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Dear Ray;

I’ve written a book about the future of software. While writing it, I came to the conclusion that your dates are way off. I talk mostly about free software and Linux, but it has implications for things like how we can have driverless cars and other amazing things faster. I believe that we could have had all the benefits of the singularity years ago if we had done things like started Wikipedia in 1991 instead of 2001. There is no technology in 2001 that we didn’t have in 1991, it was simply a matter of starting an effort that allowed people to work together.

Proprietary software and a lack of cooperation among our software scientists has been terrible for the computer industry and the world, and its greater use has implications for every aspect of science. Free software is better for the free market than proprietary software, and there are many opportunities for programmers to make money using and writing free software. I often use the analogy that law libraries are filled with millions of freely available documents, and no one claims this has decreased the motivation to become a lawyer. In fact, lawyers would say that it would be impossible to do their job without all of these resources.

My book is a full description of the issues but I’ve also written some posts on this blog, and this is probably the one most relevant for you to read: https://lifeboat.com/blog/2010/06/h-conference-and-faster-singularity

Once you understand this, you can apply your fame towards getting more people to use free software and Python. The reason so many know Linus Torvalds’s name is because he released his code as GPL, which is a license whose viral nature encourages people to work together. Proprietary software makes as much sense as a proprietary Wikipedia.

I would be happy to discuss any of this further.

Regards,

-Keith
—————–
Response from Ray Kurzweil 11/3/2010:

I agree with you that open source software is a vital part of our world allowing everyone to contribute. Ultimately software will provide everything we need when we can turn software entities into physical products with desktop nanofactories (there is already a vibrant 3D printer industry and the scale of key features is shrinking by a factor of a hundred in 3D volume each decade). It will also provide the keys to health and greatly extended longevity as we reprogram the outdated software of life. I believe we will achieve the original goals of communism (“from each according to their ability, to each according to their need”) which forced collectivism failed so miserably to achieve. We will do this through a combination of the open source movement and the law of accelerating returns (which states that the price-performance and capacity of all information technologies grows exponentially over time). But proprietary software has an important role to play as well. Why do you think it persists? If open source forms of information met all of our needs why would people still purchase proprietary forms of information. There is open source music but people still download music from iTunes, and so on. Ultimately the economy will be dominated by forms of information that have value and these two sources of information – open source and proprietary – will coexist.
———
Response back from Keith:
Free versus proprietary isn’t a question about whether only certain things have value. A Linux DVD has 10 billion dollars worth of software. Proprietary software exists for a similar reason that ignorance and starvation exist, a lack of better systems. The best thing my former employer Microsoft has going for it is ignorance about the benefits of free software. Free software gets better only as more people use it. Proprietary software is an inferior development model and an anathema to science because it hinders people’s ability to work together. It has infected many corporations, and I’ve found that PhDs who work for public institutions often write proprietary software.

Here is a paragraph from my writings I will copy here:

I start the AI chapter of my book with the following question: Imagine 1,000 people, broken up into groups of five, working on two hundred separate encyclopedias, versus that same number of people working on one encyclopedia? Which one will be the best? This sounds like a silly analogy when described in the context of an encyclopedia, but it is exactly what is going on in artificial intelligence (AI) research today.

Today, the research community has not adopted free software and shared codebases sufficiently. For example, I believe there are more than enough PhDs today working on computer vision, but there are 200+ different codebases plus countless proprietary ones. Simply put, there is no computer vision codebase with critical mass.

We’ve known approximately what a neural network should look like for many decades. We need “places” for people to work together to hash out the details. A free software repository provides such a place. We need free software, and for people to work in “official” free software repositories.

“Open source forms of information” I have found is a separate topic from the software issue. Software always reads, modifies, and writes data, state which lives beyond the execution of the software, and there can be an interesting discussion about the licenses of the data. But movies and music aren’t science and so it doesn’t matter for most of them. Someone can only sell or give away a song after the software is written and on their computer in the first place. Some of this content can be free and some can be protected, and this is an interesting question, but mostly this is a separate topic. The important thing to share is scientific knowledge and software.

It is true that software always needs data to be useful: configuration parameters, test files, documentation, etc. A computer vision engine will have lots of data, even though most of it is used only for testing purposes and little used at runtime. (Perhaps it has learned the letters of the alphabet, state which it caches between executions.) Software begets data, and data begets software; people write code to analyze the Wikipedia corpus. But you can’t truly have a discussion of sharing information unless you’ve got a shared codebase in the first place.

I agree that proprietary software is and should be allowed in a free market. If someone wants to sell something useful that another person finds value in and wants to pay for, I have no problem with that. But free software is a better development model and we should be encouraging / demanding it. I’ll end with a quote from Linus Torvalds:

Science may take a few hundred years to figure out how the world works, but it does actually get there, exactly because people can build on each others’ knowledge, and it evolves over time. In contrast, witchcraft/alchemy may be about smart people, but the knowledge body never “accumulates” anywhere. It might be passed down to an apprentice, but the hiding of information basically means that it can never really become any better than what a single person/company can understand.
And that’s exactly the same issue with open source (free) vs proprietary products. The proprietary people can design something that is smart, but it eventually becomes too complicated for a single entity (even a large company) to really understand and drive, and the company politics and the goals of that company will always limit it.

The world is screwed because while we have things like Wikipedia and Linux, we don’t have places for computer vision and lots of other scientific knowledge to accumulate. To get driverless cars, we don’t need any more hardware, we don’t need any more programmers, we just need 100 scientists to work together in SciPy and GPL ASAP!

