Tomorrow's Intelligent Machines......

10 Ways To Improve Your Brain Fitness

2012-01-13T15:53:00.000+05:30

Brain fitness has basic principles: variety and curiosity. When anything you do becomes second nature, you need to make a change. If you can do the crossword puzzle in your sleep, it's time for you to move on to a new challenge in order to get the best workout for your brain. Curiosity about the world around you, how it works and how you can understand it will keep your brain working fast and efficiently. Use the ideas below to help attain your quest for mental fitness.

1. Play Games
Brain fitness programs and games are a wonderful way to tease and challenge your brain. Suduko, crosswords and electronic games can all improve your brain's speed and memory. These games rely on logic, word skills, math and more. These games are also fun. You'll get benefit more by doing these games a little bit every day -- spend 15 minutes or so, not hours.

2. Meditation
Daily meditation is perhaps the single greatest thing you can do for your mind/body health. Meditation not only relaxes you, it gives your brain a workout. By creating a different mental state, you engage your brain in new and interesting ways while increasing your brain fitness.

3. Eat for Your Brain
Your brain needs you to eat healthy fats. Focus on fish oils from wild salmon, nuts such as walnuts, seeds such as flax seed and olive oil. Eat more of these foods and less saturated fats. Eliminate transfats completely from your diet.

4. Tell Good Stories
Stories are a way that we solidify memories, interpret events and share moments. Practice telling your stories, both new and old, so that they are interesting, compelling and fun. Some basic storytelling techniques will go a long way in keeping people's interest both in you and in what you have to say.

5. Turn Off Your Television
The average person watches more than 4 hours of television everyday. Television can stand in the way of relationships, life and more. Turn off your TV and spend more time living and exercising your mind and body.

6. Exercise Your Body To Exercise Your Brain
Physical exercise is great brain exercise too. By moving your body, your brain has to learn new muscle skills, estimate distance and practice balance. Choose a variety of exercises to challenge your brain.

7. Read Something Different
Books are portable, free from libraries and filled with infinite interesting characters, information and facts. Branch out from familiar reading topics. If you usually read history books, try a contemporary novel. Read foreign authors, the classics and random books. Not only will your brain get a workout by imagining different time periods, cultures and peoples, you will also have interesting stories to tell about your reading, what it makes you think of and the connections you draw between modern life and the words.

8. Learn a New Skill
Learning a new skill works multiple areas of the brain. Your memory comes into play, you learn new movements and you associate things differently. Reading Shakespeare, learning to cook and building an airplane out of toothpicks all will challenge your brain and give you something to think about.

9. Make Simple Changes
We love our routines. We have hobbies and pastimes that we could do for hours on end. But the more something is 'second nature,' the less our brains have to work to do it. To really help your brain stay young, challenge it. Change routes to the grocery store, use your opposite hand to open doors and eat dessert first. All this will force your brain to wake up from habits and pay attention again.

10. Train Your Brain
Brain training is becoming a trend. There are formal courses, websites and books with programs on how to train your brain to work better and faster. There is some research behind these programs, but the basic principles are memory, visualization and reasoning. Work on these three concepts everyday and your brain will be ready for anything.

Future of the computer

2011-04-28T12:03:00.002+05:30

When the advances of microprocessor technology finally hits the wall and circuits are beyond anymore shrinking, more computers are expected to use dual processing, triple processing or even more. This means that instead of using single chips to perform operations, the compter shares the job between 2 or more processors.

Already, supercomputers at companies like Intel, NASA and IBM use fleets of processors and are hence able to process jobs at amazing speeds impossible for single processor computers. Workstations, animation CAD computers and other video editing computers use the dual processor technology.

The only problem that lies here is with the operating systems. As the number of processors increase, the operating system, which takes care of all the tasks inside a computer will have to be more complex to be able to support them. Further more, the task of splitting the operations is complicated would be a big problem as more and more processors are incoperated.

To be able to let the processors run at top speeds, memory allocation areas like the RAM (random access memory), cache and also the BUS (the connection that links up the component) will also have to increase in speed and size.

However, it will still be sometime before the current technology hits a wall and we will have to resort to such tactics and the end-users and buyers like us will have nothing to worry about for the time being. No matter what, computers will only get better and faster, even if Moore's law doesn't hold anymore.

Human Brain Development

2011-04-28T11:57:00.000+05:30

The human embryo is a single cell at its conception, from that cell grows vital organs such as the brain.

The brain first appears during the first three weeks, by this time the embryo is about a tenth of an inch. The brain is developed as a bump at the end of the neural tube.

The neural tube is a group of cells that are connected into a hollow, extended structure. Glial cells create the physical structure of the brain and other central nervous systems structures. The Glial cells move to their final positions in nuclear groups and layered structures such as the cortex.

As the glial cells move they create contacts with other neurons but when they reach there final position they begin to form into thin strands called axons with the neurons that they connected to during there journey.

