fMRI Data Reveals the Number of Parallel Processes Running in the Brain

The human brain carries out many tasks at the same time, but how many? Now fMRI data has revealed just how parallel gray matter is.

The human brain is often described as a massively parallel computing machine. That raises an interesting question: just how parallel is it?

Today, we get an answer thanks to the work of Harris Georgiou at the National Kapodistrian University of Athens in Greece, who has counted the number of “CPU cores” at work in the brain as it performs simple tasks in a functional magnetic resonance imaging (fMRI) machine. The answer could help lead to computers that better match the performance of the human brain.

The brain itself consists of around 100 billion neurons that each make up to 10,000 connections with their neighbors. All of this is packed into a structure the size of a party cake and operates at a peak power of only 20 watts, a level of performance that computer scientists observe with unconcealed envy.

fMRI machines reveal this activity by measuring changes in the levels of oxygen in the blood passing through the brain. The thinking is that more active areas use more oxygen so oxygen depletion is a sign of brain activity.

Typically, fMRI machines divide the brain into three-dimensional pixels called voxels, each about five cubic millimeters in size. The complete activity of the brain at any instant can be recorded using a three-dimensional grid of 60 x 60 x 30 voxels. These measurements are repeated every second or so, usually for tasks lasting two or three minutes. The result is a dataset of around 30 million data points.

Georgiou’s work is in determining the number of independent processes at work within this vast data set. “This is not much different than trying to recover the (minimum) number of actual ‘cpu cores’ required to ‘run’ all the active cognitive tasks that are registered in the entire 3-D brain volume,” he says.

This is a difficult task given the size of the dataset. To test his signal processing technique, Georgiou began by creating a synthetic fMRI dataset made up of eight different signals with statistical characteristics similar to those at work in the brain. He then used a standard signal processing technique, called independent component analysis, to work out how many different signals were present, finding that there are indeed eight, as expected.

Next, he applied the same independent component analysis technique to real fMRI data gathered from human subjects performing two simple tasks. The first was a simple visuo-motor task in which a subject watches a screen and then has to perform a simple task depending on what appears.

In this case, the screen displays either a red or green box on the left or right side. If the box is red, the subject must indicate this with their right index finger, and if the box is green, the subject indicates this with their left index finger. This is easier when the red box appears on the right and the green box appears on the left but is more difficult when the positions are swapped. The data consisted of almost 100 trials carried out on nine healthy adults.

The second task was easier. Subjects were shown a series of images that fall into categories such as faces, houses, chairs, and so on. The task was to spot when the same object appears twice, albeit from a different angle or under different lighting conditions. This is a classic visual recognition task.

The results make for interesting reading. Although the analysis is complex, the outcome is simple to state. Georgiou says that independent component analysis reveals that about 50 independent processes are at work in human brains performing the complex visuo-motor tasks of indicating the presence of green and red boxes. However, the brain uses fewer processes when carrying out simple tasks, like visual recognition.

That’s a fascinating result that has important implications for the way computer scientists should design chips intended to mimic human performance. It implies that parallelism in the brain does not occur on the level of individual neurons but on a much higher structural and functional level, and that there are about 50 of these.

Georgiou points out that a typical voxel corresponds to roughly three million neurons, each with several thousand connections with its neighbors. However, the current state-of-the-art neuromorphic chips contain a million artificial neurons each with only 256 connections. What is clear from this work is that the parallelism that Georgiou has measured occurs on a much larger scale than this.

“This means that, in theory, an artificial equivalent of a brain-like cognitive structure may not require a massively parallel architecture at the level of single neurons, but rather a properly designed set of limited processes that run in parallel on a much lower scale,” he concludes.

Anybody thinking of designing brain-like chips might find this a useful tip.

Ref: arxiv.org/abs/1410.7100 Estimating The Intrinsic Dimension In fMRI Space Via Dataset Fractal Analysis

ORIGINAL: Tech Review

November 5, 2014

Danko Nikolic on Singularity 1 on 1: Practopoiesis Tells Us Machine Learning Is Not Enough!

If there’s ever been a case when I just wanted to jump on a plane and go interview someone in person, not because they are famous but because they have created a totally unique and arguably seminal theory, it has to be Danko Nikolic. I believe Danko’s theory of Practopoiesis is that good and he should and probably eventually would become known around the world for it. Unfortunately, however, I don’t have a budget of thousands of dollars per interview which will allow me to pay for my audio and video team to travel to Germany and produce the quality that Nikolic deserves. So, I’ve had to settle with Skype. And Skype refused to cooperate on that day even though both me and Danko have pretty much the fastest internet connections money can buy. Luckily, despite the poor video quality, our audio was very good and I would urge that if there’s ever been an interview where you ought to disregard the video quality and focus on the content – it has to be this one.

During our 67 min conversation with Danko we cover a variety of interesting topics such as:

• his personal journey into psychology and cognitive science; 
• writing a manual for the mind; 
• practopoiesis, AI and learning how to learn; 
• consciousness and free will; 
• the Penrose-Hameroff Quantum Theory of consciousness; 
• the brain-mind distinction; 
• the Human Brain Project, 
• whole brain simulation and 
• mind uploading…

As always you can listen to or download the audio file above or scroll down and watch the video interview in full.

To show your support you can write a review on iTunes or make a donation.

Who is Danko Nikolic?

The main motive for my studies is the explanatory gap between the brain and the mind. My interest is in how the physical world of neuronal activity produces the mental world of perception and cognition. I am associated with

• the Max-Planck Institute for Brain Research, 
• Ernst Strüngmann Institute, 
• Frankfurt Institute for Advanced Studies, and
• the University of Zagreb.

I approach the problem of explanatory gap from both sides, bottom-up and top-down. The bottom-up approach investigates brain physiology. The top-down approach investigates the behavior and experiences. Each of the two approaches led me to develop a theory: The work on physiology resulted in the theory of practopoiesis. The work on behavior and experiences led to the phenomenon of ideasthesia.

The empirical work in the background of those theories involved

• simultaneous recordings of activity of 100+ neurons in the visual cortex (extracellular recordings), 
• behavioral and imaging studies in visual cognition (attention, working memory, long-term memory), and 
• empirical investigations of phenomenal experiences (synesthesia).

The ultimate goal of my studies is twofold.

