(Roughly) Daily

There is a wide range of opinions on AI and what it might portend. While artificial intelligence has its skeptics, and some argue that we should slow its development, AI is here, and it’s only getting warmed up (c.f.: Ezra Klein‘s “This Changes Everything“).

As applications multiply (and get more sophisticated), there’s an understandable concern about its impact on employment. While tools like ChatGPT and DALL·E 2 are roiling the creative sphere, many economists are looking more broadly…

Like many revolutionary technologies before it, AI is likely to eliminate jobs. But, as has been the case in the past, experts argue, AI will likely offset much of that by spurring the creation of new jobs in addition to enhancing many existing jobs. The big question is: what sort of jobs?

“AI will wipe out a lot of current jobs, as has happened with all past technologies,” said Lawrence Katz, a labor economist at Harvard. “But I have no reason to think that AI and robots won’t continue changing the mix of jobs. The question is: will the change in the mix of jobs exacerbate existing inequalities? Will AI raise productivity so much that even as it displaces a lot of jobs, it creates new ones and raises living standards?”

Anu Madgavkar, who leads labor market research at the McKinsey Global Institute, estimates that one in four workers in the US are going to see more AI and technology adopted in their jobs. She said 50-60% of companies say they are pursuing AI-related projects. “So one way or the other people are going to have to learn to work with AI,” Madgavkar said.

While past rounds of automation affected factory jobs most, Madgavkar said that AI will hit white-collar jobs most. “It’s increasingly going into office-based work and customer service and sales,” she said. “They are the job categories that will have the highest rate of automation adoption and the biggest displacement. These workers will have to work with it or move into different skills.”…

“US experts warn AI likely to kill off jobs – and widen wealth inequality“

But most of these visions are rooted in an appreciation of what AI can currently do (and the likely extensions of those capabilities). What if AI develops in startling, discontinuous ways– what if it exhibits “emergence”?…

… Recent investigations… have revealed that LLMs (large language models) can produce hundreds of “emergent” abilities — tasks that big models can complete that smaller models can’t, many of which seem to have little to do with analyzing text. They range from multiplication to generating executable computer code to, apparently, decoding movies based on emojis. New analyses suggest that for some tasks and some models, there’s a threshold of complexity beyond which the functionality of the model skyrockets. (They also suggest a dark flip side: As they increase in complexity, some models reveal new biases and inaccuracies in their responses.)

Biologists, physicists, ecologists and other scientists use the term “emergent” to describe self-organizing, collective behaviors that appear when a large collection of things acts as one. Combinations of lifeless atoms give rise to living cells; water molecules create waves; murmurations of starlings swoop through the sky in changing but identifiable patterns; cells make muscles move and hearts beat. Critically, emergent abilities show up in systems that involve lots of individual parts. But researchers have only recently been able to document these abilities in LLMs as those models have grown to enormous sizes…

“The Unpredictable Abilities Emerging From Large AI Models“

Perhaps we should be thinking about AI not just functionally, but also philosophically…

The development of Artificial Intelligence is a scientific and engineering project, but it’s also a philosophical one. Lingering debates in the philosophy of mind have the potential to be substantially demystified, if not outright resolved, through the creation of artificial minds that parallel capabilities once thought to be the exclusive province of the human brain.

And since our brain is how we know and interface with the world more generally, understanding how the mind works can shed light on every other corner of philosophy as well, from epistemology to metaethics. My view is thus the exact opposite of Noam Chomsky’s, who argues that the success of Large Language Models is of limited scientific or philosophical import, since such models ultimately reduce to giant inscrutable matrices. On the contrary, the discovery that giant inscrutable matrices can, under the right circumstances, do many things that otherwise require a biological brain is itself a striking empirical datum — one Chomsky chooses to simply dismiss a priori.

Biological brains differ in important ways from artificial neural networks, but the fact that the latter can emulate the capacities of the former really does contribute to human self-understanding. For one, it represents an independent line of evidence that the brain is indeed computational. But that’s just the tip of the iceberg. The success of LLMs may even help settle longstanding debates on the nature of meaning itself…

“We’re all Wittgensteinians now“

And maybe we should be careful about “othering” AI (or, for that matter, any of the other forms for intelligence that surround us)…

I don’t think there is such a thing as an artificial intelligence. There are multiple intelligences, many ways of doing intelligence. What I envisage to be more useful and interesting than artificial intelligence as we currently conceive of it—which is this incredibly reduced version of human intelligence— is something more distributed, more widely empowered, and more diverse than singular intelligence would allow for. It’s actually a conversation between multiple intelligences, focused on some narrow goals. I have a new, very long-term, very nascent project I’m calling Server Farm. And the vision of Server Farm is to create a setting in which multiple intelligences could work on a problem together. Those intelligences would be drawn from all different kinds of life. That could include computers, but it could also include fungi and plants and animals in some kind of information-sharing processing arrangement. The point is that it would involve more than one kind of thinking, happening in dialogue and relationship with each other.

James Bridle, “There’s Nothing Unnatural About a Computer“

In the end, Tyler Cowan suggests, we should keep developing AI…

…what kind of civilization is it that turns away from the challenge of dealing with more…intelligence?  That has not the self-confidence to confidently confront a big dose of more intelligence?  Dare I wonder if such societies might not perish under their current watch, with or without AI?  Do you really want to press the button, giving us that kind of American civilization?…

We should take the plunge.  We already have taken the plunge.  We designed/tolerated our decentralized society so we could take the plunge…

“Existential risk, AI, and the inevitable turn in human history“

Still, we’re human, and we would do well, Samuel Arbesman suggests, to use the best of our human “tools”– the humanities– to understand AI…

So go study the concepts of narrative technique and use them to elucidate the behavior of LLMs. Or examine the rhetorical devices that writers and speakers have been using for millennia—and which GPT models has imbibed—and figure out how to use their “physical” principles in relating to these language models.

Ultimately, we need a deeper kind of cultural and humanistic competence, one that doesn’t just vaguely gesture at certain parts of history or specific literary styles. It’s still early days, but we need more of this thinking. To quote Hollis Robbins again: “Nobody yet knows what cultural competence will be in the AI era.” But we must begin to work this out.

“AI, Semiotic Physics, and the Opcodes of Story World“

All of which is to suggest that we are faced with a future that may well contain currently-unimaginable capabilities, that can accrue as threats or (and) as opportunities. So, as the estimable Jaron Lanier reminds us, we need to remain centered…

“From my perspective,” he says, “the danger isn’t that a new alien entity will speak through our technology and take over and destroy us. To me the danger is that we’ll use our technology to become mutually unintelligible or to become insane if you like, in a way that we aren’t acting with enough understanding and self-interest to survive, and we die through insanity, essentially.”…

The way to ensure that we are sufficiently sane to survive is to remember it’s our humanness that makes us unique…

“Tech guru Jaron Lanier: ‘The danger isn’t that AI destroys us. It’s that it drives us insane’“

All of the above-sampled pieces are eminently worth reading in full.

Apposite (and offered without comment): Theta Noir

[Image above: source]

* Alan Kay

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As we ponder progress, we might recall that it was on this date in 1979 that operators failed to notice that a relief valve was stuck open in the primary coolant system of Three Mile Island’s Unit 2 nuclear reactor following an unexpected shutdown. Consequently, enough coolant drained out of the system to allow the core to overheat and partially melt down– the worst commercial nuclear accident in American history.

Three Mile Island Nuclear Power Plant, near Harrisburg, PA