Regards,

-Keith

well-in-an-oasisIt’s easy to think of people from the underdeveloped world as quite different from ourselves. After all, there’s little to convince us otherwise. National Geographic Specials, video clips on the Nightly News, photos in every major newspaper – all depicting a culture and lifestyle that’s hard for us to imagine let alone relate to. Yes – they seem very different; or perhaps not. Consider this story related to me by a friend.

Ray was a pioneer in software. He sold his company some time ago for a considerable amount of money. After this – during his quasi-retirement he got involved in coordinating medical relief missions to some of the most impoverished places on the planet, places such as Timbuktu in Africa.

The missions were simple – come to a place like Timbuktu and set up medical clinics, provide basic medicines and health care training and generally try and improve the health prospects of native peoples wherever he went.

Upon arriving in Timbuktu, Ray observed that their system of commerce was incredibly simple. Basically they had two items that were in commerce – goats and charcoal.

According to Ray they had no established currency – they traded goats for charcoal, charcoal for goats or labor in exchange for either charcoal or goats. That was basically it.

Ray told me that after setting up the clinic and training people they also installed solar generators for the purpose of providing power for satellite phones that they left in several villages in the region.

They had anticipated that the natives, when faced with an emergency or if they needed additional medicines or supplies would use the satellite phones to communicate these needs however this isn’t what ended up happening…the-road-to-timbuktu

Two years after his initial visit to Timbuktu, Ray went back to check on the clinics that they had set up and to make certain that the people there had the medicines and other supplies that they required.

Upon arriving at the same village he had visited before Ray was surprised to note that in the short period of only two years since his previous visit things had changed dramatically – things that had not changed for hundreds, perhaps even thousands of years.

Principally, the change was to the commerce in Timbuktu. No longer were goats and charcoal the principal unit of currency. They had been replaced by a single unified currency – satellite phone minutes!

Instead of using the satellite phones to call Ray’s organization, the natives of Timbuktu had figured out how to use the phones to call out to neighboring villages. This enabled more active commerce between the villages – the natives could now engage in business miles from home – coordinating trade between villages, calling for labor when needed or exchanging excess charcoal for goats on a broader scale for example.mudshacks-in-timbuktu

Of course their use of these phones wasn’t limited strictly to commerce – just like you and I, they also used these phones to find out what was happening in other places – who was getting married, who was sick or injured or simply to communicate with people from other places that were too far away to conveniently visit.

In other words, a civilization that had previously existed in a way that we would consider highly primitive had leapfrogged thousands of years of technological and cultural development and within the briefest of moments had adapted their lives to a technology that is among the most advanced of any broadly distributed in the modern world.

It’s a powerful reminder that in spite of our belief that primitive cultures are vastly different from us the truth is that basic human needs, when enabled by technology, are very much the same no matter where in the world or how advanced the civilization.

Perhaps we are not so different after all?
Timbuktu

I am a former Microsoft programmer who wrote a book (for a general audience) about the future of software called After the Software Wars. Eric Klien has invited me to post on this blog. Here are several more sections on AI topics. I hope you find these pages food for thought and I appreciate any feedback.

The future is open source everything.

—Linus Torvalds

That knowledge has become the resource, rather than a resource, is what makes our society post-capitalist.

—Peter Drucker, 1993

Imagine 1,000 people, broken up into groups of five, working on two hundred separate encyclopedias, versus that same number of people working on one encyclopedia? Which one will be the best? This sounds like a silly analogy when described in the context of an encyclopedia, but it is exactly what is going on in artificial intelligence (AI) research today.1 Some say free software doesn’t work in theory, but it does work in practice. In truth, it “works” in proportion to the number of people who are working together, and their collective efficiency.

In early drafts of this book, I had positioned this chapter after the one explaining economic and legal issues around free software. However, I now believe it is important to discuss artificial intelligence separately and first, because AI is the holy-grail of computing, and the reason we haven’t solved AI is that there are no free software codebases that have gained critical mass. Far more than enough people are out there, but they are usually working in teams of one or two people, or proprietary codebases.

Deep Blue has been Deep-Sixed

Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.

—Alan Kay, computer scientist

The source code for IBM’s Deep Blue, the first chess machine to beat then-reigning World Champion Gary Kasparov, was built by a team of about five people. That code has been languishing in a vault at IBM ever since because it was not created under a license that would enable further use by anyone, even though IBM is not attempting to make money from the code or using it for anything.

The second best chess engine in the world, Deep Junior, is also not free, and is therefore being worked on by a very small team. If we have only small teams of people attacking AI, or writing code and then locking it away, we are not going to make progress any time soon towards truly smart software.

Today’s chess computers have no true AI in them; they simply play moves, and then use human-created analysis to measure the result. If you were to go tweak the computer’s value for how much a queen is worth compared to a pawn, the machine would start losing and wouldn’t even understand why. It comes off as intelligent only because it has very smart chess experts programming the computer precisely how to analyze moves, and to rate the relative importance of pieces and their locations, etc.

Deep Blue could analyze two hundred million positions per second, compared to grandmasters who can analyze only 3 positions per second. Who is to say where that code might be today if chess AI aficionados around the world had been hacking on it for the last 10 years?

DARPA Grand Challenge

Proprietary software developers have the advantages money provides; free software developers need to make advantages for each other. I hope some day we will have a large collection of free libraries that have no parallel available to proprietary software, providing useful modules to serve as building blocks in new free software, and adding up to a major advantage for further free software development. What does society need? It needs information that is truly available to its citizens—for example, programs that people can read, fix, adapt, and improve, not just operate. But what software owners typically deliver is a black box that we can’t study or change.

