☆ Yσɠƚԋσʂ ☆
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Ground effect vehicles are basically airplanes that are forced to fly really, really low. They take off from water and cruise just a few meters above the surface. At that altitude, the air gets compressed between the wing and the ground or water, which creates a huge cushion of extra lift. This lets the vehicle carry way more weight than a normal plane of the same size and power, making it incredibly efficient for hauling cargo over water. The trick is that it only works over flat surfaces like oceans or lakes, and the piloting can be tricky because you’re skimming the waves at high speed without actually being able to climb to a higher altitude. It’s a neat piece of engineering that trades operational flexibility for raw lifting power.
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Yes, the bed and the environment in general is part of the world model. What I mean is that’s part of object identification and recognition of what objects to use for what task, etc. It’s a separate concern from dexterity. Think of it this way. If you’re thirsty, and you pick up a cup. You’re consciously thinking about moving your hand to grab the cup and bring it to your mouth. That’s what the world model is concerned with. You’re not aware of every individual muscle movement and all the micro adjustments that need to happen in order for the task to be completed. And that’s what the running illustrates. It’s the dexterity of the system in dealing with feedback from the world and making these adjustments in response.
You absolutely do have the impact of random events when you’re doing anything in the physical world. You have wind, uneven ground, variations in weight distribution, and so on. That’s what makes this sort of stuff so difficult in practice. All the tiny little errors quickly add up, so you can’t just match expected input. You have to have a dynamic system that can adjust on the fly to the sensory data. Dealing with stuff like an uneven bed or a tilted surface is a completely separate problem of having a good enough world model internally.
kind of yeah, incidentally I experimented with a similar idea in a more restricted domain and it works pretty well https://lemmy.ml/post/41786590
Basically, the idea is to use a symbolic logic engine within a dynamic context created by the LLM. Traditionally, the problem with symbolic AI has been with creating the ontologies. You obviously can’t have a comprehensive ontology of the world because it’s inherently context dependent, and you have an infinite number of ways you can contextualize things. What neurosymbolics does is use LLMs for what they are good at, which is classifying noisy data from the outside world, and building a dynamic context. Once that’s done, it’s perfectly possible to use a logic engine to solve problems within that context.
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Binary quantization and 1 bit vectors have definitely been floating around the space for years. The big difference here is not necessarily just better raw precision but how they completely eliminate the hidden memory tax that usually comes with extreme compression. Normally when you crush a 32 bit float down to a single bit you destroy a massive amount of scale and range information. To make the model actually usable after that traditional methods usually have to store extra full precision numbers alongside those compressed blocks to act as scaling factors or zero points. So your theoretical 1 bit compression actually ends up costing something like 2 or 3 bits per parameter in practice.
TurboQuant gets around this by using the Quantized Johnson Lindenstrauss transform which is basically a mathematical guarantee that the relative distances between different data points will be preserved even when the data is aggressively shrunk. By doing this and dropping everything to just a positive or negative sign bit they completely remove the need to store any full precision scaling factors. It literally has zero memory overhead. To make sure the attention mechanism still works they use a special estimator that takes a high precision query and runs it against that low precision 1 bit cache in a way that mathematically eliminates bias.
You also have to look at how they are actually applying it in the pipeline. They don’t just take the raw 32 bit vector and smash it down to 1 bit right out of the gate. They use that PolarQuant method first to map everything to polar coordinates and capture the main structure and strength of the vector. The 1 bit QJL algorithm is only deployed at the very end as a targeted cleanup to fix residual errors left over from the first step.
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huh?
People in China enjoy genuine human rights, like right to housing, education, and healthcare. 90% of families in the country own their home giving China one of the highest home ownership rates in the world. What’s more is that 80% of these homes are owned outright, without mortgages or any other leans. https://www.forbes.com/sites/wadeshepard/2016/03/30/how-people-in-china-afford-their-outrageously-expensive-homes
The real (inflation-adjusted) incomes of the poorest half of the Chinese population increased by more than four hundred percent from 1978 to 2015, while real incomes of the poorest half of the US population actually declined during the same time period. https://www.nber.org/system/files/working_papers/w23119/w23119.pdf
From 1978 to 2000, the number of people in China living on under $1/day fell by 300 million, reversing a global trend of rising poverty that had lasted half a century (i.e. if China were excluded, the world’s total poverty population would have risen) https://www.semanticscholar.org/paper/China’s-Economic-Growth-and-Poverty-Reduction-Angang-Linlin/c883fc7496aa1b920b05dc2546b880f54b9c77a4
In fact, people in China enjoy high levels of social mobility in general https://www.nytimes.com/interactive/2018/11/18/world/asia/china-social-mobility.html
Student debt in China is virtually non-existent because education is not run for profit. https://www.forbes.com/sites/jlim/2016/08/29/why-china-doesnt-have-a-student-debt-problem/
China massively invests in public infrastructure. They used more concrete in 3 years than US in all of 20th century https://www.forbes.com/sites/niallmccarthy/2014/12/05/china-used-more-concrete-in-3-years-than-the-u-s-used-in-the-entire-20th-century-infographic/
China also built 27,000km of high speed rail in a decade https://www.railjournal.com/passenger/high-speed/ten-years-27000km-china-celebrates-a-decade-of-high-speed/
All these things translate into tangible freedoms allowing people to live their lives to the fullest. Freedom can be seen as the measure of personal agency an individual enjoys within the framework of society. A good measure of whether people genuinely feel free is to look at what people of the country have to say on the subject. Even as mainstream western media openly admits, people in China overwhelmingly see their system as being democratic, and the government enjoys broad public trust and support.
