Tag Info

Hot answers tagged

18

This is somewhat akin to asking if a hammer is the best tool for building houses. It's just one of many, and not all problems are well suited to it. Other interesting techniques include: Evolutionary Algorithms Genetic Algorithms Hill-Climbing + Simulated Annealing Expert Systems Fuzzy Logic Neural Networks are definitely pretty cool, but they are not ...


18

I study artificial intelligence in a masters program, and we use neural networks quite a lot. They are actually quite useful. I think the problem for neural nets are their name. This both confuses what a neural network actual is, and makes some people question their merits because they expect them to act like brains, when they are really a fancy type of ...


9

A neural network is a class of computing system. They are created from very simple processing nodes formed into a network. They are inspired by the way that biological systems such as the brain work, albeit many orders of magnitude less complex at the moment. They are fundamentally pattern recognition systems and tend to be more useful for tasks which can ...


7

Work in binary: 0, 1, 10, 11, 100, 101... Know your math: 0+0=00, 0+1=01, 1+0=01, 1+1=10 Know your logic: or, and, not, xor... Find that the low bit is a XOR and the high bit is a AND. Expand the principle of one bit to 8, 16, 32, 64 bits Build it with logic gates. If you want to know more, see my answer to How is fundamental mathematics efficiently ...


5

Depends heavily on how you are trying to make the prediction. Most PRNGs that I've used usually operate on a ring of numbers within a finite range. The seed decides what number you start on, and you progress around the ring one number at a time. The order of the numbers appears random, but their order is actually very deterministic. For example, lets say ...


4

Artificial intelignce and machine learning are unsolved problems in computer science and so there is no real "best approach" yet. No matter what specific techniuqe you use, success in these fields will require a strong understanding of algorithms, statistics and probability. I don't think its a good idea to limit yourself to neural networks or any other ...


4

If the random number generator is reasonably good, it is impossible to predict the Nth number given only the prior N-1 numbers. Any random number generator that is used for cryptographic purposes, for example, will be sufficiently random that you won't be able to predict the next number. If you've got a poor random number generation, on the other hand, it ...


3

Thad Starner (now at GA Tech) was able to get this to work with HMMs and a cap-mounted video camera back in 1995. I imagine you'll have some good luck along those lines with the massive increase in processing power and sensors since then. Thad Starner and Alex Pentland. Real-time american sign language recognition from video using hidden markov models. In ...


3

No, they're not, for the following reasons: Lots of algorithms are capable of attaining the Bayes error rate in the limit. ANNs are very computationally intensive. In small sample learning the most important attribute a classifier can have is making correct assumptions about the structure of your data. ANNs are hard to interpret compared to, for example, ...


2

It is a mathematical scheme for building a equation (taking multiple numerical inputs and providing a single numerical output) with adjustable coefficients weights. There are algorithms that can adjust the coefficients to make the equation approximate the expected outputs, given a training set consisting of inputs and expected outputs. The simplest example, ...


2

I know some ASL (?) has been captured and recognised by computers in the past, and that was a while ago. I remember watching videos of this on VHS. 1994 at the latest. Sign Languages (there's more than one) are typically multi-channel media — it's not just the handshapes, so the gloves may not be quite enough (depending on your aims). My ASL is almost ...


2

Well, I haven't built too many AI systems before, but let's take a naive crack at this. If I were going to use a neural network for this, the assigned tags to new parts would be the input nodes, the (vast) list of items it could be compatible with would the the output nodes, and the hidden layer would be it's user-confirmed compatibilities or ...


2

NNs are often used for tasks where we're unsure about what "features" are important. I can recognize a handwritten "2" but it's difficult to describe the essence of a "2" given the enormous variation in hand writing. In your case, the important features of your items seem to be decided by the tags. Humans have already done the hard work. Similarly NNs are ...


2

Have a Look in ada boost (adaptative booster). The idea is to use independent weak classifier (your detectors) and combine them in a clever way. It roughly goes like this: It first tries to evaluate the different detectors to detect and use an optimal linear combination of them to classify the data. It then tries to find another combination able to provide ...


2

It sounds like this would be suitable for a neural network, probably a standard feedforward type. I'm not sure how much you know about neural networks, but FYI the 'rules' it discovers won't be in a human-readable format. So if you want to run images through it and sort them, it'll do that; but if you're aiming to get a list of rules that you can see, ...


2

The crucial part of the story is this: Currently, I am in the process of building a crowd sourced platform for people who are knowledgeable to go in and mark up compatibility between those parts as its not always clear cut if they are for example: And this... Now what I want is an AI to be able to learn from the decisions of the ...


1

When you train a neural network, you need both positive and negative inputs. If you were to say, only indicate to a neural network that when given an image that it should return 1, when given an image which is not similar, it may return 1 all the same. And in this circumstance, while there is only 1 image you'd like other images to be similar to, there ...


1

Context two-layer neuronal networks and svms do linear classification = splitting good vectors into bad vectors by putting a line or flat layer between them which has one dimension less than the vector space. (line in 2d space; dot onto line). When you have three layer neuronal networks they can classify the XOR (in a,b out a xor b). So you can classify ...


1

If I understand you correctly, you essentially have a data set (crowd-)sourced from actual people, which describes the compatibility between various parts. You would like to build something which analyses this information, and figures out the associations between the parts and the compatibility flag. Essentially, extracting this type of information from a ...


1

This is a perfect application for a full-text indexer such as Lucene. Let's say your questionnaire asks about three things: smoking, diabetes and obesity. Once the text of the articles is indexed, you can use the answers you get to form queries that will return the most relevant articles first. So, for example, the query for an overweight, non-diabetic ...


1

Why not just tack a machine ID on top of the service identifier? That way you have 31 unique keys, which specify both the service and machine, and you're left with a lookup into 10000. Also, I don't really understand your math. From what I see, it looks more like: You're choosing from (200 machines) * (10 services per machine) * (10000 values per service). ...


1

There is another approach available, recently I've worked on a project that used gabor wavelets in face recognition Wikipedia entry for Gabor wavelets. And I stumbled on a interesting project that fits your need. The project revolves around a paper, rock & scissors game and the recognition of symbols used inside a game. It tracks the player trough the ...


1

Borrowing heavily from the comments: I think you should look into existing image recognition algorithms. Personally, I would simply implement an existing open source one. Once you have some understanding of and comfort with one, train it on a single sign. Use the output from your gloves to generate an image. To that end I would look for a glove that ...


1

What's the difference between having known good inputs and interacting with the environment? In both cases, you have inputs to the learning algorithm that get a value of some sort. (Similarly, perceptrons are artificial neural nets, their failing being the linear functions in the nodes.) There are various sorts of unsupervised learning algorithms, ...


1

For artificial neural networks you need training data, but maybe you want to find structure in data you haven´t seen before and for which you couldn´t do a training. This problem is called unsupervised learning. ANNs belonge to the category of supervised learning. So if you are searching for other techniques, a natural choice would be to look at ...



Only top voted, non community-wiki answers of a minimum length are eligible