• First, I would like to achieve conceptual understanding of how the dynamics of physical processes creates the mental ones. I believe that the work on practopoiesis presents an important step in this direction and that it will help us eventually address the hard problem of consciousness and the mind-body problem in general. 
• Second, I would like to use this theoretical knowledge to create artificial systems that are biologically-like intelligent and adaptive. This would have implications for our technology.

A reason why one would be interested in studying the brain in the first place is described here: Why brain?

ORIGINAL: Singularity 1 on 1

by SOCRATES

I Contain Multitudes

Our bodies are a genetic patchwork, possessing variation from cell to cell. Is that a good thing?

Olena Shmahalo for Quanta Magazine

Even healthy brains harbor genetic diversity, though scientists disagree over the extent.

Your DNA is supposed to be your blueprint, your unique master code, identical in every one of your tens of trillions of cells. It is why you are you, indivisible and whole, consistent from tip to toe.

But that’s really just a biological fairy tale. In reality, you are an assemblage of genetically distinctive cells, some of which have radically different operating instructions. This fact has only become clear in the last decade. Even though each of your cells supposedly contains a replica of the DNA in the fertilized egg that began your life, mutations, copying errors and editing mistakes began modifying that code as soon as your zygote self began to divide. In your adult body, your DNA is peppered by pinpoint mutations, riddled with repeated or rearranged or missing information, even lacking huge chromosome-sized chunks. Your data is hopelessly corrupt.

Most genome scientists assume that this DNA diversity, called “somatic mutation” or “structural variation,” is bad. Mutations and other genetic changes can alter the function of the cell, usually for the worse. Disorderly DNA is a hallmark of cancers, and genomic variation can cause a suite of brain disorders and malformations. It makes sense: Cells working off garbled information probably don’t function very well.

Most research to date has focused on how aberrant DNA drives disease, but even healthy bodies harbor genetic disorder. In the last few years, some researchers report that anywhere from 10 to 40 percent of brain cells and between 30 and 90 percent of human liver cells are aneuploid, meaning that one entire chromosome is either missing or duplicated. Copy number variations, in which chunks of DNA between 100 and a few million letters in length are multiplied or eliminated, also seem to be widespread in healthy people.

DARPA Project Starts Building Human Memory Prosthetics

The first memory-enhancing devices could be implanted within four years

Photo: Lawrence Livermore National LaboratoryRemember This? Lawrence Livermore engineer Vanessa Tolosa holds up a silicon wafer containing micromachined implantable neural devices for use in experimental memory prostheses.

“They’re trying to do 20 years of research in 4 years,” says Michael Kahana in a tone that’s a mixture of excitement and disbelief. Kahana, director of the Computational Memory Lab at the University of Pennsylvania, is mulling over the tall order from the U.S. Defense Advanced Research Projects Agency (DARPA). In the next four years, he and other researchers are charged with understanding the neuroscience of memory and then building a prosthetic memory device that’s ready for implantation in a human brain.

DARPA’s first contracts under its Restoring Active Memory (RAM) program challenge two research groups to construct implants for veterans with traumatic brain injuries that have impaired their memories. Over 270,000 U.S. military service members have suffered such injuries since 2000, according to DARPA, and there are no truly effective drug treatments. This program builds on an earlier DARPA initiative focused on building a memory prosthesis, under which a different group of researchers had dramatic success in improving recall in mice and monkeys.

Kahana’s team will start by searching for biological markers of memory formation and retrieval. For this early research, the test subjects will be hospitalized epilepsy patients who have already had electrodes implanted to allow doctors to study their seizures. Kahana will record the electrical activity in these patients’ brains while they take memory tests.

“The memory is like a search engine,” Kahana says. “In the initial memory encoding, each event has to be tagged. Then in retrieval, you need to be able to search effectively using those tags.” He hopes to find the electric signals associated with these two operations.

Once they’ve found the signals, researchers will try amplifying them using sophisticated neural stimulation devices. Here Kahana is working with the medical device maker Medtronic, in Minneapolis, which has already developed one experimental implant that can both record neural activity and stimulate the brain. Researchers have long wanted such a “closed-loop” device, as it can use real-time signals from the brain to define the stimulation parameters.

Kahana notes that designing such closed-loop systems poses a major engineering challenge. Recording natural neural activity is difficult when stimulation introduces new electrical signals, so the device must have special circuitry that allows it to quickly switch between the two functions. What’s more, the recorded information must be interpreted with blistering speed so it can be translated into a stimulation command. “We need to take analyses that used to occupy a personal computer for several hours and boil them down to a 10-millisecond algorithm,” he says.

In four years’ time, Kahana hopes his team can show that such systems reliably improve memory in patients who are already undergoing brain surgery for epilepsy or Parkinson’s. That, he says, will lay the groundwork for future experiments in which medical researchers can try out the hardware in people with traumatic brain injuries—people who would not normally receive invasive neurosurgery.

The second research team is led by Itzhak Fried, director of the Cognitive Neurophysiology Laboratory at the University of California, Los Angeles. Fried’s team will focus on a part of the brain called the entorhinal cortex, which is the gateway to the hippocampus, the primary brain region associated with memory formation and storage. “Our approach to the RAM program is homing in on this circuit, which is really the golden circuit of memory,” Fried says. In a 2012 experiment, he showed that stimulating the entorhinal regions of patients while they were learning memory tasks improved their performance.

Fried’s group is working with Lawrence Livermore National Laboratory, in California, to develop more closed-loop hardware. At Livermore’s Center for Bioengineering, researchers are leveraging semiconductor manufacturing techniques to make tiny implantable systems. They first print microelectrodes on a polymer that sits atop a silicon wafer, then peel the polymer off and mold it into flexible cylinders about 1 millimeter in diameter. The memory prosthesis will have two of these cylindrical arrays, each studded with up to 64 hair-thin electrodes, which will be capable of both recording the activity of individual neurons and stimulating them. Fried believes his team’s device will be ready for tryout in patients with traumatic brain injuries within the four-year span of the RAM program.

Outside observers say the program’s goals are remarkably ambitious. Yet Steven Hyman, director of psychiatric research at the Broad Institute of MIT and Harvard, applauds its reach. “The kind of hardware that DARPA is interested in developing would be an extraordinary advance for the whole field,” he says. Hyman says DARPA’s funding for device development fills a gap in existing research. Pharmaceutical companies have found few new approaches to treating psychiatric and neurodegenerative disorders in recent years, he notes, and have therefore scaled back drug discovery efforts. “I think that approaches that involve devices and neuromodulation have greater near-term promise,” he says.

