hedgehog

I had never heard of this show before today but what you just described makes it sound cool as fuck, I’m gonna check it out now

You should try watching the live action series next - I bet you’d love it.

The one I grabbed to test was the ROG Azoth.

I also checked my Iris and Moonlander - both cap out at 6, but I believe I can update that to be higher with QMK or add a config key via Oryx on the Moonlander to turn it on.

Per this thread from 2009, the limit was conditional upon using a particular keyboard descriptor documented elsewhere in the spec, but keyboards are not required to use that descriptor.

I tested just now on one of my mechanical keyboards, on MacOS, connected via USB C, using the Online Key Rollover Test, and was able to get 44 keys registered at the same time.

If you want to generate audiobooks using your own / a hosted TTS server, check out one of these options:

• OpenReader-WebUI - this has built-in read along capability and can be deployed as a PWA that can allow you to download the audiobooks to your phone so you can use them offline
• p0n1/epub to audiobook
• ebook2audiobook

If you don’t have a decent GPU, Kokoro is a great option as it’s fast enough to run on CPU and still sounds very good.

If you’re going to use Kokoro, Audiblez (posted by another commenter) looks like it makes that more of an all-in-one option.

If you want something that you can use without an upfront building of the audiobook, of the above options, only OpenReader-WebUI supports that. RealtimeTTS is a library that handles that, but I don’t know if there are already any apps out there that integrate it.

If you have the audiobook generation handled and just want to be able to follow along with text / switch between text and audio, check out https://storyteller-platform.gitlab.io/storyteller/

I’ve been meaning to look into Audiobook Shelf! Do you happen to know if it can be integrated with a TTS provider to narrate your ebooks, or does it only support listening to audiobooks you’ve uploaded?

From the Slashdot comments, by Rei:

Or, you can, you know, not fall for clickbait. This is one of those...

Ultimately, we found that the common understanding of AI’s energy consumption is full of holes.

"Everyone Else Is Wrong And I Am Right" articles, which starts out with....

The latest reports show that 4.4% of all the energy in the US now goes toward data centers.

without bothering to mention that AI is only a small percentage of data centre power consumption (Bitcoin alone is an order of magnitude higher), and....

In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023.

What a retcon. AI was *nothing* until the early 2020s. Yet datacentre power consumption did start skyrocketing in 2017 - having nothing whatsoever to do with AI. Bitcoin was the big driver.

At that point, AI alone could consume as much electricity annually as 22% of all US households.

Let's convert this from meaningless hype numbers to actual numbers. First off, notice the fast one they just pulled - global AI usage to just the US, and just households. US households use about 1500 TWh of the world's 24400 TWh/yr, or about 6%. 22% of 6% is ~1,3% of electricity (330 TWh/yr). Electricity is about 20% of global energy, so in this scenario AI would be 0,3% of global energy. We're just taking at face value their extreme numbers for now (predicting an order of magnitude growth from today's AI consumption), and ignoring that even a single AI application alone could entirely offset the emissions of all AI combined. Let's look first at the premises behind what they're arguing for this 0,3% of global energy usage (oh, I'm sorry, let's revert to scary numbers: "22% OF US HOUSEHOLDS!"):

• It's almost all inference, so that simplifies everything to usage growth
• But usage growth is offset by the fact that AI efficiency is simultaneously improving at faster than Moore's Law on three separate axes, which are multiplicative with each other (hardware, inference, and models). You can get what used to take insanely expensive, server-and-power-hungry GPT-4 performance (1,5T parameters) on a model small enough to run on a cell phone that, run on efficient modern servers, finishes its output in a flash. So you have to assume not just one order of magnitude of inference growth (due to more people using AI), but many orders of magnitude of inference growth.   * You can try to Jevon at least part of that away by assuming that people will always want the latest, greatest, most powerful models for their tasks, rather than putting the efficiency gains toward lower costs. But will they? I mean, to some extent, sure. LRMs deal with a lot more tokens than non-LRMs, AI video is just starting to take off, etc. But at the same time, for example, today LRMs work in token space, but in the future they'll probably just work in latent space, which is vastly more efficient. To be clear, I'm sure Jevon will eat a lot of the gains - but all of them? I'm not so sure about that.   * You need the hardware to actually consume this power. They're predicting by - three years from now - to have an order of magnitude more hardware out there than all the AI servers combined to this point. Is the production capacity for that huge level of increase in AI silicon actually in the works? I don't see it.

There’s a difference between a tool being available to you and a tool being misused by your students.

That said, I wouldn’t trust AI assessments of students to determine if they’re on track right now, either. Whatever means the AI would use needs to be better than grading quizzes, homework, etc., and while I’m not a teacher, I would be very surprised if it were better than any halfway competent teacher’s assessments (thinking in terms of high school and younger, at least - in university IME the expectation is that you self assess during the term and it’s up to you to seek out learning opportunities outside class if you need them, like going to office hours for your prof or TA).

