Category Archives: Uncategorized

Some Resources on End-to-End Sequence Prediction

Important Papers:


Important Implementations:

For reference, here are some papers on the hybrid approach:

Some Thoughts on Hours

Hours is one of the taboo topics in the tech industry. I can say couple of things, hopefully not fluffy:

  • Most hours are self-reported, so from a data perspective. It's really unclean. Funny story: Since I was 23, I work on weekends regularly, so in my past jobs, there were moments I note down some colleagues of mine who claim who work 60+ hours. What really happen is they only work 35-40. Most of them are stunned when I give them the measurement. There are few of them refused to talk with me later on. (Oh I work for some of them too.)
  • Then there is what it means by working long hours (60+ hours). And practically you should wonder why that's the case. How come one can't just issue an Unix command to solve a problem? Or if you want to know what you are doing, how come writing a 2000 words note take one more than 8 hours? How come it takes such a long time to solve your weekly issues? If we talk about coding, it also doesn't make sense. Because once you have the breakdown of a coding problem, you just have to solve them iteratively in small chunks. Usually it doesn't take more than 2 hours.
  • So here is a realistic portrait of respectable people I work with which you feel like he works long hours. What they did actually do?
    1, They do some work everyday even on holidays/vacations/weekends.
    2, They respond to you even at hours such as 1 or 2.
    3, They look agitated when things go wrong in their projects.
  • Now once you really analyze these behaviors : it doesn't really prove that the person works N hours. What it really means is that they stay up all the time. For the agitation part, it also makes more sense to say "Oh, this guy probably has anger issue, but at least he cares."
  • Sadly, there are also many people who really work more than 40, but they are also the least effective people I ever know.
  • I should mention that there are more positive part of long hours: first off learning. And my guess it is what the job description really means - you spent all your moments to learn. You might code daily but if you don't learn, then your speed won't improve at all. So this extra cost of learning is always worthwhile to pay. And that's why we always encourage members to learn.
  • Before I go, I actually follow the scheduling method from "Learning How to Learn". i.e. I took frequent breaks after 45-60 mins intense works. And my view of productivity is to continuously learn. Because new skills usually improve your workflow. Some of my past employers have huge issues with my approach. So you should understand my view is biased.
  • I would also add, there are individuals who can really work 80 hours and actually code. Usually they are either obliged by culture, influenced by drugs or shaped by their very special genes.

Hope this helps,


My Third Quick Impression on HODL - Interviews with Pieter Abbeel and Yuanqing Lin

My Third Quick Impression on Heroes of Deep Learning (HODL), from the course This time on the interviews with Pieter Abbeel and Yuanqing Lin.
* This is my 3rd write-up on HODL, unlike the previous two (Hinton and Bengio), I will summarize two interviews, Pieter Abbeel and Yuanquing Lin in one post because both of the interviews are short (<15 mins).
* Both researchers are comparatively less known than stars such as Hinton, Bengio, Lecun and Ng. But everyone knows Pieter Abbeel as a important RL researchers and lecturers and Yuanqin Lin is the head of Baidu's Institutes of Deep Learning.
* Gems from Pieter Abbeel:
- Is there anyway to learn RL from another algorithm?
- Is there anyway we can learn a game but use the knowledge to learn another game faster?
- He used to want to be a basketball player. (More like a fun fact.)
- On learning: Having a mentor is good.
* Gems from Yuanqin Lin
- Lin is the director of Baidu, when he was at NEC, he won the first Imagenet competition.
- Lin describes a fairly impressive experimental framework based on PaddlePaddle. Based on what he describe, Lin is building a framework which allow researchers to rerun an experiment using an ID. I wonder how scalable such framework is.
- Lin was a physics student specialized in Optics
- On learning: use open source framework first, but learn up basic algorithms.
That's what I have. Enjoy!
Arthur Chan

Why AIDL doesn't talk about "Consciousness" more?

Here is an answer to the question, (Rephrased from Xyed Abz) "Isn't consciousness the only algorithm we need to build to create a artificial general intelligence like humans or animals?"

My thought:

Xyed Abz: I like your question because it not exactly those "How do you build an AGI, Muahaha?"-type of fluffy topic. At least you thought about "consciousness" is important in building intelligent machine.

