2
points
ImageXav
Joined 212 karma
- I guess that's the crux of it. From an individual perspective it makes sense to stay in a stable environment, especially if a family is involved. However, I think from a societal perspective it is desirable to have people who gamble on creating new products which can raise the bar in their given industries.
Also, just because the start up fails doesn't mean it was a waste of time. If you manage to provide employment for even just 3 or 4 people for a few years, help them and yourself develop, that is a valuable success.
- I would add an aspect that is not covered here but is often ignored: the strong labour protection laws result in a mentality where if you get a good job you are much less likely to want to take risks e.g. start your own business. There was a post on the HENRY (high earner, not rich yet) UK subreddit the other day from someone who had a wealth of experience and had the opportunity to join a start up as a CTO. It honestly sounded like a great chance to initiate change. All of the comments were telling the poster that they had it good, that 99% of start ups fail, that the hours would be gruelling. I feel as though the conversation would have been quite different in a US subreddit.
A term they like to use is 'crabs in a bucket'.
- This is an interesting point. I've been trying to think about something similar recently but don't have much of an idea how to proceed. I'm gathering periodic time series data and am wondering how to factor in the frequency of my sampling for the statistical tests. I'm not sure how to assess the difference between 50Hz and 100Hz on the outcome, given that my periods are significantly longer. Would you have an idea of how to proceed? The person I'm working with currently just bins everything in hour long buckets and uses the mean for comparison between time series but this seems flawed to me.
- I've had the complete opposite experience, and feel the complete opposite way. What is there to learn from failing a leetcode? It feels like luck of the draw - I didn't study that specific problem type and so failed. Also, there is an up front cost of several months to cover and study a wide array of leetcode problems.
With a take home I can demonstrate how I would perform at work. I can sit on it, think things over in my head, come up with an attack plan and execute it. I can demonstrate how I think about problems and my own value more clearly. Using a take home as a test is indicative to me that a company cares a bit more about its hiring pipeline and is being careful not to put candidates under arbitrary pressures.
- I feel as though it also represents the fact that contributors are less invested in the project. There was a small study done a few years back hypothesizing that the number of swear words related somewhat to code quality [0] due to emotional involvement of the codebase authors. I can imagine this to be somewhat true. I would love to see this study redone now that LLMs are widespread on pre chatgpt repos (as I suspect that repos created using LLMs are going to be very sanitised).
- Even better, python has named tuples [0]. So if you have a tuple that you are sure will always have the same inputs you can declare it:
``` Point = namedtuple('Point', 'x y') pt1 = Point(1.0, 5.0) ```
And then call the X or Y coordinates either by index: pt1[0], pt1[1], or coordinate name: pt1.x, pt1.y.
This can be a really handy way to help people understand your code as what you are calling becomes a lot more explicit.
[0] https://stackoverflow.com/questions/2970608/what-are-named-t...
- Not necessarily. Interpretability of a system used to make decisions is more important in some contexts than others. For example, a black box AI used to make judiciary decisions would completely remove transparency from a system that requires careful oversight. It seems to me that the intent of the legislation is to avoid such cases from popping up, so that people can contest decisions made that would have a material impact on them, and that organisations can provide traceable reasoning.
- Something that stuck out to me in the updated blog [0] is that Demon Adam performed much better than even AdamW, with very interesting learning curves. I'm wondering now why it didn't become the standard. Anyone here have insights into this?
- I've found that YOLOv7 [0] tends to perform better across the board than anything ultralytics has produced, without the horrendous licensing.
- I think it is you who have misunderstood the Nyquist-Shannon theorem. Aliasing and noise are real concerns. Tim Wescott explains it very well [0] (Figures 3, 10 and 11). If your signal is below one half the sample rate but the noise isn't, you'll lose information about the signal. If your signal phase is shifted wrt. the sampling, you'll lose information. If your sampling period isn't representative, you'll lose information. These are not implementation details.