In the next 5 weeks major sections of the brain become recognizable and operational. Also during this time a major growth of brain rapidly occurs in the cerebral cortex. These cells then move and create other sections of the brain. These sections are decided by the neuron function. At six months of pregnancy the fetus is working as it would after birth, its forebrain, midbrain, hindbrain and optic vesicle also become visible. By seven months the brain waves of the fetus can be determine through the mother's abdomen. Around nine months the human loses the ability to create more neurons.

By this time the brain of the human is so big that the brain can not grow any further until birth. This is why a human baby is less developed than that of other animals. The human brain continues to grow until about the age of 25.

The Superior Intelligence

2011-04-28T11:51:00.000+05:30

Generally Speaking

What do we mean ? - The superior intelligence would refer to a race of computers and robots completely different in ability and functionality of those of today. We expect computers to be able to learn better without human assistance, understand scenarios and react independently to events that they come across.

So what ? - Yes, traditional AI (artificial intelligence) programs already told us of smarter, better and more human friendly computer systems in the future, but to what level ? Traditional AI allows the computer to simply make use of information provided in ways the programmer already defined and act accordingly to them. All actions and creativity of those programs are only limited to those preset by programmers. What we are talking about, however, are computer programs that can learn from observation, apply it new knowledge to use ; that is they, possess the learning capabilities similar to any human being. That being said and given their efficient

Is it Possible ?

Software wise -Today's computer programs are already capable of learning like humans, by observation, by sounds and find their way around by trail and error. Some developers have already come up with robots that are capable of learning and behaving like a human infant. It is not mainly a problem of software that is hindering the progress of computer technology but the hardware aspect. Today's computers simply lack the processing power to match up to the human brain.

Hardware wise - We already mentioned the capability in computer processing power , compared to those of the human brain. By our estimate, today's very biggest super computers are within a factor of a hundred of having the power to mimic a human mind. It is only a matter of time before computers become even more powerful than the human brain.

How long will it take ?

Right after World War I, rapid improvements in electromechanical calculators was the first sign of the beginning of the electronics race. As electronic computers surfaced during the World War II, the computation speed to price ratio increased a millionfold.

Since we know that electricity needs to travel through a conductor, it only made sense that shorter, smaller circuits and conductors will enable the electricity to travel faster, hence improving speed of the electrical component.

Once the idea got through, the race to squeeze "more" into "less" created fierce competition between markets.Components which made use of vacuum tubes were abandoned, and transistors took their place. Then, the transistors themselves were replaced by faster and smaller integrated circuits. At some point, scientists warned that the "circuit squeezing days" were over as circuits reached 3 micrometers. Then, new manufacturing techniques came out and proved the scientists wrong. Computer circuit developments sped up even more !

As these integrated circuits get more and more packed with circuits that grew increasing smaller by the day, they became so integrated, that they actually became microchips . Today's microchips producers can squeeze a few million circuits and transistors all into a chip the size of a pin head !! The small problem involved here was that the less than 0.1 micrometer circuits were so fragile and heat sensitive that they would melt down when the chips get too hot. As a result, the chips are kept under controlled temperatures by intensive cooling. (E.g. liquid helium baths)

It was not long before the question of "how much more can we squeeze the circuits ?" popped up again. Circuit sizes seem to be reaching a size that seem too small to be compressed even more and even if they were, the heat heat and other electrical interference generated would destroy the fragile circuits and signals would leak from them.

The problem has fortunately found a new solution, by a technique that seem even more unlikely ; to shrink the circuits even more. Because traditional circuits still use the property of forcing massive loads of electrons along tiny little circuits, they cause great problems about the amount of interference they can take. With this new class of components known as single-electron transistors and quantum dots, which work by the interference of electron waves, larger amounts of interference energy are needed to cause disruptions. These single electron transistors even work better as they shrink in size !! Instead of making war with the inference energy, scientists actually make use of them.

The pace of improvements in computer performance is rapid. In the 1980s, computer performance doubled every one and a half years. In the late 1990s, computer performance doubled with periods of a single year !

If the pace continues, and computer technology continues improving even more rapidly by the day, computers matching human performance would be possible in more than a decade and human like robots will appear in the 2020s !!!

Would we actually do it ?

Even today, the current technology is just barely good enough (but possible) for us to create our own "digital human" should we want it badly enough. We mentioned that we are just a fraction away from building a computer system matching the processing power of the human brain. The main barrier involved was cost. Building a system that powerful would take hundreds of billions of dollars and not many investors would be willing to sponsor any enthusiastic researchers and scientists with that amount of money.
We would expect many of you to ask : Why wouldn't anybody be interested ?

First of all, why would we waste such a massive amount of money and human resource into building a single,
"fake " human when so many real life humans are already out in the populations ? It is jst not economical to the investor's pockets because the "product" would not be at all that useful.

