10

As a full-time AI researcher myself, I'd say that a PhD in machine learning would certainly be one useful option. However, in order make much-needed progress, AI needs avoid falling into the trap of thinking that currently fashionable methods are any kind of 'silver bullet'. There's some danger that a PhD that heads straight into (say) some sub-sub-sub area ...


9

I work as a professor, and recently designed the mathematics requirements for a new AI major, in consultation with many of my colleagues at other institutions. The other answers, particularly this one do a good job of cataloging all the specific topics that might be useful somewhere in AI, but not all of them are equally useful for understanding core topics. ...


7

That question doesn't really make sense: deep learning is a sub-topic of machine learning, so you can't really 'skip' it. It's a bit like "I want to learn about trigonometry, but do I need to do geometry first?" Having said that, in order to make sense of deep learning you should really know about the general principles of machine learning, otherwise you ...


7

Welcome to AI.SE @Kate_Catelena! I teach AI courses at the undergraduate level, and so have seen a lot of semester projects over the years. Here are some templates that often lead to exciting outcomes: Pick a new board or card game, and write a program to play it. Your course has probably covered Adversarial Search, and may also have covered Monte Carlo ...


6

Over the years, many people attempted to define artificial intelligence. A lot of those definitions are summed up by Stuart Russell and Peter Norvig in their book Artificial Intelligence - A Modern Approach The definitions of AI can be summarised as falling into the following categories: Those that address thought process and reasoning (how an AI ...


6

This is a good question. I tend to think the answer is yes it is necessary to know the details, because a person without mathematical understanding of these algorithms cannot consistently make a model as good as someone who does have that understanding. The reason is right at the core of computer science: abstractions are useful, but usually obscure details....


5

Reinforcement Learning can be explained by a few equations. However I assume that this is not what you are looking at since the explanation should be for someone having a non-STEM background. Not to say non-STEM folks are not able to understand math equations, but intuition comes easier with words and examples in my opinion. Reinforcement Learning is about ...


5

Research on AI seems to be getting wider these days (2016). First, "obvious" few departments (no order): Computer Science (e.g. computation theory, algorithms): AI researchers there assume that intelligence is a kind of computation, under various forms (e.g. a neural network, a logic system). Software Engineering: Assuming we find a good model for AI, how ...


5

Also a few others options (that I listen to) include: Learning Machines 101 Machine Learning Guide Machine Learning - Software Engineering Daily


5

Great question Dennis! This is a perennial topic at AI conferences, and sometimes even in special issues of journals. The most recent one I recall was Moving Beyond the Turing Test in 2015, which ended up leading to a collection of articles in AI magazine later that year. Usually these discussions cover a number of themes: "Existing benchmarks suck". ...


5

The famous book Reinforcement learning: an introduction by Sutton and Barto provides an intuitive description of reinforcement learning (that everyone is possibly able to understand). Reinforcement learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal. The learner is not told which actions to take, ...


5

Humans are set loose in the world and go about their days doing stuff. Whenever they do specific things, their brain sends them good signals (endorphins, joy, etc.) or bad signals (pain, sadness, etc.). They learn through these signals which things they should be doing and which things they shouldn't be doing. Sometimes the signal is immediate and you know ...


5

The most I can visualize or perceive are 4 dimensions. Yes, 4, because I can also watch videos (which have 3 spatial dimensions and 1 temporal one). Remember Einstein's spacetime? When dealing with $n$-dimensional spaces, for $n > 4$, I simply do not care about visualizing them in my head, but, as someone suggests, we can think of them as "degrees of ...


4

I cannot comment about the process for AI for academia. I can compare AI for competitions and AI for business. To clarify whatever I say is about ML not any other AI techniques. The process might be different for other techniques. But most of things that I say are general enough that I am assuming should still apply. The main difference that I saw while ...


4

I have several undergraduates working on multiagent deep RL problems for their theses, but most of them have been working for 8-9 months. 2 might be a stretch. Good multiagent deep RL problems for a bachelor's thesis might look something like: Pick an older video game, which has been studied using Deep RL, but not in depth. Right now my students have been ...


