Washington: A new software will convert ordinary laptops into powerful supercomputers to extract features and patterns from enormously complex data sets.
The tool - a set of problem-solving calculations known as an algorithm - is compact enough to run on computers and laptops with as little as two gigabytes of memory.
It has been designed and developed by scientists at University of California, Davis (UC-D), and Lawrence Livermore National Laboratory.
They have already used it to probe a slew of phenomena represented by billions of data points, including analysing and creating images of flame surfaces; searching for clusters and voids in a virtual universe experiment; and identifying and tracking pockets of fluid in a simulated mixing of two fluids.
"What we've developed is a workable system of handling any data in any dimension," said Attila Gyulassy, who led the five-year development effort while pursuing a PhD in computer science at UC Davis.
"We expect this algorithm will become an integral part of a scientist's toolbox to answer questions about data," he said.
One of Gyulassy's tests of the algorithm was to use it to analyse and track the formation and movement of pockets of fluid in the simulated mixing of two fluids: one dense, one light.
The complexity of this data set is so vast - it consists of more than one billion data points on a 3-D grid - it challenges even supercomputers, Gyulassy said.
Yet the new algorithm with its streamlining features was able to perform the analysis on a laptop computer with just two gigabytes of memory, said a UC-D release.
Although Gyulassy had to wait nearly 24 hours for the little machine to complete its calculations, at the end of this process he could pull up images in mere seconds to illustrate phenomena he was interested in, such as the branching of fluid pockets in the mixture.
The paper was published in the November-December issue of IEEE Transactions on Visualisation and Computer Graphics.
Courtesy : http://www.siliconindia.com/shownews/51089
Thursday, January 15, 2009
Tuesday, November 25, 2008
Two day Workshop on Machine Learning (19-20 Dce. 2008, Mumbai))
Machine Learning
Machine Learning (ML) is a subfield of artificial intelligence (AI) that is concerned with the design and development of algorithms and techniques that allow computers to "learn". ML is one of the active areas of research in computer science and currently has a large repository of practically useable techniques and algorithms for a wide range of tasks. Typical ML problems include automatic clustering of a set of items, automatic classification (spam mail, documents, etc.), automatic learning/refinement of rules for a diagnostic system, predictive modelling, etc.
ML methods have evolved from various domains such as Statistics, Information theory, Biology and Control theory. ML has a wide spectrum of applications including natural language processing (NLP), pattern recognition, search engines, medical diagnosis, bioinformatics and chemical informatics, fraud detection, stock market analysis, speech and handwriting recognition, robotics, intelligent computer games etc.
About the Workshop
The workshop is meant to provide a comprehensive introduction to Machine Learning, focusing on conceptual understanding of popular ML algorithms and practical applications. Apart from covering popular ML techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Genetic Algorithms (GA) we will discuss modelling of a problem for using machine learning including input-output transformation. Participant will get hands on experience with these algorithms; using toolkits such as Weka.
Workshop Outline
* Introduction to Machine Learning
* Inductive Learning
* Artificial Neural Network
* Support Vector Machine
* Clustering
* Input-output transformation for ML
* Application case studies
* Lab sessions
Target Audience
The workshop is targeted at academic and industry professionals interested in Machine Learning; professionals working in the area of information retrieval, language processing, document analysis, speech recognition and students interested in the area of Machine Learning. Some familiarity with computer programming will be desirable – language does not matter.
Registration Details
Registration Fee: Rs. 1000/- per participant for academic & non-profit organizations, and Rs. 1500/- per participant for others, payable by a crossed demand draft drawn in favour of 'C-DAC Mumbai' payable at Mumbai. The fee covers lunch, refreshments, and workshop material. The complete registration form fee should be sent to the Course Administration section C-DAC, Kharghar, Mumbai.
Accommodation Availability
Limited shared Non-A/C accommodation is available at the Navi Mumbai Campus Hostel, at Rs.150/- per person per day.
Knowledge Based Computer Systems Division
The Knowledge Based Computer Systems (KBCS) division carries out research and development in selected subfields of Artificial Intelligence. Its core areas of research are Natural Language Processing, Expert Systems, Case Based Reasoning, Information Retrieval, Data Mining, Soft Computing, and Planning and Scheduling.
About C-DAC Mumbai
The Centre for Development of Advanced Computing, Mumbai (formerly National Centre for Software Technology) is a scientific society involved in the Research and Development into various areas of Software Technology and related disciplines with an objective to create focus on advanced information technologies, high-end academics & training relevant to R&D societies.
Contact Us
Knowledge Based Computer Systems (KBCS) Division
Centre for Development of Advanced Computing (Formerly NCST)
Raintree Marg, Near Bharti Vidyapeeth
Opp. Kharghar Railway Station
Sector 7, CBD Belapur
Navi Mumbai 400 614
For Details Visit: http://www.cdacmumbai.in
E-mail:kbcs[at]cdacmumbai[dot]in
Telephone: +91-22-27565303-05 Fax: +91-22-27560004
Machine Learning (ML) is a subfield of artificial intelligence (AI) that is concerned with the design and development of algorithms and techniques that allow computers to "learn". ML is one of the active areas of research in computer science and currently has a large repository of practically useable techniques and algorithms for a wide range of tasks. Typical ML problems include automatic clustering of a set of items, automatic classification (spam mail, documents, etc.), automatic learning/refinement of rules for a diagnostic system, predictive modelling, etc.
