That some of today’s cutting-edge neuroscience breakthroughs in nanotechnology, computer-brain integration and information technologies not yet recognized because they are too controversial with regard to the prevailing legal and IT policy and medical diagnostics.
The age of pharmaceutical microchipping is now upon us. Novartis AG, one of the largest drug companies in the world, has announced a plan to begin embedding microchips in medications to create “smart pill” technology.
The microchip technology is being licensed from Proteus Biomedical of Redwood City, California. Once activated by stomach acid, the embedded microchip begins sensing its environment and broadcasting data to a receiver warn by the patient. This receiver is also a transmitter that can send the data over the internet to a doctor.
“EURON is a shorthand for “EUropean RObotics research Network”. It is the community of more than 225 academic and industrial groups in Europe with a common interest in doing advanced research and development to make better robots.”
The chosen name doesn’t probably only have to do with the research on robotics but also on the huge amount of money that it will generate in the future.
The Network brings together researchers and commercial companies working on artificial perception systems to model neuronal functions and cognitive processes, to optimize existing learning algorithms and to realize intelligent artificial systems.
Cyberhand is a project funded by EU Future Emerging Technology Program robotic hand for replacement of lost limbs. The hand is designed to respond to signals from the human nervous system.
Blue Brain Project
BBP is a massive cooperative project of EPFL (Switzwerland) and IBM. It uses IBMs super computer Blue Gene to through reverse engineering copy the whole human brain.
The Berlin Brain Computer Interface (BBCI) is a collaboration between German researchers to develop BCI technology for commercial and medical uses.
A 7 million euros EC-funded collaboration among 15 different laboratories in 7 countries for the purpose of developing virtual reality environments with BCI applications.
A collaborative project of 5 European countries – paralyzed human hand.
Focuses on basic research fusing neuroscience and robotics to design, develop and test, tele-operated robotic systems to help restore personal autonomy to sensory-motor-disabled persons.
The idea behind all this is to create “smart pills” that can sense what’s happening in the body and deliver that information to the patient’s doctor. Novartis plans to start microchipping its organ transplant anti-rejection drugs and then potentially expand microchipping to otherpharmaceuticals in its product lineup. This same technology could soon end up in pills made by other drug companies, too.
Jeff Swensen for The New York Times
NELL’S TEAM Tom M. Mitchell, center, and, from left, William Cohen, Jayant Krishnamurthy, Justin Betteridge, Derry Wijaya and Bryan Kisiel.
Give a computer a task that can be crisply defined — win at chess, predict the weather — and the machine bests humans nearly every time. Yet when problems are nuanced or ambiguous, or require combining varied sources of information, computers are no match for human intelligence.
Few challenges in computing loom larger than unraveling semantics, understanding the meaning of language. One reason is that the meaning of words and phrases hinges not only on their context, but also on background knowledge that humans learn over years, day after day.
Since the start of the year, a team of researchers atCarnegie Mellon University — supported by grants from the Defense Advanced Research Projects AgencyandGoogle, and tapping into a research supercomputing cluster provided by Yahoo — has been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computer — calculating 24 hours a day, seven days a week — that resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself.
“For all the advances in computer science, we still don’t have a computer that can learn as humans do, cumulatively, over the long term,” said the team’s leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department.
The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categories — cities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like “San Francisco is a city” and “sunflower is a plant.”
NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player (category). The Indianapolis Colts is a football team (category). By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Colts — even if it has never read that Mr. Manning plays for the Colts. “Plays for” is a relation, and there are 280 kinds of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily expand.
The learned facts are continuously added to NELL’s growing database, which the researchers call a “knowledge base.” A larger pool of facts, Dr. Mitchell says, will help refine NELL’s learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.
NELL is one project in a widening field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies — formal descriptions of concepts and relationships — to help computers mimic human understanding. The ideal has been discussed for years, and more than a decade ago Sir Tim Berners-Lee, who invented the underlying software for the World Wide Web, sketched his vision of a “semantic Web.”
Today, ever-faster computers, an explosion of Web data and improved software techniques are opening the door to rapid progress. Scientists at universities, government labs, Google, Microsoft, I.B.M. and elsewhere are pursuing breakthroughs, along somewhat different paths.
For example, I.B.M.’s “question answering” machine, Watson, shows remarkable semantic understanding in fields like history, literature and sports as it plays the quiz show “Jeopardy!” Google Squared, a research project at the Internet search giant, demonstrates ample grasp of semantic categories as it finds and presents information from around the Web on search topics like “U.S. presidents” and “cheeses.”
Still, artificial intelligence experts agree that the Carnegie Mellon approach is innovative. Many semantic learning systems, they note, are more passive learners, largely hand-crafted by human programmers, while NELL is highly automated. “What’s exciting and significant about it is the continuous learning, as if NELL is exercising curiosity on its own, with little human help,” said Oren Etzioni, a computer scientist at the University of Washington, who leads a project called TextRunner, which reads the Web to extract facts.
Computers that understand language, experts say, promise a big payoff someday. The potential applications range from smarter search (supplying natural-language answers to search queries, not just links to Web pages) to virtual personal assistants that can reply to questions in specific disciplines or activities like health, education, travel and shopping.
“The technology is really maturing, and will increasingly be used to gain understanding,” said Alfred Spector, vice president of research for Google. “We’re on the verge now in this semantic world.”
With NELL, the researchers built a base of knowledge, seeding each kind of category or relation with 10 to 15 examples that are true. In the category for emotions, for example: “Anger is an emotion.” “Bliss is an emotion.” And about a dozen more.