But then why AIDL doesn't talk about the consciousness more? Part of the reasons is that the English term consciousness is fairly ambiguous. There are at least three definitions: "wakefulness" which humans are awake. A bit like you just wake up, but then you are not too aware of the surroundings. Then there is "attention" which is certain groups of world stimulation is arriving to your perception. And finally is a kind of "cognition access" which is Oh, out out all these things you attended, such as I am typing with my fingers, I feel the keyboard, I listen to the fan noice, I listen to car running outside. I decide to allow "writing" to occupy my mind.
Just a side note, these categorization are not arbitrary. Nor it is come up by me. This thinking can be traced to Christoph Koch and his long time collaborator, Francis Crick (The Nobel Prize Winner of DNA discovery). Stannish Dahaene is also another representative of such thought. I often use this school of thought to explain because they are the ones which has more backup from experiments.
So to your question, we should first ask what you actually mean by consciousness? If you meant a kind of "cognition access", yeah, I do think it is one of the keys to build intelligent machine. Because you may think that all the deep learning machines we build is only one type of "attention" we created, but there is no central binding mechanism to control them. That's what Bengio called "Cognition" in his HODL interview.
Will that be enough? Of course not. Just as I said, if you do build a binding mechanism, you are also suppose to build the perception mechanism to go around it as well. At least that's what's going on with humans.
Now, all these sound very nice, so aren't we have a theory already? Nope, even Koch and Dahaene's ideas are more hypothesis about the brain. But how does this "cognitive access" mechanism actually works? No one knows. Koch believes it is a region call claustrum in the brain which carries out such mechanism, yet there are many disagree with him. And of course, even if you find such region, it will take humans a while to reverse engineer it. So you might have heard of "cognitive architecture" which suggest different mechanism how the brain works.
Does it sound complicated? Yes, it is. Especially we really don't know what we are talking about. People who are super assertive about the brain, usually don't know what they are talk about. That's why I rather go party/dance/sing karaoke. But today is Saturday, why not?
Hope it is helpful!


Certificate Or Not

Many members at Coursera ask about if a Coursera certificate is something useful. So I want to sum up couple of my thoughts here:

* The most important thing is whether you learn something in the process. And there are many ways to learn. Taking a course is good because usually the course preparer would give you a summary of the field you are interested in.

* So the purpose of certification is mostly a way of motivation so that you can *finish* a class. Note that it is tough to *finish* a class, e.g. Coursera statistics suggest that completion rate is ~9-13%. This number might be smaller at Coursera because it doesn't cost you much to click the enroll button. But you go to understand finishing a class is no small business. And certification is a way to help you to do so. (Oh, because you paid $ ?)

* Some also ask whether a certificate is useful for resume. It's hard to say. So for now, there is a short supply of university-trained deep learning experts. If you have a lot of non-traditional experience from Coursera and Kaggle, you do get an edge. But as time goes on, when more learners have achieved status similar to yours, then your edge will fade. So if you think of certificates as part of your resume, be ready to keep on learning.


Tips for Completing Course 1 of

For people who got stuck in Course 1. Here are some tips:

  • Most assignments are straight-forward. And you can finish it within 30 mins. The key is not to overthink it. If you want to derive the equations yourself, you are not reading the question carefully.
  • When in doubt, the best tool to help you is the python print statement. Check out the size and shape of a python numpy matrix always give you insights.
  • I know a lot of reviewers claim that the exercise is supposed to teach you neural network "from scratch". So .... it depends on what you mean. Ng's assignment has bells and whistles built for you. You are really doing these out of nothing. If you write everything from C and has no reference. Yeah, then it is much harder. But that's not Ng's exercise. Once again, this goes back to the point of the assignment being straight-forward. No need to overthink them.

Hope this helps!