ML methods have evolved from various domains such as Statistics, Information theory, Biology and Control theory. ML has a wide spectrum of applications including natural language processing (NLP), pattern recognition, search engines, medical diagnosis, bioinformatics and chemical informatics, fraud detection, stock market analysis, speech and handwriting recognition, robotics, intelligent computer games etc.
About the Workshop
The workshop is meant to provide a comprehensive introduction to Machine Learning, focusing on conceptual understanding of popular ML algorithms and practical applications. Apart from covering popular ML techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Genetic Algorithms (GA) we will discuss modelling of a problem for using machine learning including input-output transformation. Participant will get hands on experience with these algorithms; using toolkits such as Weka.
Workshop Outline
* Introduction to Machine Learning
* Inductive Learning
* Artificial Neural Network
* Support Vector Machine
* Clustering
* Input-output transformation for ML
* Application case studies
* Lab sessions
Target Audience
The workshop is targeted at academic and industry professionals interested in Machine Learning; professionals working in the area of information retrieval, language processing, document analysis, speech recognition and students interested in the area of Machine Learning. Some familiarity with computer programming will be desirable – language does not matter.
Registration Details
Registration Fee: Rs. 1000/- per participant for academic & non-profit organizations, and Rs. 1500/- per participant for others, payable by a crossed demand draft drawn in favour of 'C-DAC Mumbai' payable at Mumbai. The fee covers lunch, refreshments, and workshop material. The complete registration form fee should be sent to the Course Administration section C-DAC, Kharghar, Mumbai.
Accommodation Availability
Limited shared Non-A/C accommodation is available at the Navi Mumbai Campus Hostel, at Rs.150/- per person per day.
Knowledge Based Computer Systems Division
The Knowledge Based Computer Systems (KBCS) division carries out research and development in selected subfields of Artificial Intelligence. Its core areas of research are Natural Language Processing, Expert Systems, Case Based Reasoning, Information Retrieval, Data Mining, Soft Computing, and Planning and Scheduling.
About C-DAC Mumbai
The Centre for Development of Advanced Computing, Mumbai (formerly National Centre for Software Technology) is a scientific society involved in the Research and Development into various areas of Software Technology and related disciplines with an objective to create focus on advanced information technologies, high-end academics & training relevant to R&D societies.
Contact Us
Knowledge Based Computer Systems (KBCS) Division
Centre for Development of Advanced Computing (Formerly NCST)
Raintree Marg, Near Bharti Vidyapeeth
Opp. Kharghar Railway Station
Sector 7, CBD Belapur
Navi Mumbai 400 614
For Details Visit: http://www.cdacmumbai.in
E-mail:kbcs[at]cdacmumbai[dot]in
Telephone: +91-22-27565303-05 Fax: +91-22-27560004
Sunday, August 31, 2008
Workshop on Rule Based Expert Systems @ CDAC
2 Day Workshop on Rule Based Expert Systems
17–18 October, 2008, CDAC,Navi Mumbai
Expert Systems
Expert systems are computer software that try to emulate human problem solving in highly specialised narrow domains such as financial investment planning, specialised medical diagnosis, equipment trouble shooting, etc. One of the few successful off shoots of Artificial Intelligence, the field received a lot of attention during the 80's. After a relatively dormant phase, expert systems are now attaining popularity as more and more applications are moving on to become intelligent systems, significantly enhancing their potential. The massive growth in hardware performance and capacity and evolution of software paradigms such as software as service is contributing to this revival.
Expert systems have demonstrated their ability to perform at levels comparable to human experts in a wide range of domains. Developing an expert system consist of acquiring the specialised domain knowledge from human experts through a process generally called knowledge acquisition, and structuring and representing them in a form that machines can understand - knowledge engineering. Such domain knowledge is normally represented as if – then rules; case based and network representations are also used.
Software frameworks known as expert system shells are available today to provide the user interface, user interaction management, knowledge base interpretation and reasoning. Developing an effective expert system requires clear understanding of these various processes and familiarity with the shell or software frame work used.
About the Workshop
The workshop is meant to provide a comprehensive introduction to Expert Systems, focusing on the practical application. Apart from covering topics such as representation of domain knowledge, verification and validation of rule bases, and knowledge engineering, participants are expected to build small prototype systems using the expert system shell, Vidwan – a C-DAC Mumbai product.
Workshop Outline
* Introduction to Expert Systems
* Rule Based Expert Systems
* Reasoning with Uncertainty
* Knowledge Engineering
* Creation of Knowledge Bases
* Case Study: Vidwan
Target Audience
The workshop is targeted at academicians, IT managers, consultants, domain experts, professionals working on advisory systems and potentially anyone who feels a need for building systems with human expertise.
Registration Details
Registration Fee: Rs. 1000/- per participant for academicians & non-profit organizations, and Rs. 1500/- per participant for others, payable by a crossed demand draft drawn in favour of 'C-DAC Mumbai' payable at Mumbai. It includes lunch, refreshments, and workshop material. The registration should be sent to the Course Administration section, C-DAC Kharghar, at the below given address.
Limited number of seats, register early
Venue & Contact Us
Knowledge Based Computer Systems (KBCS) Division
Centre for Development of Advanced Computing
Raintree Marg, Near Bharti Vidyapeeth
Opp. Kharghar Railway Station
Sector 7, CBD Belapur
Navi Mumbai 400 614
To send mail click here
Telephone: +91-22-27565303-05 Fax: +91-22-27560004
For More Info Visit: http://www.cdacmumbai.in/index.php/cdacmumbai/research_and_publications/research_groups/kbcs_artificial_intelligence/events
17–18 October, 2008, CDAC,Navi Mumbai
Expert Systems
Expert systems are computer software that try to emulate human problem solving in highly specialised narrow domains such as financial investment planning, specialised medical diagnosis, equipment trouble shooting, etc. One of the few successful off shoots of Artificial Intelligence, the field received a lot of attention during the 80's. After a relatively dormant phase, expert systems are now attaining popularity as more and more applications are moving on to become intelligent systems, significantly enhancing their potential. The massive growth in hardware performance and capacity and evolution of software paradigms such as software as service is contributing to this revival.