AI isn’t useless, though! It’s just being used wrong. For example, AI can improve OCR, making it more feasible for students to hand in submissions that can be automatically graded, or to improve accessibility for graders. But for that to actually be helpful we need better options on the hardware front and for better integration of those options into grading systems, like affordable batch scanners that you can just drop a stack of 50 assignments into, each a variable number of pages, with software that will automatically sort out the results by assignment and submitter, and automatically organize them into the same place that you put all the digital submissions.

What would moving your account here from Reddit entail? What would transfer with you? Your posts? Comments? Followed and moderated subreddits? Upvotes and downvotes?

They also had much higher distribution costs and a much smaller audience back then, so even if prices have gone down since the late 70s, profits haven’t.

Pac-Man was the best selling Atari 2600 game and it sold 8 million copies. Mario Kart on the Switch, by contrast, has sold over 60 million copies. A mere 1% of PC game sales are physical and a mere 16% of console game sales are physical.

Though... If a computer has a real biological brain in it doing the thinking, is it artificial intelligence?

The person who came up with the Chinese Room Argument argued that if a brain was completely synthetic, even if it were a perfect simulation of a real brain, it would not think - it would not have a genuine understanding of anything, only a simulation of an understanding. I don’t agree (though I would still say it’s “artificial”), but I’ll let you draw your own conclusions.

From section 4.3:

Consider a computer that operates in quite a different manner than an AI program with scripts and operations on sentence-like strings of symbols. The Brain Simulator reply asks us to suppose instead the program parallels the actual sequence of nerve firings that occur in the brain of a native Chinese language speaker when that person understands Chinese – every nerve, every firing. Since the computer then works the very same way as the brain of a native Chinese speaker, processing information in just the same way, it will understand Chinese. Paul and Patricia Churchland have set out a reply along these lines, discussed below. 

In response to this, Searle argues that it makes no difference. He suggests a variation on the brain simulator scenario: suppose that in the room the man has a huge set of valves and water pipes, in the same arrangement as the neurons in a native Chinese speaker’s brain. The program now tells the man which valves to open in response to input. Searle claims that it is obvious that there would be no understanding of Chinese. (Note however that the basis for this claim is no longer simply that Searle himself wouldn’t understand Chinese – it seems clear that now he is just facilitating the causal operation of the system and so we rely on our Leibnizian intuition that water-works don’t understand (see also Maudlin 1989).) Searle concludes that a simulation of brain activity is not the real thing. 

However, following Pylyshyn 1980, Cole and Foelber 1984, and Chalmers 1996, we might wonder about gradually transitioning cyborg systems. Pylyshyn writes:

If more and more of the cells in your brain were to be replaced by integrated circuit chips, programmed in such a way as to keep the input-output function each unit identical to that of the unit being replaced, you would in all likelihood just keep right on speaking exactly as you are doing now except that you would eventually stop meaning anything by it. What we outside observers might take to be words would become for you just certain noises that circuits caused you to make.

These cyborgization thought experiments can be linked to the Chinese Room. Suppose Otto has a neural disease that causes one of the neurons in his brain to fail, but surgeons install a tiny remotely controlled artificial neuron, a synron, alongside his disabled neuron. The control of Otto’s artificial neuron is by John Searle in the Chinese Room, unbeknownst to both Searle and Otto. Tiny wires connect the artificial neuron to the synapses on the cell-body of his disabled neuron. When his artificial neuron is stimulated by neurons that synapse on his disabled neuron, a light goes on in the Chinese Room. Searle then manipulates some valves and switches in accord with a program. That, via the radio link, causes Otto’s artificial neuron to release neuro-transmitters from its tiny artificial vesicles. If Searle’s programmed activity causes Otto’s artificial neuron to behave just as his disabled natural neuron once did, the behavior of the rest of his nervous system will be unchanged. Alas, Otto’s disease progresses; more neurons are replaced by synrons controlled by Searle. Ex hypothesi the rest of the world will not notice the difference; will Otto? If so, when? And why?

Under the rubric “The Combination Reply”, Searle also considers a system with the features of all three of the preceding: a robot with a digital brain simulating computer in its aluminum cranium, such that the system as a whole behaves indistinguishably from a human. Since the normal input to the brain is from sense organs, it is natural to suppose that most advocates of the Brain Simulator Reply have in mind such a combination of brain simulation, Robot, and Systems or Virtual Mind Reply. Some (e.g. Rey 1986) argue it is reasonable to attribute intentionality to such a system as a whole. Searle agrees that it would indeed be reasonable to attribute understanding to such an android system – but only as long as you don’t know how it works. As soon as you know the truth – it is a computer, uncomprehendingly manipulating symbols on the basis of syntax, not meaning – you would cease to attribute intentionality to it.