I am a beginner Silverlight programmer preparing for the interview in medical research company. Job sounds damn interesting and I would like to get there.
To show my skills and interest, I want to write a program related to the topic.
What would you suggest?
First ideas: simple statistical analysis of input data, image collections (for example, find HD DNA image and put it in Silverlight Deep Zoom), lab inventory program..
Have a look at http://research.microsoft.com/en-us/projects/bio/default.aspx the Microsoft Biology Foundation, part of Microsoft Research. Its code is OpenSource (sic) and you will find many applications there. The apps cover most of the basics, sequences, etc. and have some nice display tools.
Collection/Maintenance/Retrieval of data is very important for any organization. Try this tutorial:
Silverlight tutorial: Creating a data centric web app with DataGrid, LINQ, and WCF Web Service
You placed a "bioinformatics" tag on your question. Many bioinformatics companies consider Perl programming to be quite important.
I suggest that you perform a search on "bioinformatics Perl" and take a look at books and sites that are retrieved. Perhaps you could park yourself at a local bookstore and peruse some of those titles. Free Perl interpreters are available.
You do have a basic understanding of genetics, yes? Be very familiar with some of the terminology, so you won't have to stare off into space while you pick from genotype/phenotype or mRNA/RNA/DNA or recall what a codon is.
It wouldn't hurt to nose around PubMed and get a basic understanding of what genomes are out there and what statistical tests can be performed on them.
I like your statistics idea. Perhaps you could write a program that tells you whether to accept or reject a null hypothesis based on numbers you read in from a file. Or perhaps you could figure out how to use the statistics portion of Entrez Gene in PubMed.
Best wishes for the interview.
Related
When creating a AI talking bot what kind of methods of design should I use? Should it be one function, multiple modules, should it have classes?
Understanding language is complicated, so the goal you need to determine first is what aspect of language you want to understand.
An AI must be able to understand what the person says to it, then relate it to what it already knows, and then generate a legitimate response.
These three steps can all be thought of as nearly independent, so you need to address each on its own.
The brain, the world's best language processor, uses a Neural Network, but that's not likely to work well for you.
A logic-based proof solving system, where facts that follow from facts are derived would probably work best, and I know of at least one system that uses it fairly effectively.
I'd start with an existing AI program (like the famous Eliza) and run its output through a speech synthesizer.
Some source for Eliza is available here. One open source speech synthisizer is FreeTTS.
If you're using a language other than Java, there are similar candidates AI bots and text-to-speech code out there.
I've started to do some work in this space using this open source project called Talkify:
https://github.com/manthanhd/talkify
It is a bot framework intended to help orchestrate flow of information between bot providers like Microsoft (Skype), Facebook (Messenger) etc and your backend services. The framework doesn't really provide implementation for the bot providers yet but does provide hooks into its natural language recognition engine.
The built in natural language recognition library can be used to classify sentences to topics which you can then map to skill functions.
Give it a try! I'd really like people's input to see if how it can be improved.
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'm interested in web development and by that I mean the bigger projects like facebook or twitter. I know the basics of java, css, php and mysql. I know there is a lot more out there. I read about it. But I don't know what the purpose is and how to put in place.
Things like: Scribe, thrift, casandra, Unix/Linux, shell/perl/python scripting, PostgreSQL, MongoDB, non-relational NoSQL datastores, JVM, nginx
I want to know why they need it, how they use it and what te purpose is.
What I need is a book like technical background of facebook for dummies or so.
Are there any books or websites that explain this from scratch?
Thank you!
EDIT:
Thank you for your answers! You have been very helpful. I was in the assumption, experienced programmers know almost anything about the technology there's used today. But as I read, you can only know so much and I need to figure out which technology to use. I take on the encouragement to start building small. And will take on php and improve my skills from there.
Thanks again!
http://highscalability.com/
This is one of the best sites out there. There are several case studies describing what and why many websites use, and pointers to further references. I would also look at the Google Scalability Conference 2007 talks
http://www.google.com/search?q=Google+Scalability&hl=en&client=firefox-a&hs=YUg&rls=org.mozilla:en-US:official&prmd=v&source=univ&tbs=vid:1&tbo=u&ei=fl4OTPUkorIwueCQxQw&sa=X&oi=video_result_group&ct=title&resnum=4&ved=0CDIQqwQwAw
It's all about choosing the right tool for the job in my eyes. There is so much technology out there it's impossible to learn it all. Just choose the subset that will work for you.
The best place to start is by building small simple websites, and as you come accross problems that you need solved you research the tools needed to solve those problems.
If you attack all of the areas at once, it's going to be overwhelming and you will not get anywhere.
