Robotic Nation Evidence

Mouse brain simulated on computer

From the article:

US researchers have simulated half a virtual mouse brain on a supercomputer.
The scientists ran a "cortical simulator" that was as big and as complex as half of a mouse brain on the BlueGene L supercomputer.

In other smaller simulations the researchers say they have seen characteristics of thought patterns observed in real mouse brains.

Now the team is tuning the simulation to make it run faster and to make it more like a real mouse brain...

Also:

Half a real mouse brain is thought to have about eight million neurons each one of which can have up to 8,000 synapses, or connections, with other nerve fibres.

Right now, it takes a supercomputer with 4,096 processors to simulate the mouse brain. And there is still a lot of work to do: "For future tests the team aims to speed up the simulation, make it more neurobiologically faithful, add structures seen in real mouse brains and make the responses of neurons and synapses more detailed." However, look 20 years down the road. In 20 years, a processor like that will cost $200. There will be open source versions of simulated human brain systems. Thousands of people will have been working for years in an open source community to improve the performance and realism. And, somewhere between 2025 and 2035, that simulated brain will start talking to us.

See Robotic Nation for details.

The kind of thing you would expect in a robotic nation:

Factory Jobs: 3 Million Lost Since 2000

First:

Three weeks ago, Dawn Zimmer became a statistic. Laid off from her job assembling trucks at Freightliner's plant in Portland, Ore., she and 800 of her colleagues joined a long line of U.S. manufacturing workers who have lost jobs in recent years. A total of 3.2 million—one in six factory jobs—have disappeared since the start of 2000.
Many people believe those jobs will never come back.

"They are building a multimillion-dollar plant in Mexico and they are going to build the Freightliners down there. They came in and videotaped us at work so they could train the Mexican workers," said Zimmer, 55, who had worked at Freightliner since 1994.

Second:

Even though manufacturing jobs have been declining, the country is enjoying the lowest average unemployment rates of the past four decades. The reason: the growth in the service industries—everything from hotel chambermaids to skilled heart surgeons.

Eighty-four percent of Americans in the labor force are employed in service jobs, up from 81 percent in 2000. The sector has added 8.78 million jobs since the beginning of 2000. Although these workers have been largely sheltered from the global forces that have hit manufacturing, that could change as satellites and fiber optic cable drive down the cost of long-distance communication. Today it is call centers in India and the Philippines but tomorrow many more U.S. jobs could move off shore...

Third:

Princeton economist Alan Blinder, who was vice chairman of the Federal Reserve during the Clinton administration, says the number of jobs at risk of being shipped out of the country could reach 40 million over the next 10 to 20 years. That would be one out of every three service sector jobs that could be at risk.

The growing number of service sector jobs sounds good until you realize what is happening - factory jobs are being roboticized and exported because they are easily roboticized and exported. It has been much harder to do this with service sector jobs, although there have been several telling examples: ATMs replacing tellers, the web replacing travel agents, kiosks in airports replacing ticket agents. With a collasal 84% of jobs now in the service sector, we are in a precarious position. Those service sector jobs are at risk over the next 10 to 20 years as robotic replacement starts to become viable. And there is nothing on the horizon to replace service sector jobs in the way that manufacturing jobs have been replaced. See robotic nation for details.

Why Can't A Computer Be More Like A Brain?

From the article:

It is clear to many people that the brain must work in ways that are very different from digital computers. To build intelligent machines, then, why not understand how the brain works, and then ask how we can replicate it?

My colleagues and I have been pursuing that approach for several years. We've focused on the brain's neocortex, and we have made significant progress in understanding how it works. We call our theory, for reasons that I will explain shortly, Hierarchical Temporal Memory, or HTM. We have created a software platform that allows anyone to build HTMs for experimentation and deployment. You don't program an HTM as you would a computer; rather you configure it with software tools, then train it by exposing it to sensory data. HTMs thus learn in much the same way that children do. HTM is a rich theoretical framework that would be impossible to describe fully in a short article such as this, so I will give only a high level overview of the theory and technology. Details of HTM are available at http://www.numenta.com.

Is AI Engineering the Shortest Path to a Positive Singularity?