This article originally appeared in print as “Making a Human Memory Chip.”

ORIGINAL: IEES Spectrum

By Eliza Strickland

Posted 27 Aug 2014

Ray Kurzweil: Get ready for hybrid thinking

Two hundred million years ago, our mammal ancestors developed a new brain feature: the neocortex. This stamp-sized piece of tissue (wrapped around a brain the size of a walnut) is the key to what humanity has become. Now, futurist Ray Kurzweil suggests, we should get ready for the next big leap in brain power, as we tap into the computing power in the cloud.

ORIGINAL: TED

Jun 2, 2014

Prepare to Be Shocked. Four predictions about how brain stimulation will make us smarter

Alvaro Dominguez

Several years ago, the Defense Advanced Research Projects Agency got wind of a technique called transcranial direct-current stimulation, or tDCS, which promised something extraordinary: a way to increase people’s performance in various capacities, from motor skills (in the case of recovering stroke patients) to language learning, all by stimulating their brains with electrical current. The simplest tDCS rigs are little more than nine-volt batteries hooked up to sponges embedded with metal and taped to a person’s scalp.

It’s only a short logical jump from the preceding applications to other potential uses of tDCS. What if, say, soldiers could be trained faster by hooking their heads up to a battery?

This is the kind of question DARPA was created to ask. So the agency awarded a grant to researchers at the University of New Mexico to test the hypothesis. They took a virtual-reality combat-training environment called Darwars Ambush—basically, a video game the military uses to train soldiers to respond to various situations—and captured still images. Then they Photoshopped in pictures of suspicious characters and partially concealed bombs. Subjects were shown the resulting tableaus, and were asked to decide very quickly whether each scene included signs of danger. The first round of participants did all this inside an fMRI machine, which identified roughly the parts of their brains that were working hardest as they looked for threats. Then the researchers repeated the exercise with 100 new subjects, this time sticking electrodes over the areas of the brain that had been identified in the fMRI experiment, and ran two milliamps of current (nothing dangerous) to half of the subjects as they examined the images. The remaining subjects—the control group—got only a minuscule amount of current. Under certain conditions, subjects receiving the full dose of current outperformed the others by a factor of two. And they performed especially well on tests administered an hour after training, indicating that what they’d learned was sticking. Simply put, running positive electrical current to the scalp was making people learn faster.

Dozens of other studies have turned up additional evidence that brain stimulation can improve performance on specific tasks. In some cases, the gains are small—maybe 10 or 20 percent—and in others they are large, as in the DARPA study. Vince Clark, a University of New Mexico psychology professor who was involved with the DARPA work, told me that he’d tried every data-crunching tactic he could think of to explain away the effect of tDCS. “But it’s all there. It’s all real,” Clark said. “I keep trying to get rid of it, and it doesn’t go away.”

Now the intelligence-agency version of DARPA, known as IARPA, has created a program that will look at whether brain stimulation might be combined with exercise, nutrition, and games to even more dramatically enhance human performance. As Raja Parasuraman, a George Mason University psychology professor who is advising an IARPA team, puts it, “The end goal is to improve fluid intelligence—that is, to make people smarter.”

Whether or not IARPA finds a way to make spies smarter, the field of brain stimulation stands to shift our understanding of the neural structures and processes that underpin intelligence. Here, based on conversations with several neuroscientists on the cutting edge of the field, are four guesses about where all this might be headed. 

1. Brain stimulation will expand our understanding of the brain-mind connection.

The neural mechanisms of brain stimulation are just beginning to be understood, through work by Michael A. Nitsche and Walter Paulus at the University of Göttingen and by Marom Bikson at the City College of New York. Their findings suggest that adding current to the brain increases the plasticity of neurons, making it easier for them to form new connections. We don’t imagine our brains being so mechanistic. To fix a heart with simple plumbing techniques or to reset a bone is one thing. But you’re not supposed to literally flip an electrical switch and get better at spotting Waldo or learning Swahili, are you? And if flipping a switch does work, how will that affect our ideas about intelligence and selfhood?

Even if juicing the brain doesn’t magically increase IQ scores, it may temporarily and substantially improve performance on certain constituent tasks of intelligence, like memory retrieval and cognitive control. This in itself will pose significant ethical challenges, some of which echo dilemmas already being raised by “neuroenhancement” drugs like Provigil. Workers doing cognitively demanding tasks—air-traffic controllers, physicists, live-radio hosts—could find themselves in the same position as cyclists, weight lifters, and baseball players. They’ll either be surpassed by those willing to augment their natural abilities, or they’ll have to augment themselves.

2. DIY brain stimulation will be popular—and risky.

As word of research findings has spread, do-it-yourselfers on Reddit and elsewhere have traded tips on building simple rigs and where to place electrodes for particular effects. Researchers like the Wright State neuroscientist Michael Weisend have in turn gone on DIY podcasts to warn them off. There’s so much we don’t know. Is neurostimulation safe over long periods of time? Will we become addicted to it? Some scientists, like Stanford’s Teresa Iuculano and Oxford’s Roi Cohen Kadosh, warn that cognitive enhancement through electrical stimulation may “occur at the expense of other cognitive functions.” For example, when Iuculano and Kadosh applied electrical stimulation to subjects who were learning a code that paired various numbers with symbols, the test group memorized the symbols faster than the control group did. But they were slower when it came time to actually use the symbols to do arithmetic. Maybe thinking will prove to be a zero-sum game: we cannot add to our mental powers without also subtracting from them.

3. Electrical stimulation is just the beginning.

Scientists across the country are becoming interested in how other types of electromagnetic radiation might affect the brain. Some are looking at using alternating current at different frequencies, magnetic energy, ultrasound, even different types of sonic noise. There appear to be many ways of exciting the brain’s circuitry with various energetic technologies, but basic research is only in its infancy. “It’s so early,” Clark told me. “It’s very empirical now—see an effect and play with it.”

As we learn more about our neurons’ wiring, through efforts like President Obama’s BRAIN Initiative—a huge, multiagency attempt to map the brain—we may become better able to deliver energy to exactly the right spots, as opposed to bathing big portions of the brain in current or ultrasound. Early research suggests that such targeting could mean the difference between modest improvements and the startling DARPA results. It’s not hard to imagine a plethora of treatments tailored to specific types of learning, cognition, or mood—a bit of current here to boost working memory, some there to help with linguistic fluency, a dash of ultrasound to improve one’s sense of well-being.

