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Moving Beyond Mimicry in Artificial Intelligence

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  • I suggest much of what large pre-trained models do is a form of artificial mimicry. Rather than stochastic parrots, we might call them stochastic chameleons. Parrots repeat canned phrases; chameleons seamlessly blend in new environments. The difference might seem, ironically, a matter of semantics. However, it is significant when it comes to highlighting the capacities, limitations, and potential risks of large pre-trained models. Their ability to adapt to the content, tone, and style of virtually any prompt is what makes them so impressive—and potentially harmful. They can be prone to mimicking the worst aspects of humanity, including racist, sexist, and hateful outputs. They have no intrinsic regard for truth or falsity, making them excellent bullshitters. As the LaMDA story reveals, we are not always good at recognizing that appearances can be deceiving. — Updated on 2022-07-05 20:38:13 — Group: #Public

  • When scaled-up models unlock new capabilities, combining novel concepts coherently, explaining new jokes to our satisfaction, or working through a math problem step-by-step to give the correct solution, it is hard to resist the intuition that there is something more than mindless mimicry going on. — Updated on 2022-07-05 20:40:11 — Group: #Public

  • Can large pre-trained models really offer more than a simulacrum of intelligent behavior? 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 in the quest for artificial intelligence, and the label “foundation models” is an unfortunate misnomer.Others would argue that large pre-trained models are already making strides toward acquiring proto-intelligent abilities. For example, the way large language models can solve a math problem involves a seemingly non-trivial capacity to manipulate the parameters of the input with abstract templates. Likewise, many outputs from multi-modal models examplify a seemingly non-trivial capacity to translate concepts from the linguistic to the visual domain, and flexibly combine them in ways that are constrained by syntactic structure and background knowledge. One could see these capacities as very preliminary ingredients of intelligence, inklings of smarter aptitudes yet to be unlocked. To be sure, other ingredients are still missing, 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. — Updated on 2022-07-05 20:41:15 — Group: #Public

  • To make headway on these issues, it helps to look beyond learning function and benchmarks. Sharpening working definitions of terms such as “understanding,” “reasoning,” and “intelligence” in light of philosophical and cognitive science research is important to avoid arguments that take us nowhere. 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. — Updated on 2022-07-05 20:42:11 — Group: #Public