For a general overview on what each of the technologies does, Wikipedia gives a good overview on most technologies.
If you are interested in database content which it seems like you are, a good place to start is reading up on normalisation.
Scribe, thrift, casandra, Unix/Linux, shell/perl/python scripting, PostgreSQL, MongoDB, non-relational NoSQL datastores, JVM, nginx
Those I would search on Wikipedia for to get a quick overview. Facebook is written in PHP/MySQL. There are some books on the subject of creating social networking sites, and some books have gotten decent reviews on Amazon.com, however, I have not read any of them myself.
If I were you, I'd start with PHP/MySQL and sit down and write a simple social network. Break the project down into components and tasks and Google for each challenge you encounter such as sessions, database structure, security, friend structure, and processing POST and GET requests.
You'll learn a lot and you get the big picture. Once you see the big picture, you can take another look at different technologies that are available and then decide which component you could have developed better with other tools. I personally don't think that looking too much into the technology available is good for someone who is still in the beginning stages. Start doing, learn from it, and then your questions become much more specific and a lot of things will make more sense.
The problem you're having is you're looking at smaller, specialty products, and not at larger, more mature technologies. Wikipedia will actually give you a decent overview of most of the medium-and-large projects out there.
Cassandra, Hadoop, Mongo, and NoSQL are all lovely... but they're specialty tools. SQL is a general purpose solution that works for 99% of the sites on the net.
Unix/Linux isn't a specialty tool; you might want to try going to Ubuntu's website and installing Linux, and just using it day-to-day, the way you'd use Windows. When you need to figure out something new, like setting up a webserver, do it on the Linux box and a Windows box, and you'll eventually learn linux pretty darn well.
As far as scripting, O'Reilly makes a great line of books on Bash, Perl, and Python.
JVM is a Java Virtual Machine, which is a core of getting Java code to go. Sun's website has a great set of tutorials on learning Java.
It might be much, much easier to pick a project (or three) that you'd like to learn, and learn some of these by doing. I'd probably suggest learning some SQL before learning the newly established alternatives; that lets you learn the rest of the system, as SQL is pretty easy. Once you've got the rest of the thing solid, try swapping in a NoSQL solution at that point.
There are a lot of frameworks that do a lot of different things. You've named a lot of different things from a lot of different areas. The best way to think of these things is to group them by category. Here's an example:
Suppose you have a laptop and you want to host a website. You'll need the following at a minimum:
1) Web Server software. Two popular options are Microsoft's IIS and Apache Web Server.
That's really all you need. You can set up your www_root folder and load files into it. Assuming everything is configured properly, you can now load HTML pages into that folder and access them through your IP address. Every page you view in your web browser is in HTML format. CSS is a stylesheet language that defines how your HTML will be formatted. You can also start writing Javascript, as most modern browsers support the client-side scripting language.
Chances are you'll want the following as well:
2) Database software. Two popular options are Microsoft's SQL Server and MySQL
3) Server-side scripting. PHP is very popular, as is ASP. You'll need the runtime deployed on your server. Python, Ruby, Perl, etc all fall under this category.
4) Web Application Framework(s). This will provide you with libraries for your language of choice to help develop web applications and websites. CakePHP, Ruby on Rails, and the Google Web Toolkit are examples of web application frameworks.
Additionally, you may want to utilize:
5) Additional libraries. JQuery, for example, is quickly becoming a popular library for Javascript that handles a lot of common tasks for you. Instead of writing complex effects code and what-not yourself, just use the pre-written code in the JQuery library.
6) Data interchange technology. If you are passing a lot of information back and forth, you will likely want to encapsulate this data in a logical format. Ideally, this format would describe the data and allow your applications to easily read/process it following a standard. This is where XML and JSON come into play.
I can't recommend a good book for you to learn this stuff, but I feel that the collective replies to your question here should be more than enough to get you started.
Ultimately, what you need to do is determine what technologies you need, and then choose the right one for the job. Don't go building an application using Ruby on Rails just because it's what Twitter used, but rather choose it because it provides some advantage to you over the other options.
Screen scraping seems like a useful tool - you can go onto someone else's site and steal their data - how wonderful!
But I'm having a hard time with how useful this could be.
Most application data is pretty specific to that application even on the web. For example, let's say I scrape all of the questions and answers off of StackOverflow or all of the results off of Google (assuming this were possible) - I'm left with data that is not very useful unless I either have a competing question and answer site (in which case the stolen data will be immediately obvious) or a competing search engine (in which case, unless I have an algorithm of my own, my data is going to be stale pretty quickly).