The most powerful experimental super computers today are not used to research AI, or to imitate a human brain but are instead used for stimulations of real life events which are too complicated for normal calculations. While, they are powerful enough to do a partial imitation of the human brain, they will most likely not be used for that cause. This is again, because it is not economical. The investors and businessmen who funds such projects are not likely to waste their money on unproductive experiments that would cause so much.

While there are curious funders who pour money into AI and robotics projects that produces interesting results like a robot that can imitate a babie's curious behavouir, and robots that can create facial expressions themselves by responding to their "moods", such investors are rare and often invest only for fun and not for serious interest.

Until the cost of building a digital human brain is cheap enough, it will be hard to expect to see human-like robots walking around in the streets like in the movies any time soon.

Applied Theory (Artificial Intelligence)

2011-03-09T15:30:00.000+05:30

Ai bases its approach to creating real artificial intelligence on a solid philosophical basis. Rather than keeping our philosophy in the realm of theory, we apply it to our entire working process.

Ai’s applied philosophy is drawn from branches of the philosophy of language, logic, and radical behaviorism. It is built on four founding principles which guide our approach.

The first principle is that intelligence is in the eyes of the beholder. This means that there is no way to tell whether someone, or something, is intelligent, other than by making a subjective judgment based on observable behavior.

The second principle is that the most salient behavior that demonstrates intelligence is language, or more specifically conversational skills – the ability to interact in an "intelligent" manner with the observer.

The third principle is that this ability to use language, to converse, is a skill that can be acquired like any other skill.

Fourth, we believe that like the development of any skill, the ability to converse can only develop if a strict developmental process is followed.

A careful reading of Alan Turing’s paper "Computing Machinery and Intelligence" shows that Turing - the father of modern computing and artificial intelligence - based his approach to creating a "child machine" on the same four principles.

Subjective Intelligence

Intelligence is in the eyes of the beholder. Therefor, a machine that through conversation can fool a human into believing that it is human, must be deemed intelligent.

Language

Intelligence is measured through the social use of language. If a machine can generate language which accurately simulates the way people use language, it is fair to call that machine "intelligent".

Skill

Language has nothing to do with any type of knowledge base or rules; it is a skill that can be learned through a system of punishments and rewards.

Development

The acquisition of conversational skills has to go through an incremental developmental process. This developmental approach to learning language is the only way to create machine intelligence.

The Child Machine

2011-03-08T15:25:00.002+05:30

Hal, like any 18-month old baby, is learning the rudiments of speech. He talks about red balls and blue balls, knows his Mommy and Daddy, and likes to go to the park. A child development specialist was given transcripts of Hal's conversations with his caretakers and declared him a healthy, normal little boy. What she wasn't told is that Hal is a computer program running on a regular Windows PC.

Ai uses behaviorist principles to teach our child machine - nicknamed Hal - to hold a conversation. Our approach was outlined by the computing theory pioneer Alan Turing in his 1950 article Computing Machinery and Intelligence. Turing viewed language as the defining element of intelligence; he believed that by giving a machine the capacity to learn, and a willingness to ask questions, you could "raise" an intelligence, an entity capable of rational, engaging conversation.

Education

Learn how Hal is being educated, and read about his training process.

State of Mind

The Ai child machine is built on a statistical model of language, coupled with advanced learning algorithms.

First Words

HAL is developed to meet basic human language development milestones. Look inside to find out what HAL's talking about.

Research plan (Artifical Intelligence)

2011-03-08T15:15:00.000+05:30

Ai has developed a research program aimed at true artificial intelligence - allowing people to converse with their computers in everyday language.

In teaching a computer to use language, Ai takes a scheduled, developmental approach, applying the behaviorist model of learning.

Our research plan is based on an iterative cycle, designed to improve the language skills of the system with each software update ("brain upgrade"). The developmental milestones we set for our child machine are based on human language-use milestones, with progress being evaluated by experts in child development

Applying the principles of behaviorism, we teach language to the child-machine through a system of rewards and punishments. The child-machine thus learns to use language, rather than having language built into it. Subjective Intelligence Related Article

At the Ai research facility, trainers converse with the machine, engaging it in conversation and monitoring its progress. The trainers test the limits of the child-machine's intelligence, and share their assessments with the algorithm developers. The developers consequently update and adapt the child-machine's algorithm, or "brain", making it more "human" in its language capability. Every so often, a new version of the brain is handed to the trainers, and the process repeats.

As the trainer works with the child machine, he or she frequently reports back to the developers on its progress. Our metric for success is clearly defined as "the language capability of a human of a defined age." For the first several iterations of the child machine, we sought to have the child machine speak at the same developmental level as a 15-month old. Now we are working on raising the child to 18 months. The actual time spent training the child does not correspond to its age; rather, these measurements of linguistic ability are standard, accepted guidelines for determining if a child is making linguistic progress or not.