[0] https://www.wescottdesign.com/articles/Sampling/sampling.pdf
- 1 point
- As far as I can can tell though the core idea is the same, to focus on the differences, the implementation is different. Differential transformers 'calculates attention scores as the difference between two separate softmax attention maps'. So they must process the redundant areas. This removes them altogether, which would significantly reduce compute. Very neat idea.
However, I do think that background information can sometimes be important. I reckon a mild improvement on this model would be to leave the background in the first frame, and perhaps every x frames, so that the model gets better context cues. This would also more accurately replicate video compression.
- 10 points
- I thought the same, but the description of the cat picture is pretty spot on. I wonder if this is a dataset issue. Cat pictures are far more prevalent than abstract art on the internet so might well be overrepresented. Can Vision LLMs deal with a long tail of underrepresented objects when small? Or can they only do so at scale?
- Here's another one that I feel is often overlooked by traditional A/B testers: if you have multiple changes, don't simply test them independently. Learn about fractional factorial experiments and interactions, and design your experiment accordingly. You'll get a much more relevant result.
My impression is that companies like to add/test a lot of features separately - and individually these features are good, but together they form complex clutter and end up being a net negative.
- I don't really see why a leap to indentured servitude is necessary here. There are obviously many other ways of implementing a training relationship - see the other comments re. how it is done in Germany.
The point I'm making is that society as a whole is worse off if companies are too risk averse to hire and train juniors up to a certain level of quality. This results in too few people capable of doing a specific, presumably valuable, job well. The consequences of this are a society that underperforms it's true potential in the long run. In monetary terms, as you raise the question, that means that less taxable value is generated. So society as a whole pays the consequence.
- The most interesting point in this article for me lies towards the end: "the role of the Guild was not to form rules, mores, regulations, and laws with respect to their crafts; their role was to introduce a system of art or craft to a new individual, to instill in them the idea of standards, quality, consistency, and perfection".
A common complaint nowadays is that it is very difficult for juniors with no experience to get hired, unless they have a degree from a prestigious university, and even then that's not often a guarantee. It seems that companies are more averse than guilds to take the risk of training someone up to industry standards.
I believe that it would be very beneficial to society to create schemes that encourage learning with a similar system. Mentors can sometimes accomplish this role, but that relation is far more informal.
- 1 point
- A more niche but nonetheless interesting method that I was hoping to see discussed was magnetism. Gravitational waves are expected to decay into photons in intense magnetic fields. Or so I was told by one of my physics professors back in the day. I did understand the math somewhat back then, but it is beyond me now. It does however seem as though some people are still exploring this avenue [0].
[0] https://indico.cern.ch/event/1074510/contributions/4519384/a....
- Ahhh I see, that makes sense. Thank you for clarifying I appreciate it. I made the mistake of assuming that the paper in the documentation was the paper of interest. I will take the time to properly delve in further once the paper is released, do you have any idea when that might be? In the mean time I look forward to giving testing the method on some toy examples I have.
- So, this looks really interesting and I look forward to delving into the methodology in order to understand the algorithm better. However, what I immediately noticed from the paper linked in the documentation was that linearboost has a worse F1 score on average than the mentioned classifiers. Where it shines is energy consumption. Would it be possible to edit the title to reflect this? It's a huge gain in energy efficiency for a relatively small F1 loss, so kudos for that, but I think people might be expecting something a bit different from the title.
- I think it's important to point out for people that might be interested in this comment that a few things are wrong.
1. Standard JPEG compression uses the Discrete Cosine Transform, not the Fourier Transform.
2. It is easy to be dismissive of any technology by saying that it is 'just' X with Y, Z, etc on top
3. Vision transformers allow for much longer range context - the magic comes in part from the ability to relate between patches, as well as the learned features, which JPEG does not do.
Or, more modern Bayesian methods if you're more interested in getting the best results for a given hyperparameter sweep.
However, that is not to detract from the excellent effort made here and the great science being investigated. Write ups like this offer so much gold to the community.