Wintermute project aims to bring Artificial Intelligence to Ubuntu

Named after a computer from a very famous novel, Wintermute is an attempt to implement the world’s first personal edition of an intelligent framework of applications and libraries, and in the future, an intelligent operating system.

The Project

According to the project’s Launchpad page

Wintermute bolsters the capabilities of using neural networking to learn about its host, a pseudo-langauge engine that permits translations and grammar rulesets of any language to be incorporated into the system, and database downloads of different sets of data to permit the combination of the world’s first personal virtual self-thinking assistant.

The project aims to have something like Apple Knowledge Navigator implemented (READ: Not Clippy!), “So far, we’ve implemented an abstract system of language processing, so translations’ll be as swappable as extensions for Chromium, and we’re looking at other semantic projects (DPedia, NEPOMUK from the Semantic Desktop) to harness the need of knowledge.”, developer Jacky Alcine told us.

A Little Help

The project is looking for Programmers with either GLib, C++ or Python knowledge, Beta testers/People who can enhance the VoxForge voice modules and Designers for the Wintermute, UAIT and Intell@Ubuntu logos.

or more information see the Wintermute blog and the Wintermute Psychology and Ubuntu Artificial Intelligence Team project pages.

Robonaut 2 -The First Humanoid Heads into Space: A New Era Dawns

Another vivid sign that we have entered the dawn of the age of post-biological intelligence: NASA and General Motors announced on Tuesday that they planned to send a robot to the International Space Station, with the eventual goal of having it help the astronauts there.

Although there are already several robots in space — including the famous now AI-enhanced Mars Rovers, which have been zipping around the red planet for years — NASA and G.M. said this would be the first human-like robot to leave Earth.

Better Education Through Open Source Robots

Heather is a freelance writer, as well as a monthly contributor for OEDb, a site that helps students select among accredited online schools. She invites comments and freelancing job inquiries at heatherjohnson2323@gmail.com.

There has been a lot of talk about open source hardware lately and its potential effects on research and education. ETech 2008 showcased many examples of open hardware and offered an insightful presentation[PDF] to those who are new to the emerging technology. Likewise, popular sites like Slashdot and bloggers like Scobleizer have been discussing the growing movement.

The increasing popularity of open source software has already had a tremendous influence on education and the world as a whole. Not only are many schools now making the switch to open source programs, leading universities like UC Berkeley and Carnegie Mellon are involved with developing large open source software projects.

However, we have yet to see open hardware really take off. Ryan Singel of Wired feels that 2008 could be the year and I second that opinion. Leading the pack seems to be open source robotics, which has been embraced by several major universities.

Just last month, Willow Garage’s Steve Cousins gave a keynote speech at ETech 2008 about open source personal robots, which has brought more attention to the subject. Willow Garage is a privately funded lab that experiments with various robotics platforms.

This open source robotics movement can be felt on many college campuses as well. Carnegie Mellon, which I previously stated is involved with open source software, is also building OS personal robots. The university has recently formed a joint project called the Institute for Personal Robots in Education (IPRE).

The IPRE is a joint project between Georgia Tech and Bryn Mawr College, with sponsorship provided by Microsoft Research. Its purpose is to help advance robotics research and computer science education. The IPRE is currently selling open source robot kits, which are geared toward educators and can be integrated with computer education curricula.

Instructions can be found RobotEducation.org if you are interested in building your own educational robot.

First Robot Scientist Makes Gene Discovery

He can come up with a hypothesis, plan an experiment, reason about the results, and then plan his next steps.

Now ADAM is the first robot—but maybe not the last—to have independently discovered new scientific information, according to scientists who recently built themselves the mechanical colleague.

Ross King, of Aberystwyth University in Wales, U.K., and colleagues created ADAM by combining the most advanced robotics hardware with artificial intelligence software.

"Normal robots just do what you tell them, but ADAM is different, because it can hypothesize and try to solve a problem itself," King said.