Theory (Artificial Intelligence)

2011-03-07T15:00:00.001+05:30

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain. -- Alan Turing, 1950

At Ai, we're raising a child machine from infancy to adulthood - thus bringing Turing's vision to fruition - and creating entirely new approaches to machine learning. In our research, we take a strong behaviorist approach, meaning that we work from the principle that language is a skill, not simply the output of brain functions, and, therefore, can be learned. The research was initially led by Jason Hutchens, a world-renowned chatbot developer and winner of the Loebner Prize in Artificial Intelligence, and Dr. Anat Treister-Goren, an award-winning neurolinguist.

More on Sixth Sence Technology

2010-08-13T13:17:00.001+05:30

Ever since computers began to be a part of mankind, their evolution has been taking place at break neck speed. And now we are about to witness the power of computing and on demand information just like the Sci-fi thrillers of Hollywood have portrayed for many years. Sixth Sense Technology is one such recent invention which aims to blend in the boundaries between the virtual and the real world. The mastermind behind this futuristic technology is Pranav Mistry, a designer whiz kid.

Developmental Stages

The idea behind this technology is to simplify day-to-day tasks and integrate them with the virtual world. This technology was born with the simple modification of a ball mouse into a motion sensing device. The axial rollers found in the ball mouse were used to replicate the gestures made with hand on the computer. The much-loved sticky notes were also implemented with this, one exception being that our scribble work on them would be directly synchronized with the computer or a scheduling device which can also be organized effortlessly.

With the virtual interaction in place, the next obvious step was to bring in instant information to the user. Sixth sense technology is set to redefine the way information can be searched for. The information could be accessed by merely placing the object of interest on the interactive plane without even having to GOOGLE it! So to check your flight schedule, all that you have to do is place your ticket on the interactive surface and watch in awe as you are flooded with the details.

Device Set Up

The Sixth sense device is a complete surprise package when it comes to its functionality and hardware. Just as the device simplifies human craving for information, it simplifies the way you interact with it. You don’t have to be a rocket scientist to be absolutely at ease operating it. The device has a portable camera, projector and few color markers stuck onto the fingers for gesture tracking.

The big plus of this device is that you need not carry a monitor display with you wherever you go. Rather you can magically turn any surface of your choice into a virtual screen and start interacting with the projected information. The device is a network enabled module which allows you to access the internet, cruise through maps when you are stuck in a tour, check your mails and also doubles up as your virtual mobile phone.

Availability

If you think this technology is confined to your dreams and would cost a bomb, then its time you get ready to be blown away. Because it would come as cheap as $350 in its compact and stylish pendant avatar! If this isn’t a reason to smile, then you will be happier to know that this technology boasts of an open source software. So all you need is the basic hardware in place, a techno savvy mind and passion for cool gadgets to put together your own sixth sense device and experience the power of the digital world on the go.

Sixth sense Vs Microsoft Digital Surface

The other recent development which introduced the multi-touch functionality and gesture interpretation technology is the Microsoft Surface from the computer technology giants, the Microsoft Corporation. But Pranav Mistry’s invention wins hands down with respect to the Microsoft Surface because of its down to earth price, portability, and mass popularity. Pranav Mistry and his mentor Pattie Maes got rave reviews for their presentations in the Ted conferences held at India and USA respectively. Moreover the Microsoft Surface is aimed at the commercial market with only a few ready to experiment with it considering the huge investment involved.

Thus Sixth Sense Technology is one fuss free device which is set to bless mankind with an extra sense in spite of the individual’s spiritual status. So let us all gear up to explore the world around us Virtually!

DNA ‘Spider’ Robot Collects Nanoparticles!

2010-08-09T17:38:00.002+05:30

A DNA spider follows a path on a DNA origami scaffold towards the red-labeled goal.

Wow! Scientists are one step closer to creating molecular robots that may eventually perform very complex tasks, such as building nanomolecules… or delivering drugs to targeted diseased tissues. They have constructed DNA-based robots that can walk along a specific path, unaided, or, collect various nanoparticles along an assembly line. This news is from two studies published in Nature few month ago.

The Sixth Sense Technology....

2010-01-28T14:53:00.001+05:30

All of us are aware of the five basic senses – seeing, feeling, smelling, tasting and hearing. But there is also another sense called the sixth sense. It is basically a connection to something greater than what their physical senses are able to perceive. To a layman, it would be something supernatural. Some might just consider it to be a superstition or something psychological. But the invention of sixth sense technology has completely shocked the world. Although it is not widely known as of now but the time is not far when this technology will change our perception of the world.

Pranav Mistry, 28 year old, of Indian origin is the mastermind behind the sixth sense technology. He invented ‘ Sixth Sense / WUW ( Wear UR World) ‘ which is a wearable gestural , user friendly interface which links the physical world around us with digital information and uses hand gestures to interact with them. He is a PhD student at MIT and he won the ‘Invention of the Year 2009 ‘- by Popular Science. The device sees what we see but it lets out information that we want to know while viewing the object. It can project information on any surface, be it a wall, table or any other object and uses hand / arm movements to help us interact with the projected information. The device brings us closer to reality and assists us in making right decisions by providing the relevant information, thereby, making the entire world a computer.

