Submitter here.
I came across the Gopnik piece after hearing her discuss it on a recent episode of the Complexity podcast from the Santa Fe Institute (SFI). The series begins here: <https://www.santafe.edu/culture/podcasts/ep-1-what-is-intell...>.
As I recall that episode doesn't directly tackle what intelligence is, though numerous others from the Complexity back catalogue do, as does an episode from another podcast in the New Books Network (NBN). Two specific approaches stand out.
In the NBN episode, a discussion of the Turing Test makes specific and detailed note of how that test side-steps the question of what intelligence is entirely by focusing on what it does, and specifically whether an artificial agent can convince a human interlocutor that it is intelligent, through text-based interactions. I find this particular approach (focusing on outputs and appearances rather than inner states and motivations) generally useful, and not only for artificial behaviours. To a great extent, for example, I find what a person, organisation, or institution does far more accessible and generally useful than why it does that. This isn't to say that ends (results/actions) are more significant than means (causes/motivations/intent), but they are accessible and determinable with far less ambiguity or presumption. Knowing causes or motivations is useful for its predictive value, but given even a small sampling of behaviours and instances, it's generally possible to posit or infer these to a useful degree without deep introspection.
Another approach, taken in multiple Complexity episodes as well as writings and discussions elsewhere, former SFI president David Krakauer posits that intelligence is search, and specifically search through a pattern space for a solution or approach to some given problem. (See especially "Ingenious: David Krakauer", Nautilus 16 April 2015 <https://nautil.us/ingenious-david-krakauer-235383/>.)
I've put some thinking into an ontology of technological mechanisms, where one of those is information, consisting generally of input (sensing, parsing), storage/retrieval, output, and logic. Intelligence falls under logic, and I'd argue involves comparisons on current and prior experience (e.g., sensing and storage/retrieval), as well as applying rules, algorithms, inferences, and the like (all forms of logic, broadly). "Intelligence" then is a form of logic where logic is generally processing (as opposed to input/output/storage) of information.
At what stage a human-like or general intelligence emerges is of course somewhat nebulous. To quote a long-standing US National Parks Service observation, there's a considerable overlap between the smartest bears, and stupidest humans, when it comes to storing and/or raiding food and garbage. In the AI field, we've seen specific problems, applications, domains, or however you'd choose to call them fall into the class of those in which artificial search (or artificial intelligence, though "search" may be more accurate in the sense of "search through problem space to a useful solution) routinely bests humans, including checkers (trivial), chess (challenging), go (even more so), and now creative endeavours such as image, music, and text generation.
(A professional classical musician friend recently told me directly that at least some of the AI compositions they're encountering are not only good but show what can only be described as strong musical content and coherence as compared to the classical tradition. I'd think that the standards for popular music with its general simplicity would be far less challenging, their assessment in this case strikes me as notable.)
I'll also note I'm not especially enthusiastic about AI's potential. The field has seen many periods of apparent rapid progress followed by very long, often decades-long, "winters". Recent progress, say, 2023 onward, has been spectacular, but also seems to be somewhat stalling out and showing profound limits. That isn't to say that new approaches might come up with greater capabilities, cheaper approaches, or both. China's DeepSeek, and the story of human intelligence including the shrinking of the braincase over recent evolution despite greater apparent intelligence suggests that efficiency gains may well be the path forward, perhaps utilising something akin to Chomsky's "universal grammar" or grammar hierarchy, or notions of parsing and grouping patterns within the human brain, whether through genetic inheritance, direct experience, or education, might be ways of drastically reducing size and analysis requirements of training corpora. I think it's Krakauer again (this time in a Complexity episode) who notes that total training set humans require to acquire basic linguistic skills by, say, age 5, is roughly 5 MB of data. This is phenomenally less than current LLM AI models require, and strongly suggests far greater possible efficiencies.
Another factor, discussed in the recent Complexity series, is that humans of course not only from reading texts, but from observing and interacting with our environment. That's something AI presently does relatively little of, as I understand it, though certain domains (e.g., autonomous vehicles) may be applying this method. I'm not following progress on this at all presently, though I suspect I should.