TL;DR Picard's initial arguments are pretty weak, even admitting that Riker as opposing counsel almost had him convinced. During a recess Picard talks to Guinan where she alludes to the future subjugation of many Datas which Picard connects to slavery. Back in the courtroom Picard calls Maddox as a hostile witness and gets him to define sentience--intelligence, self-awareness, consciousness--then walks him into conceding Data meets the first two. Picard's closing boils down to, "we don't know if he meets the third--you can call Data a toaster and rule he is property--_but what if you're wrong_". The judge rules on the basis of erroring on the side of caution due to that uncertainty. It's really a great scene.

We're not there yet, obviously. No LLM brings Data's level of awareness but it's as relevant a story as ever because it isn't really about AI but othering for the purpose of subjugation.

[0] http://www.chakoteya.net/NextGen/135.htm

Apparently a combination of Mie and Rayleigh scattering.

- https://en.wikipedia.org/wiki/Mie_scattering

- https://en.wikipedia.org/wiki/Rayleigh_scattering

They specify early to mid career. Imo they're anticipating a ton of applications and bounding it makes reviewing them tractable.

Is the pride not in solving the users' problems?

> nobody talks about it, treats it with interest, or pays above market rate to work on it.

Definitely needs a citation for this one. For so many products the user isn't paying for standout design. They're paying for insight, leverage, velocity, convenience, whatever. The market definitely supports this by paying above market salaries.

Good design can be a useful differentiator but it isn't the only way for a tool or product to "spark joy" and often _fancy_ design (not good design) is used as a crutch for a subpar product.

Then, if you're like me and read this years ago, play around with the Light Mode dropdown which was new to me. :)

EMA helps because it changes more slowly than the learning network which prevents rapid collapse by forcing the predictions to align to what a historical average would predict. This is a harder and more informative task because the model can't trivially output one value and have it match the EMA target so the model learns more useful representations.

EMA has a long history in deep learning (many GANs use it, TD-learning like DQN, many JEPA papers, etc.) so authors often omit defense of it due to over-familiarity or sometimes cargo culting. :)

The online, continuous and lossy version of this problem is more like how our memory works and still largely unsolved.

[0] https://arxiv.org/abs/2112.04035

[1] https://arxiv.org/abs/1803.07770

[2] https://www.nature.com/articles/s41586-018-0102-6

Cyclists have a bad rep in SF because many (not all) ride quite dangerously. It's a common sight to see cyclists running four-way stop signs and lights without even yielding. I live adjacent to a four-way stop and there's an incident where a cyclist fails to yield nearly hourly.

Meanwhile, Waymo has millions of incident-free miles and of all the self-driving car companies generally takes safety seriously, even if they will act to protect their interests here.

Until more evidence comes out I'll be taking Waymo's side here. I want safer vehicles and Waymo is currently the best bet.

Here's the python version of what I think you're looking for. Shouldn't be too difficult to port to rust.

- https://arxiv.org/abs/1909.07528 - https://arxiv.org/abs/2212.10403 - https://arxiv.org/abs/2201.11903 - https://arxiv.org/abs/2210.13382

There are also literally hundreds of articles and tweet threads about it. Moreover, as I said, you can test many of my claims above directly using readily available LLMs.

GP has a much harder defense. They have to prove that despite all of these capabilities that LLMs are not intelligent. That the mechanisms by which humans possess intelligence is fundamentally distinct from a computer’s ability to exhibit the same behaviors so much that it invalidates any claim that LLMs exhibit intelligence.

Intelligence: “the ability to acquire and apply knowledge and skills”. It is difficult to argue that modern LLMs cannot do this. At best we can quibble about the meaning of individual words like “acquire”, “apply”, “knowledge”, and “skills”. That’s a significant goal post shift from even a year ago.

Citation needed. Numerous actual citations have demonstrated hallmarks of intelligence for years. Tool use. Comprehension and generalization of grammars. World modeling with spatial reasoning through language. Many of these are readily testable in GPT. Many people have… and I dare say that LLMs reading comprehension, problem solving and reasoning skills do surpass that of many actual humans.

> They model intelligent behavior

It is not at all clear that modeling intelligent behavior is any different from intelligence. This is an open question. If you have an insight there I would love to read it.

> They don't know or care what language is: they learn whatever patterns are present in text, language or not.

This is identical to how children learn language prior to schooling. They listen and form connections based on the cooccurrence of words. They’re brains are working overtime to predict what sounds follow next. Before anyone says “not from text!” please don’t forget people who can’t see or hear. Before anyone says, “not only from language!” multimodal LLMs are here now too!

I’m not saying they’re perfect or even possess the same type of intelligence. Obviously the mechanisms are different. However far too many people in this debate are either unaware of their capabilities or hold on too strongly to human exceptionalism.

> There is this religious cult surrounding LLMs that bases all of its expectations of what an LLM can become on a personification of the LLM.

Anthropomorphizing LLMs is indeed an issue but is separate from a debate on their intelligence. I would argue there’s a very different religious cult very vocally proclaiming “that’s not really intelligence!” as these models sprint past goal posts.