Tracking, thanks!

Tracking! Thank you.

No. There was talk of it and moving to a different software. During the big reddit exodus Lemmy became unmanagable in terms of sane moderation.

What did happen is Beehaw defederated from some instances that were the worst offenders. Sadly, a few of them were fairly large.

There is also the mission of Beehaw of being a safe space which makes modertion more difficult. Beehaw was never intended to be for everyone where everything goes.

My memory is a bit foggy because decisions were taken a few years ago but things never really happened, but the information you're looking for is probably on !support@beehaw.org

If I remember correctly the idea was to switch from Lemmy to something else

Do you know the baalspawn monster form from BG2? Something like that.

That was a joke.. I have incredibly detailed dreams, meaning that there are quite a few I remember vividly years later, but no, cannot actually recall looking into a mirror in any of them. (though in steamy dreams the parts of my body in my field of view were similar to real life)

There is a movie I supposedly saw as a 9-10 year old child, but I'm no longer sure it was not a dream. I remember it in vivid detail, but there were only so many dinosaur movies made in the 70-80s & it does not seem to be one of them. Described the plot & prominent scenes in the past on movie subreddits & also to LLMs & there is no hit..

I'm seldom conscious of my body in dreams, but when it happens it's often a low-key body horror to which I'm strangely blasé. Last dream with a mirror view, my face had a hideous trypophobia condition that should have been a lot more painful. Oh, that's happening.

Myself with a bad haircut I didn't remember getting.

For some reason my dreams always seem to lack details visually. Or at least I always remember them like that. I have no idea why, my eyesight is actually pretty good when awake.

So if I see a mirror in a dream, I just see what I assume to be my reflection, but I never see it clearly enough to make out any details, and the parts I do see aren't notably different from how I actually look.

I am however not always myself in my dreams. If I watched a movie or series or played a game right before bed then I frequently dream in character of a memorable character of that movie/series/game. But I cannot recall having come across a mirror in one of those "in character" dreams.

I think you are correct in that you should see what you expect though. I occasionally have lucid dreams, and then it really feels like whatever you expect to happen, happens. In theory that makes the dream controllable as if you change your expectations the dream changes. However getting full control over your expectations is in my experience a lot harder than it sounds.
I tend to often doubt myself so if I try to steer a dream that way, a part of me always expects it to go wrong, so it always does.
That's not related to mirrors, but I guess I still is another way dreams can tell you something about yourself.

I thought Sally Struthers used to run one.

We have literally no way of knowing. Even if you specified the category of Casio product, it would be impossible. How do we know what you don't know?

Closer to industry and physical production, there are more use cases than OCR. In data economy, statistics, and other data handling, machine learning is also a topic.

Machine learning is a wide field.

My company did a demonstration project on how AI could observe motor sensors to aid planned maintenance (recognizing and categorizing irregularities in movements).

I can't speak to concrete numbers or prevalence in different fields or job market opportunities. How much maths is involved, as I wrote, depends on the field and application.

I have time till july to think about this as you suggested. will make a post if I have chosen going in a particular direction.

Thank you so much for your advice.

I simply chose my field based on what I knew I enjoyed. I already had a job with working in systems, and I had HPC/FPGA experience from undergrad. Basically, I choose topics that I had at least a basic understanding and desired to expand into mastery.

I will be honest and say I don't think you've really thought this through. I would really weigh the pros and cons of the degree. Think about what you'd do instead of pursuing a degree. Identify a specific job you would want after you get your degree. If you only want to teach, you could be an instructor at a University, but the pay will not be good. If you want to do research, doing reaearch at a university level will require a PhD. Research centers will hire MS holders, but as engineers rather than researchers. If you want an industry job, make sure you actually need the degree you are thinking of getting.

For 99% of people, carrying on with the degree they have is the better choice. In your case I'd either make sure you consider the above or only pursue a master's if you truly cannot get the job you want with self-teaching.

Unfortunately, the field is pretty brutal about just assuming you are fully caught up to everything in the field, or that you will read the references for a given statement. You often don't need to know all the math, but at least have a general idea of what some concepts are. I've been part of 'journal clubs' for the past 5 years where the only really goal is to stay abreast of the changes going on so that a new paper doesn't send me down a rabbit hole of reading a referenced paper, which also has unknown terms, and read those references, etcetcetera.

