
> They don't get to control exactly what words they generate
We do get to control it, we just lose track of the inputs and then pretend it was all out of our control. But of course every single step was willed and controlled by us. We call it "random" for personal convenience, not because its actually true.
- HarHarVeryFunny 3y agoThese models don't output sequences - which is where you'd use beam search - they output a single word at a time. The output is a set of probabilities (from a SoftMax) which is then sampled at a given sampling "temperature" (degree of randomness). There's no point discussing it when you obviously don't have clue how these models work, won't listen when you're told, and just prefer to make stuff up.
- mike741 3y agoBeamsearch is just one example, the same applies to top sampling and greedy search. Focusing on one approach suggests you've missed the actual point: if you know how a given output is synthesized, then its not autonomous. You're trying to nitpick as an excuse to avoid a substantive response. If you want me to agree with you, you'll need to offer a counterpoint. Saying things like "It's driven by floating point values not 1's and 0's." and conflating pseudo randomness with actual randomness does not inspire confidence.
- HarHarVeryFunny 3y agoI was curious what your definition of intelligence was such that you thought GPT-4 doesn't exhibit it. It's your opinion - doesn't have to agree with mine. You seem to place an importance on whether the models are entirely predictable or not, which is why I pointed out that the output is randomly sampled.
- mike741 3y agoIt's not random, its pseudo-random. Those are not the same thing.
- HarHarVeryFunny 3y agoYou could choose to use a truly random hardware random number generator, and it would make zero difference to the system, empirically or philosophically.
- mike741 3y agoI don't believe there is such a digital generator but if there were it would make the massive difference of being unpredictable (because there would be irrational numbers impossible to fully simulate ahead of time) from the perspective of all of its creators, which would then beg the question of who made its decisions, which would spark debate about its autonomy, which would then qualify it as intelligent as per the definition you requested earlier.