I've tried to explain this paper to people in similar circumstances and have also struggled!
In my mind the key point of departure between this paper and the more standard computational functionalist approaches is the importance of metabolism. Metabolism _precedes_ organism. The body is first deeply entangled with the environment through exchanges of resources (content causality) before it is capable of building computers (vehicle causality). Having built and alphabetized the world we can understand them in terms of discrete state transitions.
I expect my explanations have been unsatisfying as we can immediately move to seeing metabolism as some alphabetized input/output system that can be immediately placed back into the computational framework. Moving outside of this framework requires engaging with the enactivist/organicist traditions, which is a rich but minority view.
Ed's main critique is about business sustainability -- it's true that there are many articles about AI on IP issues or ethics but he is unique in actually crunching the numbers on profit.
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FUTO is an organization dedicated to developing, both through in-house engineering and investment, technologies that frustrate centralization and industry consolidation. Through a combination of in-house engineering projects, targeted investments, generous grants, and multi-media public education efforts, we will free technology from the control of the few and recreate the spirit of freedom, innovation, and self-reliance that underpinned the American tech industry only a few decades ago. Our principles are here: https://futo.org/what-is-futo
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That advice makes sense if we're talking about 800B+ parameter models that require a gigantic investment of capital and time. For models that fit on a consumer GPU you're leaving chips on the table to not take advantage of training / fine-tuning. It's just too easy and powerful not to.
"Build, don't train" is poor advice for a prospective "AI Engineer" title. It should be "Know when to reach for a new model architecture, know when to reach for fine-tuning / LoRA training, know when to use an API." Only relying on an API will drastically reduce your product differentiation, to say nothing of the fact that any AI Engineer worth that title should know how to build and train models.
Do you have any recommendations on libraries for parsing CAN with Rust (or are your tools OSS)? I'll be working on a project in a couple of months that'll need to pull data off CAN.
The article's thesis is that EA's moral realism stance (which finds it's apotheosis in utilitarian moral calculus) is ill-supported and highly questionable, especially when combined with theological anti-realism.
It's definitely not written to win over any EA supporter, that's for sure.
The study only checks for myocarditis diagnosis codes 1-7 days after vaccination. That's a very short timeframe... We have multiple months worth of data at this point, I wonder what happens to the incidence rate at longer timelines.
Certainly we can say our ML models are becoming more general in the sense of being able to cross-correlate between multiple domains. This is quite a different story than "becoming a general intelligence." Intelligence is a property of a being with will. These models, and machines in general, do not posses will. It is we who define their form, their dataset, their loss function, etc. There is no self-generation that marks an intelligent being because there is no self there at all.
It is only the case that ML expands our own abilities, augments our own intelligence.
I've been publishing writing far outside consensus progressive attitudes since the pandemic began with zero issue so far. Don't make your workplace your audience or invite controversy around your workplace and you're very unlikely to have a problem.
In my mind the key point of departure between this paper and the more standard computational functionalist approaches is the importance of metabolism. Metabolism _precedes_ organism. The body is first deeply entangled with the environment through exchanges of resources (content causality) before it is capable of building computers (vehicle causality). Having built and alphabetized the world we can understand them in terms of discrete state transitions.
I expect my explanations have been unsatisfying as we can immediately move to seeing metabolism as some alphabetized input/output system that can be immediately placed back into the computational framework. Moving outside of this framework requires engaging with the enactivist/organicist traditions, which is a rich but minority view.