dentalperson

It would be mostly quiet (remember that humans only hear up to ~20 kHz).

Sure, this is a joke today, but if we continue down our current path, we would probably hit ultrasonic rates in the not too distant future.

The video was fun and insightful to watch. Big fan of sonification of computer processes. We can hear such a large and important range of frequencies (more than the 'audible range' because we hear impulses in the subsonic range as events) and it works as a nice complementary in real time for an experience that charts can't convey.

What is the training data and have you thought about how to build a causal model that would be robust to interventions (e.g. by changing or adding a gene)?

Yes, they all have significant 'ghosting' artifacts where the harmonics are a bit fuzzy if you listen closely. AFAIK all of the recent neural speech engines have this, from SoundStream to EnCodec, especially in low latency causal setups. Wavenet was a bit better in that regard but has fallen out of style due to complexity and the lack of a bottleneck. It seems like something diffusion post processing would be able to clean up.

I genuinely want to understand this mindset. Are you only against things that help extend lifespan a very large amount, or is it more nuanced?

80 years was only achieved recently because of various quality of life and medical advances. Do you feel that those are 'ok' for other reasons, or for example, that things that cure cancer, HIV, or better sanitation that increase lifespan should also be repealed if it was up to you? Or do you feel we just happen to have the perfect amount of life-extending conditions at this exact moment?

I love the math/stats history around gambling-related things, thanks for mentioning this. This method assumes the flipper can't introduce bias. ET Jaynes in his book Probability Theory also mentions that it is easy to learn to flip a fair coin in such a way that the result can be predetermined. I searched a tiny bit for this but couldn't find what he was referring to though.

Definitely bikeshedding, but it also kind of looks like the TensorFlow logo and for a second I thought there was a group breaking the project free from Google.

Thanks for the recommendations, I will check them out.

I appreciate the point about seriousness too. One part of it that I liked kind of feels like travel writing from the last century (e.g. John Muir) that goes in and out of various subjects with respect to the main one. On the other hand, the author shows their 'seriousness' and culture by being able to reference and talk about the subject in the context of many other subjects. If I try to do this it probably feels pretentious, so I admire the skill of being able to pull it off.

The essay was fun to read and had excellent balance - the author talked about distractions semi-ironically with fine control of its focused structure and pacing while keeping a light and carefree tone that was nonetheless academic. I'd like to learn to write closer to this style. Is regular writing and reading more of these literary journals the way to go about it?

The AMC series Halt And Catch Fire had the line: “Computers aren’t the thing. They’re the thing that gets us to the thing.”

There is also 10+ years research for tinnitus using Vagus Nerve Stimulation to stop it [1]. Results seem mixed. Many other drugs/neuro-stimuli for T have gotten mixed results and passed clinical trials such as Leniere only to flop in actual usage. It's interesting that the community anecdotal evidence such as r/tinnitus and TinnitusTalk are so useful for these cases because they are more likely to report negative results at the post-approval stage.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951891/

Thanks for reminding me about this. MacsBug was great. Besides being a useful development tool, MacsBug was used extensively to break shareware as well by the mac warez community. I remember seeing some guides about it that were very illuminating about how program execution and object code actually worked that made things click for me.

It was also a time when any consumer could issue a system interrupt with the same two keystrokes because it was built into mac OS, and see the cpu registers and enter hex to change the program flow. Since there was not as clean a system/program barrier as there was today and single threaded OS (I think?), before OS 8 the system froze all the time because of apps, and even non-expert users memorized the hex code for the system exit() trap.

Where did you get that 3 per 100k from? In each of the last 2 years there were 55 homicides in SF from what I can tell. Maybe you're thinking of New York, which is half of SF's rate?

https://sfist.com/2023/01/03/sf-sees-exact-same-number-of-ho... https://en.wikipedia.org/wiki/List_of_United_States_cities_b...

Any merit to this service? It claims without 'hidden fees' so I thought they would get rid of the airbnb-like fees, but it looks like there is still cleaning fees, platform fees, and taxes not included in the nightly rate (for +50% final cost over the nightly).

The install (npx dalai serve) fails silently for me. With --verbose it says `npm info run node-pty@0.10.1 install { code: 1, signal: null }`. Ubuntu 22.04.

How do they get ChatGPT not to hallucinate stuff about the articles? Everything seems fairly accurate, which is not my experience with ChatGPT when talking about technical things. Is it heavily curated/edited by humans? I noticed that the text often comes out verbatim from the articles, perhaps this indicates a clever prompt that keeps things closer to the truth by requiring verbatim output.

Ah, that makes more sense, thank you. Since this was mentioned in the tokenizer section and the number of unique tokens wasn't mentioned I misunderstood.

How does token->embedding lookup work with 1.4T BPE tokens? Since there are more tokens than the 65B parameters it must be doing some sort of interesting thing based on the merge operations. Is it different from what other GPT models with ~100k tokens are doing?

