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I need to develop a mobile app (primarily for Android, iOS, and Windows Mobile) for face detection. Obviously, OpenCV is the most well known. However, I'm unsure about the compatibility among the different OS'es. Besies OpenCV, are there other options? 2 key requirements:
-Open source/commercial libraries but must run locally/natively in devices without internet connection so Player Service API would not work
-Capable of tracking multiple faces in motion
Anyone can share their experiences/knowledge in this area? Any pointers greatly appreciated!
You are really pushing the margin a whole lot.
Face detection generally consists of three different areas.
1) Recognizing a face as a face (there is a mouth, a nose, eyes) This is useful for focusing a snapshot.
2) Recognizing facial features, looking for emotion (mouth in a smile) or eye tracking.
3) Facial recognition. Using the system to perform identification by attaching a name to a face.
You want to use a face recognition tool to perform tracking and count people entering a particular place, using a mobile phone.
First tracking is pretty difficult. Its one thing to perform simple face identity in a single frame snap shot. That's pretty easy. The problem is, you may find your frame rates so poor that you can only accommodate 1 frame every three or even every five seconds. That will make it nearly impossible to track and count faces. Counting faces is easy, but what's hard is to determine if that face in the screen was counted previously or is a new person entering the screen.
OpenCV has a whole lot of tools and examples out there for facial recognition, image tracking, etc. I'd strongly recommend playing with OpenCV and test its capabilities. I'd recommend the C/C++ versions (unless you are already a Python programmer) Here's a place to start, a blog entry I wrote a few months ago.
I really like the tutorials from Kyle Hounslow... Look him up on youtube. His videos are well thought out, they are interesting and he provides example code for all his work. Go ahead and watch all of those videos, and repeat all of those examples. Get a feel for what is available in frame rates using a laptop.
The next part of your task is porting stuff from OpenCV to Android/iOS. That's no easy task. I'm sure folks have tried, and I'm sure helpful hints are out there.
I don't mean to dissuade you from an awesome investigation but do note what you want to do is mighty difficult. You will have to invest some time to even determine where all the difficult areas are. And unfortunately you won't know effective frame rates and performance until you build some stuff and try it.
Good luck with the journey.
I need help to chose a project to work on for my master graduation, The project must involve Ai / Machine learning or Business intelegence.. but if there is any other suggestion out of these topics it is Ok, please help me.
One of the most rapid growing areas in AI today is Computer Vision. There are many practical needs where the results of your Master Thesis can be helpful. You can try research something like Emotion Detection, Eye-Tracking, etc.
An appropriate work for a MS in CS in any good University can highlight the current status of research on this field, compare different approaches and algorithms. As a practical part, it makes also a lot of fun when your program recognizes your mood properly :)
Netflix
If you want to work more on non trivial datasets (not google size, but not trivial either and with real application), with an objective measure of success, why not working on the netflix challenge (the first one) ? You can get all the data for free, you have many papers on it, as well as pretty good way to compare your results vs other peoples (since everyone used exactly the same dataset, and it was not so easy to "cheat", contrary to what happens quite often in the academic literature). While not trivial in size, you can work on it with only one computer (assuming it is recent enough), and depending on the type of algorithms you are using, you can implement them in a language which is not C/C++, at least for prototyping (for example, I could get decent results doing things entirely in python).
Bonus point, it passes the "family" test: easy to tell your parents what you are working on, which is always a pain in my experience :)
Music-related tasks
A bit more original: something that is both cool, not trivial but not too complicated in data handling is anything around music, like music genre recognition (classical / electronic / jazz / etc...). You would need to know about signal processing as well, though - I would not advise it if you cannot get easy access to professors who know about the topic.
I can use the same answer I used on a previous, similar question:
Russ Greiner has a great list of project topics for his machine learning course, so that's a great place to start.
Both GAs and ANNs are learners/classifiers. So I ask you the question, what is an interesting "thing" to learn? Maybe it's:
Detecting cancer
Predicting the outcome between two sports teams
Filtering spam
Detecting faces
Reading text (OCR)
Playing a game
The sky is the limit, really!
Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).
A starting point might be the Chen book Genetic Algorithms and Genetic Programming in Computational Finance.
Here's an AAAI writeup of an award to the National Association of Securities Dealers for a system thatmonitors NASDAQ insider trading.
Many great answers posted already, but I wanted to add my 2 cents.There is one hot topic in which big companies all around are investing lots of resources into, and is still a very challenging topic with lots of potential: Automated detection of fake news.
