IBM’s Watson scares scientists most as you can see from the pieces I selected for you. A new software can do scientıfıc work which can take years for a scientist to finish. Then scientists say they worry that machines outsmart humans. They are actually worried about their own jobs. Some of them admit this fact as you can see in one of the pieces. They think machines can answer questions but cannot figure out how to ask questions. That is plain stupid. We can build machines to detect all the problems around the world and solve them. I picked the piece about the Living Earth Simulatıon (LES)from Europe. Surely new satellite imaging systems will complete this simulation project. I added a piece showing that nano research improved infrared satellite facial recognition 20 times. Together with innovations in computers, LES can be a great success. In order to achieve those innovations on the earth we need to build the internet explanation engine as Prof. Oren Etzioni says in a piece I presented for you. But then in the last piece you will see that he sold his innovations to the search engıne giants which he is complaining against. I guess this is because he does not realize that they are blocking this innovatıon. That is well exhibited in the piece below and in others.
Will IBM‘s Watson put your job in jeopardy?
February 15, 2011: 9:33 AM ET
If IBM’s supercomputer outperforms some of Jeopardy!‘s greatest minds, what does that spell for the rest of us working stiffs? As it turns out, workers may have some cause for concern.
By Martin Ford, contributor
This week’s showdown between IBM’s Watson supercomputer and the world’s top Jeopardy! players raises several questions on the impact that artificial intelligence will have on the future job market. After all, if a machine can beat humans at Jeopardy!, will computers soon be competing with people for knowledge-based jobs?
If IBM’s (IBM) hopes for the technology are realized, workers may, in fact, have cause for concern. The company’s website says that “Watson’s first test will be on Jeopardy!, but the real test will be applying the underlying data management and analytics technology across different industries.” In a video describing Watson, an IBM executive says the technology “will revolutionize industries at a level which has never been done before.”
Unlike other advanced software applications, Watson has a command of natural language, it can simultaneously launch hundreds of information-seeking algorithms, and, perhaps most importantly, it has the ability to learn and improve its performance over time.
Watson’s creators trained it with thousands of Jeopardy! sample questions, eventually driving it to perform at championship level. Just as practice makes a person better at a particular task, machine learning techniques allow software applications to get better with experience. Watson-like technology has obvious applications in areas like customer service and support, legal research and medical diagnosis. And when it comes to learning, machines enjoy an important advantage over human workers: once a machine or software application has been trained, that knowledge and expertise can be replicated quickly and easily.
Current research in artificial intelligence goes far beyond simply building machines to answer questions based on existing information. At Cornell University, a team of researchers has built an application that analyzes raw scientific data and is then able to discover new rules or equations explaining the underlying phenomena — an accomplishment that might require years for a human scientist. In the future, it’s likely that smart applications will increasingly combine natural language capability with the ability to autonomously find, analyze and present information in just about any field of expertise.
Watson is by no means a true thinking machine. Rather, it’s a highly specialized application designed especially to play Jeopardy!. All existing practical applications of artificial intelligence are similarly specialized — we are at least decades away from creating general, human-like artificial intelligence, and it may even be unachievable.
But don’t assume that means artificial intelligence won’t replace workers. Nearly all jobs in today’s economy are specialized, and as applications like Watson become more versatile and affordable, they will be used in a variety of areas, especially in large organizations.
Machine learning is not limited to knowledge-based jobs and tasks. Heartland Robotics, a startup company founded by Rodney Brooks, a researcher at MIT, is reportedly developing a trainable manufacturing robot that will cost as little as $5,000 — a price point that makes up less than two months of a typical worker’s pay and benefits.
While Heartland is initially focusing on manufacturing, the most significant impact on workers in the United States will come if and when low-cost, trainable robots and other forms of automation are used in the service sector.
For many lower wage jobs, automation has been kept at bay by a human worker’s unique ability to recognize complex visual images and then interact with their environment accordingly. But machines and robots are growing increasingly dexterous and better at seeing and understanding the world around them. Robots in Japan, for example, are able to autonomously pick strawberries, selecting only the ripest berries based on their color.
