Could someone please explain this quote?
A good programming language is a conceptual universe for thinking about programming.
-- Alan Perlis
I tried looking for the meaning of "conceptual universe" but cannot find the meaning that would fit in the sentence.
The meaning of this quote is:
What we need is not just another programming language but a conceptual universe for thinking about software development, embodied in a kind of language that is new to computer science -- a language that will bridge the gap between programming and software development at large.
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I am doing a project at the university and I need to train an algorithm to rephrase sentences, what can you advise for implementation? Is it possible to use a translator to translate into another language in the end to get a paraphrased sentence? Also i want to use Word2Vec, or it's a bad idea?
This kind of broad-advice question – and about a very-tough problem, paraphrasing text, that is still a very active research problem – would be better answered by surveyin the research literature.
A great site for searching relevant papers – and then finding other related papers once you've set some positive examples – is http://www.arxiv-sanity.com/.
Searching for [paraphrasing] or [summarization] would give you a running start in seeing major techniques & their limitations. And, once you start bookmarking papers by the little 'disk' icon, it can autosuggest important related papers... so even if your 1st few finds are tangential or far-from-usefulness, it can lead you to the seminal papers, & prevailing cutting-edge algorithms/libraries, pretty quickly.
https://www.youtube.com/channel/UCXe-BTXAnQ9VaQQZnlC608A
This guy made machine learning script and teach machine
how to play Super Mario and complete each level.
There's FAQ document in the description of every his video
that he's using LUA to make this script but I don't even know where
to start and can't find any tutorial on youtube how to make something like this
My goal is make machine learning script for other games and see
the machine learning how to play and complete various levels
Could you please guide me where I can start and what I should learn to make script like this?
and I prefer programming language easier to learn if there is other option.
Consider the following:
Are you a beginner programmer? If so, diving into machine learning will prove to be frustrating. You need a solid foundation first in programming principles.
The next level would be to use a machine learning library. The core ML algorithms are then already written and optimized and you just need to learn how to integrate them into your own programs. Don't be fooled though - to do this well still requires a solid understanding of ML principles. There are existing libraries for Lua and Python. That is a good place to start.
I would consider looking at Andrew Ng's introductory material (book and videos) on ML. Warning! He knows what he is talking about and does offer up quite a bit of advanced material as well. Don't start with that.
There are excellent, advanced books on the subject that explain the mathematical principles behind the apparent magic of ML. If you already did all of the above and are still sticking with it, then this would be the way to go next. You could work on your own implementation of ML and apply that to any domain.
I'm gonna guess that you are at the first bullet point still. Be patient. Learn to program well. Get exposure to ML ideas. Plan on that video game ML application some years from now.
While my research area is in Machine Learning (ML), I am required to take a project in Programming Languages (PL). Therefore, I'm looking to find a project that is inclined towards ML.
One intersection I know of between the two fields is Natural Language Processing (NLP), but I couldn't find concrete papers in that topic that are related to PL; perhaps due to my poor choice of keywords in the search query.
The main topics in the PL course are : Syntax & Symantics, Static Program Analysis, Functional Programming, and Concurrency and Logic programming
If you could suggest papers or keywords that are Machine Learning enthusiast friendly, that would be highly appreciated!
Another very important intersection in these fields is probabilistic programming languages, which provide probabilistic inference over models specified as actual computer programs. It's a growing research field, including a recently started DARPA program on this topic.
If you are interested in NLP, then I would focus on two aspects of listed PL disciplines:
Syntax & Semantics - as this is incredibly closely realted to the NLP field, where in most cases the understanding is based on the various language grammars. Searching for papers regarding language modeling, information extraction, deep parsing would yield dozens of great research topics which are heavil related to the sytax/semantics problems.
logic programming -"in good old years" people believed that this is a future of AI, even though it is not (currently) true, it is still quite widely used forreasoning in some fields. In particular, prolog is a good example of language that can be used to reson (for example spatial-temporal reasoning) or even parse language (due to its "grammar like" productions).
If you wish to tackle some more ML related problem rather then NLP then you could focus on concurrency (parallelism) as it is very hot topic - making ML models more scalable, more efficient, "bigger, faster, stronger" ;) Just lookup keywords like GPU Machine Learning, large scale machine learning, scalable machine learning etc.