The Sixth Sense prototype consists of a pocket projector, mirror and a camera. The device is pendant shaped like mobile wearing devices. Both the projector and the camera are connected to the mobile computing device in the user’s pocket. The projector projects visual information enabling surfaces, walls and physical objects around us to be used as interfaces; while the camera recognizes and tracks user’s hand gestures and physical objects using computer-vision based techniques. The software program processes the video stream data captured by the camera and tracks the locations of the colored markers (visual tracking fiducials) at the tip of the user’s fingers using simple computer-vision techniques. It also supports multi touch and multi user interaction.

The device has a huge number of applications. Firstly, it is portable and easily to carry as you can wear it in your neck. The drawing application lets user draw on any surface by observing the movement of index finger. Mapping can also be done anywhere with the features of zooming in or zooming out. The camera also helps user to take pictures of the scene he is viewing and later he can arrange them on any surface. That’s not it. Some of the more practical uses are reading a newspaper. Imagine reading a newspaper and viewing videos instead of the photos in the paper. Or live sports updates while reading the newspaper. The device can also tell you arrival, departure or delay time of your air plane on your tickets. For book lovers it is nothing less than a blessing. Open any book and you will find the Amazon ratings of the book. To add to it, pick any page and the device gives additional information on the text, comments and lot more add on features. While picking up any good at the grocery store, the user can get to know whether the product is eco friendly or not. To know the time, all one has to do is to just gesture drawing circle on the wrist and there appears a wrist watch. The device serves the purpose of a computer plus saves time spent on searching information. Currently the prototype of the device costs around $350 to build. Still more work is being done on the device and when fully developed, it will definitely revolutionize the world.

Architecture of Cloud Computing

2010-01-27T15:17:00.001+05:30

Architecture

Cloud architecture, the systems architecture of the software systems involved in the delivery of cloud computing, comprises hardware and software designed by a cloud architect who typically works for a cloud integrator. It typically involves multiple cloud components communicating with each other over application programming interfaces, usually web services.

This closely resembles the Unix philosophy of having multiple programs each doing one thing well and working together over universal interfaces. Complexity is controlled and the resulting systems are more manageable than their monolithic counterparts.

Cloud architecture extends to the client, where web browsers and/or software applications access cloud applications.

Cloud storage architecture is loosely coupled, often assiduously avoiding the use of centralized metadata servers which can become bottlenecks. This enables the data nodes to scale into the hundreds, each independently delivering data to applications or users.

Types by visibility

Public cloud

Public cloud or external cloud describes cloud computing in the traditional mainstream sense, whereby resources are dynamically provisioned on a fine-grained, self-service basis over the Internet, via web applications/web services, from an off-site third-party provider who shares resources and bills on a fine-grained utility computing basis.

Hybrid cloud

A hybrid cloud environment consisting of multiple internal and/or external providers "will be typical for most enterprises". A hybrid cloud can describe configuration combining a local device, such as a Plug computer with cloud services. It can also describe configurations combining virtual and physical, colocated assets—for example, a mostly virtualized environment that requires physical servers, routers, or other hardware such as a network appliance acting as a firewall or spam filter.

Private cloud

Private cloud and internal cloud are neologisms that some vendors have recently used to describe offerings that emulate cloud computing on private networks. These (typically virtualisation automation) products claim to "deliver some benefits of cloud computing without the pitfalls", capitalising on data security, corporate governance, and reliability concerns. They have been criticized on the basis that users "still have to buy, build, and manage them" and as such do not benefit from lower up-front capital costs and less hands-on management, essentially " the economic model that makes cloud computing such an intriguing concept".

While an analyst predicted in 2008 that private cloud networks would be the future of corporate IT, there is some uncertainty whether they are a reality even within the same firm. Analysts also claim that within five years a "huge percentage" of small and medium enterprises will get most of their computing resources from external cloud computing providers as they "will not have economies of scale to make it worth staying in the IT business" or be able to afford private clouds. Analysts have reported on Platform's view that private clouds are a stepping stone to external clouds, particularly for the financial services, and that future datacenters will look like internal clouds.

The term has also been used in the logical rather than physical sense, for example in reference to platform as a service offerings, though such offerings including Microsoft's Azure Services Platform are not available for on-premises deployment.

Cloud Computing

2010-01-26T15:08:00.000+05:30

Cloud computing is Internet- ("cloud-") based development and use of computer technology ("computing"). In concept, it is a paradigm shift whereby details are abstracted from the users who no longer need knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing describes a new supplement, consumption and delivery model for IT services based on Internet, and it typically involves the provision of dynamically scalable and often virtualized resources as a service over the Internet.

