Europe’s Human Brain Project – can a billion Euros buy a brain?

In January Henry Markram got a late Christmas present.  After intense international competition, Markram’s quest for a brain simulation received one of the largest grants in the history of science – 500 million euros from Europe’s new Technology “Flagship” program.

The European Human Brain project is a large expansion of Markram’s “Blue Brain” efforts which have made amazing progress over the past several years.  With this level of funding the HBP appears to have left the USA’s DARPA SyNAPSE as something of a funding pauper.    However as politicians begin to recognize the significance of thinking computers DARPA is likely to get much higher funding.

From the HBP Executive Summary: 

We propose that the HBP should be organised in three phases, lasting a total of ten years.
For the first two and a half years (the “ramp-up” phase),
the project should focus on setting up the initial versions of
the ICT platforms and on seeding them with strategically selected data. At the end of this phase, the platforms should be
ready for use by researchers inside and outside the project.
For the following four and a half years (the “operational
phase”), the project should intensify work to generate strategic data and to add new capabilities to the platforms, while
simultaneously demonstrating the value of the platforms for
basic neuroscience research and for applications in medicine
and future computing technology.
In the last three years (the “sustainability phase”), the
project should continue these activities while simultaneously
moving towards financial self-sustainability – ensuring that
the capabilities and knowledge it has created become a permanent asset for European science and industry

Google’s Ray Kurzweil on Google’s role in the Future of Artificial Intelligence

Inventor and futurist Ray Kurzweil is generally regarded as one of the world’s top engineers working on Artificial Intelligence, and he’s certainly the world’s top *evangelist* for AI, arguing that general AI, or thinking machines, will inevitably arise, and fairly soon, as another step down the evolutionary path of the human species.    His book  “The Singularity is Near”, is the key popular work addressing what many believe will become the biggest technological theme in history – the creation of an intelligent computer that is capable of human-like thought processes.

Bill Gates has called Ray Kurzweil the leading thinker in the area of artificial intelligence.

Google very recently hired Kurzweil as Director of Engineering, promising a marriage of his ideas with the company that is probably best suited to fund and deploy general AI applications.

Here, in an interview at Singularity Hub, Kurzweil discusses Google’s role in the advancement of AI:

Ray Kurzweil On Future of AI at Google:

http://singularityhub.com/2013/01/10/exclusive-interview-with-ray-kurzweil-on-future-ai-project-at-google/

Blue Brain Project Update

Here at Technology Report we recognize that a conscious computer is likely to 1. happen within decades and 2. bring the most profound transformation of humanity in the history of … humanity.    Two of the major projects working towards the goal of a general artificial intelligence are DARPA SYNAPSE and BLUE BRAIN.   DARPA’s funding is much higher but they are newer and approaching the problem more along the lines of *computer programming* rather than *reverse engineering*.     Blue Brain’s approach is more along the lines of copying the observed neural structure of the human brain and creating a computer simulation based on those observations and activity.    Here, from their website, is a progress report on Blue Brain:

The Blue Brain Project plans to reverse engineer the human brain as a supercomputer simulation.

The project was founded in May 2005 by Henry Markram at the EPFL in Switzerland and has made notable progress in its first decade.

As of August 2012 the largest simulations are of mesocircuits containing around 100 cortical columns (image above right). Such simulations involve approximately 1 million neurons and 1 billion synapses. This is about the same scale as that of a honey bee brain. It is hoped that a rat brain neocortical simulation (~21 million neurons) will be achieved by the end of 2014. A full human brain simulation (86 billion neurons) should be possible by 2023 provided sufficient funding is received.

Latest news

July 9, 2012  – The FET Flagship Pilots final conference took place in Brussels today. Results of the recently-completed one-year pilot phase of the Human Brain Project (HBP) were presented. See the 108-page HBP report, as well as the conference statement by EC vice-president Neelie Kroes. During autumn 2012 the EU will consider the HBP and five other candidate science projects. In February 2013 a decision will be made on which of the two candidates will each receive €1 billion in funding over ten years. The chosen two projects will then run from 2013 to 2023. If the HBP is chosen, the Blue Brain Project will become a central part of it.
Jun 20, 2012  – Two newly published video talks which share lots of detail about the Blue Brain Project simulations and visualisations. The talks were given at the INCF Multiscale Modeling Program Workshop in Stockholm on May 31 and June 1, 2012.