Arthur Chan

AIDL Postings Relevant to "Threats from AGI" and Other Misc. Thoughts

Thoughts from your Humble Administrators @Aug 8, 2018 (tl;dr)
Last week is crazy - talks about FB killing AI agents which invent a language were all over the place. I believe AIDL Weekly scooped this time - we fact-checked such claims back in #18, then again #23. Of course, anyone who works on the AI/DL/ML business would instantly smell rats when hearing the term "killing" an AI agents. Then there are 30+ outlets are talking about it, none of which are directly from practicing researchers, that's a point you should start to doubt rationally.
Saying so there are many people who come to me and passionately argue that threat of AGI is a thing *now*. And we should just talk about it to avoid future humanity issues. Since I am an Acting Admin of the group, I think it's important to let you know my take.
* First of all, as long as your post is about A.I., we will keep your post regardless of your view. But we would still ask you to post brain-related topic at CNAGI, and automation-related posts are OoT. Remember, automation is a superset of A.I., and automation can mean large machinery, writing a for-loop, using Excel macros etc. Also if you are too spammy, it's also likely we would curb your posts.
* Then there is your posting - I will not judge you, but I strongly suggest you just run some deep/machine learning training yourself - for the most part, these "agents" are Unix/Windows processes these days. Btw, just like Mundher Alshabi and I discuss - you can always kill the process. (Unix: 'kill -9', Windows: Open "Control Panel"........)
* Some insist that they *don't need any experience* to reason that machines are malicious. Again, I will not judge you. But you should understand that it's much harder to consider your opinion seriously. Read up serious work then. Bostrom's Superintelligence is harder to counter, Kurzweil's LOAR is an interesting economic theory, but his predictions in AI/ML is just too lousy to take seriously for pros.......
* Some also insist that because a certain famous person says that, then it must be the true. Again, I will not judge you. Though, be careful, "argue from authority" is a dangerous way to reason.
* Finally, I hope all of you read up what "Dunning-Krueger effect" is. Basically it is a dangerous cognitive bias, but not until you reflect deeply about intelligence, human or machine, then you would understand all of us are affected by such bias.
Good Luck! And keep enjoying AIDL!
Arthur Chan

A Closer Look at "The Post-Quantum Mechanics of Conscious Artificial Intelligence"

As always, AIDL admin routinely look at whether certain post should stay to our forum. Our criterion has 3 pillars: relevancy, non-commercial and accurate. (Q13 of AIDL FAQ)

This time I look at "The Post-Quantum Mechanics of Conscious Artificial Intelligence",  the video was brought up by an AIDL member, and he recommend we started from the 40 mins mark.
So I listened through the video as recommended.

Indeed, the post is non-commercial for sure. And yes, it mentioned AGI from Roger Penrose. So it is relevant to AIDL. But is it accurate though? I'm afraid my lack of physics education background trip me. And I would judge that "I cannot decide" on the topic. Occasionally new science comes in a form no one understand yet. So calling something inaccurate without knowing is not appropriate.

As a result this post stays. But please keep on reading.

Saying so, I don't mind to give a *strong* response to the video. Due to the following 3 reasons:

1, According to Wikipedia, most of Dr. Jack Sarfatti's theory and work are not *peer-reviewed*. He has left academia from 1975. Most of his work is speculative. And most of them are self-published(!). There's no experimental proof on what he said. He was asked several times about his thought in the video. He just said "You will know that it's real". That's a sign that he doesn't really evidence.

2, Then there is the idea of "Post-Quantum Mechanics". What is it? The information we can get is really scanty.  Since I can only find a group which seems to dedicate to such study, as in here.  Since I can't quite decide if the study is valid.  I would say "I can't judge."  But I also couldn't find any other group which actively support such theory.  So may be we should call the theory at best "an interesting hypothesis".  And Sarfatti build his argument on the existence on "Post Quantum Computer". What is it?  Again I cannot quite find the answer on-line.

Also you should be aware that current quantum computer have limited capability.  D-Wave quantum computing is based on quantum annealing, with many disputed whether it is true quantume computing.  In any case, both "conventional" quantum computing and quantum annealing has nothing to do with Post-Quantum Computer. That again you should feel very suspicious.

3a, Can all these interesting theory be the mechanism of the brain or AGI? So in the video, Sarfatti mentioned brain/AGI for four times. His point are two, I would counter them right after, first is that if you believe in Penrose's theory that neurons is related to quantum entanglement, then his own theory-based on post quantum mechanics would be huge. But then once you listen to serious computational neuroscientists, they would be very cautious on whether quantum theory as the basis of neuronal exchange of information. There are many experimental evidence that neurons operate by electrical signal or chemical signal. But they are in a much bigger scale than quantum mechanics. So why would Penrose suggested that have make many learned people scratch their heads.

3b, Then there is the part about Turing machine. Sarfatti believes that because "post-quantum Computer" is so powerful so it must be the mechanism being used by the brain. So what's wrong with such arguments? So first thing: no one knows what "post quantum-computer", that I just mentioned in point 2. But then even if it is powerful, that doesn't mean the brain has to follow such mechanism. Same can be said with our current quantum computing technologies.

Finally, Sarfatti himself believes that it is a "leap of faith" to believe the consciousness is wave. I admire his compassion on speculating the world of science/human intelligence. Yet I also learn by reading Gardner's "Fads and Fallacies" that many pseudoscientists have charismatic personality.

So Members, Caveat Emptor.