Seems similar, or at least partially overlap, with what I would say is the best reference on the subject, an Introduction to Statistical Learning from Gareth James et al [1].
I wonder it this one might be a bit more accessible, although I guess the R/Python examples are helpful on the latter.
As someone who has actively written algorithms for passport security verifications, including photo strict requirements and validation, this post really struck a chord with me.
Pix money transfer/payment system. Fully digital, easy to use, fast, cheap, available 24/7, launched by Brazil Central Bank and with massive adoption in the first year.
I am browsing the web in search for NLP resources as I consider a pivot into this new area (I already have a few years in data science and image processing) and finding this post on HN is a bless! I will be going through the course for sure!
And he is going against a lot of money, hype driven startups and arguably inflated paychecks... It is expected to have people misinterpreting and criticizing him.
> Because capitalism and free enterprise are predicated on fair competition and equal access.
This is so important and seldom mentioned in internet discussions.
> Extreme inequality undermines this by allowing those who already have significant wealth to put their finger on the scales (lobbying, anticompetitive practices, etc.)
There is a number os scholars who will point that as we currently stand, those things, including the extreme inequality, are consequence of the current system.
We have been using mostly Dlib[0]. There was the need to develop solutions that can be statically compiled and produce dependency-free dlls and dlib delivered remarkbly on that aspect.
I haven't had success doing so using frameworks such as Torch and TF, even if their toolkit is better to develop new solutions.
Also we get to write code in C++, which can be a big positive when developing machine learning SDKs. I personally still do most of the prototyping in Python though.
I'll be checking the link on the post that mentions that pytorch allows models to be converted to c++, looks promising actually.
Hey, lovely answer, I will take my time to go through all of this.
I currently research mental representation such as emotions in applied computing which is near-at-hand with ToM and although many of the theories seem intuitive, the replication crisis is something that really bothered me (since I come from a STEM background).
I will read through your post and definitely check the sources, thank you very much!
As for your last point, ToM+Autism might be one of the more interesting aspects of ToM research and as far as I can tell, we are far from conclusive theories.
I believe your opinion aligns pretty well with a crescent number of researchers that see in investments like this exactly the scenario for the warp-drive you described.
In your opinion what should computer scientists be focusing on in order to achieve more advanced AI systems? I'm thinking things such as reasoning, causality, embodied cognition, goal creation, etc.
And this is without even delving into the ethics aspects of (some instances of) AI research.
As someone who have computer modelling as a core of my research it makes me happy to see this quote mentioned here.
People on other science forums are generally fast to dismiss computer models as 'unrealistic' and 'non-representative', disregarding their utility.
For those interested in models I always recommend looking at Schelling's segregation model for starters, the premises and conclusions drawn from the experiment are quite interesting.
Hey mate, awesome you messaged! I am sincerely not skilled enough to comment on the minutia of the language. I can say I really love the idea behind it and as far as I can tell the implementation is solid.
From a end user point of view, I thought the official website and documentation felt a bit lackluster, not because it was bad or anything (the Nim tutorials part I & II are a godsend), but I instinctively compared it to languages like Python. Again, this is not a fair comparison, more like a impression. I'd love to see more blog posts like this one [0] or maybe the occasional roadmap.
Ah I understand where the 'self-fulfilling prophecy' is coming, but if anything I talked about Nim to dozens of people already, even if I can't currently find utility for it on my day job (I do ML, Embedded and Image processing, so it is actually really hard to use anything other than c++)
Again, commenting from the outside, it seems it is a hard position because today upcoming languages such as rust and go have some solid enterprise backing.
If anything, I don't think you need to change my mind! The Nim development is something I keep a close eye on and I hope I will progressively incorporate it on my daily tasks. The mentioned regret on the first post has really more to do on how little spare time I have.
I wonder it this one might be a bit more accessible, although I guess the R/Python examples are helpful on the latter.
[1] https://www.statlearning.com/