Comment by mirkodrummer 4 days ago
*Outsourcing to a parrot on steroids which will make mistakes, produce stale ugly ui with 100px border radius, 50px padding and rainbow hipster shadows, write code biased towards low quality training data and so on. It's the perfect recipe for disaster
Until the thing can learn on its own and advance its capabilities to the same degree that a junior developer can, it is not intelligent enough to do that work. It doesn't learn our APIs, it doesn't learn our business domain, it doesn't learn from the countless mistakes I correct it on. What we have now is interesting, it is helping sometimes and wasteful others. It is not intelligent.
> It is not intelligent.
Which of the following would you agree to... ?
1. There is no single bar for intelligence.
2. Intelligence is better measured on a scale than with 1 bit (yes/no).
3. Intelligence is better considered as having many components instead of just one. When people talk about intelligence, they often mean different things across domains, such as emotional, social, conceptual, spatial, kinetic, sensory, etc.
4. Many researchers have looked for -- and found -- in humans, at least, some notions of generalized intellectual capability that tends to help across a wide variety of cognitive tasks.
If some of these make sense, I suggest it would be wise to conclude:
5. Reasonable people accentuate different aspects and even definitions of intelligence.
6. Expecting a yes/no answer for "is X intelligent?" without considerable explanation is approximately useless. (Unless it is a genuinely curious opener for an in-depth conversation.)
7. Asking "is X intelligent?" tends to be a poorly framed question.
Update: it might have been clearer and more helpful if I wrote this instead…
This idea of intelligence stated above seems to combine computation, memory, and self-improvement. These three concepts (as I understand them) are both different and logically decoupled.
For example, in the context of general agents, computational ability can change without affecting memory capability. Also, high computational ability does not necessarily confer self-improvement abilities. Having more memory does not necessarily benefit self-improvement.
In the case of biology, it is possible that self improvement demands energy savings and therefore sensory processing degradation. This conceptually relates to a low power CPU mode or a gasoline engine that can turn off some cylinders.
No confusion here.
This is just semantics, but you brought it up. The very first definition of intelligence provided by Webster:
1.a. the ability to learn or understand or to deal with new or trying situations : reason also : the skilled use of reason
Another thing that jumps out to me is just how fluidly people redefine "intelligence" to mean "just beyond what machines today can do". I can't help wonder much your definition has changed. What would happen if we reviewed your previous opinions, commentary, thoughts, etc... would your time-varying definitions of "intelligence" be durable and consistent? Would this sequence show movement towards a clearer and more testable definition over time?
My guess? The tail is wagging the dog here -- you are redefining the term in service of other goals. Many people naturally want humanity to remain at the top of the intellectual ladder and will distort reality as needed to stay there.
My point is not to drag anyone through the mud for doing the above. We all do it to various degrees.
Now, for my sermon. More people need to wake up and realize machine intelligence has no physics-based constraints to surpassing us.
A. Businesses will boom and bust. Hype will come and go. Humanity has an intrinsic drive to advance thinking tools. So AI is backed by huge incentives to continue to grow, no matter how many missteps economic or otherwise.
B. The mammalian brain is an existence proof that intelligence can be grown / evolved. Homo sapiens could have bigger brains if not for birth-canal size constraints and energy limitations.
C. There are good reasons to suggest that designing an intelligent machine will be more promising than evolving one.
D. There are good reasons to suggest silicon-based intelligence will go much further than carbon-based brains.
E. We need to stop deluding ourselves by moving the goalposts. We need to acknowledge reality, for this is reality we are living in, and this is reality we can manipulate.
Let me know if you disagree with any of the sentences below. I'm not here to preach to the void.
> A. Businesses will boom and bust. Hype will come and go. Humanity has an intrinsic drive to advance thinking tools. So AI is backed by huge incentives to continue to grow, no matter how many missteps economic or otherwise.
Corrected to:
A. Businesses will boom and bust. Hype will come and go. Nevertheless, humanity seems to have an intrinsic drive to innovate, which means pushing the limits of technology. People will seek more intelligent machines, because we perceive them as useful tools. So AI is pressurized by long-running, powerful incentives, no matter how many missteps economic or otherwise. It would take a massive and sustained counter-force to prevent a generally upwards AI progression.
Did Webster also redefine the term in service of other goals?
1. the ability to learn or understand or to deal with new or trying situations
Regarding the phrase statistical parrot, I would claim that statistical parrotism is an ideology. As with any ideology, what we see is a speciation event. The overpopulation of SEO parrots has driven out a minority of parrots who now respecialize in information dissemination rather than information pollution, leaving their former search-engine ecological niche and settling in a new one that allows them to operate at a higher level of density, compression and complexity. Thus it's a major step in evolution, but it would be a misunderstanding to claim that evolution is the emergence of intelligence.
The overpopulation of AI BS, prophet previsions, pseudo philosopher/anthropologist and so on, this site has been tampered with is astonishing
LLMs ARE stochastic parrots, throw whatever chatgpt slop answer but facts are facts
> Comments should get more thoughtful and substantive, not less, as a topic gets more divisive.
Over the top humor duly acknowledged.
Disastrous? Quite possibly, but my concerns are based on different concerns.
Almost everything changes, so isn’t it better to rephrase these statements as metrics to avoid fixating on one snapshot in an evolving world?
As the metrics get better, what happens? Do you still have objections? What objections remain as AI capabilities get better and better without limit? The growth might be slow or irregular, but there are many scenarios where AIs reach the bar where they are better at almost all knowledge work.
Stepping back, do you really think of AI systems as stochastic parrots? What does this metaphor buy you? Is it mostly a card you automatically deal out when you pattern match on something? Or does serve as a reusable engine for better understanding the world?
We’ve been down this road; there is already much HN commentary on the SP metaphor. (Not that I recommend HN for this kind of thing. This is where I come to see how a subset of tech people are making sense of it, often imperfectly with correspondingly inappropriate overconfidence.)
TLDR: smart AI folks don’t anchor on the stochastic parrots metaphor. It is a catchy phrase and helped people’s papers get some attention, but it doesn’t mean what a lot of people think it means. Easily misunderstood, it serves as a convenient semantic stop sign so people don’t have to dig in to the more interesting aspects of modern AI systems. For example: (1) transformers build conceptual models of language that transcend any particular language. (2) They also build world models with spatial reasoning. (3) Many models are quite resilient to low quality training data. And more.
To make this very concrete: under the assumption of universal laws of physics, people are just following the laws of physics, and to a first approximation, our brains are just statistical pattern matchers. By this definition, humans would also be “stochastic parrots”. I go all this trouble to show that this metaphor doesn’t cut to the heart of the matter. There are clearer questions to ask: they require getting a lot more specific about various forms and applications of intelligent behavior. For example
- under what circumstances does self play lead to superhuman capability in a particular domain?
- what limits exist (if any) in the self supervised training paradigm used for sequential data? If the transformer trained in this way can write valid programs then it can create almost any Turing machine; limited only by time and space and energy. What more could you want? (Lots, but I’m genuinely curious as to people’s responses after reflecting on these.)