There are already insurance companies offering coverage for legal expenses. The interesting part here is maybe that by restricting it to patents the insurer has a very clear idea of the risk being covered, as all the patents are public.
Edit: on the other hand your potential customers will be difficult to convince. To pay insurance you must have risk aversion, something I don't expect to be abundant among startups
>But we can and should do better. Why is DLL-hell still a thing? Imports should specify versions, defaulting to a default if none is specified. Languages barely even consider versions today, they are almost always hacked on via magic text strings in filenames.
So suppose you fix the version number when you import the library. You then need to copy paste the version number to other imports in your code base? Or maybe you mean each import may have different versions? which is all right until you need to pass some data between two versions of the same library. This wouldn't be so difficult if semantic versioning or another equivalent was respected, but we know it's a lie. This wouldn't be a problem at all if there were some kind of contracts in place when using modules/package/whatever instead of a silly number based on convention but how to specify the contracts is not that easy, and it's similar to say having static typing. I think improving versioning it's doable but it's very hard.
I'm all about improving tooling, I have been obsessing for a few months about the sad state of program documentation and navigation. Why is the computer doing nothing while I think so hard while I'm programming? Why there is so little money in this space?
> There is even research by now that aims to use ML to find good heuristics for a given family of problems (where the common characteristics of the problems might lend to a dominant heuristic).
I think I have spotted some papers about this for combinatorial problems but not for continuous variables, which makes sense given their relative hardness.
Do you know some papers, journals, conferences about this?
I'm interested specially about the view from people working in optimization research more than in AI.
He got 2/3 of the shares necessary for gaining control. The Bank of Portugal was the only one who could prosecute money forgery and it was being acquired with forged money. This guy was a hacker.
I doubt he would have succeeded nevertheless. In the end the law was changed to apply to him retroactively, which was unconstitutional (and I'm sure it's still unconstitutional on most parts of the world), and that only to give him more sentence time. He would have been removed from the bank, one way or another.
I'm trying to do a PhD part time while working although I don't plan to go to academia, now or in the future.
For me it's a way of focusing on some topic related to my work: I'm a machine learning engineer and I'm doing the PhD in AI/optimization. I have spent just a year on it so success is still far away.
I know people however that went back to academia but they had some previous experience for example as teaching assistants.
Road atlases were the same in Spain. I think the reason is that they are not interactive, that's the all the zoom level you are going to get and so they are extremely dense with information.
I think that for interactive use google maps are way better, they avoid information overload by presenting a very schematic representation. Old road atlases look like versions of Where's Waldo?.
I actually have wireless keyboard, mouse and earphones, both for work and for sport.
But like other commenter says I need nevertheless an space for a 27" monitor, keyboard/mouse and a comfortable office chair. I don't think I have a "mindset", I'm all about improving my day to day, I spend an insane amount of hours in front of my computer. I have been told that my next work computer is going to be a MacBook Pro so I will get a taste of the Apple experience.
I came to exactly post this. I previously had a HP ZBook with Nvidia Quadro inside and although I managed to get it running nicely I recently bought an LG Gram with integrated graphics and I'm not looking back. Both came with a Windows which I immediatly changed to Linux.
If you are not into gaming you can save big bucks and weight buying a laptop with integrated graphics. If you need a GPU for work you most probably need one with >8GB anyway.
PD: Why do so many laptops come with crapware instead of vanilla Windows? Why??? Why do laptop manufacturers tarnish their product so much? It remembers me so much of Android phones.
Exercising is doing physical exercise for the sake of it (health). Training involves planification to reach an objective: some personal record, a competition or aesthetic.
This. I get the M1 must be very good but what I really want is something I doubt I'm going to get: native Office support in Linux. I use Office rarely but on those rare occasions it sucks to use Office 365.
I have been recently looking at several "3d scanning" from monocular images/videos solutions and although they make for impressive demos I'm afraid they are a few years away from robust results.
On one hand you have traditional photogrammetry based on classical image descriptors (from the beginning of the 2000s). A nice open source solution is Meshroom [1]. I have very little experience as I have just tinkered a little but I would say they work OK with geometry of medium complexity and detailed textures. They fail horribly with untextured objects, like for example a candle. They are semi-automatic: you upload some photos and run the pipepiline but for best results it's easy to tweak as there are a lot of knobs to try.
On the other hand you have these deep learning research papers. A lot of them have Colab notebooks you can try yourself (which is awesome, thank you). They can give you these very nice demos for some kind of objects (the ones we have a lot of training data, like human bodies), but they are not truly general and sometimes will generate artifacts. There is some opportunity here if they are integrated in a semi-supervised workflow, like this [2].
I recently tried the Quest 2 and was blown away by the level of isolation from the external world it provides. It's pure concentration. I think VR has lot of potential for productivity (and education).
Regarding the price point of Simula VR my thoughts are that they are only high for a yet unproved product. On the other hand, if the product delivers I could see myself expending 2500€ for personal use. After all I'm evaluating laptops right now for 1/2 the price. And then of course a company can recover that amount multiple times if people is just 5% more productive.
Forgot to mention: I love it's Linux based, as it's my daily driver. I have seen the light but most of the people I know at work have switched to Mac, so I suppose that at some point it would make sense to give that option.
Now, NNs are the ones getting results at computer vision and natural language, and more. I think most people would say that other ML approaches are computational statistics. The goalpost for AI keeps moving.
If you are truly interested in the math of AI I think PAC Bayes learning is more appropriate and your book is Understanding Machine Learning [1] (not an easy read). A more gentle intro would be Learning From Data [2]. If someone recommends a book/paper it would be awesome, I'm always on the look.
Not the parent but I would say you just need a little of statistics and a little of calculus and linear algebra. If you are interested in theory then you need more.
For statistics I would recommend "All of Statistics".
The level of calculus required is to know to differentiate.
Algebra is more important. Any introductory linear algebra book would do. If you are able to multiply matrices and solve equations you can postpone a topic until necessary like for example eigenvectors or matrix factorization.
My advice is to first jump into the pool and learn swimming as needed. But learn swimming, use the concrete problems to motivate yourself.
What is your setup? Because I'm using Fedora and installed Unity because I was curious and deleted it after having problems (I don't remember the error though).
Edit: on the other hand your potential customers will be difficult to convince. To pay insurance you must have risk aversion, something I don't expect to be abundant among startups