11 karma · joined December 26, 2016
Could you expand on this?
where you stitch two images together, one is the working image (the one you want to modify), and the other one is the reference image, you then instruct the model what to do. I'm guessing this approach is as brittle as the other attempts you've tried so far, but I thought this seemed like an interesting approach.
The way I see RAG is it's basically some sort of semantic search, where the query needs to be similar to whatever you are searching for in the embedding space order to get good results.
Like there's a mindset where you just want to get the job done, ok cool just let the llm do it for me (and it's not perfect atm), and ill stitch everything together fix small stuff that it gets wrong etc, saves alot of time and sure I might learn something in the process as well. And then the other way of working is the traditional way, you google, look up on stackoverflow, read documentations, you sit down try to find out what you need and understand the problem, code a solution iteratively and eventually you get it right and you get a learning experience out of it. Downside is this can take 100 years, at the very least much longer than using an llm in general. And you could argue that if you prompt the llm in a certain way, it would be equivalent to doing all of this but in a faster way, without taking away from you learning.
For seniors it might be another story, it's like they have the critical thinking, experience and creativity already, through years of training, so they don't loose as much compared to a junior. It will be closer for them to treat this as a smarter tool than google.
Personally, I look at it like you now have a smarter tool, a very different one as well, if you use it wisely you can definitely do better than traditional googling and stackoverflow. It will depend on what you are after, and you should be able to adapt to that need. If you just want the job done, then who cares, let the llm do it, if you want to learn you can prompt it in certain way to achieve that, so it shouldn't be a problem. But this sort of way of working requires a conscious effort on how you are using it and an awareness of what downsides there could be if you choose to work with the llm in a certain way to be able to change the way you interact with the llm. In reality I think most people don't go through the hoops of "limiting" the llm so that you can get a better learning experience. But also, what is a better learning experience? Perhaps you could argue that being able to see the solution, or a draft of it, can be a way of speeding up learning experience, because you have a quicker starting point to build upon a solution. I dunno. My only gripe with using LLM, is that deep thinking and creativity can take a dip, you know back in the day when you stumbled upon a really difficult problem, and you had to sit down with it for hours, days, weeks, months until you could solve that. I feel like there are some steps there that are important to internalize, that LLM nowdays makes you skip. What also would be so interesting to me is to compare a senior that got their training prior to LLM, and then compare them to a senior now that gets their training in the new era of programming with AI, and see what kinds of differences one might find I would guess that the senior prior to LLM era, would be way better at coding by hand in general, but critical thinking and creativity, given that they both are good seniors, maybe shouldn't be too different honestly but it just depends on how that other senior, who are used to working with LLMs, interacts with them.
Also I don't like how LLM sometimes can influence your approach to solving something, like perhaps you would have thought about a better way or different way of solving a problem if you didn't first ask the LLM. I think this could be true to a higher degree for juniors than seniors due to gap in experience when you are senior, you sort of have seen alot of things already, so you are aware of alot of ways to solve something, whereas for a junior that "capability" is more limited than a senior.
edit: On the math side I've encountered one that seemed unique, as I haven't seen anything like this elsewhere: https://irregular-rhomboid.github.io/2022/12/07/applied-math.... However, this only points out courses that he enrolled in his math education that he thinks is relevant to ML, each course is given a very short description and or motivation as to the usefulness it has to ML.
I like this concluding remarks:
Through my curriculum, I learned about a broad variety of subjects that provide useful ideas and intuitions when applied to ML. Arguably the most valuable thing I got out of it is a rough map of mathematics that I can use to navigate and learn more advanced topics on my own.
Having already been exposed to these ideas, I wasn’t confused when I encountered them in ML papers. Rather, I could leverage them to get intuition about the ML part.
Strictly speaking, the only math that is actually needed for ML is real analysis, linear algebra, probability and optimization. And even there, your mileage may vary. Everything else is helpful, because it provides additional language and intuition. But if you’re trying to tackle hard problems like alignment or actually getting a grasp on what large neural nets actually do, you need all the intuition you can get. If you’re already confused about the simple cases, you have no hope of deconfusing the complex ones.
So to me it feels like the "going beyond undergrad math" formally is more if you want to be able to tackle the theoretical problems of DL, in which case you need all the help you can get from theory (perhaps not just math, but even physics and other fields might help as well to view a problem through more than one lens). IMO, it's like casting a wide net, where the more you know the bigger the net is and hope that something sticks. Going the math education route is a safe way to expand this net.
I tested it a bit and it seems pretty decent, although for some really niche theoretical questions it wasn't successful in retrieving the answers I wanted even if alot of the results were really good in other aspects. It could simply be because the answer is not available anywhere in hackernews.
I'm wondering if someone were to build a similar project but for other sites, what would your advice be? For instance what technical difficulties did you stumble on that you think would be good to be aware of?
Thanks in advance and once again congratulations on the project!
This discord server in particular is good if you want help, although you might not recieve instant help, you will eventually get help if you are patient. There are also other channels in the server just for the purpose of discussion, i.e discussing general topics in math.
This might not give you what you are looking for, but it's a possible alternative I guess.
I think it only supports Windows and macOS though. https://getcoldturkey.com/