To test ADAM's capabilities, King's team gave the robot the task of discovering more about the genome of baker's yeast, a simple microbe often used as a model for studying more complex biological systems.

First ADAM was given a crash course in biology, including everything that is already known about baker's yeast.

ADAM quickly set to work, formulating and testing 20 different hypotheses. The robot eventually identified the genes that code for enzymes involved in yeast metabolism—a scientific first for a robot.

Using independent experiments, King and his colleagues were able to verify ADAM's results.

Robots to Replace Scientists?

Robot scientists like ADAM might one day work alongside human researchers to boost productivity, King said.

"There are certain scientific problems that are so complicated that there are not enough people available to solve them," King said. "We need to automate in order to have a hope of solving these problems."

Robot scientists, for example, could prove valuable in drug design and screening.

King's next scientific robot, EVE, is being created specifically to help search for new drugs to treat tropical diseases such as malaria.

But King and his colleagues don't think that robots can ever completely replace human scientists.

"While robots are better at coordinating thousands of experiments," King said, "humans are better are seeing the big picture and planning the overall experiment."

Sensory Memory

Sensory memory is the first level of memory, as explained in the paragraph levels of memory. Sensory memory retains the brief impression of a sensory stimulus after the stimulus itself has ended. Imagine, you see an object. When the object has diappeared, it may still be vivid in your memory. "The sensory memory holds a short impression of sensory information even when the sensory system does not send any information anymore." Research Most research has focused on the visual and auditory systems, although there are presumably sensory registers for all our senses. For visual stimuli, we have an extremely short 'photographic' memory (about 500 milliseconds), which gives us a persistent image. In hearing we have echoic memories, which are mental echoes of stimuli. Characteristics There are various specific issues about sensory memory: first, it is a high capacity form of memory registration of visual data. Second, information in the sensory memory is un-interpreted. Third, sensory memory is short; visual information, for example, fades away in less than a second. Using the Information If we want to use the information in the sensory memory, we must quickly encode it it into a more durable form. Processing begins with attention, which selectively determines what will 'get through' for further examination and what will not. Attention allows us to focus on parts of the stimulus and thereby to recognize some of its features. Obviously, any shortcomings in sensory memory can create problems for further processing of sensory information. Sensory memory allow us to take a 'snapshot' of our environment, and to store this information for a short period. Only informatin that is transferred to another level of memory will be preserved for more than 1 à two seconds.

Portable device to detect suicide bombers

Washington, June 28 (ANI): A group of students have developed a portable device to detect the weapons of suicide bombers.

Improvised explosive devices (IEDs), the weapons of suicide bombers, are a major cause of soldier casualties in Iraq and Afghanistan.

Now, a group of University of Michigan (U-M) engineering undergraduate students have developed a new way to detect them.

The students invented portable, palm-sized metal detectors that could be hidden in trash cans, under tables or in flower pots, for example.

The detectors are designed to be part of a wireless sensor network that conveys to a base station where suspicious objects are located and who might be carrying them.

Compared with existing technology, the sensors are cheaper, lower-power and longer-range. Each of the sensors weighs about 2 pounds.

“Their invention outperforms everything that exists in the market today,” said Nilton Renno, a professor in the U-M Department of Atmospheric, Oceanic and Space Sciences.

The students undertook this project in Renno’s Engineering 450 senior level design class.

“They clearly have an excellent understanding of the problem. They also thought strategically and designed and optimized their solution. The combination of a movable command center with a wireless sensor network can be easily deployed in the field and adapted to different situations,” said Renno.

The core technology is based on a magnetometer, or metal detector, explained Ashwin Lalendran, an engineering student who worked on the project and graduated in May.

“We built it entirely in-house - the hardware and the software,” Lalendran said.