The technologies that power Watson will likely find their way into a variety of software applications and robots that can compete for both high and low skill jobs. As artificial intelligence software improves and hardware becomes dramatically faster and more affordable over the coming decade, job creation in both low and high skill occupations risks falling short of expectations. And employers in a wide range of industries may increasingly choose technology over people. Few, if any, economists seem willing to acknowledge that scenario, but if it does come to pass, what we consider unacceptable levels of unemployment today could become the new normal tomorrow.
Martin Ford is the author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future and blogs regularly at http://econfuture.wordpress.com.
Scientists Worry Machines May Outsmart Man
By JOHN MARKOFF
Published: July 25, 2009
A robot that can open doors and find electrical outlets to recharge itself. Computer viruses that no one can stop. Predator drones, which, though still controlled remotely by humans, come close to a machine that can kill autonomously.
The more disruptive any innovation, the more likely it is to get blocked, if not by gatekeepers jealously guarding their power and reward bases, then by internal processes poorly designed to accommodate innovation, or by unhelpful corporate cultures or low risk appetites…
Noam Chomsky against IBM Watson
GS: As the world’s leading linguist, what are your thoughts on Watson, the robot that will be appearing on “Jeopardy”? This appears to be the most advanced form of AI to date.
NC: I’m not impressed by a bigger steamroller.
GS: I assume that “a bigger steamroller” is a reference to Deep Blue. Watson understands spoken language and adapts its knowledge based on human interaction. What level of AI would be required to impress you?
NC: Watson understands nothing. It’s a bigger steamroller. Actually, I work in AI, and a lot of what is done impresses me, but not these devices to sell computers.
Man vs. Machine: Computable Knowledge and Language Processing
By The Astronomist on Saturday, February 19, 2011
It would be a powerful combination to have Alpha’s database and analytical skills paired with Watson’s language processing. Perhaps we will have a Watson|Alpha soon. I remarked to a colleague in jest the other day that if they made such a machine I would be out of a job, but he replied that I would still have job as long as computers only have answers and not questions. True enough. I am reminded of the Hitchhikers Guide to the Galaxy where an immense computer calculates the answer to the universe, however, with the answer in hand it is realized a bigger more powerful computer must be constructed to determine the question.
Europe’s Plan to Simulate the Entire Planet
The ‘Living Earth Simulator’ will mine economic, environmental and health data to create a model of the entire planet in real time.
When it comes to global crises, we’re not short of complex systems that look close to the edge: the climate, the food supply, energy security, the banking system and so on. Add to this the threat of war in many parts of the world and the possibility of global pandemics and it’s a wonder that anybody gets out of bed in the morning.
Science has certainly played an important role in understanding aspects of these systems but could it do more?
Today, Dirk Helbing at the Swiss Federal Institute of Technology in Zurich outlines an ambitious project to go further, much further.
Helbing’s idea is to create a kind of Manahattan project to study, understand and tackle these techno-socio-economic-environmental issues. His plan is to gather data about the planet in unheard of detail, use it to simulate the behaviour of entire economies and then to predict and prevent crises from emerging.
Think of it as a kind of Google Earth for society. We’ve all played with Google’s 3D map of the Earth that uses real data to reveal not only the town where you live and work but your home and back garden too.
Imagine a similar model that uses in real time things like financial transactions, health records, travel details, carbon dioxide emissions and so on to build a model of not just the planet but the entire society that populates it. Helbing calls it ‘reality mining’.
This model will be capable not only modelling the planet in real time but of simulating the future, rather in the manner of weather forecasters.
Helbing’s simulator will look for economic bubbles and collapses, warn of global pandemics and suggest how to tackle them, it will model and predict the outcome of regional conflicts and determine the effect of our behaviour on the climate. He even wants to create ‘situation rooms’ in which global leaders can view and manage crises as they occur.
This Google-Earth-on-steroids is to be called the Living Earth Simulator and Helbing’s plan is to have it working by 2022 at a cost of a cool EUR 1 billion, funded by the European Commission. He’s even assembled an impressive team to help, including partners from most of the top universities in Europe.
