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B.S. In Artificial Intelligence – Curriculum
71–80 of 95 posts
Re: B.S. In Artificial Intelligence – Curriculum
#72Surprised to see only 6 mentions of bullshit in this thread.
Re: B.S. In Artificial Intelligence – Curriculum
#73Disappointed 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…
Cognitive science warns away from a heavy dependence on behavioralist/probabilistic approaches in many cases.
That CogSci special requirement seems to be more reasonable one aimed to guide thinking about past AI and philosophical struggles than a generic philosophy class for an undergraduate student.
Of course, there does seem to be room for a few philosophy electives here should the student enjoy it.
Re: B.S. In Artificial Intelligence – Curriculum
#74I'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…
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, ...).
Re: B.S. In Artificial Intelligence – Curriculum
#75Earlier quoted context omitted.
Why I left the nano engineering program super fast. Come to find out, nobody hires for that. You can go into research only. While a traditional engineering degree can do research or thousands of jobs.
A traditional engineering degree at the BSc level does not do research. Any exceptions you can think of are rare and have more to do with the individual than the degree.
I disagree. You can do research in industry. It just probably won't be the kind that gets published. But yea, I meant to point out that after a Masters or PhD, there is still a ton of research in EE, civE, ME, chemE, biomed, and even IE, but there is also tons of industry jobs. Not so much in the fad fields. Even nanotech companies would rather a chemE.
Re: B.S. In Artificial Intelligence – Curriculum
#76Earlier quoted context omitted.
Why I left the nano engineering program super fast. Come to find out, nobody hires for that. You can go into research only. While a traditional engineering degree can do research or thousands of jobs.
This degree is meant for the ML high school virtuosos who know they'll be going on to grad school, just like a pre-med program. All the seven people from my undergrad's engineering physics program are in PhD programs now and I'd suspect your nanoengineering program has a similar trajectory.
Yea, but the problem is a lot of students coming out of highschool don't know that and don't learn it until it is too late. That needs to be explained up front.
Re: B.S. In Artificial Intelligence – Curriculum
#77Earlier quoted context omitted.
And since the AI knows how to design an AI system, it underwent an intelligence explosion and turned out of control...
Perhaps it will decide not to make something which could replace itself.
“If I won't do it, something else will" - rationalizations and excuses will have to evolve once AI grow out of "I was just taking orders!"
Re: B.S. In Artificial Intelligence – Curriculum
#78I know that “artificial intelligence” in its current state is a load of BS but I didn't know there are curricula for BS up until now.
Re: B.S. In Artificial Intelligence – Curriculum
#79I'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…
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 fashion, you're 90% of the way there. Knowing what these terms mean, and why you need to understand them is waaaay more important than understanding the math behind SVM. It almost becomes boring at that point, because it's the same crap over and over. Doing actual AI research, that's something completely different.
I mean just look at how very simple these Keras examples are, and these are really quite advanced and powerful deep learning models: https://github.com/keras-team/keras/tree/master/examples. You definitely do _not_ need a PhD or even a Masters, to understand, re-implement or tweak on any of these models if you understand how to rigorously test the resulting model.
Research: https://www.researchgate.net/publication/13853244_Long_Short...
Practice:
model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))Re: B.S. In Artificial Intelligence – Curriculum
#80I'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…
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…
> 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.
If you're working towards such a specialised degree, the target shouldn't be "I can use Tensorflow", it should be "I can write a simpler version of Tensorflow".