I use archive storage class on google cloud, to store old movies and wedding videos, pictures of old vacations.
For everything else I use paid onedrive subscription.
The biggest problem is user interface with s3 like storage and predictable pricing because remember you also pay for data retrieval and other storage apis, with dropbox etc you pay a fixed amount. Every year or so I roll over data into the bucket.
I find myself completely outclassed by mathematicians in my own field.
I tried to learn a little math on the side after my regular software engineer gig but I'm completely outclassed by phd's.
I am unsure of the next course of action or if software will survive another 5 years and how my career will look like in the future. Seems like I am engaged in the ice trade and they are about to invent the refrigerator.
I guess I have the opposite experience. I have a post-graduate level of mathematical education and I am dismayed at how little there is to be gained from it, when it comes to AI/ML. Diffusion Models and Geometric Deep Learning are the only two fields where there's any math at all. Many math grads are struggling to find a job at all. They aren't outclassing programmers with their leet math skillz.
The real use is in actually seeing connections. Every field has their own maths and their own terminologies, their own assumptions for theorems, etc.
More often than not this is duplicated work (mathematically speaking) and there is a lot to be gained by sharing advances in either field by running it through a "translation". This has happened many times historically - a lot of the "we met at a cafe and worked it out on a napkin" inventions are exactly that.
Math proficiency helps a lot at that. The level of abstraction you deal with is naturally high.
Recently, the problem of actually knowing every field enough, just cursorily, to make connections is easier with AI. Modern LLMs do approximate retrieval and still need a planner + verifier, the mathematician can be that.
This is somewhat adjacent to what terry tao spoke about, and the setup is sort of what alpha evolve does.
You get that impression because such advances are high impact and rare (because they are difficult). Most advances come as a sequence of field-specific assumption, field-specific empirical observation, field-specific theorem, and so on. We only see the advances that are actually made, leading to an observation bias.
Don't worry when stochastic grads get stuck math grads get going.
(One of) The value(s) that a math grad brings is debugging and fixing these ML models when training fails. Many would not have an idea about how to even begin debugging why the trained model is not working so well, let alone how to explore fixes.
Debugging ML models (large part of my job) requires very little math. Engineering experience and mindset is a lot more relevant for debugging. Complicated math is typically needed when you want invent new loss functions, or new methods for regularization, normalization or model compression.
You are perhaps talking about some simple plumbing bugs. There are other kinds:
Why didn't the training converge
Validation/test errors are great but why is performance in the wild so poor
Why is the model converging so soon
Why is this all zero
Why is this NaN
Model performance is not great, do I need to move to something more complicated or am I doing something wrong
Did the nature of the upstream data change ?
Sometimes this feature is missing, how should I deal with this
The training set and the data on which the model will be deployed are different. How to address this problem
The labelers labelled only the instances that are easy to label, not chosen uniformly from the data. How to train with such skewed label selection
I need to update model but with a few thousand data points but not train from scratch. How do I do it
Model too large which doubles can I replace with float32
So on and so forth. Many times models are given up on prematurely because the expertise to investigate lackluster performance does not exist in the team.
Literally every single example you provided does not require much math fundamentals. Just basic ML engineering knowledge. Are you saying that understanding things like numerical overflow or exploding gradients require sophisticated math background?
Numerical overflow mostly no, but in case of exploding gradient, yes especially about coming up with a way to handle it, on your own, from scratch. After all, it took the research community some time to figure out a fix for that.
But the examples you quoted were not my examples, at least not their primary movers (the NaNs could be caused by overflow but that overflow can have a deeper cause). The examples I gave have/had very different root causes at play and the fixes required some facility with maths, not to the extent that you have to be capable of discovering new math, or something so complicated as the geometry and topology of strings, but nonetheless math that requires grad school or advanced and gifted undergrad level math.
Coming back to numeric overflow that you mention. I can imagine a software engineer eventually figuring out that overflow was a root cause (sometimes they will not). However there's quite a gap between overflow recognition and say knowledge of numerical analysis that will help guide a fix.
You say > "literally every single example"...
can be dealt without much math. I would be very keen to learn from you about how to deal with this one, say. Without much math.
The labelers labelled only
the instances that are
easy to label, not chosen
uniformly from the data.
How to train with such
skewed label selection
(without relabeling properly)
This is not a gotcha, a genuine curiosity here because it is always useful to understand a solution different from your own(mine).
Maybe I don’t understand this data labeling issue - are you talking about imbalanced classification dataset? Are hard classes under-represented or missing labels completely?
None of those (but they could be added to the mix to complicate matters).
Consider the case that the labelers creates the labelled training set by cherry picking those examples that are easy to label. He labels many, but selects the items to label according to his preference.
First question, is this even a problem. Yes, most likely. But why ? How to fix it ? When are such fixes even possible.
