The Meta glasses camera has a very wide field of view. You'd need to be quite close to a person to realistically take "pervy" photos, and it'd be really obvious. Seems like it'd be no better at this than a cell phone.
For all the people represented in the training data to receive royalties would be an incredible wealth transfer to the Extremely Online. My forum posts, StackOverflow answers etc are also contributing to the model outputs. The training data, by volume, mostly belongs to blog authors, redditors, Wikipedia editors, to us!
Having a relatively new LinkedIn account now is probably a very bad move if you don't have an established network to reach out to for jobs. There are tons of AI generated profiles flooding every job post (particularly remote) from scammers who create new LinkedIn profiles. It's one of the most frequent signs of a fake submission.
Controlled burns aren't impossible in chaparral, even based on the logic of that article the controlled burns just need to be less frequent and more intense than for forest. There's no reason they couldn't be done.
A founding CTO is more effective than a hired CTO, because the founding CTO has more moral authority to create a consistent system. In other companies there's infighting between people (senior engineers, senior managers) with different architectural preferences (e.g. microservices vs monoliths, Java vs Python). These senior people get half what they want, meaning half your system works one way and half the other. A CTO can hold to their singular vision.
It could be that the moral authority stems from having as much of a full picture as a single person can have over the entire lifecycle of the company, but I think a lot is also just the effect of "I got you here."
I'm glad pg named this effect, since I've talked about the related phenomenon for CTOs with many people.
I mean, I know nothing about your network. If your network access servers are within a datacenter under your exclusive physical control, perhaps it's not an issue since it requires a man-in-the-middle position. Something like a neighborhood cabinet DSLAM could be open to abuse?
> Those customers are already untrusted, so it really does not matter.
Perhaps it doesn't matter to the health of your network, but if it leads to a customer's account being disabled due to incorrectly assigned abuse, surely it would matter to them.
It would be interesting to allow users of models to customize inference by tweaking these features, sort of like a semantic equalizer for LLMs. My guess is that this wouldn't work as well as fine-tuning, since that would tweak all the features at once toward your use case, but the equalizer would require zero training data.
The prompt itself can trigger the features, so if you say "Try to weave in mentions of San Francisco" the San Francisco feature will be more activated in the response. But having a global equalizer could reduce drift as the conversation continued, perhaps?
> The single number that should summarize your expectations about any LLM is the number of total flops that went into its training.
One thing I've been curious about is whether a model that's trained well beyond the Chinchilla level of compute will suffer more from quantization. All of that information has to live somewhere within the weights, so it stands to reason that you may have to keep more bits of information to keep that performance benefit.
If so, it would also mean that a smaller model that's been "overtrained," but which can't be quantized without suffering quality loss isn't necessarily cheaper for inference than a larger model which isn't overtrained, but which can be aggressively quantized. I haven't seen anyone discuss this, but maybe there's a paper on it.
If you could characterize what level of overtraining leads to quality loss at different levels of quantization, you could possibly figure out a more optimal model for overtraining. E.g. if you train with 10T tokens and you see quality loss at 4 bit, and you train with 20T tokens and see quality loss at 6 bit, you can fit a curve to those data points to estimate the maximum amount of tokens the model can train on with the current methodology.
> there's still no way in hell they'd get 30,000 people inside
Yeah, I'm pretty sure that would be physically impossible, unless they are planning on hosting most of the conference on the grounds, parking lot and maybe surrounding streets.
I don't think this is true. My startup had a couple desks in the Palace of Fine Arts when they had an ill-fated attempt to convert part of it to a co-working space. They shut down co-working to turn it into a full-time event space. The building is basically one giant room, with a raised mezzanine floating above part of the floor. It has one small permanent theater on the north end, and they convert the southern end into a larger theater for music shows, and this is the seating chart being shown in the above link (I saw a Ninja Sex Party show there one time, but this theater isn't reflected on the tour photos. When I was working there in 2016 it was an exhibit for Hunger Games).
I bought this book on Audible based on a random recommendation, not really knowing anything about it. On the page to buy it, it said it was released in 2020. I spent the first 80% of the book thinking that it was written in the 2010s, but intentionally written as if it were the 1980s. That is, with explanations that didn't presage the development of the Internet, and using analogies that would be understood by the people of that era. I was impressed with how perfectly the author was able to channel that era without anachronism, and even told a couple friends about this.
When I learned it was written in the 1980s, I wasn't exactly shocked. But then, I learned it was written by the Klein bottle guy, and that really was shocking. It's become one of my favorite books.
Right, but if a plane has a maximum takeoff weight of 600k lb, I doubt if it can safely takeoff while being 600k lb AND towing another x00k lb in a glider, which is what would be required to reduce cargo costs by 65%.
The cargo a plane can carry isn't limited only by the volume of the interior, but also by the amount of weight that can be carried safely in the plane. Airlines try to balance the amount of dense and non-dense (a.k.a. volumetric) cargo to solve for the joint constraint by charging different prices for cargo depending on the greater of its weight cost vs. its volume cost.
Even if it turned out to be practical to double the volume of cargo you could carry, it seems unlikely that it would allow you to double the weight of the cargo, since the engines and the airframe have all been designed around the same set of engineering requirements. The best case scenario would be a decrease in the cost of volumetric cargo, with dense cargo staying the same.