I also happen to know that there's a project at the University of Edinburgh on using machine learning to analyse source code. Here's the first publication that came out of it
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What should someone study at university level if he/she wants to get into robotics and build robotics? So far 'Mechatronics' seems to be the field I'm looking for? I looked at a few plain 'robotics' courses but they seem to be only about the electrical and computer work, and don't include any details on building the mechanical components of robots?
I'm a professional robotics research consultant, with 30 years of experience working for organizations like SRI International and JPL.
Like computers, robotics has quite a strong divide between the software and the hardware. Hardware is further subdivided into actuators and sensors.
If you'd said "I want to get into computers", I would explain that only a few hardware engineers actually design and build physical computers--most researchers assume that the hardware and firmware has been built already, and then they worry about the software--how to make the system actually work.
Similarly with robots, building the hardware is a job for the mechanical engineers (to design the structure and heat dissipation), with little bits and pieces for power electrical engineers (to spec the motors) and computer engineers (to design the firmware silicon). Next-generation robots also use industrial designers (to make the outsides look pretty, and the insides fit well together).
Research areas for actuator design include fingered hands; tentacles; hummingbird and other bird and insect wings; springy wheels; legs; non-electronic designs for high radiation areas; and surgical instruments.
With cameras in every cell phone, vision sensors are mostly a solved problem at this point. Research areas for sensor design include smart flexible tactile skin, brain wave sensors, and other biomedical sensors. There's still some room for good force sensors as well. These fall in the realms of materials engineering, computer engineering, mechanical engineering, and biomedical engineering.
In order to drive the actuators properly so they don't shake themselves apart, you need a control-theory engineer. Start with Fourier transforms so that you can then understand z-transforms. The learning curve on this mathematics is extremely steep, and careers are quite few, so either you have to be born to be a controls engineer or you should let someone else handle these lower-level details for you.
Signal processing, for the medium- and low-level sensor drivers, has been under the domain of the EEs historically. This works its way up to image processing, which falls under computer science, and then image understanding, which is in the A.I. branch of CS.
However, as I mentioned, the hardware, firmware, and drivers are all manufacturing details that you solve once and then sell forever. Anybody can buy a Lego or a Bioloids kit off the shelf now, and start working with motors. It's not like 2006, when the Fujitsu HOAP humanoid robot we were working with at JPL was a $50,000 custom-ordered special.
Most of what I consider the really interesting work starts by assuming the hardware and drivers have already been accomplished--and then, what do you do with the system? This is completely in the realm of software.
Robotic software control starts with 3D simulators, which in turn are based on forward kinematics; eventually inverse kinematics; dynamics, if you feel like it; and physics-engine simulations. Math here centers around locations [position + orientation], which are best represented by using [4x4] homogeneous coordinate transformation matrices. These are not very hard, and you can get a good background in them from any computer graphics textbook. Make sure you follow the religion of post-multiplying by matrices ending in a column vector on the right; this allows you to chain base-to-waist-to-shoulder-to-elbow-to-hand kinematics in a way that you'll be able to understand. Early textbooks proposed premultiplying using row vectors, because they thought it wouldn't make a difference. It does.
Of course the physics engines require a decent knowledge of physics.
Higher-level processing is accomplished using artificial intelligence, usually rules-based systems. Natural-language processing also can tie in linguistics and phonetics. Speech recognition and speech generation are again mostly signal processing, taught in EE and CS.
Recent advances work on Big Data, which uses statistics, Bayesian reasoning, and bases vector spaces (from mathematics).
Robotics has not yet broken out. It is still at the level cell phones were at when Gordon Gecko was walking on the beach talking into a "portable phone" the size of a shoe. I don't see robots becoming ubiquitous before 2020. Around 2025, being a robot programmer will be in demand as much as being an app programmer is today. Study lots of A.I. Start early.
State-of-the-art humanoid robot system design as of 2006 [short movie]:
http://www.seqcon.com/caseJPL.html
Very high level block diagram of components [graphic]:
http://www.seqcon.com/images/SystemSchematic640.gif
I would highly recommend looking into Artificial Intelligence for Robotics on Udacity, it is very interesting course that covers the software and AI part. Also Coursera offers a free online robotics course, and other courses as well that are very relevant and useful to Robotics.
Mechanical and electrical engineering and computer science.
Mechanical engineering will inform choices about servos, linkages, gears, and all other mechanical components.