4. The most important application may be clinical treatment.

City College’s Bikson worries that an emphasis on cognitive enhancement could overshadow therapies for the sick, which he sees as the more promising application of this technology. In his view, do-it-yourself tDCS is a sideshow—clinical tDCS could be used to treat people suffering from epilepsy, migraines, stroke damage, and depression. “The science and early medical trials suggest tDCS can have as large an impact as drugs and specifically treat those who have failed to respond to drugs,” he told me. “tDCS researchers go to work every day knowing the long-term goal is to reduce human suffering on a transformative scale.” To that end, many of them would like to see clinical trials test tDCS against leading drug therapies. “Hopefully the National Institutes of Health will do that,” Parasuraman, the George Mason professor, said. “I’d like to see straightforward, side-by-side competition between tDCS and antidepressants. May the best thing win.”

A Brief Chronicle of Cognitive Enhancement

• 500 b.c.: Ancient Greek scholars wear rosemary in their hair, believing it to boost memory.
• 1886: John Pemberton formulates the original Coca-Cola, with cocaine and caffeine. It’s advertised as a “brain tonic.”
• 1955: The FDA licenses methylphenidate—a?k?a Ritalin—for treating “hyperactivity.”
• 1997: Julie Aigner-Clark launches Baby Einstein, a line of products claiming to “facilitate the development of the brain in infants.”
• 1998: Provigil hits the U.S. market.
• 2005: Lumosity, a San Francisco company devoted to online “brain training,” is founded.
• 2020: A tDCS company starts an SAT-prep service for high-school students.

ORIGINAL: The Atlantic

Alexis C. Madrigal 

Aug 13 2014

Preparing Your Students for the Challenges of Tomorrow

Right now, you have students. Eventually, those students will become the citizens -- employers, employees, professionals, educators, and caretakers of our planet in 21st century. Beyond mastery of standards, what can you do to help prepare them? What can you promote to be sure they are equipped with the skill sets they will need to take on challenges and opportunities that we can't yet even imagine?

Following are six tips to guide you in preparing your students for what they're likely to face in the years and decades to come.

1. Teach Collaboration as a Value and Skill Set Students of today need new skills for the coming century that will make them ready to collaborate with others on a global level. Whatever they do, we can expect their work to include finding creative solutions to emerging challenges.

2. Evaluate Information Accuracy 

New information is being discovered and disseminated at a phenomenal rate. It is predicted that 50 percent of the facts students are memorizing today will no longer be accurate or complete in the near future. Students need to know 

• how to find accurate information, and 
• how to use critical analysis for 
• assessing the veracity or bias and 
• the current or potential uses of new information. 

These are the executive functions that they need to develop and practice in the home and at school today, because without them, students will be unprepared to find, analyze, and use the information of tomorrow.

3. Teach Tolerance 

In order for collaboration to happen within a global community, job applicants of the future will be evaluated by their ability for communication with, openness to, and tolerance for unfamiliar cultures and ideas. To foster these critical skills, today's students will need open discussions and experiences that can help them learn about and feel comfortable communicating with people of other cultures.

4. Help Students Learn Through Their Strengths 

Children are born with brains that want to learn. They're also born with different strengths -- and they grow best through those strengths. One size does not fit all in assessment and instruction. The current testing system and the curriculum that it has spawned leave behind the majority of students who might not be doing their best with the linear, sequential instruction required for this kind of testing. Look ahead on the curriculum map and help promote each student's interest in the topic beforehand. Use clever "front-loading" techniques that will pique their curiosity.

5. Use Learning Beyond the Classroom
New "learning" does not become permanent memory unless there is repeated stimulation of the new memory circuits in the brain pathways. This is the "practice makes permanent" aspect of neuroplasticity where neural networks that are the most stimulated develop more dendrites, synapses, and thicker myelin for more efficient information transmission. These stronger networks are less susceptible to pruning, and they become long-term memory holders. Students need to use what they learn repeatedly and in different, personally meaningful ways for short-term memory to become permanent knowledge that can be retrieved and used in the future. Help your students make memories permanent by providing opportunities for them to "transfer" school learning to real-life situations.

6. Teach Students to Use Their Brain Owner's Manual
The most important manual that you can share with your students is the owner's manual to their own brains. When they understand how their brains take in and store information (PDF, 139KB), they hold the keys to successfully operating the most powerful tool they'll ever own. When your students understand that, through neuroplasticity, they can change their own brains and intelligence, together you can build their resilience and willingness to persevere through the challenges that they will undoubtedly face in the future.

How are you preparing your students to thrive in the world they'll inhabit as adults?


ORIGINAL: Edutopia 

Judy Willis MD's Profile

August 20, 2014

Building Mind-Controlled Gadgets Just Got Easier

A new brain-computer interface lets DIYers access their brain waves

Photo: Chip AudetteEngineer Chip Audette used the OpenBCI system to control a robot spider with his mind.

The guys who decided to make a mind-reading tool for the masses are not neuroscientists. In fact, they’re artists who met at Parsons the New School for Design, in New York City. In this day and age, you don’t have to be a neuroscientist to muck around with brain signals.

With Friday’s launch of an online store selling their brain-computer interface (BCI) gear, Joel Murphy and Conor Russomanno hope to unleash a wave of neurotech creativity. Their system enables DIYers to use brain waves to control anything they can hack—a video game, a robot, you name it. “It feels like there’s going to be a surge,” says Russomanno. “The floodgates are about to open.” And since their technology is open source, the creators hope hackers will also help improve the BCI itself.

Photo: OpenBCI The OpenBCI board takes in data from up to eight electrodes.

Their OpenBCI system makes sense of an electroencephalograph (EEG), signal, a general measure of electrical activity in the brain captured via electrodes on the scalp. The fundamental hardware component is a relatively new chip from Texas Instruments, which takes in analog data from up to eight electrodes and converts it to a digital signal. Russomanno and Murphy used the chip and an Arduino board to create OpenBCI, which essentially amplifies the brain signal and sends it via Bluetooth to a computer for processing. “The big issue is getting the data off the chip and making it accessible,” Murphy says. Once it’s accessible, Murphy expects makers to build things he hasn’t even imagined yet.

