B.S. In Artificial Intelligence – Curriculum • Hacker News Board

Re: B.S. In Artificial Intelligence – Curriculum

#81

Post by Qasaur » Thu, Sep 06, 2018, 12:09 PM UTC

roymurdock wrote: post #70

hestefisk wrote: post #66

Disappointed to not see any philosophy of science subjects in here, whilst it is really the cornerstone of “thinking about AI”. Yes ethics is highly relevant, but I think it is more important for AI / ML practitioners to be able to reason about the foundations, methodology, and limitations of AI from the perspective of human knowing, what constitutes intelligence, scientific reductionism etc. I suppose US schools are…

Show me a system that can even come close to approximating “thinking” and remembering/synthesizing info on any of the deeper levels you discussed and I’ll agree. We need to improve the actual technology first before we shape it in an ethical or philosophical way. Otherwise AI will continue to be ever more fine tuned linear regressions from camera feeds stacked and trained into “smart” neural networks.

>We need to improve the actual technology first before we shape it in an ethical or philosophical way.

I disagree. How can we even begin to design or improve a system that emulates intelligence if we do not even know what it means to be intelligent?

Re: B.S. In Artificial Intelligence – Curriculum

#82

Post by tome » Thu, Sep 06, 2018, 12:35 PM UTC

Qasaur wrote: post #81

roymurdock wrote: post #70

Earlier quoted context omitted.

Show me a system that can even come close to approximating “thinking” and remembering/synthesizing info on any of the deeper levels you discussed and I’ll agree. We need to improve the actual technology first before we shape it in an ethical or philosophical way. Otherwise AI will continue to be ever more fine tuned linear regressions from camera feeds stacked and trained into “smart” neural networks.

>We need to improve the actual technology first before we shape it in an ethical or philosophical way. I disagree. How can we even begin to design or improve a system that emulates intelligence if we do not even know what it means to be intelligent?

"We" (in aggregate) are not trying to design a system that emulates intelligence. "We" are trying to solve problems, make money, and accumulate power by using techniques that are only expediently described as "intelligent" or "learning".

Re: B.S. In Artificial Intelligence – Curriculum

#83

Post by nabla9 » Thu, Sep 06, 2018, 1:21 PM UTC

iheartstartup wrote: post #46

danielmorozoff wrote: post #23

I'm a bit concerned that this will be too superficial and not really cover ML or the mathematical foundations well. ML and 'AI' are very multifaceted fields that require a strong foundation in mathematics. As such, the lack of information theory, signal processing, fourier analysis, abstract algebra (from a mathematical standpoint) not to mention CS courses is disheartening. This seems like a major tailored to the st…

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 who…

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.

Re: B.S. In Artificial Intelligence – Curriculum

#84

Post by danielmorozoff » Thu, Sep 06, 2018, 1:25 PM UTC

iheartstartup wrote: post #46

danielmorozoff wrote: post #23

I'm a bit concerned that this will be too superficial and not really cover ML or the mathematical foundations well. ML and 'AI' are very multifaceted fields that require a strong foundation in mathematics. As such, the lack of information theory, signal processing, fourier analysis, abstract algebra (from a mathematical standpoint) not to mention CS courses is disheartening. This seems like a major tailored to the st…

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 who…

I think you misunderstood me.

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.

https://en.wikipedia.org/wiki/Machine_learning *https://www.amazon.com/Artificial-Intelligence-Modern-Approa...

Re: B.S. In Artificial Intelligence – Curriculum

#85

Post by danielmorozoff » Thu, Sep 06, 2018, 1:33 PM UTC

laichzeit0 wrote: post #79

dagw wrote: post #52

Earlier quoted context omitted.