Expert systems have demonstrated their ability to perform at levels comparable to human experts in a wide range of domains. Developing an expert system consist of acquiring the specialised domain knowledge from human experts through a process generally called knowledge acquisition, and structuring and representing them in a form that machines can understand - knowledge engineering. Such domain knowledge is normally represented as if – then rules; case based and network representations are also used.
Software frameworks known as expert system shells are available today to provide the user interface, user interaction management, knowledge base interpretation and reasoning. Developing an effective expert system requires clear understanding of these various processes and familiarity with the shell or software frame work used.
About the Workshop
The workshop is meant to provide a comprehensive introduction to Expert Systems, focusing on the practical application. Apart from covering topics such as representation of domain knowledge, verification and validation of rule bases, and knowledge engineering, participants are expected to build small prototype systems using the expert system shell, Vidwan – a C-DAC Mumbai product.
Workshop Outline
* Introduction to Expert Systems
* Rule Based Expert Systems
* Reasoning with Uncertainty
* Knowledge Engineering
* Creation of Knowledge Bases
* Case Study: Vidwan
Target Audience
The workshop is targeted at academicians, IT managers, consultants, domain experts, professionals working on advisory systems and potentially anyone who feels a need for building systems with human expertise.
Registration Details
Registration Fee: Rs. 1000/- per participant for academicians & non-profit organizations, and Rs. 1500/- per participant for others, payable by a crossed demand draft drawn in favour of 'C-DAC Mumbai' payable at Mumbai. It includes lunch, refreshments, and workshop material. The registration should be sent to the Course Administration section, C-DAC Kharghar, at the below given address.
Limited number of seats, register early
Venue & Contact Us
Knowledge Based Computer Systems (KBCS) Division
Centre for Development of Advanced Computing
Raintree Marg, Near Bharti Vidyapeeth
Opp. Kharghar Railway Station
Sector 7, CBD Belapur
Navi Mumbai 400 614
To send mail click here
Telephone: +91-22-27565303-05 Fax: +91-22-27560004
For More Info Visit: http://www.cdacmumbai.in/index.php/cdacmumbai/research_and_publications/research_groups/kbcs_artificial_intelligence/events
Monday, August 4, 2008
Google’s Peter Norvig discusses the Technological Singularity & A.I.
Creating Tools of Narrow Superintelligence
“People have been saying for 50 years that artificial intelligence is just around the corner. Why the failure?”
At the start of AI in the ’60s, people got very excited. They said, now we have this new thing, computers. We could probably solve this problem very quickly. And then we discovered over time, it was harder than we thought. We thought that thinking was just being logical, being able to follow rules A, B, to C, and we discovered that it is more than that. It is analogical, it is dealing with uncertainties, that there is much more out there in the world. Understanding and dealing with it is harder than we thought.
“What is artificial intelligence?”
When I did my textbook, I had to define what AI was. One of the things I wanted to avoid was all these philosophical debates. The question of even is AI possible, what is thinking, what is intelligence, what is consciousness? In some definitions those all come into play. The way I tried to define the field was to say AI is building the best possible programs. So, you are given a task to do, you are given some robot or some computer to do it with, and there are so many possible programs. To make it simple, there are n or 2^n possible programs, and AI is finding the best one. Or at least finding a pretty good one.
“Are we heading toward a Singularity?”
I think it is interesting. I remember reading Vernor Vinge’s stories and being very interested by that. Going back and rereading I.J. Good’s ideas. It’s an interesting idea to think about, and I think that as scientists it is our responsibility to consider these kinds of ideas. We should always be saying, “What we’re working on, is it going to be a force for good or for bad? What are the effects on society going to be? And this is one of the possibilities. So, it is something we have to think about. Is it coming? If it is, what’s going to happen and how do we get ready for it?
“Is the Singularity near?”
So, I’ve looked at it and I don’t see it yet. It is like trying to get a seismograph and say, “Is there an earthquake here?” There are no signals yet, as far as I can see. On the other hand, sometimes earthquakes just come up by surprise.
“Do you know when the Singularity will occur?”
I think that is the whole problem with it. You can’t. I said, “Can you predict by the signs that it is coming?” No, the signs are not there yet. So that means I can’t predict. We just have to wait and see.
“Do you agree with Kurzweil’s forecasts regarding the Singularity?”
Ray is great and I appreciate all that he has done. I appreciate all of the advances in AI that he has done in reading and music. He is a great technologist. I think in terms of these predictions, he is doing the best he can by saying, “I want to nail down this prediction. I am going to look at the data that we have. I am going to extrapolate from that data and come up with an answer. I think the only problem is that the data is not conclusive. You look at Moore’s law and yes, you can say that computers are going at this particular rate. But I don’t think you can necessarily say that when we pass a certain number of transistors, that corresponds to a certain number of neurons, and therefore the two are equivalent. I think that’s where the analogy breaks down.
“What markers would indicate that the Singularity is upon us?”
