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.
The heat and cold are created by the compressing or decompressing the CO2 (our any other gas). If one battery is heating while the other needs heat that would imply that one is charging while the other discharges, which is rarely useful in normal operation
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 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'.
Awesome! For me, the desire is very much about the place and not personalities or any kind of ego attached to their posts, so I've avoided any kind of functionality that will allow to follow a person, or see what else a particular person has posted, but we'll see how it evolves. If you have any programming experience, I do recommend just diving in rather than waiting for someone else to do it, as I have discovered that a lot of people seem to think that they share my vision but when it comes down to the details they have their own thing in mind. So if you want this Instagram with locations to exist, you might find that someone else's vision doesn't quite meet your desires for it.
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.
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.
> I think this is over-simplified and possibly misunderstood.
I'm with you here. I wrote this because I wanted to drive people towards the book. It's incredible and I did it little justice.
> "cortical activity produces spatial patterns which somehow 'compete' and the 'winner' is chosen which is then reinforced through a 'reward'"
A slight modification: spatio-temporal patterns*. Otherwise you're dead on.
> 'Compete', 'winner', and 'reward' are all left undefined in the article.
You're right. I left these undefined because I don't believe I have a firm understanding of how they work. Here's some speculation that might help clarify.
Compete - The field of minicolumns is an environment. A spatio-temporal pattern "survives" when a minicolumn is firing in that pattern. It's "fit" if it's able to effectively spread to other minicolumns. Eventually, as different firing patterns spread across the surface area of the neocortex, a border will form between two distinct firing patterns. They "Compete" insofar as each firing pattern tries to "convert" minicolumns to fire in their specific pattern instead of another.
Winner - This has two levels. First, an individual firing pattern could "win" the competition by spreading to a new minicolumn. Second, amalgamations of firing patterns, the overall firing pattern of a cortical column, could match reality better than others. This is a very hand-wavy answer, because I have no intuition for how this might happen. At a high level, the winning thought is likely the one that best matches perception. How this works seems like a bit of a paradox as these thoughts are perception. I suspect this is done through prediction. E.g. "If that person is my grandmother, she'll probably smile and call my name". Again, super hand-wavy, questions like this are why I posted this hoping to get in touch with people who have spent more time studying this.
Reward - I'm an interested amateur when it comes to ML, and folks have been great about pointing out areas that I should go deeper. I have only a basic understanding of how reward functions work. I imagine the minicolumns as small neural networks and alluded to "reward" in the same sense. I have no idea what that reward algorithm is or if NNs are even a good analogy. Again, I really recommend the book if you're interested in a deeper explanation of this.
> the theory is not new information and seems incredibly analogous to Hebbian learning which is a long-standing theory in neuroscience.
I disagree with you here. Hebbian learning is very much a component of this theory, but not the whole. The last two constraints were inspired by it and, in hindsight, I should have been more explicit about that. But, Hebbian learning describes a tendency to average, "cells that fire together wire together". Please feel free to push back here but, the concept of Darwin Machines fits the constraints of Hebbian learning while still offering a seemingly valid description of how creative thought might occur. Something that, if I'm not misunderstanding, is undoubtedly new information.
> I don't see any evidence that the brain will always produce several candidate activity patterns before judging a winner based on consensus.
I think if you read Chapters 1-4 (about 60 pages and with plenty of awesome diagrams) you'd have a sense for why Calvin believes this (whether you agree or not would be a fun conversation).
> 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.
I disagree here. A common mistake I think we to make is assuming evolution and natural selection are equivalent. Some examples of natural selection: A diversified portfolio, or a beach with large grains of sand due to some intricacy of the currents. Dawkinsian evolution is much much rarer. I can only think of three examples of architectures that have pulled it off. Genes, and their architecture, are one. Memes (imitated behavior) are another. Many animals imitate, but only one species has been able to build architecture to allow those behaviors to undergo an evolutionary process. Humans. And finally, if this theory is right, spatiotemporal patterns and the columnar architecture of the brain is the third.
Ignoring Darwin Machines, there are only two architectures that have led to an evolutionary process. Saying we could use "any architecture" seems a bit optimistic.