Jun 11, 2012  – Scientific American has published a featured article by Henry Markram. Available online behind a $6 paywall: A countdown to a digital simulation of every last neuron in the human brain. See also the associated video animation: Neuron to cortical column.
May 24, 2012  – New video of Henry Markram talking about the Blue Brain Project. Includes Markram’s thoughts on consciousness, autism, and the Human Brain Project. Recorded in Barcelona on May 22, 2012.
Mar 30, 2012  – The ETH Board has requested CHF 85 million (€70 m) from the Swiss government to fund the Blue Brain Project during 2013 to 2016.
Jan 3, 2012  – FET Flagships mid-term conference presentation is now available online: Introducing the Human Brain Project

Blue Brain Project Progress …

BLUE BRAIN – the search for strong artificial intelligence.    Creating a mechanized “copy” of brain function.

For me it’s one of the world’s most significant and intriguing science projects along with DARPA SyNAPSE which is also working towards strong AI but in a very different way.

Using an IBM Blue Gene Supercomputer, Blue Brain is working to create a simulation of many of the interactions in the Neocortex of the brain (think “where we think”).   Progress after a few years of the project seems pretty steady although DARPA SyNAPSE has been a lot more money lately as it dips into the massive resources of the US military.

http://thebeautifulbrain.com/2011/08/bluebrain-year-two/

SyNAPSE Chip: “Someday, you’ll work for ME!”

SyNAPSE Project Chip

SyNAPSE Project AI Neuromorphic Chip

IBM’s Aug 18th Press Release announced another significant milestone for the DARPA SyNAPSE project, the world’s best funded and arguably the “most likely to succeed” approach to creating a general artificial intelligence.

The release notes that the new chips represent a departure from traditional models of computing:

…. cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

As we’ve noted here many times, another remarkable project is the Blue Brain Project in Europe spearheaded by Dr. Henry Markram.     That team has joined with many others and is in the process of applying to the European Union for substantial funding – perhaps as much as 1.6 billion dollars.    Although Blue Brain tends to shy away from stating that their objective is a general artificial intelligence,  I would argue that they should have that goal and also that they are much more likely to be funded by stating that goal in no uncertain terms.

Unfortunately there remain many both in and outside of technology circles who believe the search for a general artificial intelligence is either dangerous or a waste of time and money.   Both these scenarios are possible but unlikely.   Sure, intelligence can be dangerous but given human history compared to technology history it seems odd to argue that we are more likely to create a Frankenstein than a helpful machine process.    Computers don’t kill people, people kill people.

In terms of a waste of time and money, clearly we humans have overrated our intelligence for some time – probably since the beginning of self-awareness.   There are few rational reasons to reject the idea that we cannot duplicate processes that are similar to our own thinking in a machine.   The advantages of machine based intelligence are likely to be  substantial – probably on the order of a new human age with vastly improved resource efficiency, poverty reduction, and more.  Thus the costs – currently measured in the low tens of millions – pale in comparison to almost all other government projects – many with massively dubious and negative ROIs.

SyNAPSE Update from Dr. Dharmendra Modha’s Team

Dr. Dharmendra Modha and his SyNAPSE gang recently published an excellent paper about “Cognitive Computing” that updates what appears to be excellent progress in the effort to create a general artificial intelligence:

http://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltext

One of the paper’s most notable items asserts that within a decade the project expects to have the computational scale needed for human level modelling, though it also notes that this is not the same as creating a model of the human brain – this may require computational structures yet to be invented.    However on balance it would seem the SyNAPSE project continues to build on their core assumptions, taking us ever closer to the holy grail of technology – a general artificial intelligence.

More at Dr. Modha’s blog , where we learn more about the new approaches the SyNAPSE team at IBM will take in an effort to achieve human quality cognition in a machine:

18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers.

In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.

Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

Brain-i-Nets Mission = General Artificial Intelligence.

As the hunt for general artificial intelligence aka “thinking, conscious machines” heats up some of the more promising projects are getting a modest level of funding, and it now seems likely we’ll have some significant progress over the next few years. Hopefully

We’ve written a lot about DARPA SyNAPSE and the BLUE BRAIN projects, but a new exciting one in Europe (not sure about the name though) is called …

—————————–

Brain‐i‐Nets

Novel Brain‐Inspired Learning Paradigms for Large‐Scale
Neuronal Networks

• Our vision: To build a novel type of computing
machinery, exhibiting human‐like learning
capabilities.

• Our approach: To port leaning and adaptation
mechanisms in the brain to brain‐inspired
hardware.

• Our path: An interdisciplinary roadmap from
neuroscience to technology.
•Explore: Use cutting‐edge techniques to gain
insight in how the brain learns.

•Understand: Understand the results through
mathematical analysis and simulation.

• Implement: Study the implementation in novel
brain‐inspired hardware.

————————-

Read more at the University of Heidleberg’s Brain-i-Nets website

IBM’s Artificial Intelligence – is the cat brain out of the bag or not?