You would look in two ways. You would look at the theoretical. Are we almost ready to build something that we think could do the whole job? I think now, we’re not. There is no consensus on how you would go about building a machine that would improve itself indefinitely. The other thing you would want to look at is the practical. What is it that machines are capable of doing now and how fast is that progressing? So, certainly we have seen a lot of advances. We have seen machines doing more and more. But it does not seem like we are at the point where it is imminent.
“Do you see technology’s exponential growth or double-exponential growth at play in Google’s operation?”
You have to look at different applications. In many places we see more like linear growth. One of the good examples is work in machine translation, translating from Chinese to English. There we have taken an approach by saying the more you know about language, the better you do. We collect more and more examples of language, feed them into our learning algorithms, and as you add more language examples, performance goes up linearly. We don’t really expect it to be exponential. If anything, we expect it to start to level off at some point, but we have not reached that point yet. We still put in more data and it performs better. In other places you see examples where you need to reach a certain threshold of data before you get results at all. Maybe something like Google as a whole is an example of that. When the web was teeny, a search engine would not be that useful. But now that there are tens of billions of pages out there, almost any query you can do, you get something back. So, it reaches this threshold of usefulness just because of the data that is there, and on top of that we are trying to improve it all the time.
“Google and AI are so often mentioned in the same sentence. What’s Google doing that other search engines are not doing?”
We have the majority of users compared to the other engines. Two, I think we have more of a technological focus. Yahoo seems like they are focused on a full solution to the user. They are a content company, an entertainment company, as well as a technology company. We seem much more focused on technology. I think people look at that and say, “If something’s going to happen, maybe it’s going to come from these guys who are so focused on the technology.”
“What is going on at Google that reflects advancement in AI?”
Ai is prevalent throughout Google in the sense that we have data everywhere. There is far too much data for a human programmer to make sense of it. So, we have been forced to go to machine-learning algorithms throughout the company for almost everything we do. So, in that sense, it is one of the most AI-driven companies that I know of. Now, we have not done anything at the general intelligence level. What we have not tried to say is that we are here to solve all your answers for you. You don’t even need to do the Google queries anymore, we’ll find out what you need and drive to the store and buy it for you rather than give you information on it. We are not attempting to do that. We are just trying to do a better job of what we can do, which is connect you with the information.
“Can we build greater-than-human intelligence?”
Sure. We already have that in so many ways. My tiny little desktop calculator has greater-than-human intelligence. It does square roots much faster than I can. Google has greater intelligence in that it knows about more pages and can find them faster than any human could. Now, it doesn’t understand them the way a human does, so it’s capabilities are very limited. It has superhuman intelligence in some directions and subhuman intelligence in other directions. Maybe someday we will have a more general superhuman intelligence. Until that day, what is interesting is putting it together. What can we take from people to connect them with other people and use these narrow superintelligent tools to make those connections better?
“What are your thoughts on SIAI’s Singularity Summit?”
I think it is a fascinating area just thinking about what the possibilities are for the future. I think it is our responsibility to think about it. If there is going to be some possible negative consequences, we need to prepare for that. If we can have positive consequences quicker, it is our duty to try to do that to make the world a better place. Regardless of how high a probability or what timeframe in the future you put the Singularity, you have got to start thinking about it now. And the other reason I’m here is that a bunch of my friends are here that I wanted to talk to. A bunch of good thinkers that I have not met yet are here that I wanted to meet. So, it just seemed like a great place to be.
“Does Google anticipate future technologies?”
Certainly you want to plan for technological progress. You want to plan even for the unknowns. You want to say, I want to be in a position at some time in the future where I can take advantage of these opportunities. I don’t think that you can say the answers are always going to come out right. If I say that in six years I am going to know, maybe in six years maybe you will know, maybe you won’t. Maybe in six years you will find out there is no answer. That’s it’s impossible. There are limits on what you can do, and some of the advances in computer science have been finding those limits. In basic mathematics, going back to Gödel, showing that there are mathematical truths that cannot be proved, there are limits to what mathematics can do. In computer science, showing that there are problems that are exponentially difficult, that even with this exponential growth in computers we are not going to be able to solve. Now, the question is, where is general intelligence? Is that something that is below this line of a solvable problem, or is it above the line? I don’t think we know yet.
“Why support the Singularity Institute?”
Because it is a fascinating problem and it affects everybody in the world. This could change our lives. I think we want to know about that. We want to be able to participate and have the best chance we can for a good outcome.
“Is Friendly AI possible?”
I think the idea of Friendly AI is very interesting. At Google, we are not doing anything specifically on that, but in my earlier work at NASA. We did work on program verification and properties of programs. At NASA it is very important that software does not crash because if the software crashes, the spaceship crashes, there are people on board, they die. Bad things happen. One of the things you want to be able to do is verify that software is correct. Some of that is done through traditional testing, a quality assurance type of approach, and some of it is done through mathematical proofs of the program. Proofs of correctness or proofs of properties. You could prove a property like, this program will not get into a deadlock state. That’s a good thing to be able to know. Now, if you could define what “friendly” means, then there is a chance of using that same technology to say, “I can prove that this program will never be unfriendly.” The problem is that it is much easier to define the state of deadlock than it is to define the state of Friendliness.
“What does a post-Singularity society look like to you?”