The term cloud is used as a metaphor for the Internet, based on the cloud drawing used to depict the Internet in computer network diagrams as an abstraction of the underlying infrastructure it represents. Typical cloud computing providers deliver common business applications online which are accessed from a web browser, while the software and data are stored on servers.

These applications are broadly divided into the following categories: Software as a Service (SaaS), Utility Computing, Web Services, Platform as a Service (PaaS), Managed Service Providers (MSP), Service Commerce, and Internet Integration.

Human Thoughts Control New Robot

2010-01-22T12:27:00.003+05:30

Scientists have created a way to control a robot with signals from a human brain.

By generating the proper brainwaves—picked up by a cap with electrodes that sense the signals and reflect a person's instructions—scientists can instruct a humanoid robot to move to specific locations and pick up certain objects.

The commands are limited to moving forward, picking up one of two objects and bringing it to one of two locations. The researchers have achieved 94 percent accuracy between the thought commands and the robot's movements.

"This is really a proof-of-concept demonstration," said Rajesh Rao, a researcher from the University of Washington who leads the project. "It suggests that one day we might be able to use semi-autonomous robots for such jobs as helping disabled people or performing routine tasks in a person's home."

The person wearing the electrode cap watches the robot's movement on a computer screen through two cameras installed on and above the robot.

When the robot's camera sees the objects that are to be picked up it passes on the information to the user's computer screen. Each object lights up randomly on the computer screen. When a person wants something picked up and it happens to light up, the brain registers surprise and sends this brain activity to the computer and then to the robot as the choice object. The robot then proceeds to pick up the object.

A similar algorithm is used to decide where the robot will go.

"One of the important things about this demonstration is that we're using a 'noisy' brain signal to control the robot," Rao said. "The technique for picking up brain signals is non-invasive, but that means we can only obtain brain signals indirectly from sensors on the surface of the head, and not where they are generated deep in the brain. As a result, the user can only generate high-level commands such as indicating which object to pick up or which location to go to, and the robot needs to be autonomous enough to be able to execute such commands."

2010-01-21T13:25:00.003+05:30

Robotics Timeline

2010-01-21T13:01:00.003+05:30

* Robots capable of manual labour tasks
o 2009 - robots that perform searching and fetching tasks in unmodified library environment, Professor Angel del Pobil (University Jaume I, Spain), 2004
o 2015-2020 - every South Korean household will have a robot and many European, The Ministry of Information and Communication (South Korea), 2007
o 2018 - robots will routinely carry out surgery, South Korea government 2007
o 2022 - intelligent robots that sense their environment, make decisions, and learn are used in 30% of households and organizations - TechCast
o 2030 - robots capable of performing at human level at most manual jobs Marshall Brain
o 2034 - robots (home automation systems) performing most household tasks, Helen Greiner, Chairman of iRobot

* Military robots
o 2015 - one third of US fighting strength will be composed of robots - US Department of Defense, 2006
o 2035 - first completely autonomous robot soldiers in operation - US Department of Defense, 2006
o 2038 - first completely autonomous robot flying car in operation - US Department of Technology, 2007

* Developments related to robotics from the Japan NISTEP 2030 report :
o 2013-2014 — agricultural robots (AgRobots).
o 2013-2017 — robots that care for the elderly
o 2017 — medical robots performing low-invasive surgery
o 2017-2019 — household robots with full use.
o 2019-2021 — Nanorobots
o 2021-2022 — Transhumanism

Robotics in 2020

2010-01-20T15:07:00.006+05:30

Robots will be commonplace: in home, factories, agriculture, building & construction, undersea, space, mining, hospitals and streets for repair, construction, maintenance, security, entertainment, companionship, care.

Purposes of these Robots:

* Robotized space vehicles and facilities
* Anthropomorphic general-purpose robots with hands like humans used for factory jobs - Intelligent robots for unmanned plants - Totally automated factories will be commonplace.
* Robots for guiding blind people
* Robots for almost any job in home or hospital, including Robo-surgery.
* Housework robots for cleaning, washing etc - Domestic robots will be small, specialized and attractive, e.g. cuddly

Properties of these robots:

* Autonomous, with environmental awareness sensors
* Self diagnostic self repairing
* Artificial brains with ten thousand or

International Robot Exhibition (IREX), organized by Japan Robot Association (JARA), is a biennal robot exhibition since 1973, which features state-of-the art robot technologies and products.

Bits vs. atoms

2010-01-19T16:30:00.000+05:30

Engelberger and others at the show drew a sharp contrast between the explosive growth of the computer industry over the past few decades and the relative stagnation of the robotics field. While venture capitalists were lining up to fund computer start-ups, Engelberger, despite his impressive résumé, was unable to get financing for his robot that would help people live at home rather than go into a nursing home.

The robotics industry today is about as far along the road to widespread commercial acceptance as the PC industry was in the 1970s. The differences are that robotics don't have an equivalent of Moore's Law, the industry hasn't settled on standards, there's not much in the way of venture capital money and there's really no viable commercial application - killer or otherwise, said Paolo Pirjanian, chief scientist at Evolution Robotics .

