AStrangeMorrow
You can definitely do similar things with say hosted LLMs + a lib like outlines , or with API models and the proper output validation layer, but again way slower and more expensive.
And on the opposite side you can train dedicated classification models that will be even cheaper and faster to run than jev. But, well, you need to train them (costly, time consuming, and data might be hard to come by depending on target). Here you are a nice zero-short system, that can handle complex/messy data out of the box.
At 6 feet / 183 cm that it takes 184 lbs / 83 kg to be overweight. From what I could find, for regular gymgoers the typical weight for people around 6feet is 180-190lbs, which put many people around the overweight threshold. I am myself at 24.9, and while not skinny I am definitely in the skinnier half of the people at my local gym.
Many active gym people have pretty high BMIs but fairly low fat (because muscle is dense), and unsurprisingly have better outcomes than the average person (if you ignore the share that uses/overuses anabolic steroids and co)
The map of France shows maybe 100 castles? If you include castles, forts and chateaux (which it seems the map aims to do) France has around 45000 of them, including 15k medieval castles (military), an other 15k from the Renaissance period (mix of military and luxury), and 20k more recent ones (some military, many luxury type).
I know if at least 20 medieval castles within 1h of my hometown and not a single one is here.
For Spain maybe 10k to 20k castles and forts. Out of which at least 2500 genuine medieval castles (not just big houses with fortifications)
I remember spending 2h writing a question for what I thought was a complex c++/compiler issue. 10s of thousands of lines proprietary codebase, so I couldn’t include everything obviously, but also couldn’t create a “minimal working example” to reproduce the issue. So I included as many things is I could to try to get pointer on how to track that behavior I was seeing. Of course the second I post it I got a -1 plus “can’t reproduce”/“please add minimal example”.
An other time, I had a question that was very similar to an existing one, but different setup and the answer did not solve my problem at all. Mentioned all that, linked the other question and specifically wrote that it was NOT addressing my problem. Posted it, soon after tagged as duplicate with that one answer that did NOT solve the problem.
After that I rarely asked questions again.
Also the points system made it frustrating as a new user: someone 2 years ago asks a basic language question “+50 upvotes”. You asked a similar question, asking extra clarification on an aspect “-2, already answered, read the doc” and so on. And with such a big deal made about reputation it felt like just being born early and being able to be an early adopter meant you got east points. For new users, though luck.
- a long uninterrupted night cycle and a short (20-60 mins) afternoon nap. Around 2pm. - a night cycle split into two halves. With a 1-2h break (maybe up to 3h) starting around midnight to 2am.
The former is still very common, and imho stretching the definition of multiple cycles. The latter is more historical (more common when there are long winter nights and no electricity).
Also making it a “Western” problem is kinda weird? There are other cultures where single cycle sleep has existed. Even hunter-gatherer groups with little to no contact with the west. And alternatively afternoon naps are still quite common is some western areas. I guess the main thing that prevent it would be the classic work day schedule.
But more generally while recordings might be copyrighted, the voice itself isn’t so copying a voice isn’t a crime, at least as it currently stands. You cannot however use said voice for deceptive practices. You can however for advertisement (needs permission). And in the US you can for satire, at least in the US, withOUT permission (falls under the 1st amendment).
But If I send a message with more than one question, there is what feels like a 10% change they will answer all my questions and a 90% chance they will either answer one or none and I’ll just get an “ok”. And I am talking about 5 lines messages not 50 lines.
So I have to send my questions one at a time, wait sometimes minutes to hours for an answer, then send the next one.
Of course I can also call, but often can’t reach them. Or can reach the front desk that doesn’t have the answers. I understand people are busy but it turns something that should be one message into a cat and mouse game
But they picked 9 family names per group. Which sounds quite low. And combined that with first names to reach 500 first+last names per group.
I wonder how much of the bias we see has to do with the names actually picked versus it being racially motivated (absolutely not denying that this probably is a factor, but might not be the only one).
