There's quite a bit of misinformation in this comment.
- Tensorflow has very little use for the mathematical concept of a "Tensor", apart from the fact that it is a multidimensional array as a way of organizing data.
- Again, most of what is covered in an Information theory class is coding theory, which is not directly applicable to ML. There are a few superficial connections, however, nothing enough to justify a whole class.
- A class on Harmonic analysis, again, though a beautiful subject, does not have any significant overlap with ML, apart from a few superficial similarities to do with convolution.
- Most ML Ph.d.s don't take these classes, and go on to have very successful careers.
This comment is very typical of a kind of snobbery in ML observers that goes along the lines of "you need to understand all these deep and hard concepts before you start to touch ML". Actually, you don;t. ML is, right now, still quite a young field as far as its branching off from statistics goes. We are still building the groundwork of this skyscraper.
We welcome everyone with any background, and hey, even those with none.
Signal processing can be useful in a few ML domains (eg speech recognition), it would be useful as an elective at least. The best ML researcher I know had a leg up on the whole GPU DNN thing (way back in 2010!) because of his very strong signal processing background (he has an EE degree).
But even a degree specifically on ML isn’t going to cover all of its use cases, I guess (CV, speech recognition, ...).
I do not believe "you need to understand all these deep and hard concepts before you start to touch ML." That is a contortion of what I said.
First point: ML is not a young field- term was coined in 1959. Not to mention the ideas are much older. *
Second Point: ML/'AI' relies on a slew of various concepts in maths. Take any 1st year textbook -- i personally like Peter Norvig's. I find the breadth of the field quite astounding.
Third Point: Most PhDs are specialists-- aka, if I am getting a PhD in ML, i specialize in a concrete problem domain/subfield, so I can specialize in all subfields. For example, I work on event detection and action recognition in video models. Before being accepted into a PhD you must pass a Qual, which ensures you understand the foundations of the field. So comparing to this is a straw man argument.
If your definition of ML is taking a TF model and running it, then I believe we have diverging assumptions of what the point of a course in ML is. Imo the point of an undergraduate major is to become acquainted with the field and be able to perform reasonably well in it professionally.
The reason why so many companies (Google,FB,MS etc) are paying for this talent, is that it is not easy to learn and takes time to master. Most people who just touch ML have a surface level understanding.
I have seen people who excel at TF (applied to deep learning) without having an ML background, but even they have issues when it comes to understanding concepts in optimization, convergence, model capacity that have huge bearings on how their models perform.
The problem with this discussion is that people take field and discuss it as a one single thing.
Imagine B.S degree in medicine and people mixing up the concept of surgeon, medical physicist, ER nurse, practical nurse and hygienist as the same. It would make no sense to put people with different levels of education and specialties into same program.
My worry is that this type B.S degree misleads people. It's not preparing people to continue into ML R&D but at the same time it's not providing solid background for numeric programming or data science programmers.
It would be more beneficial to have B.S degrees with emphasis in numeric programming and data science to prepare programmers for ML, data science, scientific computing, or game development. Then have different pipeline for people who need to study more statistics, math and computer science for ML R&D.
It's like every second post on AI/ML tries to convince everyone how difficult it is and how you need 16 years and 3 PhD's to even approach the level of mastery that they have of this subject.
While may or may not be true - definitely not helpful for a student aspiring to learn this stuff.
he misses many points, including the meaning of the phrase "long-term greedy". It is not about "everyone else in the market was short-term greedy and, as a result, we took all their money. Since traders like money, this was inspiring.", but a philosophy of enlightened self-interest, which I suspect would resonate with the class and professor more. Yet he subtly uses this as an anecdote about how MBAs are dumb and the classes "herd mentality". What aarogance!
if you've read the article, you'll the discussion at hand was concerning stereoscopic 3d, not the fancy light field tech which may be around the corner, nor not.
what matters in the end is being right. When opnions have to turn into action, knowing what is the right thing to do (knowing what technology to use, for example), and a careful balance of tradeoffs is always the sign of good leadership. This may include taking very strong stands on various things.
I've found people have tendencies to delude themselves in certain ways, however. Either being too positive or negative. The former is less harmful than the latter, but both are miscalibrations of judgement.
It'd be interesting to explore bounds on the entropy of these passwords. It'd be a shame if you could guess the rest of the password from the first 2-3 characters.
The same idea can be extended to what you are proposing. The neural net has to be given passwords with high entropy so that it can learn to generate passwords with high entropy only. My post was more of a demo of what can be achieved with neural nets in this regard.
the social nut in VR is going to be the hardest one to crack. It will be big when the solution appears. But we're not close to one yet.
The approach we're taking now is the "avatars and voice" approach, where you interact with other people through VR puppets, essentially. These puppets can mimic your body language and facial expressions and are supposed to increase the depth of human communication through nonverbal cues. The truth is, after experimenting heavily with this, I've found most of it to be pretty gimmicky.
It seems the human mind naturally gravitates to such ideas (e.g. second life), but it never pans out. I'm not sure what the right answer is, or if theres even one.
- Tensorflow has very little use for the mathematical concept of a "Tensor", apart from the fact that it is a multidimensional array as a way of organizing data.
- Again, most of what is covered in an Information theory class is coding theory, which is not directly applicable to ML. There are a few superficial connections, however, nothing enough to justify a whole class.
- A class on Harmonic analysis, again, though a beautiful subject, does not have any significant overlap with ML, apart from a few superficial similarities to do with convolution.
- Most ML Ph.d.s don't take these classes, and go on to have very successful careers.
This comment is very typical of a kind of snobbery in ML observers that goes along the lines of "you need to understand all these deep and hard concepts before you start to touch ML". Actually, you don;t. ML is, right now, still quite a young field as far as its branching off from statistics goes. We are still building the groundwork of this skyscraper.
We welcome everyone with any background, and hey, even those with none.