Yes, this is a problem - the most challenging samples might not even be present in your training data. This means your model will not perform well if real world data has lots of challenging samples.
This can be partially solved if we make some assumptions about your labeller:
1. they have still picked enough challenging samples.
2. their preferences are still based on features you care about.
3. he labelled the challenging samples correctly.
And probably some other assumptions should hold for distribution of labels, etc. But what we can do in this situation is first try to model that labeller preferences, by training a binary classifier - how likely he would choose this sample for labelling from the real-world distribution? If we train that classifier, we can then assign its confidence as a sample weight when preparing our training dataset (less likely samples get more weight). This would force our main classifier to pay more attention to the challenging samples during training.
This could help somewhat if all assumptions hold, but in practice I would not expect much improvement, and the solution above can easily make it worse - this problem needs to be solved by better labelling.
By using the (estimated) Radon Nikodym derivative between the the two measures -- the measure from which the labelers samples and the deployed to measure from which the on-deployment items are presumably sampled.
For this to work the two measures need to be absolutely continuous with each other.
This is close to your pre-penultimate paragraph and that's mathy enough. This done right can take care of bias but may do so at the expense of variance, so this Radon Nikodym derivative that is estimated needs to be done so under appropriate regularization in the function space.
Thinking of the solution in these terms requires mathematical thinking.
Now let's consider the case where some features may be missing on instances at the time of deployment but always present in training and the features are uncorrelated with each other (by construction).
IMO Computer Science doesn't have enough mathematics in the core curriculum. I think more CS students should be double majoring or minoring in Physics and/or Math. The skills you gain in analyzing problems and constructing models in Physics, finding truth/false values and analyzing problems in math, and the algorithmic skills in CS really compliment each other.
Instead of people "hacking" university education to make them purely fotm job training centers. The real hack would be something that really drills down at the fundamentals. CS, Math, Physics, and Philosophy to get an all around education in approaching problems from fundamentals I think would be the optimal school experience.
The big thing that made it all click for mathematics was that I stopped thinking about mathematics the way that it was taught to me and I started thinking about it the way that it naturally felt correct to me
So in my specific case I stopped thinking about mathematics as: how to interpret a sequence of symbols
But instead I decided to start thinking about it as “the symbols tell me about the multidimensional topological coordinate space that I need to inhabit
So now when I look at a equation (or whatever) my first step is “OK how do I turn this into a topology so that I can explore the toplogical space the way that a number would”
Kind of like if you were to extend Nagle’s “what it’s like to be a bat” but instead of being a bat you’re a number
> Seems like I am engaged in the ice trade and they are about to invent the refrigerator.
The way I like to look at it is that I'm engaged in the ice trade and they are about to invent everything else that will end mine and every other current trade. Which leaves me with two practical options: a) deep despair. b) to become a Jacks of all trades, master of none, but oftentimes better than a master of one. The Jacks can, for now, capitalize in the thing that the Machines currently lack, which is agency.
Don't despair. The key to becoming proficient in advanced subjects like this one is to first try to understand the fundamentals in plain language and pictures in your mind. Ignore the equations. Ask AI to explain the topic at hand at the most fundamental level.
Once the fundamental concepts are understood, what problem is being solved and where the key difficulties are, only then the equations will start to make sense. If you start out with the math, you're making your life unnecessarily hard.
Also, not universally true but directionally true as a rule of thumb, the more equations a text contains the less likely it is that the author itself has truly grasped the subject. People who really grasp a subject can usually explain it well in plain language.
The problem is that equations give the illusion of conciseness and brevity but in reality always heavily depend on context.
You give a physicist an equation of a completely unrelated field in mathematics and it will make zero sense to them because they lack the context. And vice versa. The only people who can readily read and understand your equations are those that already understand the subject and have learned all the context around the math.
Therefore it's pointless to try to start with the math when you're foreign to a field. It simply won't make any sense without the context.
Of course, but everything depends on context. Stating a mathematical theorem in English will also make no sense to someone who's not acquainted with the field
It's interesting to me, as if you read Benjamin Franklins biography, he mentions creating a literary circle being super important. I suspect many of our important thoughts leaders through history created small social circles where they hyperfocused on their domain with friends in a more social way
ACE 6.
I did good career wise, failure was just not an option to me so I worked hard. At school I was put into accelerated classes for gifted kids. But I feel like I could have done a lot better with more support at home. I see that people get points for diversity etc. in college or hiring, but there is no such support for people who suffer silently, and even if there was I have too much pride to let others know just for some brownie points. Life was particularly hard before I found employment, roughly from the age of 18 to 26. That was also the period when my mental health challenges were more difficult and I had not yet learned how to cope. I have not yet gotten any ‘treatment’ for mental health issues. I got some money after making some fortunate investment decisions that allowed me to climb out of poverty, it might be trivial sum for most people out there but it was a lot to me. Lately I get feelings of being an orphan, just out there on my own and not having my own people to share things with or support in life.
