cstejerean

the problem with gastown is it tries to use agents for supervision when it should be possible to use much simpler and deterministic approaches to supervision, and also being a lot more token efficient

how long ago was this past? A review with latest models should absolutely catch the issue you describe, in my experience.

Well of course, we basically expanded the ASD definition to cover a wide range in order to ensure that everyone gets access to support if needed, but in the process turned "autism" into a grab bag of different conditions which makes discussions about it difficult because everyone is talking about something else.

Why do I have a feeling that most of that $30B was spent on paying for consultants, most of which were also essentially making things up as they went along.

Playing against an AI that's really dumb gets boring quickly. Playing against an AI that's way too good gets annoying quickly.

I want an AI that can play like a human at my level would, such that the game is competitive and fun.

> the Waymo ADS’s perception system assigned a low damage score to the object;

and Tesla would do better how in this case? It also routinely crashes into stationary objects, presumably because the system assumes it wouldn't cause damage.

Completely agree. It's been 18 years since Nvidia released CUDA. AMD has had a long time to figure this out so I'm amazed at how they continue to fumble this.

> we didn’t see “Joe Smith” level of granularity, it was at Zip code levels

So you got aggregated analytics instead of data about individual users.

Meanwhile other companies are selling your name, phone number, address history, people you are affiliated with, detailed location history, etc.

Which one would you say is "selling user data"?

None of the tech companies are selling your data to advertisers. They allow advertisers to target people based on the data, but the data itself is never sold. And it would be dumb to sell it because selling targeted ads is a lot more valuable than selling data.

Just about everyone else other than the tech companies are actually selling your data to various brokers, from the DMV to the cellphone companies.

The problem is you're limited to 24 GB of VRAM unless you pay through the nose for datacenter GPUs, whereas you can get an M-series chip with 128 GB or 192 GB of unified memory.

The authors of the metric found some cases where it works better is not the same thing as it being widely considered to be better. When it comes to typical video compression and scaling artifacts VMAF does really well. To prove something is better than VMAF on video compression it should be compared on datasets like MCL-V, BVI-HD, CC-HD, CC-HDDO, SHVC, IVP, VQEGHD3 and so on (and of course Netflix Public).

TID2013 for example is an image dataset with many artifacts completely unrelated to compression and scaling.

- Additive Gaussian noise - Additive noise in color components is more intensive than additive noise in the luminance component - Spatially correlated noise - Masked noise - High frequency noise - Impulse noise - Quantization noise - Gaussian blur - Image denoising - JPEG compression - JPEG2000 compression - JPEG transmission errors - JPEG2000 transmission errors - Non eccentricity pattern noise - Local block-wise distortions of different intensity - Mean shift (intensity shift) - Contrast change - Change of color saturation - Multiplicative Gaussian noise - Comfort noise - Lossy compression of noisy images - Image color quantization with dither - Chromatic aberrations - Sparse sampling and reconstruction

Doing better on TID2013 is not really an indication of doing better on a video compression and scaling dataset (or being more useful for making decisions for video compression and streaming).

Citation needed.

That's slightly different though, sounds like fob went from being blocked by your body to no longer being blocked by your body.