We’ve profiled two of the world’s most promising AI efforts here at Technology Report.     Blue Brain in Switzerland and DARPA SyNAPSE here in the USA, a newer project that appears to be getting better funding thanks to backing from the US Defense Department.   Both of these projects rely on IBM supercomputers for their simulations of neurons and their interactions, and both are optimistic about the potential to develop thinking machines within the next decade.

The project leader of Blue Brain, Dr. Henry Markram, has been very vocal and very critical of claims by  the IBM team leader Dr. Dharmendra S. Modha.    Markram’s concerns are expressed here in his Technology Report guest post about the IBM project claims.

We asked Dr. Modha for a response but didn’t hear back, so I’d like to refer folks to the Modha blog here, especially to the post called ‘The Cat is Out of the bag and BlueMatter”, which details progress in the SyNAPSE project and explains the claims made that they are simulating brain activity that is roughly equivalent to that we’d see from a cat.  Here’s an excerpt from that post:

Towards this end, we are announcing two major milestones.

First, using Dawn Blue Gene / P supercomputer at Lawrence Livermore National Lab with 147,456 processors and 144 TB of main memory, we achieved a simulation with 1 billion spiking neurons and 10 trillion individual learning synapses. This is equivalent to 1,000 cognitive computing chips each with 1 million neurons and 10 billion synapses, and exceeds the scale of cat cerebral cortex. The simulation ran 100 to 1,000 times slower than real-time.

Second, we have developed a new algorithm, BlueMatter, that exploits the Blue Gene supercomputing architecture to noninvasively measure and map the connections between all cortical and sub-cortical locations within the human brain using magnetic resonance diffusion weighted imaging. Mapping the wiring diagram of the brain is crucial to untangling its vast communication network and understanding how it represents and processes information.

Finally, here is an excellent presentation by Dr. Modha that outlines in simple terms what they are trying to do with DARPA SyNAPSE, which is build a human scale brain by 2018.

New Artificial Intelligence Initiative Begins in group of European Universities

A new initiative to develop something of a “strong AI” computer has begun in Europe, coordinated by this group:  brain-i-nets.eu

Strong AI is, for many, the “holy grail” of computing and in general terms means the development of a machine that thinks as well or better than a human.    Computers already have surpassed humans in most very focused intelligence tasks such as mathematical calculations, game playing, most forms of data analysis, and many more of the tasks we often use to define  “intelligence”.     However no machine comes close to the capabilities of a full human brain at this time, our remarkable three pound package that includes a high level of consciousness and self-awareness, adaptive mechanisms, deep creativity, and more.

With a fairly modest budget compared to the DARPA SyNAPSE,  Brain-i-nets goals appear more modest.   Science Daily suggests the goal as:

The scientists want to design a new generation of neuro-computers based on the principles of calculation and learning mechanisms found in the brain, and at the same time gain new knowledge about the brain’s learning mechanisms.

The Brain-i-nets website puts this …. somewhat differently, leading one to wonder if they were writing for public understanding or a science fiction movie script:

The goal of this project is to produce a set of rules for synaptic plasticity and network reorganisation that describe the actual adaptive processes that take place in the living brain during learning and to port these rules into current and next-generation neuromorphic hardware.

“Future Emerging Technologies” (FET) is the key agency behind   “Brain-i-Nets” which appears to have a budget of about 2.6 million Euros (though I’m not clear if this is just the matching partner funding or the entire budget).

The research partnership includes the University College London, the Ecole Polytechnique Federale de Lausanne, the French Centre National de la Recherche Scientifique, Ruprecht-Karls-Universität Heidelberg und the University of Zurich.

Neuroscience Expert Dr. Henry Markram on the IBM “Cat Brain” Simulation: “IBM’s claim is a HOAX”

Editors Note:   We’re hoping for more information from Dr. Modha who is also welcome to a Guest post here at Technology Report.

——   Guest Post by Dr. Henry Markram of the Blue Brain Project —-

IBM’s claim is a HOAX.

This is a mega public relations stunt – a clear case of scientific deception of the public. These simulations do not even come close to the complexity of an ant, let alone that of a cat. IBM allows Mohda to mislead the public into believing that they have simulated a brain with the complexity of a cat – sheer nonsense.

