Philosophers like Kant, Descartes and Locke wanted to draw a distinction between a priori and a posteriori beliefs (or knowledge). I believe AI will be very much bound by these discussions. Some knowledge can be had purely by reason. Some other knowledge will require observations.
Consider a calculator, a machine capable of valid transformations wrt the semantics of arithmetic.
Does a calculator reason its way to 2 + 2 = 4? No, it merely transforms.
An LLM is simply a much more complex version of this; except that its complexity hides a lot of what is otherwise reducible to a transformation of semantic, conceptual, and inferential relations. There are lots of NLP models prior to the introduction of modern "AI" that would make these representations more clear.
However I don't believe any of the aforementioned philosophers would ever consider reason itself as a transformation, as Kant especially spent a lot of time differentiating apprehension from transition.
I’m not so sure. While I completely agree on pure transformation, I continue to believe that the most concerning aspect of neural networks is that they really strengthen the argument that consciousness is the illusion.
The interplay between deductive frameworks and inductive frameworks is still always buoyed by the black swan problem… which is a the heart of Hume’s solipsism. Kant’s pure reason never considered instinctual logical gates that come prepackaged in the mind. That seems frighteningly close to a prediction algorithm, no matter how much we wrap it in a concept of understanding. In the end, we’re just building a model with a minimal error rate.
Western tradition has firmly put pure reasoning in a cage. It serves its purpose only as a source of hypotheses, conjecture and thought experiments. Abrahamic religions, while still not accepted sensu proprio by many, are much less mystical than the Eastern Religions. You can see the difference in the unusual creative process of Ramanujan.
Which tradition are we to follow then whilst training the AI models? Pushed beyond their limits today's models tend to hallucinate and cannot be accepted sensu proprio. Philosophers will call it speculative reasoning. And yet, some speculation can be useful in breaking new grounds. Anyway, I have no answers. Just musings.
> I continue to believe that the most concerning aspect of neural networks is that they really strengthen the argument that consciousness is the illusion.
Based on what? Just because you can model the weather using math doesn't imply at all that the weather is the product of a mathematical model itself. I don't see how reasoning is different.
We deliberately construct LLMs so that they approximate certain relations typical of reasoning. Suppose they become extraordinarily successful at doing so. What exactly have we demonstrated? That these effects can be artificially reproduced through such a mechanism. But how would it follow that the thing being reproduced must itself be nothing more than that mechanism?
Based on the face that we have zero explanation for consciousness. Based on the face that biologically neural networks are exactly a kind of neural network.
My point isn’t that we’re able to explain consciousness as an illusion. It’s that we’re able to explain consciousness-like behavior presumably without consciousness, and the infrastructure look uncomfortably like we do.
The model supports both instruction and RL fine-tuning. It looks like people have started training it already [1]. The RL tuning may be only available to the Cloudflare customers, I couldn't be sure.
The biggest upside to using a zero-shot classification model is that it's actually zero-shot, i.e., you don't need to construct task-specific training sets.
If it needs to be fine-tuned, then why not fine-tune smaller and cheaper models that are only as big as the task requires?
I hear what you're saying. But this is not such a model. The article says that various internal teams had to fine-tune Clef to suit their specific needs. They're even rolling out a RL training product line for it.
Fair. Though in this case my actual classifier (dino v3 features fed into a very shallow DNN) works surprisingly well and is insanely faster than using an LLM -- so it really was the one shot performance I was hoping for.
Please do not encourage your children to carry and brandish replica guns. This is never a good idea. Unfortunately I see this more frequently than I like. Kids don't just carry, they aim and pretend to shoot. Even in an open carry state like Florida brandishing a gun is illegal.
I know, it's a different world. I'm 62. As a kid, my friends and I chased one another around the neighborhood with toy guns for hours, raising a racket. My kids are 24 and 26 now, but it would have been unthinkable to let them have toy guns.
I'm in my 50s and did the same. But even then the toy guns were often bright orange.
Nerf guns are the modern equivalent of our dart guns and kids run around with them today all the time. They are pretty unmistakeable, I'm sure by design.
Clearly toy guns. Gun-like, but nobody with the slightest understanding of guns would think they were real. I have no problem with kids having toy guns. I do have a problem with kids in public having realistic replica guns. Bright orange isn't enough.
I think the answer is that other than in organized events realistic replicas should be subject to the same restrictions on actions that real guns are.