I guess by definition you can’t know what it’s like beyond the Singularity, but if you expand the definition of Singularity a little bit to say “advanced general AI,” I like Barney Pell’s description. It’s when a computer can get most jobs that a high school graduate gets today. How does that change the world? So, one thing is, it makes the world a much richer place. It changes what each of us is able to do, because we are now freed up from doing a lot of labor that we were forced to do before. I remember reading Freeman Dyson’s biography, and one of the things he said was very interesting. The middle class women of his age were much more feminist than the middle class women of today, and the reason was that they all had servants who stayed home and did the cooking, cleaned the house and took care of the kids. So, they were free to go out and do their suffragette movements or whatever it was they were working on. Whereas today, the middle class, we don’t have servant, you often have two parents working and so there is much less free time. Now, after the Singularity in Barney’s sense, we are going to be back to that kind of a lifestyle where not just the middle class but everybody essentially has these servants that can take care of business. So, suddenly, a lot of your lifetime is freed up. Now it’s a question of what do you want to do with it?
“Will the Singularity be democratizing?”
I don’t think it will be more democratizing or more equalizing. Certainly it will raise all boats. Everyone will be at a higher level of living, have more free time, and so on. But I think that the people at the higher end of the economic scale will get an even increasing share, and so this gap between the haves and the have-nots will grow, even as the have-nots have much more.
“Do you have concerns regarding future technologies?”
I certainly do. I think there are a lot of things to worry about, and I don’t think it’s new with AI or nanotechnology or biotechnology. I think we have been dealing with it for many years. I grew up worrying about nuclear war, trying to protest and help through that a little bit. It seems like we made it through that crisis, though there is still a chance of some problems there. There have been all sorts of potential threats. So, for fifty years it has been within the bounds of a high school chemistry students to make up a batch of botulism and drop it in the water supply and kill thousands or maybe hundreds of thousands of people, but no one has decided to do that yet. I’m not sure why. I’m not sure if there really are constraints that are going to continue to hold. I know there is going to be continued threats that smaller and smaller groups of people can do more and more damage. I don’t know how much these ethical constraints or other kinds of constraints are going to stop that from happening.
“Assuming Strong AI is likely, do you anticipate a hard take-off or a soft take-off?”
I think I agree with the soft take-off for the reasons you mentioned. Also, just looking at Ray’s curve, exponential growth means the same percentage change every year. So, I think we will keep on looking at that. Now, at Google, you are very happy with your results and we are happy that we can deliver them to you. From the inside I think it’s kind of like making sausages. We worry more about the results that aren’t good than the results that are good. You do a search and get six out of ten good results and you’re really happy. We say, damn, there were four bad ones in there. We’ve really got to fix that. So, we will keep on doing that. We will keep on coming up with improvements. We will give you better results. We will give you better ways to interact with us. We know there are lots of things now that you want to be able to do but can’t do. For example, if you don’t know any of the words, what do you do? Well, we put all the onus on you. So, you have to fall back and say, I’m looking up some medical problem, I don’t know the specific medical terms, but you know enough to get to a document that will have the terms, and then you read that, and then you can go back and make the actual query. We want to make that step faster. We want to get you directly to the right answer, even if you didn’t know the right words to begin with. Throughout, we want to make the interactions faster, we want to correct you with the right documents or connect you with the right person. We think we will add continual improvements to that and you will be getting happier and happier. I don’t see a hard take-off, but I do see you being able to do more over time.
“What are your thoughts on molecular manufacturing and bio-engineering?”
It’s fascinating to me. I’m trying to learn something about it and educating myself. I have a long way to go. I think it does tie-in, because it is this mix between the physical world and the world of information. I think it is likely that in the coming century that a large part of manufacturing with cells or with atoms. I kind of think it will be more done with cells than with atoms, just because they are bigger, they are easier to manipulate. They have already got this three gigabyte computer on board, we just have to figure out how to program it.
Note: Taken from: http://www.focusonit.info/?p=32796
“People have been saying for 50 years that artificial intelligence is just around the corner. Why the failure?”
At the start of AI in the ’60s, people got very excited. They said, now we have this new thing, computers. We could probably solve this problem very quickly. And then we discovered over time, it was harder than we thought. We thought that thinking was just being logical, being able to follow rules A, B, to C, and we discovered that it is more than that. It is analogical, it is dealing with uncertainties, that there is much more out there in the world. Understanding and dealing with it is harder than we thought.
“What is artificial intelligence?”
When I did my textbook, I had to define what AI was. One of the things I wanted to avoid was all these philosophical debates. The question of even is AI possible, what is thinking, what is intelligence, what is consciousness? In some definitions those all come into play. The way I tried to define the field was to say AI is building the best possible programs. So, you are given a task to do, you are given some robot or some computer to do it with, and there are so many possible programs. To make it simple, there are n or 2^n possible programs, and AI is finding the best one. Or at least finding a pretty good one.
“Are we heading toward a Singularity?”
I think it is interesting. I remember reading Vernor Vinge’s stories and being very interested by that. Going back and rereading I.J. Good’s ideas. It’s an interesting idea to think about, and I think that as scientists it is our responsibility to consider these kinds of ideas. We should always be saying, “What we’re working on, is it going to be a force for good or for bad? What are the effects on society going to be? And this is one of the possibilities. So, it is something we have to think about. Is it coming? If it is, what’s going to happen and how do we get ready for it?
“Is the Singularity near?”
So, I’ve looked at it and I don’t see it yet. It is like trying to get a seismograph and say, “Is there an earthquake here?” There are no signals yet, as far as I can see. On the other hand, sometimes earthquakes just come up by surprise.
“Do you know when the Singularity will occur?”
I think that is the whole problem with it. You can’t. I said, “Can you predict by the signs that it is coming?” No, the signs are not there yet. So that means I can’t predict. We just have to wait and see.
“Do you agree with Kurzweil’s forecasts regarding the Singularity?”