Control theory is the junction of mechanical and electrical engineering. You'll need that.
So much of control is digital these days, so EE and computer science will be a part of it.
It's a big field. Good luck.
Industrial robotics is usually handeled by Mechanical Engineers, and sport/team robotics by electical engr, electronics engr, or computer science majors. It all depends on what you mean by "robotics". Also, in case nobody else mentions it, a Masters degree is strongly encouranged.
As an added bonus the math used in industrial robotics, is directly linked to math for game development. There isn't really a clear cut line of who is supposed to be doing what in robotics.
Mechtronics is the current field of study for those interested in robotics. It combines mechanical, electrical, controls, and software as relates to robotics.
In the past we came from many different backgrounds, mechanical engineers, electrical, electronics, and software. I am an Application Engineer for robot manufacturer. I started out in Avionics, moved to automated test equipment, then to automated material delivery systems, I became a robotics service technician and manager then moved over to application programing and training.
One final note, be prepared to keep learning. This is a field that is constantly changing and evolving.
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I'm looking for a book similar to "Introduction to Algorithms" by Thomas Cormen geared towards DSP algorithms. Is there anything as thorough as Cormen on the DSP market?
EDIT
I should say I'm looking for a book that's analogous to The Joy of Cooking.
Disclaimer - I am not familiar with the Cormen book so I'm not quite sure what you're looking for.
I'm a huge fan of "A Digital Signal Processing Primer" by Ken Steiglitz. It introduces DSP concepts like sampling, as well as simple filtering implementations, without relying just on math for explanation. Cookbook equivalent: You know how to boil water on a stove, but you're nervous about the rest.
A more advanced book, more of a practitioner's handbook than a text, is "Theory and Application of Digital Signal Processing" by Lawrence Rabiner and Bernard Gold. Their explanation of the overlap-save FFT technique for convolution, in particular, is the best I've ever come across. Cookbook equivalent: Maybe Joy of Cooking, maybe the Cordon Bleu tome.
And "Telecommunications Breakdown" by Richard Johnson and William Sethares is great for taking some DSP concepts and bringing them to life by implementing a radio in software. Cookbook equivalent: A tour through a specific cuisine, and explains what "braising" is along the way.
Hope these are of use to you!
For theory, I like Understanding DSP by Rick Lyons, which also has some nice "recipe-type nuggets".
More practical, and much more "nuggetty" is Streamlining DSP, same author. There's some really interesting stuff in there (IMHO!). Some of it is of the "lost knowledge" variety - especially in these days of just running Matlab's filter design functions. Some of it relates to limited hardware machines (which is great for tiny microcontroller or FPGA implementations).
The articles are written by serious, practicing DSP engineers (many of whom hang out on news:comp.dsp) in a very accessible style.
(I'm afraid I'm no good with cooking analogies though :)
Just for the record and benefit to others, I would recommend The Scientist and Engineer's Guide to Digital Signal Processing.
This is a good book for beginners.
There are a few online books available as well at the great DSPRelated.com:
INTRODUCTION TO DIGITAL FILTERS WITH AUDIO APPLICATIONS by JULIUS O. SMITH
MATHEMATICS OF THE DISCRETE FOURIER TRANSFORM (DFT) WITH AUDIO APPLICATIONS by JULIUS O. SMITH
This is not a book but I'm sure it'll be a valuable resource: The Ecole Polythechnique de Lausanne is starting a free online course on digital signal processing on February 18th 2013: https://www.coursera.org/course/dsp.
Also, the guys teaching it co-authored a book on the topic: http://www.sp4comm.org/
A second vote for the Rick Lyons book. You might also want to get a couple of DSP "bibles", e.g. Oppenheim & Schafer and Proakis & Manolakis, which are more theoretical but cover more ground.
The DSP handbook: algorithms, applications and design techniques - Bateman, Andrew, Paterson-Stephens, Iain 2002
and
Introduction to digital signal processing - Meddins, Bob 2000
Have basically made my ADSP module a breeze (so far). They are also at the top of the suggested reading list. As such, both are fairly beginner friendly, and the latter includes Matlab examples.
The former is probably more Delia, while the latter is more 'my first cook book'.
I will also add Vetterli, Kovacevic, and Goya's Foundations of Signal Processing, which can be downloaded for free.