This is even more relevant nowadays where most of us are connecting though social media and there's a huge crisis looming over.
Fake news, content removal, source reliability... The problem is huge and very exciting. It is as I said challenging as it can be seen from many perspectives (from analising images to detect fakes using adversarial netwotks to detecting fake written news based on text content (NLP) or using graph theory to find sources) and the possbilities for a research proyect are endless.
I suggest you read some general articles (e.g this or this) or have a look at research articles from the last couple of years (a quick google seach will throw you a lot of related stuff).
I wish I had the opportunity of starting over a project based on this topic. I think it's going to be of the upmost relevance in the next few years.
I was wondering if you creative minds out there could think of some situations or applications in the web environment where Neural Networks would be suitable or an interesting spin.
Edit: Some great ideas here. I was thinking more web centric. Maybe bot detectors or AI in games.
To name a few:
Any type of recommendation system (whether it's movies, books, or targeted advertisement)
Systems where you want to adapt behaviour to user preferences (spam detection, for example)
Recognition tasks (intrusion detection)
Computer Vision oriented tasks (image classification for search engines and indexers, specific objects detection)
Natural Language Processing tasks (document/article classification, again search engines and the like)
The game located at 20q.net is one of my favorite web-based neural networks. You could adapt this idea to create a learning system that knows how to play a simple game and slowly learns how to beat humans at it. As it plays human opponents, it records data on game situations, the actions taken, and whether or not the NN won the game. Every time it plays, win or lose, it gets a little better. (Note: don't try this with too simple of a game like checkers, an overly simple game can have every possible game/combination of moves pre-computed which defeats the purpose of using the NN).
Any sort of classification system based on multiple criteria might be worth looking at. I have heard of some company developing a NN that looks at employee records and determines which ones are the least satisfied or the most likely to quit.
Neural networks are also good for doing certain types of language processing, including OCR or converting text to speech. Try creating a system that can decipher capchas, either from the graphical representation or the audio representation.
If you screen scrap or accept other sites item sales info for price comparison, NN can be used to flag possible errors in the item description for a human to then eyeball.
Often, as one example, computer hardware descriptions are wrong in what capacity, speed, features that are portrayed. Your NN will learn that generally a Video card should not contain a "Raid 10" string. If there is a trend to add Raid to GPUs then your NN will learn this over time by the eyeball-er accepting an advert to teach the NN this is now a new class of hardware.
This hardware example can be extended to other industries.
Web advertising based on consumer choice prediction
Forecasting of user's Web browsing direction in micro-scale and very short term (current session). This idea is quite similar, a generalisation, to the first one. A user browsing Web could be proposed with suggestions with other potentially interesting websites. The suggestions could be relevance-ranked according to prediction calculated in real-time during user's activity. For instance, a list of proposed links or categories or tags could be displayed in form of a cloud and font size indicates rank score. Each and every click a user makes is an input to the forecasting system, so the forecast is being constantly refined to provide user with as much accurate suggestions, in terms of match against user's interest, as possible.
Ignoring the "Common web problems" angle request but rather "interesting spin" view.
One of the many ways that a NN can be viewed/configured, is as a giant self adjusting, multi-input, multi-output kind of case flow control.
So when you want to offer match ups that are fuzzy, (not to be confused directly with fuzzy logic per se, which is another area of maths/computing) NN may offer a usable alternative.
So to save energy, you offer a lift club site, one-offs or regular trips. People enter where they are, where they want to go and at what time. Sort by city and display in browse control.
Using a NN you could, over time, offer transport owners to transport seekers by watching what owners and seekers link up. As a owner may not live in the same suburb that a seeker resides. The NN learns over time what variances in owners, seekers physical location difference appear to be acceptable. So it can then expand its search area when offering a seekers potential owners.
An idea.
Search! Recognize! Classify! Basically everything search engines do nowadays could benefit from a dose of neural networks and fuzzy logic. This applies in particular to multimedia content (e.g. content-indexing images and videos) since that's where current search technologies are lagging behind.
One thing that always amazes me is that we still don't have any pseudo-intelligent firewalling technology. Something that says "hey his range of urls is making too much requests when they are not supposed to", blocks them, and sends a report to an administrator. That could be done with a neural network.
On the nasty part of things, some virus makers could find lucrative uses to neural networks. Adaptative trojans that "recognized" credit card numbers on a hard drive (instead of looking for certain cookies) or that "learn" how to mask themselves from detectors automatically.