Ray is great and I appreciate all that he has done. I appreciate all of the advances in AI that he has done in reading and music. He is a great technologist. I think in terms of these predictions, he is doing the best he can by saying, “I want to nail down this prediction. I am going to look at the data that we have. I am going to extrapolate from that data and come up with an answer. I think the only problem is that the data is not conclusive. You look at Moore’s law and yes, you can say that computers are going at this particular rate. But I don’t think you can necessarily say that when we pass a certain number of transistors, that corresponds to a certain number of neurons, and therefore the two are equivalent. I think that’s where the analogy breaks down.
“What markers would indicate that the Singularity is upon us?”
You would look in two ways. You would look at the theoretical. Are we almost ready to build something that we think could do the whole job? I think now, we’re not. There is no consensus on how you would go about building a machine that would improve itself indefinitely. The other thing you would want to look at is the practical. What is it that machines are capable of doing now and how fast is that progressing? So, certainly we have seen a lot of advances. We have seen machines doing more and more. But it does not seem like we are at the point where it is imminent.
“Do you see technology’s exponential growth or double-exponential growth at play in Google’s operation?”
You have to look at different applications. In many places we see more like linear growth. One of the good examples is work in machine translation, translating from Chinese to English. There we have taken an approach by saying the more you know about language, the better you do. We collect more and more examples of language, feed them into our learning algorithms, and as you add more language examples, performance goes up linearly. We don’t really expect it to be exponential. If anything, we expect it to start to level off at some point, but we have not reached that point yet. We still put in more data and it performs better. In other places you see examples where you need to reach a certain threshold of data before you get results at all. Maybe something like Google as a whole is an example of that. When the web was teeny, a search engine would not be that useful. But now that there are tens of billions of pages out there, almost any query you can do, you get something back. So, it reaches this threshold of usefulness just because of the data that is there, and on top of that we are trying to improve it all the time.
“Google and AI are so often mentioned in the same sentence. What’s Google doing that other search engines are not doing?”
We have the majority of users compared to the other engines. Two, I think we have more of a technological focus. Yahoo seems like they are focused on a full solution to the user. They are a content company, an entertainment company, as well as a technology company. We seem much more focused on technology. I think people look at that and say, “If something’s going to happen, maybe it’s going to come from these guys who are so focused on the technology.”
“What is going on at Google that reflects advancement in AI?”
Ai is prevalent throughout Google in the sense that we have data everywhere. There is far too much data for a human programmer to make sense of it. So, we have been forced to go to machine-learning algorithms throughout the company for almost everything we do. So, in that sense, it is one of the most AI-driven companies that I know of. Now, we have not done anything at the general intelligence level. What we have not tried to say is that we are here to solve all your answers for you. You don’t even need to do the Google queries anymore, we’ll find out what you need and drive to the store and buy it for you rather than give you information on it. We are not attempting to do that. We are just trying to do a better job of what we can do, which is connect you with the information.
“Can we build greater-than-human intelligence?”
Sure. We already have that in so many ways. My tiny little desktop calculator has greater-than-human intelligence. It does square roots much faster than I can. Google has greater intelligence in that it knows about more pages and can find them faster than any human could. Now, it doesn’t understand them the way a human does, so it’s capabilities are very limited. It has superhuman intelligence in some directions and subhuman intelligence in other directions. Maybe someday we will have a more general superhuman intelligence. Until that day, what is interesting is putting it together. What can we take from people to connect them with other people and use these narrow superintelligent tools to make those connections better?
“What are your thoughts on SIAI’s Singularity Summit?”
I think it is a fascinating area just thinking about what the possibilities are for the future. I think it is our responsibility to think about it. If there is going to be some possible negative consequences, we need to prepare for that. If we can have positive consequences quicker, it is our duty to try to do that to make the world a better place. Regardless of how high a probability or what timeframe in the future you put the Singularity, you have got to start thinking about it now. And the other reason I’m here is that a bunch of my friends are here that I wanted to talk to. A bunch of good thinkers that I have not met yet are here that I wanted to meet. So, it just seemed like a great place to be.
“Does Google anticipate future technologies?”
Certainly you want to plan for technological progress. You want to plan even for the unknowns. You want to say, I want to be in a position at some time in the future where I can take advantage of these opportunities. I don’t think that you can say the answers are always going to come out right. If I say that in six years I am going to know, maybe in six years maybe you will know, maybe you won’t. Maybe in six years you will find out there is no answer. That’s it’s impossible. There are limits on what you can do, and some of the advances in computer science have been finding those limits. In basic mathematics, going back to Gödel, showing that there are mathematical truths that cannot be proved, there are limits to what mathematics can do. In computer science, showing that there are problems that are exponentially difficult, that even with this exponential growth in computers we are not going to be able to solve. Now, the question is, where is general intelligence? Is that something that is below this line of a solvable problem, or is it above the line? I don’t think we know yet.
“Why support the Singularity Institute?”
Because it is a fascinating problem and it affects everybody in the world. This could change our lives. I think we want to know about that. We want to be able to participate and have the best chance we can for a good outcome.
“Is Friendly AI possible?”
I think the idea of Friendly AI is very interesting. At Google, we are not doing anything specifically on that, but in my earlier work at NASA. We did work on program verification and properties of programs. At NASA it is very important that software does not crash because if the software crashes, the spaceship crashes, there are people on board, they die. Bad things happen. One of the things you want to be able to do is verify that software is correct. Some of that is done through traditional testing, a quality assurance type of approach, and some of it is done through mathematical proofs of the program. Proofs of correctness or proofs of properties. You could prove a property like, this program will not get into a deadlock state. That’s a good thing to be able to know. Now, if you could define what “friendly” means, then there is a chance of using that same technology to say, “I can prove that this program will never be unfriendly.” The problem is that it is much easier to define the state of deadlock than it is to define the state of Friendliness.