—Richard Stallman

The hardest computing challenges we face are man-made: language, roads and spam. Take, for instance, robot-driven cars. We could do this without a vision system, and modify every road on the planet by adding driving rails or other guides for robot-driven cars, but it is much cheaper and safer to build software for cars to travel on roads as they exist today — a chaotic mess.

At the annual American Association for the Advancement of Science (AAAS) conference in February 2007, the “consensus” among the scientists was that we will have driverless cars by 2030. This prediction is meaningless because those working on the problem are not working together, just as those working on the best chess software are not working together. Furthermore, as American cancer researcher Sidney Farber has said, “Any man who predicts a date for discovery is no longer a scientist.”

Today, Lexus has a car that can parallel park itself, but its vision system needs only a very vague idea of the obstacles around it to accomplish this task. The challenge of building a robot-driven car rests in creating a vision system that makes sense of painted lines, freeway signs, and the other obstacles on the road, including dirtbags not following “the rules”.

The Defense Advanced Research Projects Agency (DARPA), which unlike Al Gore, really invented the Internet, has sponsored several contests to build robot-driven vehicles:


Stanley, Stanford University’s winning entry for the 2005 challenge. It might not run over a Stop sign, but it wouldn’t know to stop.

Like the parallel parking scenario, the DARPA Grand Challenge of 2004 required only a simple vision system. Competing cars traveled over a mostly empty dirt road and were given a detailed series of map points. Even so, many of the cars didn’t finish, or perform confidently. There is an expression in engineering called “garbage in, garbage out”; as such, if a car sees “poorly”, it drives poorly.

What was disappointing about the first challenge was that an enormous amount of software was written to operate these vehicles yet none of it has been released (especially the vision system) for others to review, comment on, improve, etc. I visited Stanford’s Stanley website and could find no link to the source code, or even information such as the programming language it was written in.

Some might wonder why people should work together in a contest, but if all the cars used rubber tires, Intel processors and the Linux kernel, would you say they were not competing? It is a race, with the fastest hardware and driving style winning in the end. By working together on some of the software, engineers can focus more on the hardware, which is the fun stuff.

The following is a description of the computer vision pipeline required to successfully operate a driverless car. Whereas Stanley’s entire software team involved only 12 part-time people, the vision software alone is a problem so complicated it will take an effort comparable in complexity to the Linux kernel to build it:

Image acquisition: Converting sensor inputs from 2 or more cameras, radar, heat, etc. into a 3-dimensional image sequence

Pre-processing: Noise reduction, contrast enhancement

Feature extraction: lines, edges, shape, motion

Detection/Segmentation: Find portions of the images that need further analysis (highway signs)

High-level processing: Data verification, text recognition, object analysis and categorization

The 5 stages of an image recognition pipeline.

A lot of software needs to be written in support of such a system:


The vision pipeline is the hardest part of creating a robot-driven car, but even such diagnostic software is non-trivial.

In 2007, there was a new DARPA Urban challenge. This is a sample of the information given to the contestants:


It is easier and safer to program a car to recognize a Stop sign than it is to point out the location of all of them.

Constructing a vision pipeline that can drive in an urban environment presents a much harder software problem. However, if you look at the vision requirements needed to solve the Urban Challenge, it is clear that recognizing shapes and motion is all that is required, and those are the same requirements as had existed in the 2004 challenge! But even in the 2007 contest, there was no more sharing than in the previous contest.

Once we develop the vision system, everything else is technically easy. Video games contain computer-controlled drivers that can race you while shooting and swearing at you. Their trick is that they already have detailed information about all of the objects in their simulated world.

After we’ve built a vision system, there are still many fun challenges to tackle: preparing for Congressional hearings to argue that these cars should have a speed limit controlled by the computer, or telling your car not to drive aggressively and spill your champagne, or testing and building confidence in such a system.2

Eventually, our roads will get smart. Once we have traffic information, we can have computers efficiently route vehicles around any congestion. A study found that traffic jams cost the average large city $1 billion dollars a year.

No organization today, including Microsoft and Google, contains hundreds of computer vision experts. Do you think GM would be gutsy enough to fund a team of 100 vision experts even if they thought they could corner this market?

There are enough people worldwide working on the vision problem right now. If we could pool their efforts into one codebase, written in a modern programming language, we could have robot-driven cars in five years. It is not a matter of invention, it is a matter of engineering.

1 One website documents 60 pieces of source code that perform Fourier transformations, which is an important software building block. The situation is the same for neural networks, computer vision, and many other advanced technologies.

2 There are various privacy issues inherent in robot-driven cars. When computers know their location, it becomes easy to build a “black box” that would record all this information and even transmit it to the government. We need to make sure that machines owned by a human stay under his control, and do not become controlled by the government without a court order and a compelling burden of proof.

With our growing resources, the Lifeboat Foundation has teamed with the Singularity Hub as Media Sponsors for the 2010 Humanity+ Summit. If you have suggestions on future events that we should sponsor, please contact [email protected].

The summer 2010 “Humanity+ @ Harvard — The Rise Of The Citizen Scientist” conference is being held, after the inaugural conference in Los Angeles in December 2009, on the East Coast, at Harvard University’s prestigious Science Hall on June 12–13. Futurist, inventor, and author of the NYT bestselling book “The Singularity Is Near”, Ray Kurzweil is going to be keynote speaker of the conference.

