nirvael

Kind of, Soundcloud has very aggressive copyright claims which is a problem for mixes.

I'm confused, surely there's a contradiction between "piracy was so good, we had this panacea of completely open, free, infinite music" and "streaming is bad even though it's the same infinite music library but we charge a small subscription". I get that artists aren't paid enough, but it's better than the $0 they get from piracy.

I actually had a similar idea that I cam across in my own research in brain-to-video applications. You could theoretically train the model in real-time by using brain data as a reward signal, such that the model would find the image or video that most strongly produces the desired response (e.g. an increase in certain brainwave bands associated with pleasure, or alertness, or fear).

I can imagine this has some very undesirable usecases if used in the wrong way.

Anyone have a link or more info on the "mind reading startup" being referred to?

Yeah you can email me (alex dot whelan at live dot co dot uk) or you can contact me through instagram (@aphynia)

I've been working on my own GAN for a while, trained on a few different datasets. Then the EEG data is fed into the GAN in real-time and used to interpolate within it's latent space. I assign each brainwave band (e.g. alpha, beta, etc.) to it's own point within that latent space, so the core idea is that as alpha increases (for example) the output heads towards that point in the space and that's reflected in the output image. Happy to talk more.

Using a Muse EEG headset to read brain activity and use that to drive the output of a GAN. Similar to other projects that try to visualise or decode thoughts, but at the moment it's an art project. Obviously quite limited by compute and hardware. I'm sort of looking for collaborators / co-founders / opportunities in the AI + neuroscience + creativity space.

Using a Muse EEG headset to read brain activity and use that to drive the output of a GAN. Similar to other projects that try to visualise or decode thoughts, but at the moment it's an art project. Obviosuly quite limited by compute and hardware. I'm sort of looking for collaborators / co-founders / opportunities in the AI + neuroscience + creativity space.

Yeah you're right I didn't think that through, why would you charge a battery while discharging another, just use the energy directly.

I'm sort of thinking out loud here but could you have two batteries running simultaneously but on opposite cycles, so while one is cooling the other is heating? Obviously it wouldn't be 100% efficient but it might reduce some wasted energy.

I was also confused by this. I think you're right, but in the original text they specifically mention a 'static background' that they remove, so it's not just a simple 'wrong way round' error, it's a fundamental misunderstanding of what's happening. Makes me wonder if the author actually knew what they were doing, or just using an LLM to vibe-code everything.

I considered it but the full OS route seemed cleaner, I wanted to use the Pi for it.

I have very recently been setting up HA and various Hue products and tbh I'm quite disappointed in both.

For one thing, the Hue bridge requiring an ethernet cable is mind-blowing to me. My router doesn't have an ethernet port, and yes it does say on their website, but I wouldn't have expected it to be a requirement, especially when it actually has wifi capabilities on-device, they're just disabled.

Setting up HA was a real pain, it's definitely not "Plug and play". I used a Pi 4 and got a bunch of weird errors, and debugging it is not for the faint-hearted. Eventually I did get it working by downgrading to a year-old version, but now it doesn't recognise my Tapo (TP-Link's brand) lights, the integration just doesn't work.

Overall, it's quite a hassle to get set up so I see why people don't bother unless they've got a lot of free time or are really into it, or just pay the premium for an all-Hue system that does actually work out of the box.

I have been thinking about a similar idea for a few months - a location-focussed social media. But my idea is more like Instagram with an extra location layer. You have a 'local' feed that shows public profiles of people in your area. You can then add those local people to some kind of 'friends' list - they can then see a more private profile, and you see their posts regardless of distance.

The key idea is that you can only add 'friends' if you've actually met them once in real life. So it wouldn't be overrun by celebrities and pseudo-social relationships, influencers, etc. I'm hoping it would foster more local connections - e.g. if someone often runs into a certain person at the same places and has similar interests, maybe they'll add each other as 'friends'.

Yesterday I was scraping NASA's SDO for images of the Sun, which I'll use to train a GAN to generate similar-looking images and video. This will be used in album artwork and audio-reactive videos for an EP that I recently finished.

Thanks for the considered reply.

I think this is over-simplified and possibly misunderstood. I haven't read the book this article references but if I am understanding the main proposal correctly then it can be summarised as "cortical activity produces spatial patterns which somehow 'compete' and the 'winner' is chosen which is then reinforced through a 'reward'".

