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Why do I read these hoping to find novel insights or a different way to see things: > [...] There are two ways to look at this issue. Some researchers think th

Why do I read these hoping to find novel insights or a different way to see things: > [...] There are two ways to look at this issue. Some researchers think that the kind of intelligence found in biological agents is cut from a fundamentally different cloth than the kind of statistical pattern-matching large models excel at. For these skeptics, scaling up existing approaches is but a fool’s errand [...] > Others would argue that large pre-trained models are already making strides toward acquiring proto-intelligent abilities. [...] One could see these capacities as very preliminary ingredients of intelligence [...] and there are compelling reasons to doubt that simply training larger models on more data, without further innovation, will ever be enough to replicate human-like intelligence. > Regardless of how we answer these questions, we need to tread carefully when deploying large pre-trained models in the real world; not because they threaten to become sentient or superintelligent overnight, but because they emulate us, warts and all.

nonsensikal 4y ago
> In a particularly influential article, Emily Bender, Timnit Gebru, and colleagues ... This quote was included to virtue signal. It assured the article would be garbage. The thesis (buried a dozen paragraphs in) is > I suggest much of what large pre-trained models do is a form of artificial mimicry. > Here’s the thing about mimicry: It need not involve intelligence, or even agency. > a kind of biological mimicry that can be seen as solving a matching problem > Artificial mimicry in large pre-trained models also solves a matching problem > We also need a better understanding of the mechanisms that underlie the performance of large pre-trained models to show what may lie beyond artificial mimicry. Wait what? The whole thesis is that AIs are parrots. But parrots are living intelligence creatures..... But the entire argument is smuggled in on the assumption > Parrots repeat phrases without understanding what they mean This is really simple and boring. Parrots, "parrot". Parroting isn't understanding. AIs, "parrot". therefore AIs dont understand The novel contribution is zero, and all this has already been refuted and death marched. AGI is inevitable because computation is universal and intelligence is substrate independent.
- balsam 4y ago
  nautilus is an organ of the mediocracy and sadly, the title is more valuable than the article.
- deleted 4y ago
  [deleted]
- fzingle 4y ago
  > AGI is inevitable because computation is universal and intelligence is substrate independent. What the article is asking, albeit obliquely is: how do we know that to be true? It is very difficult to prove when definitions for intelligence and consciousness are fuzzy and not widely agreed upon.
  - tbrownaw 4y ago
    I read it more as asking about deep-NN models specifically. If it really was trying to suggest that computation isn't universal or that our intelligence is non-physical or something, that would be a whole different problem.
  - nh23423fefe 4y ago
    I see people assert this all the time. Intelligence is the ability to achieve goals. Consciousness is contents of what you are aware of. The definitions are irrelevant. People want a definition to do demarcation. But demarcation is boring. I don't care whether some threshold entity is on this or that side of the line. The existence of some distinct territories is enough for me. There is no reason to imagine AIs are prohibited from occupying territory on both sides of the line. AIs aren't climbing up a ladder, non local exploration is possible
    - visarga 4y ago
      > Intelligence is the ability to achieve goals A more precise definition by François Chollet: The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty https://arxiv.org/pdf/1911.01547.pdf https://arxiv.org/pdf/1911.01547.pdf So a system is more intelligent if it can solve harder tasks with fewer trials and prior knowledge. Intelligence is always defined over a scope of tasks, for example human intelligence only applies to the space of tasks and domains that fit within the human experience. Our intelligence does not have extreme generalization even though we have broad generalization (defined as adaptation to unknown unknowns across a broad category of related tasks).

nonsensikal 4y ago

> In a particularly influential article, Emily Bender, Timnit Gebru, and colleagues ... This quote was included to virtue signal. It assured the article would be garbage. The thesis (buried a dozen paragraphs in) is > I suggest much of what large pre-trained models do is a form of artificial mimicry. > Here’s the thing about mimicry: It need not involve intelligence, or even agency. > a kind of biological mimicry that can be seen as solving a matching problem > Artificial mimicry in large pre-trained models also solves a matching problem > We also need a better understanding of the mechanisms that underlie the performance of large pre-trained models to show what may lie beyond artificial mimicry. Wait what? The whole thesis is that AIs are parrots. But parrots are living intelligence creatures..... But the entire argument is smuggled in on the assumption > Parrots repeat phrases without understanding what they mean This is really simple and boring. Parrots, "parrot". Parroting isn't understanding. AIs, "parrot". therefore AIs dont understand The novel contribution is zero, and all this has already been refuted and death marched. AGI is inevitable because computation is universal and intelligence is substrate independent.

balsam 4y ago
nautilus is an organ of the mediocracy and sadly, the title is more valuable than the article.
deleted 4y ago
[deleted]
fzingle 4y ago
> AGI is inevitable because computation is universal and intelligence is substrate independent. What the article is asking, albeit obliquely is: how do we know that to be true? It is very difficult to prove when definitions for intelligence and consciousness are fuzzy and not widely agreed upon.
- tbrownaw 4y ago
  I read it more as asking about deep-NN models specifically. If it really was trying to suggest that computation isn't universal or that our intelligence is non-physical or something, that would be a whole different problem.
- nh23423fefe 4y ago
  I see people assert this all the time. Intelligence is the ability to achieve goals. Consciousness is contents of what you are aware of. The definitions are irrelevant. People want a definition to do demarcation. But demarcation is boring. I don't care whether some threshold entity is on this or that side of the line. The existence of some distinct territories is enough for me. There is no reason to imagine AIs are prohibited from occupying territory on both sides of the line. AIs aren't climbing up a ladder, non local exploration is possible
  - visarga 4y ago
    > Intelligence is the ability to achieve goals A more precise definition by François Chollet: The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty https://arxiv.org/pdf/1911.01547.pdf https://arxiv.org/pdf/1911.01547.pdf So a system is more intelligent if it can solve harder tasks with fewer trials and prior knowledge. Intelligence is always defined over a scope of tasks, for example human intelligence only applies to the space of tasks and domains that fit within the human experience. Our intelligence does not have extreme generalization even though we have broad generalization (defined as adaptation to unknown unknowns across a broad category of related tasks).