The project got its start in 2011, when Russomanno was a student in Murphy’s physical computing class at Parsons and told his professor he wanted to hack an EEG toy made by Mattel. The toy’s EEG-enabled headset supposedly registered the user’s concentrated attention (which in the game activated a fan that made a ball float upward). But the technology didn’t seem very reliable, and since it wasn’t open source, Russomanno couldn’t study the game’s method of collecting and analyzing the EEG data. He decided that an open-source alternative was necessary if he wanted to have any real fun.

Happily, Russomanno and his professor soon connected with engineer Chip Audette, of the New Hampshire R&D firm Creare, who already had a grant from the U.S. Defense Advanced Research Projects Agency (DARPA) to develop a low-cost, high-quality EEG system for “nontraditional users.” Once the team had cobbled together a prototype of their OpenBCI system, they decided to offer their gear to the world with a Kickstarter campaign, which ended in January and raised more than twice the goal of US $100,000.

Murphy and Russomanno soon found that production would be more difficult and take longer than expected (as is the case with so many Kickstarter projects), so they had to push back their shipping date by several months. Now, though, they’re in business—and Russomanno says that shipping a product is only the beginning. “We don’t just want to sell something; we want to teach people how to use it and also develop a community,” he says. OpenBCI wants to be an online portal where experimenters can swap tips and post research projects.

So once a person’s brain-wave data is streaming into a computer, what is to be done with it? OpenBCI will make some simple software available, but mostly Russomanno and Murphy plan to watch as inventors come up with new applications for BCIs.

Audette, the engineer from Creare, is already hacking robotic “battle spiders” that are typically steered by remote control. Audette used an OpenBCI prototype to identify three distinct brain-wave patterns that he can reproduce at will, and he sent those signals to a battle spider to command it to turn left or right or to walk straight ahead. “The first time you get something to move with your brain, the satisfaction is pretty amazing,” Audette says. “It’s like, ‘I am king of the world because I got this robot to move.’ ”

In Los Angeles, a group is using another prototype to give a paralyzed graffiti artist the ability to practice his craft again. The artist, Tempt One, was diagnosed with Lou Gehrig’s disease in 2003 and gradually progressed to the nightmarish “locked in” state. By 2010 he couldn’t move or speak and lay inert in a hospital bed—but with unimpaired consciousness, intellect, and creativity trapped inside his skull. Now his supporters are developing a system called the BrainWriter: They’re using OpenBCI to record the artist’s brain waves and are devising ways to use those brain waves to control the computer cursor so Tempt can sketch his designs on the screen.

Another early collaborator thinks that OpenBCI will be useful in mainstream medicine. David Putrino, director of telemedicine and virtual rehabilitation at the Burke Rehabilitation Center, in White Plains, N.Y., says he’s comparing the open-source system to the $60,000 clinic-grade EEG devices he typically works with. He calls the OpenBCI system robust and solid, saying, “There’s no reason why it shouldn’t be producing good signal.”

Putrino hopes to use OpenBCI to build a low-cost EEG system that patients can take home from the hospital, and he imagines a host of applications. Stroke patients, for example, could use it to determine when their brains are most receptive to physical therapy, and Parkinson’s patients could use it to find the optimal time to take their medications. “I’ve been playing around with these ideas for a decade,” Putrino says, “but they kept failing because the technology wasn’t quite there.” Now, he says, it’s time to start building.

Learn More BCI BMI DIY The Human OS brain-computer interface brain-machine interface electrophysiology makers neural engineering neurotech open source hardware

ORIGINAL: IEEE Spectrum

By Eliza Strickland

11 Aug 2014

Palm's Jeff Hawkins is building a brain-like AI. He told us why he thinks his life's work is right

Inside a big bet on future machine intelligence

Feature Jeff Hawkins has bet his reputation, fortune, and entire intellectual life on one idea: that he understands the brain well enough to create machines with an intelligence we recognize as our own.

If his bet is correct, the Palm Pilot inventor will father a new technology, one that becomes the crucible in which a general artificial intelligence is one day forged. If his bet is wrong, then Hawkins will have wasted his life. At 56 years old that might sting a little.

"I want to bring about intelligent machines, machine intelligence, accelerated greatly from where it was going to happen and I don't want to be consumed – I want to come out at the other end as a normal person with my sanity," Hawkins told The Register. "My mission, the mission of Numenta, is to be a catalyst for machine intelligence."

A catalyst, he says, staring intently at your correspondent, "is something which accelerates a reaction by a thousand or ten thousand or a million-fold, and doesn't get consumed in the process."

His goal is ambitious, to put it mildly.

Before we dig deep into Hawkins' idiosyncratic approach to artificial intelligence, it's worth outlining the state of current AI research, why his critics have a right to be skeptical of his grandiose claims, and how his approach is different to the one being touted by consumer web giants such as Google.

AI researcher Jeff Hawkins

The road to a successful, widely deployable framework for an artificial mind is littered with failed schemes, dead ends, and traps. No one has come to the end of it, yet. But while major firms like Google and Facebook, and small companies like Vicarious, are striding over well-worn paths, Hawkins believes he is taking a new approach that could take him and his colleagues at his company,Numenta, all the way.

For over a decade, Hawkins has poured his energy into amassing enough knowledge about the brain and about how to program it in software. Now, he believes he is on the cusp of a great period of invention that may yield some very powerful technology.

Some people believe in him, others doubt him, and some academics El Reg has spoken with are suspicious of his ideas.

One thing we have established is that the work to which Hawkins has dedicated his life has become an influential touchstone within the red-hot modern artificial intelligence industry. His 2004 book, On Intelligence, appears to have been read by and inspired many of the most prominent figures in AI, and the tech Numenta is creating may trounce other commercial efforts by much larger companies such as Google, Facebook, and Microsoft.

"I think Jeff is largely right in what he wrote in On Intelligence," explains Hawkins' former colleague Dileep George (now running his own AI startup, Vicarious, which recently received $40m in funding from Mark Zuckerberg, space pioneer Elon Musk, and actor-turned-VC Ashton Kutcher). "Hierarchical systems, associative memory, time and attention – I think all those ideas are correct."

One of Google's most prominent AI experts agrees: "Jeff Hawkins ... has served as inspiration to countless AI researchers, for which I give him a lot of credit," explains former Google brain king and current Stanford Professor Andrew Ng.

Some organizations have taken Hawkins' ideas and stealthily run with them, with schemes already underway at companies like IBM and federal organizations like DARPA to implement his ideas in silicon, paving the way for neuromorphic processors that process information in near–real time, develop representations of patterns, and make predictions. If successful, these chips will make Qualcomm's "neuromorphic" Zeroth processors look like toys.