Also speaking at the H+ Summit @ Harvard is Aubrey de Grey, a biomedical gerontologist based in Cambridge, UK, and is the Chief Science Officer of SENS Foundation, a California-based charity dedicated to combating the aging process. His talk, “Hype and anti-hype in academic biogerontology research: a call to action”, will analyze the interplay of over-pessimistic and over-optimistic positions with regards of research and development of cures, and propose solutions to alleviate the negative effects of both.

The theme is “The Rise Of The Citizen Scientist”, as illustrated in his talk by Alex Lightman, Executive Director of Humanity+:

“Knowledge may be expanding exponentially, but the current rate of civilizational learning and institutional upgrading is still far too slow in the century of peak oil, peak uranium, and ‘peak everything’. Humanity needs to gather vastly more data as part of ever larger and more widespread scientific experiments, and make science and technology flourish in streets, fields, and homes as well as in university and corporate laboratories.”

Humanity+ Summit @ Harvard is an unmissable event for everyone who is interested in the evolution of the rapidly changing human condition, and the impact of accelerating technological change on the daily lives of individuals, and on our society as a whole. Tickets start at only $150, with an additional 50% discount for students registering with the coupon STUDENTDISCOUNT (valid student ID required at the time of admission).

With over 40 speakers, and 50 sessions in two jam packed days, the attendees, and the speakers will have many opportunities to interact, and discuss, complementing the conference with the necessary networking component.

Other speakers already listed on the H+ Summit program page include:

  • David Orban, Chairman of Humanity+: “Intelligence Augmentation, Decision Power, And The Emerging Data Sphere”
  • Heather Knight, CTO of Humanity+: “Why Robots Need to Spend More Time in the Limelight”
  • Andrew Hessel, Co-Chair at Singularity University: “Altered Carbon: The Emerging Biological Diamond Age”
  • M. A. Greenstein, Art Center College of Design: “Sparking our Neural Humanity with Neurotech!”
  • Michael Smolens, CEO of dotSUB: “Removing language as a barrier to cross cultural communication”

New speakers will be announced in rapid succession, rounding out a schedule that is guaranteed to inform, intrigue, stimulate and provoke, in moving ahead our planetary understanding of the evolution of the human condition!

H+ Summit @ Harvard — The Rise Of The Citizen Scientist
June 12–13, Harvard University
Cambridge, MA

You can register at http://www.eventbrite.com/event/648806598/friendsofhplus/4141206940.

An obvious next step in the effort to dramatically lower the cost of access to low Earth orbit is to explore non-rocket options. A wide variety of ideas have been proposed, but it’s difficult to meaningfully compare them and to get a sense of what’s actually on the technology horizon. The best way to quantitatively assess these technologies is by using Technology Readiness Levels (TRLs). TRLs are used by NASA, the United States military, and many other agencies and companies worldwide. Typically there are nine levels, ranging from speculations on basic principles to full flight-tested status.

The system NASA uses can be summed up as follows:

TRL 1 Basic principles observed and reported
TRL 2 Technology concept and/or application formulated
TRL 3 Analytical and experimental critical function and/or characteristic proof-of concept
TRL 4 Component and/or breadboard validation in laboratory environment
TRL 5 Component and/or breadboard validation in relevant environment
TRL 6 System/subsystem model or prototype demonstration in a relevant environment (ground or space)
TRL 7 System prototype demonstration in a space environment
TRL 8 Actual system completed and “flight qualified” through test and demonstration (ground or space)
TRL 9 Actual system “flight proven” through successful mission operations.

Progress towards achieving a non-rocket space launch will be facilitated by popular understanding of each of these proposed technologies and their readiness level. This can serve to coordinate more work into those methods that are the most promising. I think it is important to distinguish between options with acceleration levels within the range human safety and those that would be useful only for cargo. Below I have listed some non-rocket space launch methods and my assessment of their technology readiness levels.

Spacegun: 6. The US Navy’s HARP Project launched a projectile to 180 km. With some level of rocket-powered assistance in reaching stable orbit, this method may be feasible for shipments of certain forms of freight.

Spaceplane: 6. Though a spaceplane prototype has been flown, this is not equivalent to an orbital flight. A spaceplane will need significantly more delta-v to reach orbit than a suborbital trajectory requires.

Orbital airship: 2. Though many subsystems have been flown, the problem of atmospheric drag on a full scale orbital airship appears to prevent this kind of architecture from reaching space.

Space Elevator: 3. The concept may be possible, albeit with major technological hurdles at the present time. A counterweight, such as an asteroid, needs to be positioned above geostationary orbit. The material of the elevator cable needs to have a very high tensile strength/mass ratio; no satisfactory material currently exists for this application. The problem of orbital collisions with the elevator has also not been resolved.

Electromagnetic catapult: 4. This structure could be built up the slope of a tall mountain to avoid much of the Earth’s atmosphere. Assuming a small amount of rocket power would be used after a vehicle exits the catapult, no insurmountable technological obstacles stand in the way of this method. The sheer scale of the project makes it difficult to develop the technology past level 4.

Are there any ideas we’re missing here?


Paul J. Crutzen

Although this is the scenario we all hope (and work hard) to avoid — the consequences should be of interest to all who are interested in mitigation of the risk of mass extinction:

“WHEN Nobel prize-winning atmospheric chemist Paul Crutzen coined the word Anthropocene around 10 years ago, he gave birth to a powerful idea: that human activity is now affecting the Earth so profoundly that we are entering a new geological epoch.

