nbeleski

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.

[1] https://www.statlearning.com/

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.

Awesome work!

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!

Can this statically compiled? Why would anyone opt for this instead of Torch or DLIB?

I wonder how the passive voice detection is done... could be useful for a colleague research of writting patterns.

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.

[0] http://dlib.net/

Hey, thanks por posting this and adding to the discussion. You raise some points that I feel are very important.

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.

Would you kindly expand on the issues with ToM and embodied cognition that were affected by the Replication Crisis?

You mention a main component that was badly hit, what would that be?

Thanks in advance!

I had the same two games in mind when I saw it! Looks promising.

I wonder how the AI behaves... Dwarf Fortress is such a gem of a game.

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.

This is lovely, thank you for your work. I will be forwarding this to a number of colleagues.

I am speaking from memory, but iirc Libet's study wasn't without flaws and it was contested many times during the years.

More researchs like this are positive, as this is a far from solved field.

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.

thanks for this! It is probably a smarter way to share the pic given the number o dead links...

Such an amazing piece of work from one of the greatest thinkers of the past century.

I wonder how many CS students finish their degree without having read Turing papers.

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.

[0]: https://nim-lang.org/blog/2018/06/07/create-a-simple-macro.h...

I haven't used Nim for anything else since then. If I had to give a simplified critique, the language in itself is still springing to life and the user base and documentation are small.

I currently don't have the time to sink into something that could end up being a gimmick only, while there is some hardcore optimization needed in one of our services that could be approached through a better parallelization using Rust.

While from my point of view Nim is only interesting, Rust on the other hand seems promising. (I don't mean to compare different purpose languages, the problem stands on my spare time to study and the company needs, if that makes sense.)

I used AoC to try and learn a bit of Nim as I liked the idea of writting simple scripts that could run many times faster than Python. I somewhat regret it though.

I actually considered Rust given its popularity. I program C++ for a living, but I'd love to find some use for Rust.

Rust will probably my language of choice for this year's challenge.

Isn't most of DAI stability artificial through bots because it is just a cryptocurrency-collateralised derivative contract which uses ETH for the collateral?

It will eventually fail too. When ETH collapses they won't be able to hold the stability for long.

I remember recently reading about the myopia increase in China and it was largely attributed to children staying much more inside, out of the sunlight, causing the eye to under-develop iirc.

I wonder how much these are correlated.

> working in a nice company that lets me solve hard software problems is the most important thing

A thousand times this. Working for a small company it is expected that they won't be able to pay a highly competitive salary, but I have quite a few benefits.

However the most important part by far is being able to work solving software problems. Sometimes when doing explorative work I actually get to implement and (hopefully) reproduce recent scientific papers results. The highly technical work plus the low-pressure enviroment has been great for my mental health.

I even get to leave early the days I have classes, with no reduced pay.

I am of the opinion that in a couple of decades we will look back at all these social networks and find that they caused a lot of problems, in social and personal levels.

Facebook, Twitter, Instagram, even Linkedin, do they really bring more benefits than detriments? I personally don't think so.

Given the results obtained by the OpenAI in Dota[1] (with asymmetrical teams nonetheless) I am pretty confident RL could be used to train a pretty efficient pokemon pvp agent. From my experiences the nuances and mindgames/predictions in a pokemon battle are much simpler than those in a high level chess/go game.

I would say the model isn't as straightforward as the Mario or Sonic AI players, but is still achievable. Actually, I wish I had more time because this is definetly a project I would like to tackle.

[1] https://blog.openai.com/openai-five/

Choosing somethind to dedicate time and effort is consistently one of the hardest decisions one can take. Choosing something (or sometimes someone) can mean you are giving up on an (almost) infinite number of other options.

I encouter this pretty frequently between people getting into serious cientific research.

Computer Science is a much bigger field than it once was. There are many aspects where compsi is useful from a interdisciplinary point of view, and this imho, entails working with researchers from different specific areas.

You shouldn't expect a computer scientist to deeply understand sociological/psycological/philosophical questions, but they can and should to some degree identify where knowledge from other fields is important. My understanding is that they are not asking researchers to predict the future (although this is apparently what the interviewee imply), but simply identify that there might be (societal) consequences, good or bad.

This is why in areas like social robotics and HRI you will find more and more articles that are written by researches from different areas. E.g. a Computer Scientist and a Psychologist.

We already have a number on embedded processors and controllers in cars today, including systems responsible for critical operational parts of the car.

How autonomous systems will make anything different? Why do you believe people aren't currently hacking cars?

I don't intend to be critical at all, I believe this is a very interesting point too that should be discussed. (I hope to check the links on my interval).