On the show floor, several vendors displayed small demo robots that used sensors to navigate the show floor - literally technologies in search of an application. Unfortunately, the economics are such that it's extremely difficult to build a true robot that can interact with its environment at a cost that would attract consumers, Pirjanian said.

The vacuum cleaner is a good example. Electrolux tried to market a robotic vacuum cleaner called Trilobite that uses ultrasound to get around, but at $1,800 consumers weren't biting. The Roombas and e-Vacs are affordable - between $150 and $250 - but they lack the sophisticated capabilities that one would want in a robotic vacuum cleaner, such as obstacle avoidance, the ability to go up and down steps, and the ability to know where it had already vacuumed.

The Future Of Robots

2010-01-16T11:12:00.000+05:30

Engineers built humanoid robots that can recognize objects by color by processing information from a camera mounted on the robot's head. The robots are programmed to play soccer, with the intention of creating a team of fully autonomous humanoid robots able to compete against a championship human team by 2050. They have also designed tiny robots to mimic the communicative "waggle dance" of bees.

A world of robots may seem like something out of a movie, but it could be closer to reality than you think. Engineers have created robotic soccer players, bees and even a spider that will send chills up your spine just like the real thing.
They're big ... they're strong ... they're fast! Your favorite big screen robots may become a reality.
Powered by a small battery on her back, humanoid robot Lola is a soccer champion.
"The idea of the robot is that it can walk, it can see things because it has a video camera on top," Raul Rojas, Ph.D., professor of artificial intelligence at Freie University in Berlin, Germany, told Ivanhoe.
Using the camera mounted on her head, Lola recognizes objects by color. The information from the camera is then processed in this microchip, which activates different motors.
"And using this camera it can locate objects on the floor for example a red ball, go after the ball and try to score a goal," Dr. Rojas said. A robot with a few tricks up her sleeve.

German engineers have also created a bee robot. Covered with wax so it's not stung by others, it mimics the 'waggle' dance -- a figure eight pattern for communicating the location of food and water.
"Later what we want to prove is that the robot can send the bees in any decided direction using the waggle dance," Dr. Rojas said.
Robots like this could one day become high-tech surveillance tools that secretly fly and record data ... and a robot you probably won't want to see walking around anytime soon? The spider-bot.

Artificial Intelligence - Chatterbot Eliza description

2010-01-15T11:15:00.001+05:30

Artificial Intelligence - Chatterbot Eliza program is an Eliza like chatterbot.

This program is an Eliza like chatterbot.The implementation of the program has been improved, the repetitions made by the program are better handled, the context in a conversation is also better handled, the program can now correct grammatical errors that can occure after conjugating verbs.

Finaly, the database is bigger than the last time, it includes some of the script that originaly was used in the first implementation of the chatterbot Eliza by Joseph Weizenbaum. And also,most of the chatterbots that have been written this days are largely based on the original chatterbot Eliza that was written by Joseph Weizenbaum which means that they use some appropriate keywords to select the responses to generate when they get new inputs from the users.

More generaly,the techique that are in use in a "chatterbot database" or "script file" to represent the chatterbot knowledge is known as "Case Base Reasoning" or CBR. A very good example of an Eliza like chatterbot would be "Alice",these program has won the Loebner prize for most human chatterbot three times (www.alicebot.org).

The goal of NLP and NLU is to create programs that are capable of understanding natural languages and also capable of processing it to get input from the user by "voice recognition" or to produce output by "text to speech".

During the last decades there has been a lot of progress in the domains of "Voice Recognition" and "Text to Speech",however the goal of NLU that is to make software that are capable of showing a good level of understanding of "natural languages" in general seems quiet far to many AI experts. The general view about this subject is that it would take at list many decades before any computer can begin to really understand "natural language" just as the humans do.

Technologies of affective computing

2010-01-14T13:16:00.000+05:30

Emotional speech
Emotional speech processing recognizes the user's emotional state by analyzing speech patterns. Vocal parameters and prosody features such as pitch variables and speech rate are analyzed through pattern recognition.
Emotional inflection and modulation in synthesized speech, either through phrasing or acoustic features is useful in human-computer interaction. Such capability makes speech natural and expressive. For example a dialog system might modulate its speech to be more puerile if it deems the emotional model of its current user is that of a child.

Facial expression
The detection and processing of facial expression is achieved through various methods such as optical flow, hidden Markov model, neural network processing or active appearance model. More than one modalities can be combined or fused (multimodal recognition, e.g. facial expressions and speech prosody or facial expressions and hand gestures) to provide a more robust estimation of the subject's emotional state.

Body gesture
Body gesture is the position and the changes of the body. There are many proposed methods to detect the body gesture. Hand gestures have been a common focus of body gesture detection, apparentness methods and 3-D modeling methods are traditionally used.