“What does a post-Singularity society look like to you?”
I guess by definition you can’t know what it’s like beyond the Singularity, but if you expand the definition of Singularity a little bit to say “advanced general AI,” I like Barney Pell’s description. It’s when a computer can get most jobs that a high school graduate gets today. How does that change the world? So, one thing is, it makes the world a much richer place. It changes what each of us is able to do, because we are now freed up from doing a lot of labor that we were forced to do before. I remember reading Freeman Dyson’s biography, and one of the things he said was very interesting. The middle class women of his age were much more feminist than the middle class women of today, and the reason was that they all had servants who stayed home and did the cooking, cleaned the house and took care of the kids. So, they were free to go out and do their suffragette movements or whatever it was they were working on. Whereas today, the middle class, we don’t have servant, you often have two parents working and so there is much less free time. Now, after the Singularity in Barney’s sense, we are going to be back to that kind of a lifestyle where not just the middle class but everybody essentially has these servants that can take care of business. So, suddenly, a lot of your lifetime is freed up. Now it’s a question of what do you want to do with it?
“Will the Singularity be democratizing?”
I don’t think it will be more democratizing or more equalizing. Certainly it will raise all boats. Everyone will be at a higher level of living, have more free time, and so on. But I think that the people at the higher end of the economic scale will get an even increasing share, and so this gap between the haves and the have-nots will grow, even as the have-nots have much more.
“Do you have concerns regarding future technologies?”
I certainly do. I think there are a lot of things to worry about, and I don’t think it’s new with AI or nanotechnology or biotechnology. I think we have been dealing with it for many years. I grew up worrying about nuclear war, trying to protest and help through that a little bit. It seems like we made it through that crisis, though there is still a chance of some problems there. There have been all sorts of potential threats. So, for fifty years it has been within the bounds of a high school chemistry students to make up a batch of botulism and drop it in the water supply and kill thousands or maybe hundreds of thousands of people, but no one has decided to do that yet. I’m not sure why. I’m not sure if there really are constraints that are going to continue to hold. I know there is going to be continued threats that smaller and smaller groups of people can do more and more damage. I don’t know how much these ethical constraints or other kinds of constraints are going to stop that from happening.
“Assuming Strong AI is likely, do you anticipate a hard take-off or a soft take-off?”
I think I agree with the soft take-off for the reasons you mentioned. Also, just looking at Ray’s curve, exponential growth means the same percentage change every year. So, I think we will keep on looking at that. Now, at Google, you are very happy with your results and we are happy that we can deliver them to you. From the inside I think it’s kind of like making sausages. We worry more about the results that aren’t good than the results that are good. You do a search and get six out of ten good results and you’re really happy. We say, damn, there were four bad ones in there. We’ve really got to fix that. So, we will keep on doing that. We will keep on coming up with improvements. We will give you better results. We will give you better ways to interact with us. We know there are lots of things now that you want to be able to do but can’t do. For example, if you don’t know any of the words, what do you do? Well, we put all the onus on you. So, you have to fall back and say, I’m looking up some medical problem, I don’t know the specific medical terms, but you know enough to get to a document that will have the terms, and then you read that, and then you can go back and make the actual query. We want to make that step faster. We want to get you directly to the right answer, even if you didn’t know the right words to begin with. Throughout, we want to make the interactions faster, we want to correct you with the right documents or connect you with the right person. We think we will add continual improvements to that and you will be getting happier and happier. I don’t see a hard take-off, but I do see you being able to do more over time.
“What are your thoughts on molecular manufacturing and bio-engineering?”
It’s fascinating to me. I’m trying to learn something about it and educating myself. I have a long way to go. I think it does tie-in, because it is this mix between the physical world and the world of information. I think it is likely that in the coming century that a large part of manufacturing with cells or with atoms. I kind of think it will be more done with cells than with atoms, just because they are bigger, they are easier to manipulate. They have already got this three gigabyte computer on board, we just have to figure out how to program it.
Note: Taken from: http://www.focusonit.info/?p=32796
Thursday, July 3, 2008
MaTra : English to Hindi MT system
MaTra
Translating Languages ....
Revolutionizing Lives................!
India is linguistically rich nation with 22 national languages recognized by the constitution of India, of which Hindi is official Union Language. Besides these there are 844 different dialects that are practiced in various parts of the country. Being a developing nation India is in great need of information revolution for the inclusive growth of the society residing in geographically different areas and speaking different languages. Internet has broken the geographical barrier but the language barrier remains unanswered as only less than 10% of population speaks English. ‘MaTra2’ is an attempt to break the same barrier. This current version of Matra2 is a step towards the mission of Information Revolution to revolutionalize millions of lives in this nation.
MaTra2 is a Fully-Automatic Indicative English-Hindi Machine Translation System. It translates the text in English into Hindi. Though the system is designed to support any domain, currently it is focusing ‘News’ and ‘Medical’ domains only. System works well for the simple sentences. We are working towards translation of other types of sentences such as compound, complex, interrogative, exclamatory, etc. These advances will be integrated into the system soon.
Using MaTra2 is simple, to try it please click here.
or use:
http://202.141.152.9/matra/index.jsp
Translating Languages ....
Revolutionizing Lives................!