4

In addition to the points already listed in John's answer, some factors that can help to reduce / mitigate the risk of overfitting to commonly-used benchmarks as a research community are: Competitions with instances of problems hidden from entrants: as far as I'm aware this is particularly popular in game AI (see the General Game Playing competition and ...


4

I'll add a few, though I'm also not sure what exactly would constitute an "academic" podcast. I'm not going to link everything, they should be easy enough to find. Partially derivative Data sceptic This Week in Machine Learning and AI Concerning AI Exponential View Talking Machines


4

Adding something to nbro answer, from my personal experience there are also some hints that can quickly tell you if you're dealing with a good machine learning paper, i.e. worth to read in its entirety or not. In random order: Clear contribution description: machine learning and artificial intelligence in general are both broad fields. A paper can be about ...


3

Let me answer your questions one by one. Submit it to a conference Let's start with the optimistic case. Say your paper gets accepted! You can upload your preprint on arXiv with the "arXiv.org perpetual, non-exclusive license to distribute this article (Minimal rights required by arXiv.org)". It is a non-Creative Common License that does not provide any ...


3

Just A Rather Very Intelligent System (J.A.R.V.I.S.) in Iron Man (and related films, such as The Avengers) is something (a personal assistant) that people are already trying to develop, so JARVIS is a quite realistic artificial intelligence. Examples of existing personal assistants are Google Assistant (integrated into Google Home devices), Cortana, Siri and ...


3

Some other details you could mention are: total number of model parameters (e.g. 1.2M or 0.15M) & depth of the network (e.g. 38-layered network) family/style of the network architecture (e.g. encoder-decoder arch., LSTM) specifics of connections between network layers (e.g. residual-, dense-, skip-connections) specifics of individual components of the ...


3

As far as simple algorithms like Gradient Descent are concerned, you need to have a good grasp of partial derivatives. Especially if you want to implement neural networks. Also most algorithms are vectorised to improve computing speed and so you need to be comfortable with matrix math. This involves being really quick and comfy with dimensions of matrices, ...


3

I think the answer depends very much on why you are reading the paper, what are you trying to get out of it? There are plenty of papers that I "read" (or often really just quickly skim through) where I'll definitely not understand all the math. More often than not, this will be because I don't actually care to deeply understand it. There is plenty ...


3

Of course, whether or not you will need to know and use C++ depends on the topics you will research during your Ph.D. or job. If you'll need just to use and/or combine some existing ML models (yes, in a Ph.D., you're expected to come up with new ideas/tools), then you won't probably need to know C++, as the most commonly used libraries for machine learning ...


2

To better understand my point of view, I am using deep learning for geomatics and teledetection purposes. So after reading with interest the great two previous answers, I would like to add my small contribution to this thread. First, I would like to emphasis Insight knowledge: I do agree with the second point of Dennis's answer, "Simple" and well ...


2

Here are some possible options Music Generation using GA/MA Open AI's gym projects 2048 on RL and search algorithms Fixing bugs in the source code of some AI software project


2

There are several conferences dedicated to AGI or human-level intelligence, such as AGI Conferences (organized by AGI society) Biologically Inspired Cognitive Architectures (organized by BICA Society) Advances in Cognitive Systems IEEE Task Force on Towards Human-like Intelligence The conferences focus on topics such as cognitive architectures, autonomy, ...


2

I would like to mention WOPR from War Games, maybe is an old movie for your students, but it is a more realistic IA centered around the problem of playing board games (if you exclude the part about deciding that a game is not worth the time). Also I remember an artificial assistant in "The Time machine" that was more convincing than J.A.R.V.I.S because it ...


2

Based on past publications, here are some journals and conferences where you can possibly publish or present a research paper on geometric deep learning or graph neural networks Neural Information Processing Systems (NIPS) International Conference on Learning Representations (ICLR) Conference on Computer Vision and Pattern Recognition (CVPR) International ...


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