It's a much smaller model, but IMO, the results are much lower quality too.

However the fact you can run it on your PC means you can tweak all the settings and have many goes at getting better results, partially offsetting that.

My maths comes up with a different result, although the conclusion is the same.

Didn't even put the seat down

The energy requirements are much higher than just melting... The main energy consumer is removing the oxygen

Yes, it's a lot. But it can run when energy is cheap, such as when there is excess solar and wind.

There are other factories already that power up the most energy hungry devices only when power is cheap.

Doesn't look awfully hard to repair... Just replace the ties, which are standard parts you can bring in in bulk by train.

Replacing just say 1 in 10 will allow you to use the railway for light vehicles, which then allows you to do the rest of the repair all in parallel.

>They were not able to find significant improvements with scaling anymore.

GPT-3 has a window size of 2048 tokens ChatGPT has a window size of 8192 tokens. The compute cost is superliner, so I suspect the compute required for ChatGPT is a minimum of 10x what GPT-3 used. And GPT-3 cost ~12M USD. (At market rates - I assume they got a deep discount)

So I suspect they did scale compute as much as they could afford.

I guess you're not German...

In Germany it is considered rude(and possibly illegal too) to publish a photo on the internet unless you have permission of everyone in the frame.

This photo has enough people in the background that I'm pretty sure you didn't get permission.

This isn't true.

The model generates 1 token at a time, and if you look at the network connection you can see it slowly loading the response.

I'm pretty sure the speed the answer is returned is as fast as openAI can generate it on their cluster of GPU's.

Oh, and this paper has code published.

This paper has their algorithm in pseudocode:

https://www.cs.cmu.edu/~mmv/papers/11rssw-BiswasVeloso2.pdf

It is their culture, yes. But they are a huge behemoth with lots of revenue and money in the bank, so I think it'll be at least 20 years till they collapse.

OpenAI keep a big database of all the output.

That in itself serves the same purpose of a watermark.

OpenAI can take any bit of text, and search their database and see if it came from their service.

If you want to make the assumption that most buildings don't have any curves in their roofs...

Then take your point cloud, extract the largest polygons... There are classical algorithms for such things.

From the polygons, turning that into a plan should be quite straightforward.

While ML could be applied... I think you'll get better results quicker with classical methods.

> Industry convenience should not trump public health and yet it occurs on a regular basis.

The regulatory body for making this decision is the FAA. The FAA banning leaded fuels is like NASA banning rockets or the Treasury banning dollars.

The real question is why have we the people set the system up so that the regulatory body making this decision doesn't have the people's best interests in mind?

> while I'm pretty sure you can't ask Github Copilot that

You can comment out the code, then write underneath:

"# Version above not working due to TypeError. Fixed version below:"

Then use Copilot completion. It will fix whatever the bug was.

> too early to consider diffusion as a serious alternative to autoregression for generative language modelling at scale

This blog post explores lots of ideas and has conjectures about why they may or may not work...

But it seems this stuff could just be tried.... Burn up some TPU credits and simply run each of the types of model you talk about and see which does best.

Hard numbers are better than conjecture. Then focus future efforts on improving the best numbers.

Any electronics are 'dangerous' because they have sharp edges... Or might be a choking hazard if the screws are swallowed...

Top K sorting is linear in computational complexity, and I doubt it will dominate because it just needs to be done on a single number rather than a vector of 512 numbers.

There is no latency constraint - it's a pure streaming operation, and total data to be transferred is 1 gigabyte for the whole set of vectors - which is well within the read performance of apples ssd's.

This is also the naive approach - there are probably smarter approaches by doing an approximate search with very low resolution vectors (eg. 3 bit depth), and then a 2nd pass of the high resolution vectors of only the most promising few thousand results.

It should scale to 1 million images without much slowdown.

1 million images * 512 vector length= 512 million multiples, which the neural engine ought to be able to do in ~100ms

I think 60 minutes is crap.

I want deliveries within 5 minutes.

The products can already be boxed and attached to a drone. And then when someone orders that product, the drone takes off and flies at 100 mph to your address.

100 mph for 5 minutes is 8 miles, which covers a 16 mile diameter circle, a few of which would fully cover even large cities.

And there are plenty of drones which can fly 100 mph - world record small drones go nearly 200 mph.

They still are, yea.

But let's not let facts get in the way of popular opinion.

You could get a similar outcome by discarding results of the first 2 or so examples of each session as 'practice' ones, then recording data from the rest.

I've got a feeling chatGPT benefits massively from it's human-curated finetuning feedback loop.

Thats hard to reproduce without tens of thousands of man-hours upvoting/downvoting/editing the bots responses.