Educators are now administering the Turing test in reverse: What are questions that only humans can answer well? What kinds of thinking does writing make possible for us? 

GPT-3 threatens to “[undermine] the kind of writing intensive course that had served as the backbone of [his] teaching for two decades.” “I was less worried about whether GPT-3 is genuinely intelligent,” Symons writes, “and more worried about whether the development of these tools would make us less intelligent.” 

natural-language processing is going to force engineers and humanists together. They are going to need each other despite everything. Computer scientists will require basic, systematic education in general humanism: The philosophy of language, sociology, history, and ethics are not amusing questions of theoretical speculation anymore. They will be essential in determining the ethical and creative use of chatbots, to take only an obvious example.

The extraordinary ignorance on questions of society and history displayed by the men and women reshaping society and history has been the defining feature of the social-media era.

Emergent abilities are not present in small models but can be observed in large models.

Houston, we have a Capability Overhang problem: Because language models have a large capability surface, these cases of emergent capabilities are an indicator that we have a ‘capabilities overhang’ – today’s models are far more capable than we think, and our techniques available for exploring the models are very juvenile. We only know about these cases of emergence because people built benchmark datasets and tested models on them. What about all the capabilities we don’t know about because we haven’t thought to test for them? There are rich questions here about the science of evaluating the capabilities (and safety issues) of contemporary models. 

As the metaphor suggests, though, the prospect of a capability overhang isn’t necessarily good news. As well as hidden and emerging capabilities, there are hidden and emerging threats. And these dangers, like our new skills, are almost too numerous to name.

There’s a concept in AI that I’m particularly fond of that I think helps explain what’s happening. It’s called “capability overhang” and refers to the hidden capacities of AI: skills and aptitudes latent within systems that researchers haven’t even begun to investigate yet. You might have heard before that AI models are “black boxes” — that they’re so huge and complex that we don’t fully understand how they operate or come to specific conclusions. This is broadly true and is what creates this overhang.

Which is why I wonder if this may be the end of using writing as a benchmark for aptitude and intelligence.

Perhaps there are reasons for optimism, if you push all this aside. Maybe every student is now immediately launched into that third category: The rudiments of writing will be considered a given, and every student will have direct access to the finer aspects of the enterprise. Whatever is inimitable within them can be made conspicuous, freed from the troublesome mechanics of comma splices, subject-verb disagreement, and dangling modifiers.

I’ve also long held, for those who are interested in writing, that you need to learn the basic rules of good writing before you can start breaking them—that, like Picasso, you have to learn how to reliably fulfill an audience’s expectations before you get to start putting eyeballs in people’s ears and things.

“In literacy education, particularly for developing writers, instructors are looking for the level of desirable difficulty, or the point at which you are working yourself just as hard so that you don’t break but you also improve,” Laffin told Motherboard. “Finding the right, appropriate level of desirable difficulty level of instruction makes their capacity to write grow. So if you are doing compensation techniques that go beyond finding that level of desirable difficulty and instructing at that place, then you’re not helping them grow as a writer.”

# Input Input: 123, Output: Input: 121, Output: Input: 111, Output: Input: 123454321, Output: Input 123123, Output: # Instruction Output true if input is a palindrome # Output Input: 123, Output: false Input: 121, Output: true Input: 111, Output: true Input: 123454321, Output: true Input 123123, Output: false