The Anthropocene has yet to be accepted as a geological time period, but if it is, it may turn out to be the shortest — and the last. It is not hard to imagine the epoch ending just a few hundred years after it started, in an orgy of global warming and overconsumption.

Let’s suppose that happens. Humanity’s ever-expanding footprint on the natural world leads, in two or three hundred years, to ecological collapse and a mass extinction. Without fossil fuels to support agriculture, humanity would be in trouble. “A lot of things have to die, and a lot of those things are going to be people,” says Tony Barnosky, a palaeontologist at the University of California, Berkeley. In this most pessimistic of scenarios, society would collapse, leaving just a few hundred thousand eking out a meagre existence in a new Stone Age.

Whether our species would survive is hard to predict, but what of the fate of the Earth itself? It is often said that when we talk about “saving the planet” we are really talking about saving ourselves: the planet will be just fine without us. But would it? Or would an end-Anthropocene cataclysm damage it so badly that it becomes a sterile wasteland?

The only way to know is to look back into our planet’s past. Neither abrupt global warming nor mass extinction are unique to the present day. The Earth has been here before. So what can we expect this time?”

Read the entire article in New Scientist.

Also read “Climate change: melting ice will trigger wave of natural disasters” in the Guardian about the potential devastating effects of methane hydrates released from melting permafrost in Siberia and from the ocean floor.

Artificial brain ’10 years away’

By Jonathan Fildes
Technology reporter, BBC News, Oxford

A detailed, functional artificial human brain can be built within the next 10 years, a leading scientist has claimed.

Henry Markram, director of the Blue Brain Project, has already simulated elements of a rat brain.

He told the TED Global conference in Oxford that a synthetic human brain would be of particular use finding treatments for mental illnesses.

Around two billion people are thought to suffer some kind of brain impairment, he said.

“It is not impossible to build a human brain and we can do it in 10 years,” he said.

“And if we do succeed, we will send a hologram to TED to talk.”

‘Shared fabric’

The Blue Brain project was launched in 2005 and aims to reverse engineer the mammalian brain from laboratory data.

In particular, his team has focused on the neocortical column — repetitive units of the mammalian brain known as the neocortex.

Neurons

The team are trying to reverse engineer the brain

“It’s a new brain,” he explained. “The mammals needed it because they had to cope with parenthood, social interactions complex cognitive functions.

“It was so successful an evolution from mouse to man it expanded about a thousand fold in terms of the numbers of units to produce this almost frightening organ.”

And that evolution continues, he said. “It is evolving at an enormous speed.”

Over the last 15 years, Professor Markram and his team have picked apart the structure of the neocortical column.

“It’s a bit like going and cataloguing a bit of the rainforest — how may trees does it have, what shape are the trees, how many of each type of tree do we have, what is the position of the trees,” he said.

“But it is a bit more than cataloguing because you have to describe and discover all the rules of communication, the rules of connectivity.”

The project now has a software model of “tens of thousands” of neurons — each one of which is different — which has allowed them to digitally construct an artificial neocortical column.

Although each neuron is unique, the team has found the patterns of circuitry in different brains have common patterns.

“Even though your brain may be smaller, bigger, may have different morphologies of neurons — we do actually share the same fabric,” he said.

“And we think this is species specific, which could explain why we can’t communicate across species.”

World view

To make the model come alive, the team feeds the models and a few algorithms into a supercomputer.

“You need one laptop to do all the calculations for one neuron,” he said. “So you need ten thousand laptops.”

Computer-generated image of a human brain

The research could give insights into brain disease

Instead, he uses an IBM Blue Gene machine with 10,000 processors.

Simulations have started to give the researchers clues about how the brain works.

For example, they can show the brain a picture — say, of a flower — and follow the electrical activity in the machine.

“You excite the system and it actually creates its own representation,” he said.

Ultimately, the aim would be to extract that representation and project it so that researchers could see directly how a brain perceives the world.

But as well as advancing neuroscience and philosophy, the Blue Brain project has other practical applications.

For example, by pooling all the world’s neuroscience data on animals — to create a “Noah’s Ark”, researchers may be able to build animal models.

“We cannot keep on doing animal experiments forever,” said Professor Markram.

It may also give researchers new insights into diseases of the brain.

“There are two billion people on the planet affected by mental disorder,” he told the audience.

The project may give insights into new treatments, he said.

The TED Global conference runs from 21 to 24 July in Oxford, UK.

Singularity Hub

Create an AI on Your Computer

Written on May 28, 2009 – 11:48 am | by Aaron Saenz |

If many hands make light work, then maybe many computers can make an artificial brain. That’s the basic reasoning behind Intelligence Realm’s Artificial Intelligence project. By reverse engineering the brain through a simulation spread out over many different personal computers, Intelligence Realm hopes to create an AI from the ground-up, one neuron at a time. The first waves of simulation are already proving successful, with over 14,000 computers used and 740 billion neurons modeled. Singularity Hub managed to snag the project’s leader, Ovidiu Anghelidi, for an interview: see the full text at the end of this article.

The ultimate goal of Intelligence Realm is to create an AI or multiple AIs, and use these intelligences in scientific endeavors. By focusing on the human brain as a prototype, they can create an intelligence that solves problems and “thinks” like a human. This is akin to the work done at FACETS that Singularity Hub highlighted some weeks ago. The largest difference between Intelligence Realm and FACETS is that Intelligence Realm is relying on a purely simulated/software approach.