India is linguistically rich nation with 22 national languages recognized by the constitution of India, of which Hindi is official Union Language. Besides these there are 844 different dialects that are practiced in various parts of the country. Being a developing nation India is in great need of information revolution for the inclusive growth of the society residing in geographically different areas and speaking different languages. Internet has broken the geographical barrier but the language barrier remains unanswered as only less than 10% of population speaks English. ‘MaTra2’ is an attempt to break the same barrier. This current version of Matra2 is a step towards the mission of Information Revolution to revolutionalize millions of lives in this nation.
MaTra2 is a Fully-Automatic Indicative English-Hindi Machine Translation System. It translates the text in English into Hindi. Though the system is designed to support any domain, currently it is focusing ‘News’ and ‘Medical’ domains only. System works well for the simple sentences. We are working towards translation of other types of sentences such as compound, complex, interrogative, exclamatory, etc. These advances will be integrated into the system soon.
Using MaTra2 is simple, to try it please click here.
or use:
http://202.141.152.9/matra/index.jsp
Thursday, April 17, 2008
Inovation at Geneva....
Bangalore: Geneva Software Technologies, a pioneer in research and development for last fifteen years in the language technology, has commercially launched the Geneva E2C, a software product by which one can send multi-lingual SMSs from desktops to mobile phones, breaking the so-called language barrier in the technology scenario. The company has also taken to the market the Geneva Messaging Service (GMS), a tool which helps one send SMSs in 12 Indian languages to any other mobile phone, irrespective of any difference between the technologies used in source phone and receiving phone. GMS has already been launched by four telecom service providers- BSNL, MTNL, Spice and Idea.
There are three components of technology in the new products. VIVID is the fist one where characters from different languages are created from corresponding matrixes of glyphs or pixels and dots so that it can be received on any platform. The second component Natural Language Framework (NLF) has the capability to move a given software application from its source language to the target language extrinsically or intrinsically with reference to the user interface, data services, internationalization features, personalization and cultural data. The third component is a SIM Card Based Multi-lingual Messaging Application. This invention is about a system and a method for a Subscriber Identity Module (SIM) card based multi-lingual messaging application for mobile equipments. This particularly relates to a system and a method for implementing device independent multi-lingual messaging application using standard GSM or CDMA protocols. Geneva has patented these three technologies in USA and PCT Switzerland.
Geneva E2C supports 11 Indian languages - Hindi, Bengali, Assamese, Telugu, Tamil, Kannada, Malayalam, Oriya, Punjabi, Gujarati, Marathi - and English. It also has the capability to translate messages from one language to another. The natural language lay out will help one easily type vernacular languages.
Another product developed by Geneva R&D team is Natural Disaster Information system (NDIS), which provides SMS alerts on the occasions of natural disasters in vernacular language. Many states including AP and Tamil Nadu have implemented this facility. Geneva was nominated for the 3 GSM Mobile Innovation Awards 2006.
Monday, March 24, 2008
Vivekanand and ambiguity.......!
Yesterday I was reading a Marathi news paper "TARUN BHARAT".It had a special section about the stories and one of them was about the Swami Vivekananda. Story was some thing like.... leave it I will tell the story later....first see how the sentence is ambiguous in meaning.
Sentence: "Give him what you want" or "What you want, give it to him".
The above sentence is always taken as ...ex: you are told to give ur friend a pen or book. You give him book, as you wanted to give him that.
But there is another meaning to the sentence "Give him what you want" and that goes like this...
ex: you are asked what u want from the pen and book? say ur answer is pen. then you should give him pen and not the book.
Logically say ...what you want=X ..then..... give him what you want= give him X.
Isn't it funny?
And this sentence relates to the Vivekananda as:
Once Vivekananda and his friend had gone to the Vivekananda's home for the lunch break from school. The friend was from a poor family and was very hungry. Vivekananda took him to home and asked mother to give them some thing to eat. she was engaged in some work and said go and take LADDUS from the dabba. She actualy said,"There are two laddus in dabba, give him what you want." And Vivekananda gave him the bigger one took a smaller for himself. Mother had seen this and when vivekanand came back to home she asked him,"didn't you liked the laddu?"
He told he liked it and his friend too. Then she asked," why did you gave him the bigger one and took the smaller?" Intelligent Vivekanand replied ,"You had told me to give him what I want, I wanted the bigger one and So I gave it to him."
Salute to great Vivekanand!
Sentence: "Give him what you want" or "What you want, give it to him".
The above sentence is always taken as ...ex: you are told to give ur friend a pen or book. You give him book, as you wanted to give him that.
But there is another meaning to the sentence "Give him what you want" and that goes like this...
ex: you are asked what u want from the pen and book? say ur answer is pen. then you should give him pen and not the book.
Logically say ...what you want=X ..then..... give him what you want= give him X.
Isn't it funny?
And this sentence relates to the Vivekananda as:
Once Vivekananda and his friend had gone to the Vivekananda's home for the lunch break from school. The friend was from a poor family and was very hungry. Vivekananda took him to home and asked mother to give them some thing to eat. she was engaged in some work and said go and take LADDUS from the dabba. She actualy said,"There are two laddus in dabba, give him what you want." And Vivekananda gave him the bigger one took a smaller for himself. Mother had seen this and when vivekanand came back to home she asked him,"didn't you liked the laddu?"
He told he liked it and his friend too. Then she asked," why did you gave him the bigger one and took the smaller?" Intelligent Vivekanand replied ,"You had told me to give him what I want, I wanted the bigger one and So I gave it to him."
Salute to great Vivekanand!
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