The Bank of Australia was a failed financial institution of early colonial New South Wales. It was formed in 1826 and collapsed in 1843. (from the usual source)
I came to this piece by way of Molly White+Nikhil Suresh+Iris Meredith+Gary Marcus (a kind of second-set Dead compilation) & it was a gas the whole way, inasmuch as this subject can ever be a gas. I have no more tech knowledge than how to on-turn the devices in question, and can even find that challenging, but it's exhilarating and heartening to know that such sharp & clear-thinking (& *young*) people are engaged in this work. And y'all can all write like motherfuckers, that's the most fun thing. It's not just that I, like Lord Peter Wimsey, find it so easy to get drunk on words that I am seldom perfectly sober, it's that sharp writing plainly made is one of the surest indicators of quality thought. I also take it, perhaps speciously, as a guarantee of the ethical bona fides of the author, though their willingness (compulsion?) to come at tech from a social-justice perspective, and the degree to which they engage with the economy of tech (which means so much more than the money involved and must encompass a thorough and detailed survey of all the pieces -- technological, societal, governmental, financial, and above all political, which leads inevitably to race as pretty much everything does in the US) show a desire to do that now very old-fashioned thing, speak truth to power in hopes of making the world a better place.
Though it’s good to be aware that the recent Nobel prize winners, at least in chemistry or medicine (whichever prize was awarded for the Alpha Fold work) was not for generative AI nor large language models — indeed, the Alpha Fold work and much of Deep Mind’s other work is closer to what Gary Marcus and his cohort envisions than it is to the increasing-scale-will-be-magical thinking of Open AI. That said, its not clear to what extent Open AI is working on what Marcus has described as “algebraic AI”, and others have described as “Good Old-Fashioned AI (GOFAI)” — supplementing the statistical models with reasoning about facts, since they’re pretty secretive about what’s going into their systems.
The other prize awarded, for Hopfield nets (Hinton as well as Hopfield, I think?) comes closer, as it was for the fundamental work enabling things like LLMs
The Bank of Australia was a failed financial institution of early colonial New South Wales. It was formed in 1826 and collapsed in 1843. (from the usual source)
If you couldn't access the Potemkin Ai pdf, try here: https://direct.mit.edu/books/oa-edited-volume/5319/chapter/3800165/Planetary-Potemkin-AI-The-Humans-Hidden-inside
I came to this piece by way of Molly White+Nikhil Suresh+Iris Meredith+Gary Marcus (a kind of second-set Dead compilation) & it was a gas the whole way, inasmuch as this subject can ever be a gas. I have no more tech knowledge than how to on-turn the devices in question, and can even find that challenging, but it's exhilarating and heartening to know that such sharp & clear-thinking (& *young*) people are engaged in this work. And y'all can all write like motherfuckers, that's the most fun thing. It's not just that I, like Lord Peter Wimsey, find it so easy to get drunk on words that I am seldom perfectly sober, it's that sharp writing plainly made is one of the surest indicators of quality thought. I also take it, perhaps speciously, as a guarantee of the ethical bona fides of the author, though their willingness (compulsion?) to come at tech from a social-justice perspective, and the degree to which they engage with the economy of tech (which means so much more than the money involved and must encompass a thorough and detailed survey of all the pieces -- technological, societal, governmental, financial, and above all political, which leads inevitably to race as pretty much everything does in the US) show a desire to do that now very old-fashioned thing, speak truth to power in hopes of making the world a better place.
Would recommend Professor Ethan Mollick on Substack and The AI Daily Brief on YouTube and Spotify and then theres the recent Nobel AI prize winners
Though it’s good to be aware that the recent Nobel prize winners, at least in chemistry or medicine (whichever prize was awarded for the Alpha Fold work) was not for generative AI nor large language models — indeed, the Alpha Fold work and much of Deep Mind’s other work is closer to what Gary Marcus and his cohort envisions than it is to the increasing-scale-will-be-magical thinking of Open AI. That said, its not clear to what extent Open AI is working on what Marcus has described as “algebraic AI”, and others have described as “Good Old-Fashioned AI (GOFAI)” — supplementing the statistical models with reasoning about facts, since they’re pretty secretive about what’s going into their systems.
The other prize awarded, for Hopfield nets (Hinton as well as Hopfield, I think?) comes closer, as it was for the fundamental work enabling things like LLMs