When you were interviewing the candidates I was also interviewing you.
You opened yourself like a book.
π
2016-05-09
2016-04-20
Hypes
Nowadays a quick way to filter a job/school application is to see whether and how it says the candidate wants to do machine learning.
(Some neural network probably already existed precisely for this.)
Machine learning by itself is not the problem (quite on the contrary).
The problem is whether you can even form your own independent opinions.
When something (investment, technology, or research field) becomes hot it is already too late to bandwagon.
Those pioneers you see today started (and stuck to) their stuff when it is not yet hot.
Stick with your passion, belief, and opinion might not lead to success, but at least you can have fun, face less competition, and success/fail in your own style.
And if you are smart and creative enough you can have the cake and eat it.
Say your expertise and/or interests are about user interface design. But you also want to do some machine learning like everyone else.
You can switch field, and compete with a lot of smart people who have more passion and knowledge.
Or you can stick with user interfaces, and use machine learning to make them better. You can pick up something new without ditching what you already have.
2016-03-27
Equality versus fairness
A CEO gets paid 100 times the average employee salary. It is definitely not equal. But is it fair? The answer depends on whether the CEO has contributed 100 times than the average employees.
It is not always easy to tell fairness from equality, but it is important not to confuse the two.
Fairness should be maintained, but it is unfair and counter-productive to enforce equality.
Algorithmic species
Algorithms can already devise algorithms and write programs.
It is just a matter of time before they can do that in a scale massive enough to displace many, if not most, programming jobs, just like what robots have already done to the manufacturing jobs.
2016-03-08
δΈθη₯η§
“You can observe a lot by just watching” – Yogi Berra
Sometimes some very little things can reveal a lot of useful information. Just watch carefully.
2016-02-10
It might not be good to be a good student
It is usually not too hard for smart kids to perform well in schools; just excel in what you are told to do, such as taking courses.
This is a deterministic process with well-defined goals and tasks that reward smartness and hard working.
However, real world is chaotic and ambiguous. You have to figure out what to do, with shifting targets and ever-changing environments.
This is why school performance does not directly translate to real-life performance: the required mentality and skills are not the same.
This is also why being a good student might not be a good thing for you. You are so used to this deterministic input-output process that you might be very frustrated by the non-deterministic nature of the real world, when starting your first job or research project.
In contrast, not-so good students might adapt better to the real world, because they already have enough failure experiences and are not yet cast into conformity.
PS
I was lucky to be a student who was considered good in performance and bad in behavior.
π
2016-02-01
The cat experiment
Once, when I was around 9 or 10, I was visiting my aunt’s place.
One of the cousins, X, and I were standing near the swimming pool. The family cat walked by. Cousin X and I got into the discussion about whether cats can swim. I have seen a few dogs and at least one horse swam, so I was pretty sure the answer is yes (cats seem more agile). Cousin X disagreed (he is older but not necessarily smarter), so we decided to have a bet.
Clearly, the only way to settle the bet is to experiment, so I grabbed the cat and threw it into the pool.
(That was before the age of YouTube and Google, BTW.)
What followed was amazing, and happened like within a few milliseconds. The cat sprang on the water surface like a trampoline, and immediate landed back near my feet. It was dripping, so it clearly fell into the water, but I had no idea how it managed to jump back. Meanwhile, our debate remained unsettled.
I am trying to come up with a very concrete way to tell a new PhD student how to decide whether someone is suitable for (scientific) research. So here is my try. Let me know if you have better ideas.
Do you like to ask questions that seem interesting at least to you (e.g. whether cats can swim)?
Do you enjoy finding the answers yourself through investigations and experiments (e.g. grab the cat and throw it into the pool, and observe what happens)?
Are you very comfortable with the consequences, regardless of the outcomes of the experiments (e.g. the cat neither swam nor sank and my aunt beat me up)?
Can you do this continuously as a career? Imagine it is Friday lunch time, and all the works you have done this week have turned out to be failures (e.g. no other ways you have tried can tell you whether cats can swim).
You have no idea what is going to happen this afternoon when you try your 101th experiment with that cat.
If you hesitate for any of these questions or you think I am crazy, you are probably not suitable for research. At least, you will not be happy or successful.
Talent and personality are important; you have to be sufficiently smart and tough for research. But passion is even more important; the only way to be truly happy and productive is to do what you really like.
2016-01-29
School versus job performance
How predictive is the school performance for the eventual job (and life) performance of an individual?
This is a very important question. The schools are supposed to educate what is actually useful. (But clearly that is not the case in practice.)
This is also a very broad question under perennial discussion.
In my personal experiences on the creative side of computer science (e.g. research and development for the cutting edge of graphics and HCI), there is a weak positive correlation between school and job performance (around 0.2 to 0.3 if I have to be numeric).
Good school performance reflects positive traits such as talent and work ethics, but also negative traits such as conformity, lack of creativity, and risk aversion.
This is why standard statistics, like grades, schools, and rankings, are not enough and sometimes even misleading. We have to look at more practical evidence, such as publications, projects, and recommendations.
This is also why recruiting top students and employees is very challenging. Top schools and companies do have advantages in attracting top talents, but we only get what we look for. Many of the best people I have worked so far had been bypassed by the traditional screening standards. Conversely, I have also seen many weak people in top institutions.
Maybe one day data analysis and machine learning will solve this problem.
Before that, I rely on the good old way of people reading.
2015-09-10
Failure cases
Successes are good, but when I look at them I am not sure what I can learn.
In contrast, when I look at failure cases, I know exactly what to avoid.
I have been considering sharing my failure cases for a while, following some prior examples such as this failure CV.
My paper reviews are not suitable because my co-authors might not be comfortable with sharing and my single-authored papers usually just got in.
Thus, I am sharing all my grant proposals + reviews, funded or not.
To avoid pissing off the funding agencies and to benefit those in real needs, please ping me for the information if interested.