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13.08.2025 05:25
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Adoption of AI can’t increase productivity *and* create a cornucopia of new well-paid jobs. That’s a fantasy world.
Based on the behaviour of corporations over the last decades, do you expect they will voluntarily pass on those increased profits to their workers? Or will it accelerate the movement of wealth to the company owners, as has been the case since the 1970s?
Forget AI for a moment, and imagine we wave a magic wand and immediately increase the profit margins for all those companies through some other means — e.g. eliminate corporate taxation.
In either sense, it’s a reduction of money spent on personnel costs (i.e. wages) per dollar of GDP — that’s what increased productivity means.
Let’s do a thought experiment.
If we take their predictions at face value, what AI will deliver is either increased revenue for companies adopting AI, with no change in personnel costs; a massive reduction in personnel costs for the same amount of revenue; or some combination of the two.
AI accelerationists frequently hype the amazing increases in productivity that AI will deliver, and the enormous benefits to society that will result.
I’ve found it’s more visually appealing to just paste in his head at the inflection points
But to win in the market, #neuromorphic companies will need to clearly demonstrate the benefits over commodity competitors.
In a new paper @natcomms.nature.com, Sadique Sheik and I show how #neuromorphic compute can succeed, with inspiration from the success of tensor processors.
Sending raw sensory data to the cloud is so 2022.
More and more compute capacity is rushing to the network edge — in-sensor computing is the next big thing, and #neuromorphic processor cores are ready to step into the commercial limelight.
tfw most of your favourite albums are available in their "20th anniversary edition" #genxthings
A few months ago I had the great pleasure to chat with @giuliadangelo.bsky.social and @fabhertz.bsky.social for their new #Sottosoglia #podcast. We covered the differences between working in #academia and the #startup life, as well as a lot of very personal ground...
If #Colesworths is engaging in #anti-competitive behaviour, then recommend changes that will increase #competition, instead of some pricing trims around the edges. #accc #auspol
What a horrifying article to see posted by the premier science journal. Attacks on scientists and on science are direct attacks on the rational basis for liberal democracies. None of this is normal or acceptable, and leads very quickly to a very dark place.
Dance-off for tenure 💯
This is horrible to post, but I may as well post it. We are essentially shutting down research operations in my group, which is focused on treatments for pediatric brain cancer. I’m a well funded investigator, and there’s no choice. Science can’t function without the stability of NIH
What an incredible failure of #privatised #healthcare. Healthcare and medicine shouldn’t make a profit, and shouldn’t be privately funded or delivered. Shut down Australia’s private healthcare industry, and fully fund #Medicare. #Auspol
A toddler in cardiac distress is ignored for hours on a Saturday morning, and tragically dies. Months later, a woman goes into labour on a Saturday, needs an emergency caesarean, but no theatre staff are present to operate. 50 minutes later she gives birth vaginally, and the baby doesn’t survive.
Consequently, the hospital cuts staffing numbers to save money, especially on weekends.
A private healthcare company, given a contract by the #LNP to run a public hospital, is placed under financial pressure by the awful Australian private health insurance industry, which doesn’t pay for health services that people need.
What an incredible cluster-f*** of late-stage capitalism and neoliberal privatised healthcare policy. #LNP
Inform them clearly about the statistics of the PhD-to-Professor pipeline. Advise them about the political wrangling and support needed to succeed in academia.
But beyond this superficial similarity, *mechanistically* they are all fundamentally different and incomparable.
The early network layers produce things which look like receptive fields — because any optimisation method attempting to maximise information about visual scenes will extract the statistics of visual scenes. PCA, ICA, CNNs… it’s not surprising.
Good point for same constraints producing rhyming solutions to a common problem. We see that superficially with networks trained on vision tasks.
It’s often noted how wild and specific the structures resulting from e.g. evolutionary algorithms are. So why would we expect to learn something concrete about the mechanisms of biological systems, from studying synthetically evolved systems? Same goes for trained NNs.
Bio brains and trained networks are both the end-point of an optimization process, but unless the constraints and loss function are identical, there is no reason to believe the solutions will be in any way comparable.
1/7. Public discourse about racial equity has changed a lot over the years. At @commhsp.bsky.social we’ve been monitoring those changes and their consequences. In a new 538 article, @efranklinfowler.bsky.social and I share what we’ve been seeing and why it matters.
abcnews.go.com/538/national...
Absolutely nobody can be surprised, let along shocked by this, right? Surely #Europe spent the months since November planning to assist #Ukraine without yoosa?
Government is big business. *Somebody* has to do the work required to keep public agencies grinding along. If you fire full-time staff, you spend much more in personnel contracting, consultants and private firms taking up the contracts.
🤔 Funny, sounds just like #LNP policy… #Dutton #auspol