“Our sensors are small, flexible to deploy, inexpensive and scalable. It’s extremely novel technology,” he added. (ANI)

Sensor Network Simulator and Emulator

The Necessity of Network Simulation

The emergence of wireless sensor networks brought many open issues to network designers. Traditionally, the three main techniques for analyzing the performance of wired and wireless networks are analytical methods, computer simulation, and physical measurement. However, because of many constraints imposed on sensor networks, such as energy limitation, decentralized collaboration and fault tolerance, algorithms for sensor networks tend to be quite complex and usually defy analytical methods that have been proved to be fairly effective for traditional networks. Furthermore, few sensor networks have come into existence, for there are still many unsolved research problems, so measurement is virtually impossible. It appears that simulation is the only feasible approach to the quantitative analysis of sensor networks.

Why a New Simulator

ns2, perhaps the most widely used network simulator, has been extended to include some basic facilities to simulate sensor networks. However, one of the problems of ns2 is its object-oriented design that introduces much unnecessary interdependency between modules. Such interdependency sometimes makes the addition of new protocol models extremely difficult, only mastered by those who have intimate familiarity with the simulator. Being difficult to extend is not a major problem for simulators targeted at traditional networks, for there the set of popular protocols is relatively small. For example, Ethernet is widely used for wired LAN, IEEE 802.11 for wireless LAN, TCP for reliable transmission over unreliable media. For sensor networks, however, the situation is quite different. There are no such dominant protocols or algorithms and there will unlikely be any, because a sensor network is often tailored for a particular application with specific features, and it is unlikely that a single algorithm can always be the optimal one under various circumstances.

Many other publicly available network simulators, such as JavaSim, SSFNet, Glomosim and its descendant Qualnet, attempted to address problems that were left unsolved by ns2. Among them, JavaSim developers realized the drawback of object-oriented design and tried to attack this problem by building a component-oriented architecture. However, they chose Java as the simulation language, inevitably sacrificing the efficiency of the simulation. SSFNet and Glomosim designers were more concerned about parallel simulation, with the latter more focused on wireless networks. They are not superior to ns2 in terms of design and extensibility.

Features of SENSE

SENSE is designed to be an efficient and powerful sensor network simulator that is also easy of use. We identify the three most critical factors as:

• Extensibility: The enabling force behind the fully extensibility network simulation architecture is our progress on component-based simulation. We introduced a component-port model that frees simulation models from interdependency usually found in an object-oriented architecture, and then proposed a simulation component classification that naturally solves the problem of handling simulated time. The component-port model makes simulation models extensible: a new component can replace an old one if they have compatible interfaces, and inheritance is not required. The simulation component classification makes simulation engines extensible: advanced users have the freedom to develop new simulation engines that meet their needs.

• Reusability: The removal of interdependency between models also promotes reusability. A component developed for one simulation can be used in another if it satisfies the latter's requirements on the interface and semantics. There is another level of reusability made possible by the extensive use of C++ template: a component is usually declared as a template class so that it can handle different type of data.

• Scalability: Unlike many parallel network simulators, especially SSFNet and Glomosim, parallelization is provided as an option to the users of SENSE. The reflects our belief that completely automated parallelization of sequential discrete event models, however tempting it may seem, is impossible, just as automated parallelization of sequential programs. Even if it possible, it is doomed to be inefficient. Therefore, parallelizable models require more effort than sequential models, but a good portion of users are not interested in parallel simulation at all. In SENSE, a parallel simulation engine can only execute components of compatible components. If a user is content with the default sequential simulation engine, then every component in the model repository can be reused.

Currently Available Components and Simulation Engines (as of Oct 21, 2006)

• Battery Model:

  • Linear Battery

  • Discharge Rate Dependent and/or Relaxation Battery

• Application Layer:

  • Random Neighbor

  • Constant Bit Rate

• Network Layer:

  • Simple Flooding

  • A simplified version of ADOV without route repairing

  • A simplified version of DSR without route repairing

  • Self Selective Routing (SSR)

  • Self Healing Routing (SHR)

• MAC Layer:

  • NullMAC

  • IEEE 802.11 with DCF

• Physical Layer: Duplex Transceiver

• Wireless Channel:

  • Free Space

  • Adjacency Matrix

• Simulation Engine: CostSimEng (sequential)