For example, in France there is the national BAC end of high school exam. If you you at the names X grade distribution, and look at the higher “very good” bracket: some names are heavily under-represented (less than 5% of say “Jordan” get that grade) while some are over-represented (35% of “Josephine” get such a grade). The exam is for the most part anonymous, but some names are definitely heavily correlated with lower/higher income groups. So nothing surprising: Josephines tend to come from richer families, thus in average get better education/support, thus better grades. Same thing is true with family names to a smaller extent.
So I wonder how much of the bias we see, be it from real persons or the AI has more to do with a class thing than a racial thing. Again those are not neatly separate things, but still
Anyway doesn’t really matter, it was more to see if anyone else was a listener of that podcast.
Funnily at my company, the few engineer that did the majority of the work before AI still do the majority of the work now. By majority I mean tackling both more issues and better.
However there is a general verboseness and over engineering trend across the board.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
They do quickly acknowledge it, but definitely not a balanced set.
I work on a lot of 2D system, and the only way to debug is often to plot 1000s of results and visually check it behaves as expected. Sometimes I will fix an issue, look at the results, and it seems resolved (was present is say 100 cases) only to realize that actually there are still 5 cases where it is still present. Sure I could amend the last commit, but I actually keep it as a trace of “careful this first version mostly did the job but actually not quite”
Did it make a mistake because I didn’t follow instructions properly or hallucinated some content?
Did it make a mistake because the prompt was unclear/open to interpretation or plain wrong?
Did it make a mistake because it lacked some context? Or too much context and it starts getting confused?
Is not handling edge cases automatically when that was not requested a mistake?
I am not just trying to defend LLMs, in many cases they make obvious mistakes and just don’t follow my arguably clear instructions properly. But sometimes it is not so clear cut. Maybe I didn’t link a relevant file (you can argue it could have looked to it), maybe my prompt just wasn’t that clear etc
If I take the example of code, but that extends to many domains, it can sometimes produce near perfect architecture and implementation if I give it enough details about the technical details and fallpits. Turning a 8h coding job into a 1h review work.
On the other hand, it can be very wrong while acting certain it is right. Just yesterday Claude tried gaslighting me into accepting that the bug I was seeing was coming from a piece of code with already strong guardrails, and it was adamant that the part I was suspecting could in no way cause the issue. Turns out I was right, but I was starting to doubt myself
I guess it is different once you look at people renting, and also you could track a specific person posts to see when they are posting away from home for example. But as far as revealing your home address, sadly there are many other ways in a lot of cases
Steam link: https://store.steampowered.com/app/2147950/4D_Golf
The person goes over quite a few technical details on their Youtube, though they talk about a bunch of other coding experiments too.
The former for me have a bit more exhale and round sound while the “in” are a tad drier.
For example “fin” and “faim” are distinct for me. However “faim” and “feint”
An other thing for me is that it has gotten a lot harder for small teams with few ressources, let one person, to release anything that can really compete with anything the big player put out.
Quite a few years back I was working on word2vec models / embeddings. With enough time and limited ressources I was able to, through careful data collection and preparation, produce models that outperformed existing embeddings for our fairly generic data retrieval tasks. You could download from models Facebook (fasttext) or other models available through gensim and other tools, and they were often larger embeddings (eg 1000 vs 300 for mine), but they would really underperform. And when evaluating on general benchmarks, for what existed back then, we were basically equivalent to the best models in English and French, if not a little better at times. Similarly later some colleagues did a new architecture inspired by BeRT after it came out, that outperformed again any existing models we could find.
But these days I feel like there is nothing much I can do in NLP. Even to fine-tune or distillate the larger models, you need a very beefy setup.
Like I had multiple companies quote me $300-500 based on the job for things that take me maybe 2-3 hours total to do, including learning about it (will be faster next time), getting the materials, and doing the job.
When you have a few of these a months they add up. It is usually nothing for a month and then 4-5 things to fix/improve the next
Also remember a teacher telling us about that story of a company finding a woman was pregnant from her shopping behavior and pushing relevant recommendation. Prompting people around her like her dad or something to find out she was pregnant