I've been having fun trying to implement a bot based on a neural net for the Diplomacy board game, interacting via DAIDE protocols. It turns out to be extremely tricky, so I've turned to XCS to simplify the problem.
Suppose EBay used neural nets to predict how likely a particular item was to sell; predict what the best day to list items of that type would be, suggest a starting price or "buy it now price"; or grade your description based on how likely it was to attract buyers? All of those could be useful features, if they worked well enough.
Neural net applications are great for representing discrete choices and the whole behavior of how an individual acts (or how groups of individuals act) when mucking around on the web.
Take news reading for instance:
Back in the olden days, you picked up usually one newspaper (a choice), picked a section (a choice), scanned a page and chose an article (a choice), and read the basics or the entire article (another choice).
Now you choose which news site to visit and continue as above, but now you can drop one paper, pick up another, click on ads, change sections, and keep going with few limits.
The whole use of the web and the choices people make based on their demographics, interests, experience, politics, time of day, location, etc. is a very rich area for NN application. This is especially relevant to news organizations, web page design, ad revenue, and may even be an under explored area.
Of course, it's very hard to predict what one person will do, but put 10,000 of them that are the same age, income, gender, time of day, etc. together and you might be able to predict behavior that will lead to better designs. Imagine a newspaper (or even a game) that could be scaled to people's needs based on demographics. An ad man's dream !
How about connecting users to the closest DNS, and making sure there are as few bounces as possible between the request and the destination?
Friend recommendation in social apps (Linkedin,facebook,etc)
As I learn more about Computer Science, AI, and Neural Networks, I am continually amazed by the cool things a computer can do and learn. I've been fascinated by projects new and old, and I'm curios of the interesting projects/applications other SO users have run into.
The Numenta Platform for Intelligent Computing. They are implementing the type of neuron described in "On Intelligence" by Jeff Hawkins. For an idea of the significance, they are working on software neurons that can visually recognize objects in about 200 steps instead of the thousands and thousands necessary now.
Edit: Apparently version 1.6.1 of the SDK is available now. Exciting times for learning software!!
This isn't AI itself, but OpenCyc (and probably it's commercial big brother, Cyc) could provide the "common sense" AI applications need to really understand the world in which they exist.
For example, Cyc could provide the enough general knowledge that it could begin to "read" and reason about encyclopedic content such as Wikipedia, or surf the "Semantic Web" acting as an agent to develop some domain-specific knowledge base.
w:
Arthur L. Samuel (1901 – July 29,
1990) was a pioneer in the field of
computer gaming and artificial
intelligence. The Samuel
Checkers-playing Program appears to be
the world's first self-learning
program...
Samuel designed various
mechanisms by which his program could
become better. In what he called rote
learning, the program remembered every
position it had already seen, along
with the terminal value of the reward
function. This technique effectively
extended the search depth at each of
these positions. Samuel's later
programs reevaluated the reward
function based on input professional
games. He also had it play thousands
of games against itself as another way
of learning. With all of this work,
Samuel’s program reached a respectable
amateur status, and was the first to
play any board game at this high of
level.
Samuel: Some Studies in Machine Learning Using the Game of Checkers (21 page pdf file). Singularity is near! :)
One of my own favorites is Donald Michie's 1960, Project: MENACE - Matchbox Educable Naughts and Crosses Engine. In this project Michie used a collection of matchboxes with colored beads that he taught to play Tic-Tac-Toe. This was to demonstrate that machines could in some sense learn from their previous successes and failures.
More information as well as a computer simulation of the experiment are here: http://www.adit.co.uk/html/menace_simulation.html
http://alice.pandorabots.com/
- This bot is able to have pretty intelligent conversation with us.
http://www.triumphpc.com/johnlennon/
recreating the personality and thoughts of John Lennon.. you can have a chat with him on this site.
http://AngelCog.org is quite interesting. The project is based around the idea that to make a true AI, you must do it in three stages:
1) Try to process logic in general, and be able to describe anything.
2) Logically process code, and process "Stories" about the real world.
3) Logically process it's own code, and talk to people.
The project is based around the idea, that once a program is logically processing it's own code, it is already an AI. Of course it also needs to be able to understand the "Real world". That's the "other half".
As far as I'm aware, no one else has a project based on the assumption that to make a proper AI, the AI must understand the language in which it is written. So lets say an AI is written in C++. Well then it must master C++ and be able to read and write and alter C++ programs, especially itself!!