So what to make of this plan and it’s ambition. At first glance, it seems a somewhat worrying, even frightening, vision of the future. A Living Earth Simulator will change how we see ourselves and our planet in ways that are hard to imagine right now.
There’s no question that we need to better understand the global nature of the society we live in and the effects that it has on the planet. We also need to know how to leverage the benefits of these global systems while limiting the downsides they can generate.
This capability is coming whether we like it or not. Clearly, the computing infrastructure of the near future will be increasingly capable of such a task.
The great worry, of course, is that it will not be the great public universities and government-funded research institutes that complete this task. The huge benefits of a Living Earth Simulator will make it a valuable tool for insurance companies, financial traders, global businesses and even search engines.
It’s not hard to imagine a company like Google wanting and even building such a model. And if that seems hard to swallow, there are plenty of organisations that may be even less palatable operators of such a system. Imagine a Goldman Sachs Earth Simulator or one run by the People’s Liberation Army. EUR 1 billion is just a small fraction of the money these organisations play with.
When viewed through that prism, it seems clear and even necessary that such a project is publicly funded and managed. Should the European Commission agree, Helbing, who is a world leader in the new science of techno-socio-economic studies, may well be the man who leads it.
A Living Earth Simulator is coming, one way or another, perhaps even to your living room or mobile communicator. The only question is who builds it.
Ref: arxiv.org/abs/1004.4969: The FuturICT Knowledge Accelerator
By Clay Dillow Posted 05.18.2010
Quantum Dot Infrared Photodector Tech Rensselaer Polytechnic Institute
Researchers at Rensselaer Polytechnic Institute have turned a sheet of nano-thin gold into what could be the next big advance in infrared technology. Taking advantage of the unique properties of gold at the nanoscale, scientists there have created a “microlens” system that could boost detectivity in quantum-dot-based IR detectors by 20 times.
New York Times, August 4, 2011, 4:33 pm
A Call to Rethink Internet SearchBy STEVE LOHR
“We could soon view today’s keyword searching with the same nostalgia and amusement reserved for bygone technologies such as electric typewriters and vinyl records.”
So declares Oren Etzioni, a computer scientist at the University of Washington, in an essay published Thursday in the science journal Nature. (Available online to subscribers or for a single copy purchase of $32.)
The missing ingredients, he writes, are mainly the necessary investments in money and science by leading technology companies and universities. The better world of search, according to Mr. Etzioni, will be services that field spoken or typed questions and generate useful answers. Or, as he writes, “natural-language searching and answering, rather than providing the electronic equivalent of the index at the back of a reference book.”
Many people have lamented the shortcomings of Internet search, but Mr. Etzioni’s critique is provocative and informed by his own research, and he describes the way ahead and the technologies needed to get there.
One threat to progress, Mr. Etzioni writes, is the keyword search box, an innovation-retarding trap that “exerts a powerful gravitational pull.”
Oren Etzioni is a Venture Partner with Madrona and founder of Decide, a recent Madrona investment. Oren also founded Farecast, an early market leader in travel metasearch. Farecast was acquired by Microsoft in 2008. He chairs Madrona’s Technology Advisory Board and is a Professor at the University of Washington’s Department of Computer Science & Engineering. He is the author of more than 100 academic papers on topics including Web search, data mining, agent technology, and various aspects of Artificial Intelligence. He was the Chief Technology Officer and board member of Go2Net Inc. (acquired by InfoSpace) and a founder of Netbot, Inc. (acquired by Excite). He received the NSF Young Investigator Award, was chosen to be an AAAI Fellow, and in 2007 received the Robert S. Engelmore Memorial Award for “long-standing technical and entrepreneurial contributions to Artificial Intelligence”.
At Netbot, Dr. Etzioni helped to conceive and design the web’s first major comparison-shopping agent. In 1995, he and his student Erik Selberg developed MetaCrawler, the Web’s premier Meta-search engine for several years. He also is a co-founder of Clearforest (acquired by Reuters).
He received his undergraduate degree in computer science from Harvard University, and his MS and Ph.D. in computer science from Carnegie Mellon University.