My wife is startled sometimes when I react as I do, she doesn’t understand where it comes from. I have forgetfulness and emotional regulation issues. But she is a nice person and helps me out.
I worry about money and the future all the time.
I would like to thank my mom and God for everything, and Jensen Huang.
Will delete this later.
I am suffering from this too, and I am not sure who to turn to or what to do about it.
I work remotely and all my interaction with coworkers is maybe half an hour everyday where we talk about work and nothing else. If I challenge myself to go to office, I find that its only about 33% occupied.
I have few friends but they don't call me anymore and live far apart.
Tried to meet women but it didn't work out.
I think the silver lining of loneliness being so prevalent is that other people are feeling it too and want to do something about it. Knowing nothing about your situation I would guess that statistically if you're feeling lonely then your friends are also likely to feel lonely -- maybe they also say that their friends don't call them. I think this is a great opportunity in that there's desire from other people to connect with you that you could tap into.
Speaking from my own experience, it's very uncomfortable to be forward about trying to make friends / strengthen friendships. It reminds me of the stigma that online dating had when I first did it circa 2010 -- it has a connotation of being "desperate", and maybe that cool people wouldn't have to be desperate. But in my personal experience I've seen this shift a lot in the last year, where there is a lot of relief and appreciation when I mention wanting more friends and hint that I want to be friends with the person I'm talking to (they often respond by saying that they want more friends too). So I personally decided that I would rather risk looking desperate than feel lonely, and I recommend that tradeoff, especially because it's not perceived negatively like we worry it will be.
That's not to say that I think there's some easy option that I think you're avoiding; from my experience it has taken a number of incremental steps (and it's not like I've fully "solved" it). I guess I'm just trying to say don't get discouraged by the magnitude of the situation -- it might seem like a single phone call to a friend could never make a difference, but things like that add up over time.
As for practical suggestions, I've found video games to be a good activity to do with people that I don't live near. I've also found Buddhist meditation to be a great option for this -- it has the same sort of social structure and benefits as organized religion, but without requiring any particular sorts of beliefs or devotions. A lot of that has moved online these days -- I haven't tried this out but there are things like this https://www.sit-heads.com/
There are a lot of group sports (like run clubs or pickle ball) and art classes that are open sign ups where you don't need to know anyone to join, and you can just casually meet people. They might not become friends outside of the activity, but it's an easy entry point to feeling like you're part of your community. There used to be a lot of board game meet ups like this too, but the pandemic kind of killed the scene.
I feel that is an answer for americans.
There is no sports or arts classes where I live. Most people work and go home to watch TV.
A lot of places in the world don't have such option with activities or outdoors.
(Now I'm a bit confused on the difference between Gurugram, and Gurgaon, so I'm hoping I did not mix the locations. If I did I'm terribly sorry. Also, understandably I haven't tried these, so I can't say if they are any good. But there is really only one way to figure that out :) )
I just want to second the recommendation for the board game meetup. Those can be a lot of fun, and a good way get out of the house and socialize. It's also good to bring your own favorite game in case the selection there isn't to your liking; you might find others who love your game but never tried it.
Same city. India is trying to replace British era city names. Bombay/Mumbai. Bangalore/Bengaluru. Hard to get people to stop using an old name they like though!
You have a pretty good variety of coworking spaces nearby at many different price points. Some pretty upscale ones in horizon center! Plenty of decent ones down golf course road. Why not join?
Sounds like you need a more drastic change to get you to snap out of your patterns. Maybe try moving to a new place? I suspect that if you move to a place that has been seeing a lot of population growth in the past couple of years (like Austin or Denver), it's probably easier to meet friends because there will be others in the same situation. You should also try to find an activity or hobby that you are into that has a social component to it. Or even something like volunteering or mentoring-- anything to get you out of the house and communicating more with people in the real world.
I had basically the same problem, and it doesn't help that I'm picky about the people whose company I enjoy, I found going to the mosque more helped me start developing an irl social circle again.
If you're not religious maybe something like a library or a hackspace could help? Going to hackspace helped alleviate some of my loneliness, even though I never managed to develop a social circle out of it.
Some time ago it occurred to me that the price you pay in time and effort absorbing the material from a book like this one is usually incomparably higher than the price of the book itself.
I can assure you that most (if not all) C++11/14 developers that have some prior experience with those standards will find our book useful in various possible ways.
If you don't want to take my word for it, check out the acknowledgements in the book itself -- it has been thoroughly reviewed endorsed by many top-notch ISO committee members and C++ experts.
For everything else I use paid onedrive subscription. The biggest problem is user interface with s3 like storage and predictable pricing because remember you also pay for data retrieval and other storage apis, with dropbox etc you pay a fixed amount. Every year or so I roll over data into the bucket.
But for infrequently accessed data its fine.
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