Animals or humans don't cause any net emissions, because the same carbon was captured from the atmosphere in the first place. No new carbon is added. Also, the same amount of methane is broken down in the atmosphere as is created. Increasing co2 is only possible by burning fossil fuels.
Talking in terms of "carbon" is misleading. Methane is much more potent than CO2. I don't know why you think methane is broken down at the same rate as it is added.
- Cattle release methane
- Forests are burnt to make room for crops/grazing
- Fertilizer for crops for cattle produces nitrous oxide
I do not claim this adds up to 60%, but to suggest it is zero is incorrect.
Methane is broken down with a few years delay, but still the same amount is breaking down as is produced. Think of a long pipe which takes a few years to travel through and it's fed at a constant rate. Total methane in the atmosphere stays constant.
But that still means you have a couple of years with a higher concentration of methane, and given the higher impact this is obviously very relevant, no?
No new carbon would be added if we were talking about hunting buffalo on the great plains. But we're talking about industrial agriculture and each calorie produced does have associated fossil fuel emissions
ELIZA is better, because this doesn't seem to generate anything coherent. You can try the original ELIZA with DOCTOR script here: https://anthay.github.io/eliza.html
My guess is that they'll release something impressive in September, and they want to give Ternus an early win as you said. Maybe a new completely product or Vision Air.
This is about the old autopilot, not FSD, and there doesn't seem to be anything new in the article. This is based on the same leaked data which has been public since 2023. The title seems to be inaccurate, as there's nothing to indicate that they hid fatal accidents.
No one is producing Bitcoin at loss, because it doesn't make any sense, but it might happen temporarily. Imagine the mining cost as a distribution curve, and bitcoin miners filling the distribution from the cheapest upwards to where the cost equals the revenue, i.e. the highest cost miner is at break-even, so that total of 3.125 (+transaction fees) bitcoin worth of hashrate is produced every 10 minutes. All but the highest cost miner operate at profit.
> No one is producing Bitcoin at loss, because it doesn't make any sense
The cost of producing bitcoin is a combination of the marginal cost (running the miner you already have, i.e. mostly power) and paying for the miner that you bought.
If you buy a miner expecting a certain profitability but then the economics change, you can both end up with a loss long term (never able to recoup the cost of the miner) and still be better off continuing to mine (because the cost of the miner is a sunk cost, and as long as the revenue is larger than the marginal cost of running it, you'll at least recoup some of it).
Both of you are right. There is one more edge case: if you commit to buying electricity in advance it might cost you extra to not consume it. It would still be in your interest to use the power at a net marginal loss rather than not using it and paying a fine for failing the contract.
I once worked in a meatball factory. I touched almost every single meatball with nitrile gloves. There's probably a lot of earlier process steps where humans are touching the food with gloves.
Is there an error in the visualization? It shows that every vector is rotated the same amount. My understanding was that they are randomized with different values, which results in a predictable distribution, which is easier to quantize.
That's actually correct and intentional. TurboQuant applies the same rotation matrix to every vector. The key insight is that any unit vector, when multiplied by a random orthogonal matrix, produces coordinates with a known distribution (Beta/arcsine in 2D, near-Gaussian in high-d). The randomness is in the matrix itself (generated once from a seed), not per-vector. Since the distribution is the same regardless of the input vector, a single precomputed quantization grid works for everything. I've updated the description to make this clearer.
Thanks. However, from this visualization it's not clear how the random rotation is beneficial. I guess it makes more sense on higher dimensional vectors.
I believe they are all rotated by the same random matrix, the purpose being (IIUC) to distribute the signal evenly across all dimensions. So effectively it drowns any structure that might be present in noise. That's essential for data efficiency in addition to avoiding bias related issues during the initial quantization step. However there are still some other issues due to bias that are addressed by a second quantization step involving the residual.
That said, I don't believe the visualization is correct. The grid for one doesn't seem to match what's described in the paper.
Also it's entirely possible I've misunderstood or neglected to notice key details.
I think electric motors are just barely suitable for mobile / humanoid robots. Compared to human muscles, they're heavy and they overheat quickly. Current humanoid robots are spending most of their energy in carrying around just themselves, which is a huge amount of dead weight, while being unsafe for anyone to be around.
If someone invents a new type of 'artificial muscle' which has low inertia, high force/torque density, and can work without overheating, that would instantly kill all other robotics companies.
Electric motors are heavy, but they have better energy efficiency than muscles.
The reason why muscles do not overheat as much as electric motors, despite lower efficiency, is because they have good liquid cooling, by blood. The cooling system of animals has outstanding reliability, due to self repair. Liquid cooling is also possible for electric motors, but it is usually avoided due to high cost and low reliability.
The weight problem of the electric motors is solved by keeping them in the body of the robot and transmitting the movement towards the moving parts that need low inertia by various means (ropes, cables, levers, hydraulic/pneumatic fluids).
This is also done with muscles, which frequently are far away from the bone that they are moving and the movement is transmitted by long thin tendons, to reduce the inertia of the moving limb.
Electric motors can have quite a low efficiency in slow speed / high torque regime, where robots operate, because torque scales linearly with current, and power loss (resistance -> heat) scales quadratically to current (P=UI, U=RI -> P=RI^2). In addition, when the motor is statically loaded, when it's just holding a position, it wastes a lot of power (depending on the torque) with zero efficiency, if it doesn't have mechanical brakes.
I think only 1X is using tendon drives for all (?) joints in their robots, but most robots use them just for hands. 1X uses tendons just for moving the motor one link upwards the chain, not through multiple joints to the body (elbow motor is in shoulder etc.). Transmitting power from the body to remote joints with tendons is quite hard problem, and I'm not aware of anyone doing it this way. One problem is that there is no obvious way to decouple the motion of tendons when they go through a joint, e.g. if the elbow joint moves then it affects all tendons which go through the elbow. Also, it's rather complex and there is friction, stretching and vibration and so on, which might be hard to model / simulate.
Hydraulics might work, but Boston Dynamics gave up on it, so I think it's probably not worth it. Hydraulics could in theory be very good because it needs just one motor for the pump and the fluid can distribute the power to many actuators.
Moving the motors to the body is a good way to solve the mass / inertia problem, but no one has really figured out how to do it. Whatever 1X is doing might be the sweet spot.
reply