So my question is, under what circumstances could the data from one app be useful to some external app? I'm looking for a practical example to illustrate the point.
It's useful when a site publicly provides data that is (still) not available as an XML service. I had a client who used scraping to pull flight tracking data into one of his company's intranet applications.
The technique is also used for research. I had a client who wanted to compare the contents of several online dictionaries by part of speech, and all of these sites had to be scraped.
It is not a technique for "stealing" data. All ordinary usage restrictions apply. Many sites implement CAPTCHA mechanisms to prevent scraping, and it is inappropriate to work around these.
A good example is StackOverflow - no need to scrape data as they've released it under a CC license. Already the community is crunching statistics and creating interesting graphs.
There's a whole bunch of popular mashup examples on ProgrammableWeb. You can even meet up with fellow mashupers (O_o) at events like BarCamps and Hack Days (take a sleeping bag). Have a look at the wealth of information available from Yahoo APIs (particularly Pipes) and see what developers are doing with it.
Don't steal and republish, build something even better with the data - new ways of understanding, searching or exploring it. Always cite your data sources and thank those who helped you. Use it to learn a new language or understand data or help promote the semantic web. Remember it's for fun not profit!
Hope that helps :)
If the site has data that would benefit from being accessible through an API (and it would be free and legal to do so), but they just haven't implemented one yet, screen scraping is a way of essentially creating that functionality for yourself.
Practical example -- screen scraping would allow you to create some sort of mashup that combines information from the entire SO family of sites, since there's currently no API.
Well, to collect data from a mainframe. That's one reason why some people use screen scraping. Mainframes are still in use in the financial world and often it's running software that has been written in the previous century. The people who wrote it might already be retired and since this software is very critical for these organizations, they really hate it when some new code needs to be added. So, screenscraping offers an easy interface to communicate with the mainframe to collect information from the mainframe and then send it onwards to any process that needs this information.
Rewrite the mainframe application, you say? Well, software on mainframes can be very old. I've seen software on mainframes that was over 30 years old, written in COBOL. Often, those applications work just fine and companies don't want to risk rewriting parts because it might break some code that had been working for over 30 years! Don't fix things if they're not broken, please. Of course, additional code could be written but it takes a long time for mainframe code to be used in a production environment. And experienced mainframe developers are hard to find.
I myself had to use screen scraping too in a software project. This was a scheduling application which had to capture the output to the console of every child process it started. It's the simplest form of screen scraping, actually, and many people don't even realize that if you redirect the output of one application to the input of another, that it's still a kind of screen scraping. :)
Basically, screen scraping allows you to connect one (web) application with another one. It's often a quick solution, used when other solutions would cost too much time. Everyone hates it, but the amount of time it saves still makes it very efficient.
Let's say you wanted to get scores from a popular sports site that did not offer the information available with an XML feed or API.
For one project we found a (cheap) commercial vendor that offered translation services for a specific file format. The vendor didn't offer an API (it was, after all, a cheap vendor) and instead had a web form to upload and download from.
With hundreds of files a day the only way to do this was to use WWW::Mechanize in Perl, screen scrape the way through the login and upload boxes, submit the file, and save the returned file. It's ugly and definitely fragile (if the vendor changes the site in the least it could break the app) but it works. It's been working now for over a year.
One example from my experience.
I needed a list of major cities throughout the world with their latitude and longitude for an iPhone app I was building. The app would use that data along with the geolocation feature on the iPhone to show which major city each user of the app was closest to (so as not to show exact location), and plot them on a 3D globe of the earth.
I couldn't find an appropriate list in XML/Excel/CSV type format anywhere easily, but I did find this wikipedia page with (roughly) the info I needed. So I wrote up a quick script to scrape that page and load the data into a database.
Any time you need a computer to read the data on a website. Screen scraping is useful in exactly the same instances that any website API is useful. Some websites, however, don't have the resources to create an API themselves; screen scraping is the developer's way around that.
For instance, in the earlier days of Stack Overflow, someone built a tool to track changes to your reputation over time, before Stack Overflow itself provided that feature. The only way to do that, since Stack Overflow has no API, was to screen scrape.
The obvious case is when a webservice doesn't offer reverse search. You can implement that reverse search over the same data set, but it requires scraping the entire dataset.
This may be fair use if the reverse search also requires significant pre-processing, e.g. because you need to support partial matching. The data source may not have the technical skills or computing resources to provide the reverse search option.
I use screen scraping daily, I run some eCommerce sites and have screen-scraping scripts running daily to gather product lists automatically from my suppliers wholesale sites. This allows me to have upto date information on all the products available to me from several suppliers and allows me to flag non-economical margins due to price changes.
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.