It's still a "toy" right now however, and is still in the "First stage" of development. ("Try to process logic in general, and be able to describe anything."). But the developer is looking for help.
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Welcome!
I very enjoyed programming artificial intelligence in my studies - neural networks, expert machines and other. But in work I develop mainly web applications.
And now I think about returning to such programming, maybe in hobby, or maybe in work. Are there areas where AI is commonly used in applications development and programmer with such skills can search work?
Or maybe I can sold some ideas to my boss and use AI to extend some of our applications.
What are you experience and ideas with using AI in applications?
I recently started reading the book Programming Collective Intelligence. It's an excellent book which discusses exactly what you are looking for - using AI techniques in web applications.
The book is written clearly, is easy to understand, explains everything in terms of real applications (it covers how some commonly used technology works: Google Pagerank, Amazons recommendation system, matchmaking websites, link recommendation systems, bayesian spam filters and more) and it uses actually useful examples using real data (ebay API, facebook API etc are used to collect data). In one chapter, it even explains how you can draw graphs (I mean the data structure, not bar/line/etc graphs) optimally (so that no nodes are too close together, minimum overlapping lines etc), which could be useful for, for example, mapping social networks.
I would recommend having a look at it and see the different ways AI can be applied to web applications.
As a counter-example, parsing data acquired from water testing equipment would probably be a bad place to use artificial intelligence:
The Daily WTF: No, We Need a Neural Network
Just a reminder for all of us to choose the right tool for the right job.
Neural networks are great for working on images, so one area of web applications you could use AI for would be identifying and/or manipulating patterns in images over large sets of data. For example, a site like Flickr or Facebook might have some interesting training material to identify people based on face or associating groupings of pixels (those being the features you work with) with certain items mentioned in captions or tags.
In terms of text manipulation, there's a lot of stuff, but it's usually icing on the cake for other web apps. I'm talking mostly in the areas of automatic completion in search bars and back-end things the user doesn't usually see, like automatic machine translation or improved search capability.
The problem with putting AI at the front of an application's offering is that usually, artificial intelligence is not a feature in and of itself, but rather a way of negotiating large data sets effectively without regular prompts from the designer. In general, a user will associate with an application on a one-to-one basis, and therefore judges it only on the quality of a relatively low number of responses.
Email spam filtering systems - definitely.
Any other security applications which need to spot patterns for malicious stuff.
You probably could analyze the behavior of the visitors of your web applications ; how do they navigate inside the website to provide a better, optimized interface. Now it depends on what kind of web applications you're working on. For on line shopping you can come with suggestions extrapolated from customers habits.
You can also detect "abnormal" behavior and fraud. Fraud and bot detection can take advantage of AI.
Forecasting, of course.
It has immense value for businesses (i.e.: inventory optimization) and is especially valuable in the time of global crisis.
Games do need AI.
Expert systems too.
Outside of games, I've seen very few commercial uses of AI.
It could, in theory, be very useful in industrial robotics and imaging, but those fields also tend to be very conservative, and uncomfortable with non-deterministic algorithms.
You might want to research what iRobot does, but even them use rather simple algorithms in their commercial robots.
In the area of cognitive architectures (e.g. Soar, ACT-R, etc), rather than concentrating on algorithms like A* and games, researchers investigate models of human behavior including decision-making, cultural interchange and learning. They often focus on cognitive plausibility, i.e. how close does a model track what a human would do, including timing, etc.
These systems tend to be strictly research-based with limited commercial applications. So far anyway. Military applications, especially for training, are fairly common though.
Image Processing for detecting cancer! (We actually code IEEE papers about it, creating the algoritms is way harder than coding them so we write papers about the performance of other papers)
Risk assessment is a pretty good case for neural networks, mostly because they're pretty good at pattern matching. Insurance and credit companies use them to some degree for determining the risk of a customer.
I have done some extensive research on using Artificial Neural Networks for classification of underwater sound sources. The algorithm seemed to work quite well, especially that I devoted a big portion of the work on figuring out what combination of fourier transform coefficient composed the best set for the classification (with Principal Component Analysis).
Anything (seriously):
http://highlevellogic.blogspot.com/2010/09/high-level-logic-rethinking-software.html
The High Level Logic (HLL) Open Source project is about finding and coding high level logic under which all the other AI (and in fact, all programming) fits. There are serious concrete ideas and code. HLL is already an application framework.