Visual aesthetics
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing Website as data source. They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. The work is demonstrated in the ACQUINE system on the Web.

Potential applications
In e-learning applications, affective computing can be used to adjust the presentation style of a computerized tutor when a learner is bored, interested, frustrated, or pleased. Psychological health services, i.e. counseling, benefit from affective computing applications when determining a client's emotional state. Affective computing sends a message via color or sound to express an emotional state to others.
Robotic systems capable of processing affective information exhibit higher flexibility while one works in uncertain or complex environments. Companion devices, such as digital pets, use affective computing abilities to enhance realism and provide a higher degree of autonomy.

Other potential applications are centered around social monitoring. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry. Affective computing has potential applications in human computer interaction, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.

Affective computing is also being applied to the development of communicative technologies for use by people with autism.

Affective computing

2010-01-13T13:06:00.000+05:30

Affective computing is a branch of the study and development of artificial intelligence that deals with the design of systems and devices that can recognize, interpret, and process human emotions. It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science. While the origins of the field may be traced as far back as to early philosophical enquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing. A motivation for the research is the ability to simulate empathy. The machine should interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate response for those emotions.

Areas of affective computing

1. Detecting and recognizing emotional information
2. Emotion in machines

Technologies of affective computing

1. Emotional speech
2. Facial expression
3. Body gesture
4. Visual aesthetics
5. Potential applications

Application examples

1. Wearable computer applications make use of affective technologies, such as detection of biosignals
2. Human–computer interaction
3. Kismet
4. ACQUINE Aesthetic Quality Inference Engine

Philosophy of artificial intelligence

2010-01-12T13:03:00.000+05:30

In 1950 Alan M. Turing published "Computing machinery and intelligence" in Mind, in which he proposed that machines could be tested for intelligence using questions and answers. This process is now named the Turing Test. The term Artificial Intelligence (AI) was first used by John McCarthy who considers it to mean "the science and engineering of making intelligent machines". It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity. AI is studied in overlapping fields of computer science, psychology, neuroscience and engineering, dealing with intelligent behavior, learning and adaptation and usually developed using customized machines or computers.

Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, natural language, speech and facial recognition. As such, the study of AI has also become an engineering discipline, focused on providing solutions to real life problems, knowledge mining, software applications, strategy games like computer chess and other video games. One of the biggest difficulties with AI is that of comprehension. Many devices have been created that can do amazing things, but critics of AI claim that no actual comprehension by the AI machine has taken place.

The debate about the nature of the mind is relevant to the development of artificial intelligence. If the mind is indeed a thing separate from or higher than the functioning of the brain, then hypothetically it would be much more difficult to recreate within a machine, if it were possible at all. If, on the other hand, the mind is no more than the aggregated functions of the brain, then it will be possible to create a machine with a recognisable mind (though possibly only with computers much different from today's), by simple virtue of the fact that such a machine already exists in the form of the human brain.

Neural Networks and Parallel Computation

2010-01-11T11:07:00.000+05:30

In the quest to create intelligent machines, the field of Artificial Intelligence has split into several different approaches based on the opinions about the most promising methods and theories. These rivaling theories have lead researchers in one of two basic approaches; bottom-up and top-down. Bottom-up theorists believe the best way to achieve artificial intelligence is to build electronic replicas of the human brain's complex network of neurons, while the top-down approach attempts to mimic the brain's behavior with computer programs.

The neuron "firing", passing a signal to the next in the chain.

Research has shown that a signal received by a neuron travels through the dendrite region, and down the axon. Separating nerve cells is a gap called the synapse. In order for the signal to be transferred to the next neuron, the signal must be converted from electrical to chemical energy. The signal can then be received by the next neuron and processed.
Warren McCulloch after completing medical school at Yale, along with Walter Pitts a mathematician proposed a hypothesis to explain the fundamentals of how neural networks made the brain work. Based on experiments with neurons, McCulloch and Pitts showed that neurons might be considered devices for processing binary numbers. An important back of mathematic logic, binary numbers (represented as 1's and 0's or true and false) were also the basis of the electronic computer. This link is the basis of computer-simulated neural networks, also know as Parallel computing.

An Introduction to Artificial Intelligence.

2010-01-10T10:59:00.000+05:30

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".
In order to classify machines as "thinking", it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex

problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception have aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the human brain, made up of billions of neurons, and arguably the most complex matter in the universe. Perhaps the best way to gauge the intelligence of a machine is British computer scientist Alan Turing's test. He stated that a computer would deserves to be called intelligent if it could deceive a human into believing that it was human.

Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics,

to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. Over the next four decades, despite many stumbling blocks, AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.

AI has always been on the pioneering end of computer science. Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence. The theory and insights brought about by AI research will set the trend in the future of computing. The products available today are only bits and pieces of what are soon to follow, but they are a movement towards the future of artificial intelligence. The advancements in the quest for artificial intelligence have, and will continue to affect our jobs, our education, and our lives.