He has also inspired software adaptations of his work, such as CEPT, which has built an intriguing natural language processing engine partly out of Hawkins' ideas.

How we think: time and hierarchy

Hawkins' idea is that to build systems that behave like the brain, you have to be able to 

• take in a stream of changing information, 
• recognize patterns in it without knowing anything about the input source, 
• make predictions, and 
• react accordingly. 

The only context you have for this analysis is an ability to observe how the stream of data changes over time.

Though this sounds similar to some of the data processing systems being worked on by researchers at Google, Microsoft, and Facebook, it has some subtle differences.

Part of it is heritage – Hawkins traces his ideas back to his own understanding of how our neocortex works based on a synthesis of thousands of academic papers, chats with researchers, and his own work at two of his prior tech companies, Palm and Handspring, whereas the inspiration for most other approaches are neural networks based on technology from the 80s, which itself was refined out of a 1940s paper [PDF], "A Logical Calculus of the Ideas Immanent in Nervous Activity".

"That may be the right thing to do, but it's not the way brains work and it's not the principles of intelligence and it's not going to lead to a system that can explore the world or systems that can have behavior," Hawkins tells us.

So far he has outlined the ideas for this approach in his influential On Intelligence, plus a white paper published in 2011, a set of open source algorithms called NuPIC based on his Hierarchical Temporal Memory approach, and hundreds of talks given at universities and at companies ranging from Google to small startups.

Six easy pieces and the one true algorithm

Hawkins' work has "popularized the hypothesis that much of intelligence might be due to one learning algorithm," explains Ng.

Part of why Hawkins' approach is so controversial is that rather than assembling a set of advanced software components for specific computing functions and lashing them together via ever more complex collections of software, Hawkins has dedicated his research to figuring out an implementation of a single, basic approach.

This approach stems from an observation that our brain doesn't appear to come preloaded with any specific instructions or routines, but rather is an architecture that is able to take in, process, and store an endless stream of information and develop higher-order understandings out of that.

The manifestation of Hawkins' approach is the Cortical Learning Algorithm, or CLA.

"People used to think the neocortex was divided into sensory regions and motor regions," he explains. "We know now that is not true – the whole neocortex is sensory and motor."

Ultimately, the CLA will be a single system that involves both sensory processing and motor control – brain functions that Hawkins believes must be fused together to create the possibility of consciousness. For now, most work has been done on the sensory layer, though he has recently made some breakthroughs on the motor integration as well.

To build his Cortical Learning Algorithm system, Hawkins says, he has developed six principles that define a cortical-like processor. These traits are

• "on-line learning from streaming data", 
• "hierarchy of memory regions", 
• "sequence memory", 
• "sparse distributed representations",
•  "all regions are sensory and motor", and 
• "attention".

These principles are based on his own study of the work being done by neuroscientists around the world.

Now, Hawkins says, Numenta is on the verge of a breakthrough that could see the small company birth a framework for building intelligence machines. And unlike the hysteria that greeted AI in the 70s and 80s as the defense industry pumped money into AI, this time may not be a false dawn.

"I am thrilled at the progress we're making," he told El Reg one sunny afternoon at Numenta's whiteboard-crammed offices in Redwood City, California. "It's accelerating. These things are compounding, and it feels like these things are all coming together very rapidly."

The approach Numenta has been developing is producing better and better results, he says, and the CLA is gaining broader capabilities. In the past months, Hawkins has gone through a period of fecund creativity, and has solved one of the main problems that have bedeviled his system (temporal pooling), he says. He sees 2014 as a critical year for the company.

He is confident that he has bet correctly – but it's been a hard road to get here.

That long, hard road

Hawkins' interest in the brain dates back to his childhood, as does his frustration with how it is studied.

Growing up, Hawkins spent time with his father in an old shipyard on the north shore of Long Island, inventing all manner of boats with his father, an inventor with the enthusiasm for creativity of a Dr. Seuss character. In high school, the young Hawkins developed an interest in biophysics and, as he recounts in his book On Intelligence, tried to find out more about the brain at a local library.

"My search for a satisfying brain book turned up empty. I came to realize that no one had any idea how the brain actually worked. There weren't even any bad or unproven theories; there simply were none," he wrote.

This realization sparked a lifelong passion to try to understand the grand, intricate system that makes people who they are, and to eventually model the brain and create machines built in the same manner.

Hawkins graduated from Cornell in 1979 with a Bachelor of Science in Electronic Engineering. After a stint at Intel, he applied to MIT to study artificial intelligence, but had his application rejected because he wanted to understand how brains work, rather than build artificial intelligence. After this he worked at laptop start-up GRiD Systems, but during this time "could not get my curiosity about the brain and intelligent machines out of my head," so he did a correspondence course in physiology and ultimately applied to and was accepted in the biophysics program at the University of California, Berkeley.

When Hawkins started at Berkeley in 1986, his ambition to study a theory of the brain collided with the university administration, which disagreed with his course of study. Though Berkeley was not able to give him a course of study, Hawkins spent almost two years ensconced in the school's many libraries reading as much of the literature available on neuroscience as possible.

This deep immersion in neuroscience became the lens through which Hawkins viewed the world, with his later business accomplishments – Palm, Handspring – all leading to valuable insights on how the brain works and why the brain behaves as it does.

The way Hawkins recounts his past makes it seem as if the creation of a billion-dollar business in Palm, and arguably the prototype of the modern smartphone in Handspring, was a footnote along his journey to understand the brain.

This makes more sense when viewed against what he did in 2002, when he founded the Redwood Neuroscience Institute (now a part of the University of California at Berkeley and an epicenter of cutting-edge neuroscience research in its own right), and in 2005 founded Numenta with Palm/Handspring collaborator Donna Dublinksy and cofounder Dileep George.

These decades gave Hawkins the business acumen, money, and perspective needed to make a go at crafting his foundation for machine intelligence.

Controversy

His media-savvy, confident approach appears to have stirred up some ill feeling among other academics who point out, correctly, that Hawkins hasn't published widely, nor has he invented many ideas on his own.

Numenta has also had troubles, partly due to Hawkins' idiosyncratic view on how the brain works.

In 2010, for example, Numenta cofounder Dileep George left to found his own company, Vicarious, to pick some of the more low-hanging fruit in the promising field of AI. From what we understand, this amicable separation stemmed from a difference of opinion between George and Hawkins, as George tended towards a more mathematical approach, and Hawkins to a more biological one.