At inference, how many of those tokens are used? (they mention most tokens are used only once during training, so the must be very long sequences.)

The options grant appraisal for startup employees seems very optimistic - 70% it's worth nothing. 25% payout is equivalent to all the backpay you would have gotten if you had worked at a big tech co. instead. 4% life changing money but not the richest person you know. 1% you are the richest person you know.

Was this the generally the way people thought about it back then? I remember seeing fairly pessimistic valuations around 2013 though, and I see it more like 10-15% chance of big tech salary equivalent, and 1-2% for life changing.

I've seen this use of constants inside big O notation in leetcode and 'informal' discussions. Is it pedantic to say that O(NM) == O(N) if M is a constant (in this case since it's bound by 26)? Or is this the current and expected usage?

Can you answer this question: In the case of business failure, will customers lose their existing 20 years they paid for and have to pay the new management company all over again?

>What happens to all of Anja's clients if the business exits?

>All biological goods stored with Anja will stay in our New Jersey lab regardless

>of what happens to Anja as a business. Client relationship management and

>connection will be securely transferred to a responsible, experienced party if

>necessary.

Another comment asked this and other questions but this one was not answered other than 'there is insurance'.

How does this work? Will customers lose their existing 20 years they paid for and have to pay the new party all over again? How is Anja incentivized to find a good party to transfer to at the time of business failure? How is the funds for the lab guaranteed after Anja goes out of business?

Cryonics organizations have solved this problem with a non-profit structure, but I'm really curious how it is handled in this for-profit case.

These points and the Guzey essay were discussed in depth in Science Fictions by Stuart Ritchie, which features examples of negligence and fraud in science. Having read and enjoyed the message in Why We Sleep, my first reaction before reading based on the length of the essay was that the motivation was axe-grindy, but the further I read the more it made sense and the more egregious Walker's claims seemed. The lack of a response from Walker is another serious concern (especially after Andrew Gelman highlights this). It seems that he is trying to avoid a Streisand Effect by ignoring it completely. If that is the case, Walker's strategy seems to be working because on the comments on new threads like this, many mention mention Walker but seem unaware of the criticisms.

Yes, once you have felt and identified a chanterelle with success a few times, it's very easy for an experienced forager to tell the difference between gills and folds (especially in the larger species such as C. californicus). The folds are often veiny and cannot be moved or 'plucked' like gills. In general though, identification should be done using as many factors as possible (e.g. using a identification key which is like a decision tree). For example, besides gills and smell, another way to differentiate the (non- or only mildly-toxic) false chanterelle is the firmness.

> The notion that somehow aging is “special” and therefore shouldn’t be regulated like “normal” diseases is not convincing. The same argument could be made about any number of other conditions. For example cancer – for years we’ve been told it’s not one disease but hundreds of diseases, and therefore we need to think about it differently. And yet, cancer medications including personalized therapies such as CAR-T seem perfectly capable of getting approved within the existing framework of the FDA.

This seems to miss the main point about regulation that the longevity researchers face. Aging is not recognized as a disease by the FDA, so aging treatments are not allowed to have the same treatment and development pipeline. To 'be regulated like “normal” diseases' is in fact what many longevity companies would benefit from.

I mostly remember this because of the brief appearance in "Halt and Catch Fire", which captured the fascination of early webcams in a few seconds.

I made up these phrases to give a simple explanation instead of causal inference terms, and they are probably not good terms, so sorry for the confusion. If I describe it in more standard vocabulary, 'at any of those instances' refer to doing interventions (such as randomized controlled trials).

'causal path' refers to a path in a directed acyclic graph that models the causality. https://cran.r-project.org/web/packages/ggdag/vignettes/intr... has some examples.

The reason it is causally related is that if you were to not push the pedal at any of those instances, the car would move slower than if you were to push it faster.

There's also a causal effect from biking to showering, although there are a few other things in the causal path such as getting sweaty. If you walked home instead of biking, you might avoid the shower. So biking does cause you take a shower.

A buyback does not trigger a taxable event, unlike a dividend. So the tax deferred value of buybacks is compounded over the years, which can be significant.

Yes, I'm surprised more HN folk don't call out #1. To be clear, the author is purporting that lower performance on an IQ test when doing another task means multitasking implies that a lower IQ. This seems crazy to me, but maybe I've been multitasking too much.

I have a SpiffChorder [1] built by Greg Priest-Dorman. It has 7 keys (three for thumb). I can write at about 40-50 wps and code in emacs reasonably, but it is slower, so I only use it when I am eating something or have one hand busy. Greg mentioned having two keyboards in each hand should be faster than a single traditional keyboard, but I never tried that.

1. https://chorder.cs.vassar.edu/doku.php?id=spiffchorder:forsi...