- https://www.newsweek.com/most-china-call-their-nation-democracy-most-us-say-america-isnt-1711176
- https://www.csmonitor.com/World/Asia-Pacific/2021/0218/Vilified-abroad-popular-at-home-China-s-Communist-Party-at-100
- https://www.bloomberg.com/opinion/articles/2020-06-26/which-nations-are-democracies-some-citizens-might-disagree
- https://web.archive.org/web/20230511041927/https://6389062.fs1.hubspotusercontent-na1.net/hubfs/6389062/Canva images/Democracy Perception Index 2023.pdf
- https://www.tbsnews.net/world/china-more-democratic-america-say-people-98686
- https://web.archive.org/web/20201229132410/https://en.news-front.info/2020/06/27/studies-have-shown-that-china-is-more-democratic-than-the-united-states-russia-is-nearby-and-ukraine-is-at-the-bottom/
That’s part of the idea with the whole mixture of experts (MoE) approach in newer models actually.
Rather than using a single neural net that’s say 512 wide, you split it into eight channels/experts of 64. If the neural net can pick the correct channel for each inference, then you only have to run 1/8th of the neurons on every forward pass. Of course, once you have your 8 channels/experts in parallel, you now need to decide which expert/channel to use for each token you want to process. This is called a router which takes in an input and decides which expert/channel to send it to. The router itself is a tiny neural network. It is a matrix that converts the input vectors to a router choice. And the router itself has a small set of trainable weights that gets trained together with the MoE.
The trick they use is pretty clever. When you ask an AI to write code, it doesn’t always get it right. Sometimes the code has bugs, sometimes it misunderstands the problem entirely. A naive way to address that is to generate a few solutions and test each one. The odds that at least one works go way up. ATLAS generates multiple attempts, running each through a test suite. Each retry also gets told what went wrong with the previous attempt, so it can try to avoid the same mistake.
But this can be pretty slow since you have to run the code in an isolated environment, check the outputs, wait for it to finish. Doing that for every candidate quickly adds up. So ATLAS has another shortcut for avoiding unnecessary testing. Instead of simply generating solutions and testing all of them, it tries to predict which one is most likely correct before running any tests.
ATLAS also asks the model for an embedding of what it just wrote which acts as a fingerprint. Two similar pieces of code will produce similar fingerprints. A well-written, confident solution will produce a different fingerprint than a confused, buggy one.
These fingerprints get fed into a separate, much smaller neural network called the Cost Field. This little network was trained ahead of time on examples where they already knew which solutions were correct and which were wrong. It learned to assign a score to each fingerprint. Correct solutions get a low score and incorrect ones get a high one.
So the process is to generate multiple solutions, get their fingerprints, score each one, and pick the lowest. Only that one gets tested. The Cost Field picks correctly about 88% of the time according to the repo.
Completely agree, and now it’s just hip to say how much you hate AI. This kind of performative action doesn’t really accomplish anything, but it lets people feel good about themselves and gain social acceptance. Actually building an alternative takes work. The whole Linux analogy is very apt here because we’ve always had alternatives to corporate offerings, but most people don’t want to invest the time into learning how to use them.
I’d argue it’s inevitable for the simple reason that the whole AI as a service business model is a catch 22. Current frontier models aren’t profitable, and all the current service providers live off VC funding. And if models become cheap enough to be profitable, then they’re cheap enough to run locally too. And there’s little reason to expect that models aren’t going to continue being optimized going forward, so we are going to hit an inflection point where local becomes the dominant paradigm.
We’ve seen the pendulum swing between mainframe and personal computer many times before. I expect this will be no different.
I absolutely hate the fact that every chat app now has a proprietary protocol and you have to install a bunch of apps to talk to different people. It’s such a travesty.