Hawkins has also come in for a bit of a drubbing from the intelligentsia, with NYU psychology professor Gary Marcus dismissing Numenta's approach in a New Yorker article titled "Steamrolled by Big Data".

Other academics El Reg interviewed for this article did not want to be quoted, as they felt Hawkins' lack of peer reviewed papers combined with his entrepreneurial persona reduced the credibility of his entire approach.

Hawkins brushes off these criticisms and believes they come down to a difference of opinion between him and the AI intelligentsia.

"These are complex biological systems that were not designed by mathematical principles [that are] very difficult to formalize completely," he told us.

"This reminds me a bit of the beginning of the computer era," he said. "If you go back to the 1930s and early 40s, when people first started thinking about computers they were really interested in whether an algorithm would complete, and they were looking for mathematical completeness, a mathematical proof, that if you implemented something like an algorithm today when we build a computer, no one sits around saying "Let's look at the mathematical formalism of this computer.' It reminds me a little about that. We still have people saying 'You don't have enough math here!' There's some people that just don't like that."

Hawkins' confidence stems from the way Numenta has built its technology, which far from merely taking inspiration from the brain – as many other startups claim to do – is actively built as a digital implementation of everything Hawkins has learned about how the dense, napkin-sized sheet of cells that is our neocortex works.

"I know of no other cortical theories/models that incorporate any of the following: 

• active dendrites, 
• differences between proximal and distal dendrites, 
• synapse growth and decay, 
• potential synapses, 
• dendrite growth, 
• depolarization as a mode of prediction, 
• mini-columns, 
• multiple types of inhibition and their corresponding inhibitory neurons, 
• etcetera. 

The new temporal pooling mechanism we are working on requires metabotropic receptors in the locations they are, and are not, found. Again, I don't know of any theories that have been reduced to practice that incorporate any, let alone all of these concepts," he wrote in a post to the discussion mailing list for NuPic, an open source implementation of Numenta's CLA, in February.

Deep learning is the new shallow learning

But for all the apparent rigorousness of Hawkins' approach, during the years he has worked on the technology there has been a fundamental change in the landscape of AI development: the rise of the consumer internet giants, and with them the appearance of various cavernous stores of user data on which to train learning algorithms.

Google, for instance, was said in January of 2014 to be assembling the team required for the "Manhattan Project for AI", according to a source who spoke anonymously to online publication Re/code. But Hawkins thinks that for all its grand aims, Google's approach may be based on a flawed presumption.

The collective term for the approach pioneered by companies like Google, Microsoft, and Facebook is "Deep Learning", but Hawkins fears it may be another blind path.

"Deep learning could be the greatest thing in the world, but it's not a brain theory," he says.

Deep learning approaches, Hawkins says, encourage the industry to go about refining methods based on old technology, itself based on an oversimplified version of the neurons in a brain.

Because of the vast stores of user data available, the companies are all compelled to approach the quest of creating artificial intelligence through building machines that compute over certain types of data.

In many cases, much of the development at places like Google, Microsoft, and Facebook has revolved around vision – a dead end, according to Hawkins.

"Where the whole community got tripped up – and I'm talking fifty years tripped up – is vision," Hawkins explains. "They said, 'Your eyes are moving all the time, your head is moving, the world is moving – let us focus on a simpler problem: spatial inference in vision'. This turns out to be a very small subset of what vision is. Vision turns out to be an inference problem. What that did is they threw out the most important part of vision – you must learn first how to do time-based vision."

The acquisitions these companies have made speak to this apparent flaw.

Google, for instance, hired AI luminary and University of Toronto professor Geoff Hinton and his startup DNNresearch last year to have him apply his "Deep Belief Networks" approach to Google's AI efforts.

In a talk given at the University of Toronto last year, Hinton said he believed more advanced AI should be based on existing approaches, rather than a rethought understanding of the brain.

"The kind of neural inspiration I like is when making it more like the brain works better," Hinton said. "There's lots of people who say you ought to make it more like the brain – like Henry Markram [of the European Union's brain simulation project], for example. He says, 'Give me a billion dollars and I'll make something like the brain,' but he doesn't actually know how to make it work – he just knows how to make something more and more like the brain. That seems to me not the right approach. What we should do is stick with things that actually work and make them more like the brain, and notice when making them more like the brain is actually helpful. There's not much point in making things work worse."

Hawkins vehemently disagrees with this point, and believes that basing approaches on existing methods means Hinton and other AI researchers are not going to be able to imbue their systems with the generality needed for true machine intelligence.

Another influential Googler agrees.

"We have neuroscientists in our team so we can be biologically inspired but are not slavish to it," Google Fellow Jeff Dean (creator of MapReduce, the Google File System, and now a figure in Google's own "Brain Project" team, also known as its AI division) told us this year.

"I'm surprised by how few people believe they need to understand how the brain works to build intelligent machines," Hawkins says. "I'm disappointed by this."

Hinton's foundational technologies, for example, are Boltzmann machines - advanced "stochastic recurrent neural network" tools that try to mimic some of the characteristics of the brain, which sit at the heart of Hinton's "Deep Belief Networks" (2006).

"The neurons in a restricted Boltzmann machine are not even close [to the brain] – it's not even an approximation," Hawkins explains.

Even Google is not sure about which way to bet on how to build a mind, as illustrated by its buy of UK company "DeepMind Technologies" earlier this year.

That company's founder, Demis Hassabis, has done detailed work on fundamental neuroscience, and has built technology out of this understanding. In 2010, it was reported that he mentioned both Hawkins' Hierarchical Temporal Memory and Hinton's Deep Belief Nets when giving a talk on viable general artificial intelligence approaches.

Facebook has gone down similar paths by hiring the influential artificial intelligence academic Yann LeCun to help it "predict what a user is going to do next," among other things.

Microsoft has developed significant capabilities as well, with systems like the Siri-beater "Cortana" and various endeavors by the company's research division, MSR.

Though the techniques these various researchers employ differ, they all depend on training a dataset over a large amount of information, and then selectively retraining it as information changes.

These AI efforts are built around dealing with problems backed up by large and relatively predictable datasets. This has yielded some incredible inventions, such as

• reasonable natural language processing, 
• image detection, and 
• video tagging.