Which sort of makes Intelligence Realm similar to the Blue Brain Project that Singularity Hub also discussed. Both are computer simulations of neurons in the brain, but Blue Brain’s ultimate goal is to better understand neurological functions, while Intelligence Realm is seeking to eventually create an AI. In either case, to successfully simulate the brain in software alone, you need a lot of computing power. Blue Brain runs off a high-tech supercomputer, a resource that’s pretty much exclusive to that project. Even with that impressive commodity, Blue Brain is hitting the limit of what it can simulate. There’s too much to model for just one computer alone, no matter how powerful. Intelligence Realm is using a distributed computing solution. Where one computer cluster alone may fail, many working together may succeed. Which is why Intelligence Realm is looking for help.

The AI system project is actively recruiting, with more than 6700 volunteers answering the call. Each volunteer runs a small portion of the larger simulation on their computer(s) and then ships the results back to the main server. BOINC, the Berkeley built distributed computing software that makes it all possible, manages the flow of data back and forth. It’s the same software used for SETI’s distributed computing processing. Joining the project is pretty simple: you just download BOINC, some other data files, and you’re good to go. You can run the simulation as an application, or as part of your screen saver.

Baby Steps

So, 6700 volunteers, 14,000 or so platforms, 740 billion neurons, but what is the simulated brain actually thinking? Not a lot at the moment. The same is true with the Blue Brain Project, or FACETS. Simulating a complex organ like the brain is a slow process, and the first steps are focused on understanding how the thing actually works. Inputs (Intelligence Realm is using text strings) are converted into neuronal signals, those signals are allowed to interact in the simulation and the end state is converted back to an output. It’s a time and labor (computation) intensive process. Right now, Intelligence Realm is just building towards simple arithmetic.

Which is definitely a baby step, but there are more steps ahead. Intelligence Realm plans on learning how to map numbers to neurons, understanding the kind of patterns of neurons in your brain that represent numbers, and figuring out basic mathematical operators (addition, subtraction, etc). From these humble beginnings, more complex reasoning will emerge. At least, that’s the plan.

Intelligence Realm isn’t just building some sort of biophysical calculator. Their brain is being designed so that it can change and grow, just like a human brain. They’ve focused on simulating all parts of the brain (including the lower reasoning sections) and increasing the plasticity of their model. Right now it’s stumbling towards knowing 1+1 = 2. Even with linear growth they hope that this same stumbling intelligence will evolve into a mental giant. It’s a monumental task, though, and there’s no guarantee it will work. Building artificial intelligence is probably one of the most difficult tasks to undertake, and this early in the game, it’s hard to see if the baby steps will develop into adult strides. The simulation process may not even be the right approach. It’s a valuable experiment for what it can teach us about the brain, but it may never create an AI. A larger question may be, do we want it to?

Knock, Knock…It’s Inevitability

With the newest Terminator movie out, it’s only natural to start worrying about the dangers of artificial intelligence again. Why build these things if they’re just going to hunt down Christian Bale? For many, the threats of artificial intelligence make it seem like an effort of self-destructive curiosity. After all, from Shelley’s Frankenstein Monster to Adam and Eve, Western civilization seems to believe that creations always end up turning on their creators.

AI, however, promises rewards as well as threats. Problems in chemistry, biology, physics, economics, engineering, and astronomy, even questions of philosophy could all be helped by the application of an advanced AI. What’s more, as we seek to upgrade ourselves through cybernetics and genetic engineering, we will become more artificial. In the end, the line between artificial and natural intelligence may be blurred to a point that AIs will seem like our equals, not our eventual oppressors. However, that’s not a path that everyone will necessarily want to walk down.

Will AI and Humans learn to co-exist?

Will AI and Humans learn to co-exist?

The nature of distributed computing and BOINC allow you to effectively vote on whether or not this project will succeed. Intelligence Realm will eventually need hundred of thousands if not millions of computing platforms to run their simulations. If you believe that AI deserves a chance to exist, give them a hand and recruit others. If you think we’re building our own destroyers, then don’t run the program. In the end, the success or failure of this project may very well depend on how many volunteers are willing to serve as mid-wives to a new form of intelligence.

Before you make your decision though, make sure to read the following interview. As project leader, Ovidiu Anghelidi is one of the driving minds behind reverse engineering the brain and developing the eventual AI that Intelligence Realm hopes to build. He’s didn’t mean for this to be a recruiting speech, but he makes some good points:

SH: Hello. Could you please start by giving yourself and your project a brief introduction?

OA: Hi. My name is Ovidiu Anghelidi and I am working on a distributed computing project involving thousands of computers in the field of artificial intelligence. Our goal is to develop a system that can perform automated research.

What drew you to this project?

During my adolescence I tried understanding the nature of question. I used extensively questions as a learning tool. That drove me to search for better understanding methods. After looking at all kinds of methods, I kinda felt that understanding creativity is a worthier pursuit. Applying various methods of learning and understanding is a fine job, but finding outstanding solutions requires much more than that. For a short while I tried understanding how creativity is done and what exactly is it. I found out that there is not much work done on this subject, mainly because it is an overlapping concept. The search for creativity led me to the field of AI. Because one of the past presidents of the American Association of Artificial Intelligence dedicated an entire issue to this subject I started pursuing that direction. I looked into the field of artificial intelligence for a couple of years and at some point I was reading more and more papers that touched the subject of cognition and brain so I looked briefly into neuroscience. After I read an introductory book about neuroscience, I realized that understanding brain mechanisms is what I should have done all along, for the past 20 years. To this day I am pursuing this direction.