Here are the scientific reasons why this is a hoax and misleading PR stunt:

How complex is their model?
They claim to have simulated over a billion neurons interacting. Their so called “neurons” are the tiniest of points you can imagine, a microscopic dot. Over 98% of the volume of a neuron is branches (like a tree). They just cut off all the branches and roots and took a point in the middle of the trunk to represent a entire neuron. In real life, each segment of the branches of a neuron contains dozens of ion channels that powerfully controls the information processing in a neuron. They have none of that. Neurons contain 10’s of thousands of proteins that form a network with 10’s of millions of interactions. These interactions are incredibly complex and will require solving millions of differential equations. They have none of that. Neurons contain around 20’000 genes that produce products called mRNA, which builds the proteins. The way neurons build proteins and transport them to all the corners of the neuron where they are needed is an even more complex process which also controls what a neuron is, its memories and how it will process information. They have none of that. They use an alpha function (up fast down slow) to simulate a synaptic event. This is a completely inaccurate representation of a synapse. There are at least 6 types of synapses that are highly non-linear in their transmission (i.e. that transform inputs and not only transmit inputs). In fact you would need a 10’s of thousands of differential equations to simulate one synapse. Synapses are also extremely complex molecular machines that would themselves require thousands of differential equations to simulate just one. They simulated none of this. There are complex differential equations that must be solved to simulate the ionic flow in the branches, to simulate the ion channels biophysics, the protein-protein interactions, as well as the complete biochemical and genetic machinery as well as the synaptic transmission between neurons. 100’s of thousands of more differential equations. They have none of this. Then there are glia – 10 times more than neurons..And the blood supply…and more and more. These “points” they simulated and the synapses that they use for communication are literally millions of times simpler than a real cat brain. So they have not even simulated a cat’s brain at more than one millionth of it’s complexity.

Is it nonetheless the biggest point neuron simulation ever run?
No. These people simulated 1 billion points interacting. They used a formulation to model the summing up and threshold spiking of the “points” called the Izhikevik Formulation (an extremely simple equation). Eugene Izhikevik himself already in 2005 ran a simulation with 100 billion such points interacting just for the fun of it: (over 60 times larger than Modha’s simulation). This simulation ran on a cluster of desktop PCs and which every graduate student can run This is no technical achievement and certainly not even a record number of point neurons. That model exhibited oscillations, but that always happens so even simulating 100 Billion such points interacting is light years away from a brain.
see: http://www.izhikevich.org/human_brain_simulation/Blue_Brain.htm#Simulation%20of%20Large-Scale%20Brain%20Models

Is the simulator they built a big step?
Not even close. There are numerous proprietary and peer-reviewed neurosimulators (e.g., NCS, pNEURON, SPLIT, NEST) out there that can handle very large parallel models that are essentially only bound by the available memory. The bigger the machine you have available, the more neurons you can simulate. All these simulators apply optimizations for the particular platform in order to make optimal use of the available hardware. Without any comparison to existing simulators, their publication is a non-peer reviewed claim.

Did they learn anything about the brain?
They got very excited because they saw oscillations. Oscillations are an obligatory artifact that one always gets when many points interact. These findings that they claim on the neuroscience side may excite engineers, but not neuroscientists.

Why did they get the Gordon Bell Prize?
They submitted a non-peer reviewed paper to the Gordon Bell Committee and were awarded the prize almost instantly after they made their press release. They seem to have been very successful in influencing the committee with their claim, which technically is not peer-reviewed by the respective community and is neuroscientifically outrageous.

But is there any innovation here?
The only innovation here is that IBM has built a large supercomputer – which is irrelevant to the press release.

Why did IBM let Mohda make such a deceptive claim to the public?
I don’t know. Perhaps this is a publicity stunt to promote their supercompter. The supercomputer industry is suffering from the financial crisis and they probably are desperate to boost their sales. It is so disappointing to see this truly great company allow the deception of the public on such a grand scale.

But have you not said you can simulate the Human brain in 10 years?
I am a biologist and neuroscientist that has studied the brain for 30 years.  I know how complex it is. I believe that with the right resources and the right strategy it is possible. We have so far only simulated a small part of the brain at the cellular level of a rodent and I have always been clear about that.

Would other neuroscientists agree with you?
There is no neuroscientist on earth that would agree that they came even close to simulating the cat’s brain – or any brain.

But did Mohda not collaborate with neuroscientists?
I would be very surprised if any neuroscientists that he may have had in his DARPA consortium realized he was going to make such an outrages claim. I can’t imagine that that the San Fransisco neuroscientists knew he was going to make such a stupid claim. Modha himself is a software engineer with no knowledge of the brain.

But did you not collaborate with IBM?
I was collaborating with IBM on the Blue Brain Project at the very beginning because they had the best available technology to faithfully allow us to integrate the diversity and complexity found in brain tissue into a model. This for me is a major endeavor to advance our insights into the brain and drug development. Two years ago, when the same Dharmendra Mhoda claimed the “mouse-scale simulations”, I cut all neuroscience collaboration with IBM because this is an unethical claim and it deceives the public.

What IBM allowed Modha to do here is not only wrong, but outrageous. They deceived millions of people.

Henry Markram
Blue Brain Project