I'm not in a great position to give useful advice due to that. it might be a good approach to find a youtuber who gives a 20-30 minute review/summary of a single paper that looks interesting, and of the video makes sense, then try to read the paper, and if you get stuck, see if the YouTuber has a video on the concept that got you stuck.

I think I have to get used to reading scientific papers. could you tell me where to start, I mean some papers which doesn't require a lot of pre-knowledge?

I am sorry for the terse response.

here is some context:

I have done bachelors in CS and didn't do very well. I wasn't that bad at it either. now the reason I am interested in research/teaching because I love learning new things and teaching helps retain stuff I have already learned. yes I am willing to give hours into it.

it's really hard to pick a subject though, I was thinking of AI/ML because it's currently hot and one might have more opportunity in it. besides it I would want to know other fields for research. you have PhD in CS, could you guide me what we do and dont etc. what's the process if I chose let's say cryptography ( which I like ) then what would my next step will be?

the other person said AI/ML is saturated so don't know what to do. it's for sure that I want to get into teaching/research just don't know if it will be AI/ML.

I work in AI/ML, and it's such a hilariously broad and meaningless field I don't even do remotely the same thing as my coworker in the same team hired on the same day. They work in agentic llm swarm research I work in CV/RL. Here's three papers I had to read recently, and not fully understand but extract usable insight from (why did they do what they did and can I use that for myself)

Upsample anything

Dinov3

LTGNN line transformer graph neural networks for robust line feature matching just kidding this one isn't on arxiv

It takes me a couple hours to deeply read a paper, I'm not like, getting 100% from it till like the 4th read through and a couple pages of notes, or reading a couple background papers (such as the JBU paper for UpAnything). Try reading one or two and if it seems interesting then maybe that's an area in AI ML that might be interesting to you.

Remember the people doing the most math are the people doing ai ML academically in a masters or PhD, us commercial users tend to do less math and more "this new model came out can we apply it to our problem"

I don't mean python and SQL. Anyone with a decent software background can pick that stuff up. As an example, my background is in high performance computing, networking, systems, and FPGAs. All of these are fields (well, maybe not FPGAs) that ML/AI rely heavily on, but if AI/ML disappeared tomorrow they'd still be incredibly relevant. Another approach is to study a domain science and apply AI/ML. For example, high energy physics leverages AI/ML, at least in research.

TBH, "wanting a good job" is a very generic response that needs more context. Are you moving from somewhere other than software? If you already have a software background, you might be better of self-teaching. If you are actually interested in research/teaching, that needs to be thought through very thoroughly. I say this as someone who just finished their PhD in CS, just to give context as to why I am being particularly intense on this point.

yes, I did okay in algorithms class. but I am sure if I give enough efforts I can do this

Are you in CS now? How did you do in the algorithms class. Treat that class as the deciding factor of how much you will like it.

You would be better served by getting skills in another field that AI/ML relies on or where AI/ML can be applier

you mean something like python or SQL ? yes I want to master these tools but it takes a lot of time to do so.

main reason for having this degree is to get a decent job. besides it would be great if I can get into research area and teaching, I like doing that too

Asking how the job will be seems like the wrong question to me, when you’re trying to answer what the studies will be

I want my study to be fruitful. that's why I also want to know about job opportunity. it's just that I don't see use cases of AI/ML in daily life. the only thing installed is an OCR. wanted some perspective that's all.

Asking how the job will be seems like the wrong question to me, when you're trying to answer what the studies will be. I would check with your uni instead.

Studies could be focused on maths or not. Jobs can be focused on maths if you're developing models and ml in depth, but is likely unnecessary if you're just training and assessing and embedding models, outside of maybe statistics, depending on use and needs.

LLM is a hot topic, but I suspect it did not overturn general ml.

I'm not in the field specifically, but we explore and discuss it at work, other team trained a model, and we use coding tools.

I would not enter AI/ML for one simple reason: it is oversaturated. Everyone and their brother is in tgis field, and even those outside of it still study it and us it. Unfortunately the way to stand out within AI/ML field is to have a very good mathematics/statistics foundation and really understand the theory behind it. You would be better served by getting skills in another field that AI/ML relies on or where AI/ML can be applier.

BTW, you should also state what you plan on doing with your degree. There is no point in getting a degree if there isn't a concrete benefit to doing so.

I'm glad! Thank you for following up.