Interesting, in Scandinavia Engineering Physics is quite prestigious and many graduates end up as SWEs. In fact many researchers across Sweden hold Engineering Physics degrees in CS and other disciplines outside physics.
In Sweden I definitely observed this. I worked with quite a few SWE's who went to a technical university (LTH, Uppsala as primary examples) and held engineering physics degrees. From talking and working with people, though, I think it's because it's quite easy in Swedish technical university curricula to take SWE courses that count towards your degree. There is also quite much more of an applied engineering approach vs. theoretical than you would expect which might also play a role in this.
Just want to shime in and recommend you to visit Chongqing. Incredibly interesting city to explore, great food and still has some rural China vibe that the other first tier cities start to lack.
Why not pick a problem the interviewer have not solved, and let the interviewee and interviewer solve the problem together. This shows how the candidate is thinking, and how they are working together with others. It will also let the interviewer get a more un-biased view of the difficulty of the algorithm or CS problem.
I think China will create an AI bubble, currently studying in Beijing and all EE and CS students I know focus all their time at either stats, ML or data mining. While I think China might have a big shot at the AI race, I don't think it's useful to have to only focus on AI because it's hot right now, they may lack progress in other fundamental fields that may lead to more important breakthrough and technological change.
It's not just China. At my German university all courses related to AI/ML/data mining/stats are oversubscribed by a factor of 2 to 5. One professor went so far as to stress that he didn't have any experience with deep learning and he'd only cover Bayesian statistics in order to discourage students. I don't think it worked.
I'm pretty sure that there's a similar surge of interest in all countries.
>>I'm pretty sure that there's a similar surge of interest in all countries.
Yup. Same here in India.
People talk as though singularity is next year or so, and like Y2K jobs they have to do AI programming jobs.
Went to campus hiring like a few weeks back. And everyone had one or two show-and-tell AI/ML projects on their resume. Like every one. Everything from fitness apps to face emotion recognition.
Those students will be awfully disappointed when they graduate and discover that most commercial software development work involves just shuffling bits around with no AI in sight.
I could be quite wrong but wouldn’t a course in Bayesian statistics be a lot more useful for someone interested in inference or however you will say it?
It would certainly be useful. I'm interviewing a lot of people for ML roles, and I've noticed a new group of "deep learning only" people coming out of colleges. They know their DL, but have no exposure to what I guess we're calling "traditional ML". Since DL isn't appropriate for all problems, they don't have the skills we want.
We’ve been here before with Japan in the 80s, and it led to an extended 2nd AI winter.
China’s efforts are much larger than Japan’s were, and they have a bigger housing bubble to go along with that to boot. It is interesting to see how history repeats itself.
All it takes for an AI winter to happen is for the hype to go way beyond the reality, the AI doesn’t have to not make money, it just has to make not enough money as expected. It is very much like a housing crash: the houses are still useful to live in, just that their value is not as high as expected.
I think China has plenty of data as well, of its own people at least. It's a surveillance state, and the big corps work closely with the government and so also the universities. My point is more that they have a chance of winning the ML race, but that doesn't matter if there will be more important technological breakthroughs in other areas that will have greater impact on AI than ML.
China does want to gather data, it has deployed tons of sensors and cameras to monitor its population's habits on the street. With the personal credit score implemented it can monitor its population even more thoroughly. How much data it gathers on its population thanks to the Great Firewall we can only speculate.
Granted, population data is one thing since you can incentivize people to share it. Data gathering that requires investment of money into sensors and time for careful placement of them is a completely different thing entirely
Anyone with experience of tech work in SE Asia? I am currently studying in Beijing, and I am really surprised how nice the life-style is here. But from what I've read, software engineering is not the best profession here opportunity-wise.
I think this course is about Computer Architecture as in Computer Organization, ie a course about the layer between digital circuits and operating systems. You probably confuse the term with Software Architecture.
I'm a teen I guess. Haskell as a first language worked great for me, and I happened to write a game and a few scripts in Python two months ago so I think I can answer your question.
So, what made learning Haskell a good experience I think was mainly the strong type system. You could basically even early on use functions you barely knew what its name meant solely based on its type signature. It also made the progress of writing a program very nice, you write it and then it fails, you go back and try fix it, and when it compiles it usually worked.
There are some things to keep in mind though. Wild recursion is really the GOTO of functional languages, it's important to teach how to use the generalized folds and traversals early on. Secondly it is important to have simple analogies to concepts such as functors, monoids and monads etc. If you make sure to not care about these strange names then you can go pretty far by just trying to make the type signatures stick together, and it's not important for a beginner to grasp these directly either.
Python was quite smooth to learn coming from Haskell, the syntax is similar and it simply felt like programming in Haskell's do-blocks. It was very easy to get quickly going, I managed to write a little script talking to a JSON API almost directly after reading the official tutorial. I also digged a little bit into the realm of OOP, I wrote a simple game using Kivy. It felt quite nice actually, I could get some code re-use, although it was harder to get that (as newbie) than in Haskell.
EDIT: Something like typed Racket might been even better when I think about it, since Racket got a lot of resources for complete beginners. Then you get the benefit of easy to use syntax too, although syntax is probably not the biggest problem when starting out. For the record I usually recommend Python or Racket when people ask me where to start, because in the end I don't think it matter much what language you go with first, just make sure not to limit the paradigm of languages you learn afterwards.