'Compete', 'winner', and 'reward' are all left undefined in the article. Even given that, the theory is not new information and seems incredibly analogous to Hebbian learning which is a long-standing theory in neuroscience. Additionally, the metaphor of evolution within the brain does not seem apt. Essentially what is said is that given a sensory input, we will see patterns emerge that correspond to a behaviour deemed successful. Other brain patterns may arise but are ignored or not reinforced by a reward. This is almost tautological, and the 'evolutionary process' (input -> brain activity -> behaviour -> reward) lacks explanatory power. This is exactly what we would expect to see. If we observe a behaviour that has been reinforced in some way, it would obviously correlate with the brain producing a specific activity pattern. I don't see any evidence that the brain will always produce several candidate activity patterns before judging a winner based on consensus. The tangent of cortical columns ignores key deep brain structures and is also almost irrelevant, the brain could use the proposed 'evolutionary' process with any architecture.

This should be its own submission, I hadn't heard of it and its interesting.

Longmont Potion Castle vibes

prompt: "domain name for site that sells tractors"

result: "nomadventurequest.com"

...at least we know that nomadventurequest.com is available I suppose, saves me less than one minute to look that up.

Gotcha. To go back to the article, it compares Tesla vs Toyota's market cap (558 vs 329) and then their enterprise value (553 vs 466) and concludes that if we look at EV then Toyota has almost caught up. What this reveals is that the market has decided that Toyota is in a good position ("valuable") because they have a market cap reasonably close to Tesla despite a lot more debt, so there is inherently some extra value in Toyota (according to the market) not captured by just looking at the diff between Tesla and Toyota market cap. It also reveals that Toyota believes they're undervalued which is why they're using debt financing instead of shares, whereas Tesla thinks the opposite.

Ok I understand what you're saying, but this is still treating debt as a negative despite the fact it has a positive influence on the enterprise value.

Imagine this example. I'm CEO of Company2 and I take out a 1B loan. Enterprise value is still 2B, because 1B debt is cancelled by the 1B I now have in cash. I then waste all the cash on whatever. My company's enterprise value is now 3B, because the debt has increased it without being cancelled by the cash.

I think the confusion lies in the word "value". To use your example of Company, imagine another company (Company2) exactly the same as Company but without the debt. Enterprise value of Company is 3B, enterprise value of Company2 is 1B. But Company2 would clearly be preferential to acquire, because it has no debt, despite the much lower enterprise value. So enterprise "value" is not a useful measure of "value". Am I misunderstanding something here?

You make a good point. It's possible it's not useful to them and that's why they can't generate enough revenue from it. I assume the sales pitch to pharma is that it's a wide database, rather than deep.

These are different scenarios, clinical trials are experiments not just genetic information gathering.

That's the conflict at the heart of the business. But do the public care enough about that? Obviously a HN audience does, but people use Google and Facebook products every day with the awareness that all that data is sold directly to advertisers. With the right messaging ("yes, we sell your data, but it's to drug companies to help make drugs that can cure your illnesses") it's possible that the conflict isnt too much of an issue.

Seems like 23andMe is two businesses: consumer and a B2B data business.

The consumer side is clearly struggling because of the problems mentioned in the article (they only need one test in their life, public perception is bad because their security has had breaches). So this needs a pivot where you can change the public's perception from a one-time test to continuous health monitoring through blood markers or something similar, expand to tests other than genetic and make it a repeatable, accurate test that gives you more information (and obviously stop leaking people's data).

But why not focus on the B2B side? Sell access to their databases. I'm sure computational biology and/or pharma companies need this information. It makes sense to do vertical integration by manufacturing your own drugs, but not for a cash-strapped business that has little incoming revenue to sustain further development. Presumably they are selling their genetic data, but I don't get why it's not giving them a revenue stream. Let GSK manufacture the drugs using your genetic info, with a profit share for any drugs made this way. They mention a collaboration with GSK in the article, but why was this stopped?

Found the reference (see section 2): https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla...

This is actually incorrect, there's not that much data left to train on. I remember reading an article about it, might have been one of Gwern's or something about Chinchilla scaling, but to produce an order of magnitude increase we need an order of magnitude more data and there just isn't that amount available.