> the president's name will start with a letter in the first half of the alphabet

Sorry, this triggered my fact-checkitis. I opened Wikipedia's list of presidents of the US and ran this in the web developer console:

    Array.from(document.querySelectorAll(
        '#mwgA tr > :nth-child(3) span[data-sort-value]'
    ))
    .map(x => x.getAttribute('data-sort-value')[0])
    .reduce(({total, firstHalf}, c) => ({
        total: total+1, 
        firstHalf: c < 'N' ? firstHalf+1 : firstHalf
    }), {total: 0, firstHalf: 0});

and it responded

    Object { total: 47, firstHalf: 31 }

Then I used Agresti and Coull's method for a 95 % confidence interval around this (add two successes and two failures, compute Wald interval as usual). That gives us

    65 % ± 13 %

In other words, the 95 % confidence interval of the proportion lies in the span from 52 % to 78 %. That is significantly different from the null hypothesis.

I didn't expect it, but we might actually have reason to believe US presidents more often than not do have names starting with letters from the first half of the alphabet.

Edit: But I just realised English letter frequencies have 54 % of the probability mass in the first half of the alphabet, so that's probably a better null hypothesis than the even split. It's also compatible with the actual presidential statistics.

I don't really know what it means. It seems to sort of entangle Google Search as a product with a lot of constituent parts and "search" as a specific operation over Google's index of sites. Talking about the product, ML has been a core part of Google's search product for a really long time. AI summaries in Google Search distill information from sites discovered from a "normal" Google search. The Search product, which now incorporates AI, and advertising enabled by it, are still far and away Google's biggest revenue drivers.

If we literally mean AI as in people paying to use/deploy AI via Google, that's still a small part of their revenue, far less than advertising revenue. If we mean AI as in "every Google product that uses ML", then I guess that's been their core product for a long time.

In 2016 Google was riding high on DeepMind with Alpha Go, etc. I don't think that it was a big stretch to say they would dominate AI in 10 years. It does if you define the meaning of AI though (LLMs, ML, DRL, etc)

I would say this statement is incorrect, and if I had to guess, it will be for the next 5 years. Google’s main product is ads, followed by search, gmail, and Google Docs suite, and Gemini is not on track to dethrone either any time soon.

Was Google's main product search in 2016? That doesn't ring true. Google's main product today is not search. Neither is it AI. So I guess 50% is a fair characterization. But that was the floor for that prediction in 2016. So... meh.

It’s still search. They had an AI based search for quite some time. Now it spits out a summary.

Nah. way before that page/brin wanted to create ai (agi) long before that when google was started or in its infancy, but it seemed completely unbelievable (the kind of stuff elon musk would say)

I can't find the reference to it, but I remember them saying it in wired or similar.

You don't know anyone who bought storage from google for google photos or drive?

LLMs obviously qualify as an example of the bitter lesson. The point I was replying to was using Stockfish as an example of how the bitter lesson was wrong, and I was pointing out that Stockfish-like systems are the core example used in the actual essay

That's sort of a loaded question depending on what you mean by "smaller sub expert models inside".

At the most abstract level, you can observe patterns in a model's activations and theorize that particular collections of components contribute to particular kinds of operations. There are many flavors of this intuition. In that sense, you can think of there being "submodels" within a model, insofar as different parts of the network can participate in specialized computations. Some reading that might interest you in this area would include things like mechanistic interpretability.

There are also Mixture of Expert models, which are a dominant architecture for LLMs now, in which you essentially have a bunch of smaller FFNs that get routed over/aggregated together when the LLM performs a forward pass. It would be a mistake to treat any single expert as a better task specific model though. They are trained as part of an ensemble, and they're almost always being combined with other experts in the forward pass, so they're more like (to use a loose analogy) regions of the brain that tend to correspond more towards particular actions than others. You couldn't just extract one and say "This is now our Chess model that performs as well as the larger LLM we took it from". You could potentially use the relevant experts as a basis for training a smaller task-specific model, and there is work around expert pruning and other distillation techniques, but it's not as simple as "This smaller inner model is a complete task-specific model as is". And more importantly, that ensemble of experts *is* the LLM you're comparing it to, at least in major part. It doesn't really make sense to treat the LLM as some foreign model that calls the expert. The experts themselves don't often correlate to easily interpretable task domains, as they've been learned via joint computation with the other experts and broader LLM.

Then there's tool calling. Today LLMs are trained to make tool calls (basically execute functions) for particular tasks. Those tools can be smaller specialized models, or deterministic code, or anything really that can be fired from a host environment.

Explain what you mean by not being able to get to Mars?

What’s the value of predictions if they’re not good enough to make decisions on?

Augmented reality a 'big' enabler for AI? Sry what?

Just because some people thought that VR or AR are the big thing, doesn't mean it has any tangable relevance.

I was wearing glasses and got lasic, for me the whole AR/VR thingy was quite weird from the get go. Oh you want people constantly wearing something on their face for what benefit?

I even own a HTC Vive Pro and especially when you realize what it actually means to have Virtual Reality without the benefits of a real holodeck, its not what the movies and everyone depicted.

Every book like Neuromancer was always cool because you imagined (and it was depicted like this) like a place to be. It ignored everything inbetween. He drove a motorcycle and he was able to feel it. In a holodeck you feel things too.

I also don't get your Jessica Alba reference tbh?

I just realized that its not social reasons but taste or preference.

I think personal taste/preference not having to wear something, not having to handle it day to day (charging, configuraing etc.) is the stronger thing than social reasons.

I don't like wearing glasses. I should wear my glasses more often, but I don't. But that isn't going to stop me from buying the Even Reality G2