ML and 'AI' are very multifaceted fields that require a strong foundation in mathematics. As a mathematician with a strong foundation in all those things you mention (and more) I don't think it's really necessary. I've never found my knowledge of algebra tensors in any way useful or relevant when working with tensorflow for example. On rare occasions I might get some insight like that working with the Fourier transfo…

> A large part of ML today is very much an applied practical field. Agreed. Once you understand the difference between bias, variance, training, test and development sets, cross-validation, feature selection, normalization, precision, recall, F-score, Matthews correlation coefficient, regularization, imputation techniques for missing values, overfitting, etc. I.e. you know how to build and test models in a rigorous f…

I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience

Re: B.S. In Artificial Intelligence – Curriculum

#86

Post by dagw » Thu, Sep 06, 2018, 2:04 PM UTC

danielmorozoff wrote: post #85

laichzeit0 wrote: post #79

Earlier quoted context omitted.

> A large part of ML today is very much an applied practical field. Agreed. Once you understand the difference between bias, variance, training, test and development sets, cross-validation, feature selection, normalization, precision, recall, F-score, Matthews correlation coefficient, regularization, imputation techniques for missing values, overfitting, etc. I.e. you know how to build and test models in a rigorous f…

I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience

To answer that question and create a plan to tackle that problem requires knowledge and experience

Sure, but you don't necessarily need knowledge about tensor fields and Fourier analysis.

Re: B.S. In Artificial Intelligence – Curriculum

#87

Post by laichzeit0 » Thu, Sep 06, 2018, 2:06 PM UTC

danielmorozoff wrote: post #85

laichzeit0 wrote: post #79

Earlier quoted context omitted.

> A large part of ML today is very much an applied practical field. Agreed. Once you understand the difference between bias, variance, training, test and development sets, cross-validation, feature selection, normalization, precision, recall, F-score, Matthews correlation coefficient, regularization, imputation techniques for missing values, overfitting, etc. I.e. you know how to build and test models in a rigorous f…

I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience

What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You need a Computer Science degree to develop and debug software". The libraries, frameworks and operationalising of ML models is mature enough at this point that it's accessible to a wider audience than just people with research-level academic backgrounds.

I'm not saying that your average web developer with no formal training can or even should be putting this kind of stuff in production. But someone with an undergraduate degree in Computer Science that's had a year or two of calculus and linear algebra and first year mathematical statistics should have no problem whatsoever in practice doing ML/AI. I mean look at this:

  from sklearn.model_selection import KFold
  # Define 10 fold cross-validation
  cv = KFold(n_splits=10)
  from sklearn.model_selection import GridSearchCV
  svm_model = GridSearchCV(svc_pipeline, param_grid=svc_parameters, scoring='f1_micro', cv=cv)
  svm_model.fit(X_train, y_train)

A few lines and you're doing hyperparameter optimization on an SVM model with cross-validation. What a time to be alive.

Re: B.S. In Artificial Intelligence – Curriculum

#88

Post by ptd » Thu, Sep 06, 2018, 3:15 PM UTC

hestefisk wrote: post #66

Disappointed to not see any philosophy of science subjects in here, whilst it is really the cornerstone of “thinking about AI”. Yes ethics is highly relevant, but I think it is more important for AI / ML practitioners to be able to reason about the foundations, methodology, and limitations of AI from the perspective of human knowing, what constitutes intelligence, scientific reductionism etc. I suppose US schools are…

Any resources you would recommend to learn more about the philosophy behind AI/ML?

Re: B.S. In Artificial Intelligence – Curriculum

#89

Post by msie » Thu, Sep 06, 2018, 3:33 PM UTC

I find it odd that a Bachelor of Science is abbreviated as B.S. here. I thought it would be BSc. Interestingly, if you search the site for BSc you get a number of hits where it's used properly.

Re: B.S. In Artificial Intelligence – Curriculum

#90

Post by noetic_techy » Thu, Sep 06, 2018, 4:03 PM UTC

I'm of the opinion that 5-12 of these 32 classes are not relevant and just add additional debt: Anything with the word BSAI in the descriptor, and the ethics class can be gleaned from reading a single book. I'm sure I will get a lot of consternation from that statement, but it's time to eliminate the bloat from college and realize that college debt is real and weighing down an entire generation, and the truth is no matter how you spin it, most of this stuff is NOT necessary for your field. If you don't use the knowledge, your neurons will atrophy in that area.