What’s your time table for success? How long till we have a distributed AI running around using your system?

I have been working on this project for about 3 years now, and I estimate that we will need another 7–8 years to finalize the project. Nonetheless we do not need that much time to be able to use some its features. I expect to have some basic features that work within a couple of months. Take for example the multiple simulations feature. If we want to pursue various directions in different fields (i.e. mathematics, biology, physics) we will need to set up a simulation for each field. But we do not need to get to the end of the project, to be able to run single simulations.

Do you think that Artificial Intelligence is a necessary step in the evolution of intelligence? If not, why pursue it? If so, does it have to happen at a given time?

I wouldn’t say necessary, because we don’t know what we are evolving towards. As long as we do not have the full picture from beginning to end, or cases from other species to compare our history to, we shouldn’t just assume that it is necessary.

We should pursue it with all our strength and understanding because soon enough it can give us a lot of answers about ourselves and this Universe. By soon I mean two or three decades. A very short time span, indeed. Artificial Intelligence will amplify a couple of orders of magnitude our research efforts across all disciplines.

In our case it is a natural extension. Any species that reaches a certain level of intelligence, at some point in time, they would start replicating and extending their natural capacities in order to control their environment. The human race did that for the last couple thousands of years, we tried to replicate and extend our capacity to run, see, smell and touch. Now it reached thinking. We invented vehicles, television sets, other devices and we are now close to have artificial intelligence.

What do you think are important short term and long term consequences of this project?

We hope that in short term we will create some awareness in regards to the benefits of artificial intelligence technology. Longer term it is hard to foresee.

How do you see Intelligence Realm interacting with more traditional research institutions? (Universities, peer reviewed Journals, etc)

Well…, we will not be able to provide full details about the entire project because we are pursuing a business model, so that we can support the project in the future, so there is little chance of a collaboration with a University or other research institution. Down the road, as we we will be in an advanced stage with the development, we will probably forge some collaborations. For the time being this doesn’t appear feasible. I am open to collaborations but I can’t see how that would happen.

I submitted some papers to a couple of journals in the past, but I usually receive suggestions that I should look at other journals, from other fields. Most of the work in artificial intelligence doesn’t have neuroscience elements and the work in neuroscience contains little or no artificial intelligence elements. Anyway, I need no recognition.

Why should someone join your project? Why is this work important?

If someone is interested in artificial intelligence it might help them having a different view on the subject and seeing what components are being developed over time. I can not tell how important is this for someone else. On a personal level, I can say that because my work is important to me and by having an AI system I will be able to get answers to many questions, I am working on that. Artificial Intelligence will provide exceptional benefits to the entire society.

What should someone do who is interested in joining the simulation? What can someone do if they can’t participate directly? (Is there a “write-your-congressman” sort of task they could help you with?)

If someone is interested in joining the project they need to download the Boinc client from the http://boinc.berkeley.edu site and then attach to the project using the master Url for this project, http://www.intelligencerealm.com/aisystem. We appreciate the support received from thousands of volunteers from all over the world.

If someone can’t participate directly I suggest to him/her to keep an open mind about what AI is and how it can benefit them. He or she should also try to understand its pitfalls.

There is no write-your-congressman type of task. Mass education is key for AI success. This project doesn’t need to be in the spotlight.

What is the latest news?

We reached 14,000 computers and we simulated over 740 billion neurons. We are working on implementing a basic hippocampal model for learning and memory.

Anything else you want to tell us?

If someone considers the development of artificial intelligence impossible or too far into the future to care about, I can only tell him or her, “Embrace the inevitable”. The advances in the field of neuroscience are increasing rapidly. Scientists are thorough.

Understanding its benefits and pitfalls is all that is needed.

Thank you for your time and we look forward to covering Intelligence Realm as it develops further.

Thank you for having me.

U.S. News and World Report — May 12, 2009, by KEVIN McGILL

BATON ROUGE, La.—Combining human and animal cells to create what are sometimes called “human-animal hybrids” would be a crime in Louisiana, punishable by up to 10 years in prison, under legislation approved Tuesday by a state Senate panel.

Scientific researchers in some areas have tried to create human embryonic stem cells, which scientists say could be used to develop treatment for a variety of human ailments, by placing human DNA into animal cells. But such practices are controversial for a number of reasons.

Sen. Danny Martiny’s bill, approved without objection by members of the Senate Judiciary Committee, was designed to outlaw such practices. It defines and criminalizes various ways of making human-animal hybrids, including combining human sperm and an animal egg, combining animal sperm with a human egg, and the use of human brain tissue or neural tissue to develop a human brain in an animal.

The bill by Martiny, R-Kenner, goes next to the full Senate.

Attorney Dorinda Bordlee, an anti-abortion activist and an opponent of human embryonic stem cell research, said the bill would not stop common medical practices such as the use of pig valves in human heart surgery; nor would it prohibit research in which human brain cells are grown in mouse brains. The growth of a few thousand cells in a mouse brain would not violate the bill’s prohibition of a “non-human life form engineered such that it contains a human brain or a brain derived wholly or predominantly from human neural tissues,” Bordlee said.

The idea of using animal-human “hybrid” embryos drew fire last year in Britain as authorities pondered whether to let scientists try it. Opponents objected to mixing human and animal material and worried that such research could lead to genetically modified babies.

Another element of the argument: Regardless of whether animal cells are used, the creation of embryonic stem cells for research is opposed by some because it destroys the embryo, considered by some to be a human life.