From the article:

As a result of the specifics of my AI research, I have come to a position somewhat more radical than that of most Singularity pundits. Kurzweil estimates 2045 for the Singularity, and 2029 for human-level AI via a brain emulation methodology. I think this is basically a plausible scenario (Though I do think that, if a human-level AI takes 16 years to create a Singularity, this slow pace will be due to intentional forbearance and caution rather than technological obstacles. I believe that a human-level AI, once it exists, will be able to improve its intelligence at a rapid rate, making Singularity imminent within months or a few years at most). But I also think a much more optimistic scenario is plausible.

At the 2006 conference of the World Transhumanist Association, I gave a talk entitled "Ten Years To the Singularity (If We Really, Really Try)"6. That talk summarized my perspective fairly well (briefly and nontechnically, but accurately). I believe that the creation of a superhumanly intelligent AI system is possible within 10 years, and maybe even within a lesser period of time (3-5 years). Predicting the exact number of years is not possible at this stage. But the point is, I believe that I have arrived at a detailed software design that is capable of giving rise to intelligence at the human level and beyond. If this is correct, it means that the possibility is there to achieve Singularity faster than even Kurzweil and his ilk predict. Furthermore, having arrived at one software design that appears Singularity-capable, I have become confident there are many others as well. There may be other researchers besides me, actively working on projects with the capability of achieving massive levels of intelligence.

But the "If We Really, Really Try" part is also critical. My own software design, the Novamente Cognition Engine, is large and complex. It would take me decades to complete the implementation, testing and teaching on my own. If the advent of superhuman AI is to be accelerated in the manner I'm describing, a coordinated effort among a team of gifted computer scientists will be required. Currently I am trying to pull together such an effort in the context of a small software company, Novamente LLC7. I am optimistic about this venture. However, objectively, it is certainly not impossible that neither I nor anyone else with a viable AI design will succeed in pulling together the needed resources. In this case, the Kurzweil-style projections may come out correct—but not because the Singularity couldn't have arisen sooner if people had focused their efforts on the right things.

Nice video:

Christine Peterson - Singularity Institute

An attempt to build an ethical robotic soldier

From the article:

WAR is expensive and it is bloody. That is why America’s Department of Defence wants to replace a third of its armed vehicles and weaponry with robots by 2015. Such a change would save money, as robots are usually cheaper to replace than people. As important for the generals, it would make waging war less prey to the politics of body bags. Nobody mourns a robot.

NASA to Test Portable Robot Surgeon

From the article:

The portable robot, which can be controlled over the Internet by a human surgeon many miles away, is being developed with money from the U.S. Defense Department to be used to treat wounded soldiers on a battlefield, to perform complicated surgery on patients in remote areas of the developing world and to help sick astronauts in space.

The difference between the robot surgeon demonstrated at the University of Washington on Wednesday and others that are being used today in American hospitals involves portability and communications, said Professor Blake Hannaford, co-director of the UW BioRobotics Lab.

All the portable parts of this device weigh about 50 pounds and can be transported and reconstructed by non-engineers at remote sites. Robot surgeons currently being used in hospitals weigh several thousand pounds, are not portable and can't be easily broken down and reconstructed.

New Robot Eyes Humans with Human-Like Eyes

"Presently, Domo can identify objects, reach for them and place them on shelves. Unlike an assembly-line robot, Domo can sense its surroundings using a pair of video cameras for eyes; they are connected to 12 computers. The cameras are built into remarkably human-looking "eyeballs," for a reason, said Domo's developer, Aaron Edsinger."

Assistive robot adapts to people, new places

From the article:

A robot like Domo could help elderly or wheelchair-bound people with simple household tasks like putting away dishes. Other potential applications include agriculture, space travel and assisting workers on an assembly line, says Aaron Edsinger, an MIT postdoctoral associate who has been working on Domo for the last three years.

A computer can now recognise classes of things as accurately as a person can

From the article:

Thomas Serre and his colleagues at the Massachusetts Institute of Technology have built a computer processing system that tries to work in this general way. Among the tasks that computers are bad at is recognising broad categories of images. Tell one to search for something specific, such as a rectangle or even a human face, and it can make a reasonable fist of the task. Ask it to find “animals” among photographs of dragonflies, trees, sharks, cars and monkeys, and it falls over. Indeed a monkey—or even a human baby—would leave it in the dust.

That, at least, was how it used to be. But as Dr Serre describes in this week's Proceedings of the National Academy of Sciences, his computer handles this problem rather well. In a recent test it even did a little better than humans.