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An LLM doesn't "think", it just puts one word in front of another based on the likelihood of it having been placed in that sequence before. On top of that funct

An LLM doesn't "think", it just puts one word in front of another based on the likelihood of it having been placed in that sequence before. On top of that function is an application layer that contributes additional rules. At no point are any of these pieces "thinking" in any sense. As for speed of output as a metric, I can write you a Python script in 60 seconds that can generate thousands of HTTP requests, print thousands of words, and even find and display for you a decent recipe for chilli con carne. Despite this incredible capability for output, it's about as frightening as a satsuma. Just because something can do something a lot isn't a reason to abandon sense. While these misguided cinematic fantasies captivate everyone's attention, automated decision making is already deciding whether refugees are settled or sent back to be killed in their originating countries, whether people are given housing or not, or "deserve" welfare. The emerging research illustrates quite clearly the problematic biases entrenched in these models. Why this doesn't dominate AI discourse instead of hallucinatory speculating about some theoretical future is beyond me.

TeMPOraL 3y ago
> An LLM doesn't "think", it just puts one word in front of another based on the likelihood of it having been placed in that sequence before. You are confusing LLMs with Markov chains.
- rainonmoon 3y ago
  Luckily for me, I'm not.
  - ilaksh 3y ago
    It will make more sense to call it thinking if you realize that thinking does not need to connote all of the other human or animal cognitive characteristics that people tend to bundle with it. ChatGPT can think and reason but it doesn't have a high bandwidth sensory experience or subjective stream of consciousness. It doesn't think in the same way as humans. Etc. Etc. But thinking is still the best way to describe what it's doing. It has a model of the world and what you are asking it to do. It absolutely has to synthesize information to be able to fulfill requests.
ilaksh 3y ago
Sure but the point is that ChatGPT can already write those same scripts in 6 seconds instead of 60. Whether you want to call that thinking or not. And the level of script sophistication and speed will continue to increase. For those examples you gave we don't even need a leading-edge LLM. And automated decision making is exactly what I am warning about. As the speed, effectiveness and performance of the models increases, it becomes uneconomic and uncompetitive to keep humans in the loop. The most obvious real danger is in warfare. Racial bias is one aspect of the problem but overall it's more than that. It's that the efficiency pushes towards fewer and fewer check-ins with humans so you are relying more and more on the AI training. And so any type of defect at all or misalignment with your goals can be very problematic for you.

TeMPOraL 3y ago

> An LLM doesn't "think", it just puts one word in front of another based on the likelihood of it having been placed in that sequence before. You are confusing LLMs with Markov chains.

rainonmoon 3y ago
Luckily for me, I'm not.
- ilaksh 3y ago
  It will make more sense to call it thinking if you realize that thinking does not need to connote all of the other human or animal cognitive characteristics that people tend to bundle with it. ChatGPT can think and reason but it doesn't have a high bandwidth sensory experience or subjective stream of consciousness. It doesn't think in the same way as humans. Etc. Etc. But thinking is still the best way to describe what it's doing. It has a model of the world and what you are asking it to do. It absolutely has to synthesize information to be able to fulfill requests.

ilaksh 3y ago

Sure but the point is that ChatGPT can already write those same scripts in 6 seconds instead of 60. Whether you want to call that thinking or not. And the level of script sophistication and speed will continue to increase. For those examples you gave we don't even need a leading-edge LLM. And automated decision making is exactly what I am warning about. As the speed, effectiveness and performance of the models increases, it becomes uneconomic and uncompetitive to keep humans in the loop. The most obvious real danger is in warfare. Racial bias is one aspect of the problem but overall it's more than that. It's that the efficiency pushes towards fewer and fewer check-ins with humans so you are relying more and more on the AI training. And so any type of defect at all or misalignment with your goals can be very problematic for you.