A report earlier this year by researchers with Advanced Cell Technology in Worcester, Mass., cast doubt on the effectiveness of using human DNA in animal eggs to make hybrid cloned embryos. The animal eggs don’t reprogram human DNA in the right way to generate stem cells, researchers reported.

(Crossposted on the blog of Starship Reckless)

Eleven years ago, Random House published my book To Seek Out New Life: The Biology of Star Trek. With the occasion of the premiere of the Star Trek reboot film and with my mind still bruised from the turgid awfulness of Battlestar Galactica, I decided to post the epilogue of my book, very lightly updated — as an antidote to blasé pseudo-sophistication and a reminder that Prometheus is humanity’s best embodiment. My major hope for the new film is that Uhura does more than answer phones and/or smooch Kirk.

Coda: The Infinite Frontier

star-trekA younger science than physics, biology is more linear and less exotic than its older sibling. Whereas physics is (mostly) elegant and symmetric, biology is lunging and ungainly, bound to the material and macroscopic. Its predictions are more specific, its theories less sweeping. And yet, in the end, the exploration of life is the frontier that matters the most. Life gives meaning to all elegant theories and contraptions, life is where the worlds of cosmology and ethics intersect.

Our exploration of Star Trek biology has taken us through wide and distant fields — from the underpinnings of life to the purposeful chaos of our brains; from the precise minuets of our genes to the tangled webs of our societies.

How much of the Star Trek biology is feasible? I have to say that human immortality, psionic powers, the transporter and the universal translator are unlikely, if not impossible. On the other hand, I do envision human genetic engineering and cloning, organ and limb regeneration, intelligent robots and immersive virtual reality — quite possibly in the near future.

Furthermore, the limitations I’ve discussed in this book only apply to earth biology. Even within the confines of our own planet, isolated ecosystems have yielded extraordinary lifeforms — the marsupials of Australia; the flower-like tubeworms near the hot vents of the ocean depths; the bacteriophage particles which are uncannily similar to the planetary landers. It is certain that when we finally go into space, whatever we meet will exceed our wildest imaginings.

Going beyond strictly scientific matters, I think that the accuracy of scientific details in Star Trek is almost irrelevant. Of course, it puzzles me that a show which pays millions to principal actors and for special effects cannot hire a few grad students to vet their scripts for glaring factual errors (I bet they could even get them for free, they’d be that thrilled to participate). Nevertheless, much more vital is Star Trek’s stance toward science and the correctness of the scientific principles that it showcases. On the latter two counts, the series has been spectacularly successful and damaging at the same time.

The most crucial positive elements of Star Trek are its overall favorable attitude towards science and its strong endorsement of the idea of exploration. Equally important (despite frequent lapses) is the fact that the Enterprise is meant to be a large equivalent to Cousteau’s Calypso, not a space Stealth Bomber. However, some negative elements are so strong that they almost short-circuit the bright promise of the show.

I cannot be too harsh on Star Trek, because it’s science fiction — and TV science fiction, at that. Yet by choosing to highlight science, Star Trek has also taken on the responsibility of portraying scientific concepts and approaches accurately. Each time Star Trek mangles an important scientific concept (such as evolution or black hole event horizons), it misleads a disproportionately large number of people.

The other trouble with Star Trek is its reluctance to showcase truly imaginative or controversial ideas and viewpoints. Of course, the accepted wisdom of media executives who increasingly rely on repeating well-worn concepts is that controversial positions sink ratings. So Star Trek often ignores the agonies and ecstasies of real science and the excitement of true or projected scientific discoveries, replacing them with pseudo-scientific gobbledygook more appropriate for series like The X-Files, Star Wars and Battlestar Galactica. Exciting ideas (silicon lifeforms beyond robots, parallel universes) briefly appear on Star Trek, only to sink without a trace. This almost pathological timidity of Star Trek, which enjoys the good fortune of a dedicated following and so could easily afford to cut loose, does not bode well for its descendants or its genre.

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On the other hand, technobabble and all, Star Trek fulfills a very imporant role. It shows and endorses the value of science and technology — the only popular TV series to do so, at a time when science has lost both appeal and prestige. With the increasing depth of each scientific field, and the burgeoning of specialized jargon, it is distressingly easy for us scientists to isolate ourselves within our small niches and forget to share the wonders of our discoveries with our fellow passengers on the starship Earth. Despite its errors, Star Trek’s greatest contribution is that it has made us dream of possibilities, and that it has made that dream accessible to people both inside and outside science.

Scientific understanding does not strip away the mystery and grandeur of the universe; the intricate patterns only become lovelier as more and more of them appear and come into focus. The sense of excitement and fulfillment that accompanies even the smallest scientific discovery is so great that it can only be communicated in embarrassingly emotional terms, even by Mr. Spock and Commander Data. In the end these glimpses of the whole, not fame or riches, are the real reason why the scientists never go into the suspended animation cocoons, but stay at the starship chart tables and observation posts, watching the great galaxy wheels slowly turn, the stars ignite and darken.

Star Trek’s greatest legacy is the communication of the urge to explore, to comprehend, with its accompanying excitement and wonder. Whatever else we find out there, beyond the shelter of our atmosphere, we may discover that thirst for knowledge may be the one characteristic common to any intelligent life we encounter in our travels. It is with the hope of such an encounter that people throng around the transmissions from Voyager, Sojourner, CoRoT, Kepler. And even now, contained in the sphere of expanding radio and television transmissions speeding away from Earth, Star Trek may be acting as our ambassador.