It has not and cannot, however, yield a framework for a general intelligence, as it doesn't have the necessary architecture for data 

• apprehension, 
• analysis, 
• retention, and 
• recognition 

that our own brains do, Hawkins claims.

Hawkins' focus on time is why he believes his approach will win – something that the consumer internet giants are slowly waking up to.

It's all about time

"I would say that Hawkins is focusing more on how things unfold over time, which I think is very important," Google's research director Peter Norvig told El Reg via email, "while most of the current deep learning work assumes a static representation, unchanging over time. I suspect that as we scale up the applications (i.e., from still images to video sequences, and from extracting noun-phrase entities in text to dealing with whole sentences denoting actions), that there will be more emphasis on the unfolding of dynamic processes over time."

Another former Googler concurs, with Andrew Ng telling us via email, "Hawkins' work places a huge emphasis on learning from sequences. While most deep learning researchers also think that learning from sequences is important, we just haven't figured out ways to do so that we're happy with yet."

Geoff Hinton echoes this praise. "He has great insights about the types of computation the brain must be doing," he tells us – but argues that Jeff Hawkins' actual algorithmic contributions have been "disappointing" so far.

An absolutely crucial ingredient to AI

Time "is one hundred per cent crucial" to the creation of true artificial intelligence, Hawkins tells us. "If you accept the fact intelligent machines are going to work on the principles of the neocortex, it is the entire thing, basically. The only way."

"The brain does two things: 

• it does inference, which is recognizing patterns, and 
• it does behavior, which is generating patterns or generating motor behavior," 

Hawkins explains. "Ninety-nine percent of inference is time-based – language, audition, touch – it's all time-based. You can't understand touch without moving your hand. The order in which patterns occur is very important."

Numenta's approach relies on time. Its Cortical Learning Algorithm (white paper) amounts to an engine for

• processing streams of information, 
• classifying them, 
• learning to spot differences, and 
• using time-based patterns to make predictions about the future.

As mentioned above, there are several efforts underway at companies like IBM and federal research agencies like DARPA to implement Hawkins' systems in custom processors, and these schemes all recognize the importance of Hawkins' reliance on time.

"What I found intriguing about [his approach] – time is not an afterthought. In all of these [other] things, time has been an afterthought," one source currently working on implementing Hawkins' ideas tells us.

So far, Hawkins has used his system to make predictions of diverse phenomena such as 

• hourly energy use and 
• stock trading volumes, and 
• to detect anomalies in data streams. 

Numenta's commercial product, Grok, detects anomalies in computer servers running on Amazon's cloud service.

Hawkins described to us one way to understand the power of this type of pattern recognition. "Imagine you are listening to a musician," he suggested. "After hearing her play for several days, you learn the kind of music she plays, how talented she is, how much she improvises, and how many mistakes she makes. Your brain learns her style, and then has expectations about what she will play and what it will sound like. As you continue to listen to her play, you will detect if her style changes, if the type of music she plays changes, or if she starts making more errors. The same kind of patterns exist in machine-generated data, and Grok will detect changes."

Here again the wider AI community appears to be dovetailing into Hawkins' ideas, with one of Andrew Ng's former Stanford students Honglak Lee having published a paper called "A classification-based polyphonic piano transcription approach using learned feature representations" in 2011. However, the method if implementation is different.

Obscurity through biology

Part of the reason why Hawkins' technology is not more widely known is because for current uses it is hard for it to demonstrate a vast lead over rival approaches. For all of Hawkins' belief in the tech, it is hard to demonstrate a convincing killer application for it that other approaches can't do. The point, Hawkins says, is that the CLA's internal structure gets rid of some of the stumbling blocks that exist in the future of other approaches.

Hawkins believes the CLA's implicit dependence on time means that eventually it will become the dominant approach.

"At the bottom of the [neocortex's] hierarchy are fast-changing patterns and they form sequences – some of them are predictable and some of them are not – and what the neocortex is doing is trying to understand the set of patterns here and give it a constant representation – a name for the sequence, if you will – and it forms that as the next level of the hierarchy so the next level up is more stable," Hawkins explains.

"Changing patterns lead to changing representations in the hierarchy that are more stable, and then it learns the changes in those patterns, and as you go up the hierarchy it forms more and more stable representations of the world and they also tend to be independent of your body position and your senses."

A comparison between Hawkins' Hierarchical Temporal Memory cells (right), 

a neural network neuron (center), and the brain's own neuron (left)

He believes his technology is more effective than the approaches taken by his rivals due to its use of sparse distributed representations as an input device to a storage system he terms "sequence memory".

Sequence memory refers to how information makes its way into the brain as a stream of information that comes in from both external stimuli and internal stimuli, such as signals from the broader body.

Sparse Distributed Representations (SDRs) are partially based on the work of mathematician Pentti Kanerva on "Sparse Distributed Memory" [PDF].

They refer to how the brain represents and stores information. They are designed to mimic the way our brain is believed to encode memories, which is through neuron firings across a very large area in response to inputs. To achieve this, SDRs are written, roughly, as a 2000-bit string of which perhaps two percent are active. This means that you don't need to read all active bits in an SDR to say that it is similar to another, because it merely needs to share a few of the activated bits to be considered similar, due to the sparsity.

Hawkins believes SDRs give input data inherent meaning through this representation approach.

"This means that if two vectors have 1s in the same position, they are semantically similar. Vectors can therefore be expressed in degrees of similarity rather than simply being identical or different. These large vectors can be stored accurately even using a subsampled index of, say, 10 of 2,000 bits. This makes SDR memory fault tolerant to gaps in data. SDRs also exhibit properties that reliably allow the neocortex to determine if a new input is unexpected," the company's commercial website for Grok says.

But what are the drawbacks?

So if Hawkins thinks he has the theory and is on the way to building the technology, and other companies are implementing it, then why are we even calling what he is doing a "bet"? The answer comes down to credibility.

Hawkins' idiosyncratic nature and decision to synthesize insights from two different fields – neuroscience and computer science – are his strengths, but also his drawbacks.

"No one knows how the cortex works, so there is no way to know if Jeff is on the right track or not," Dr. Terry Sejnowski, the laboratory head of the Computational Neurobiology Laboratory at the SALK Institute for Biological Studies, tells us. "To the extent that [Hawkins] incorporates new data into his models he may have a shot, and there will be a flood of data coming from the BRAIN Initiative that was announced by Obama last April."

Hawkins says that this response is typical of the academic community, and that there is enough data available to learn about the brain. You just have to look for it.

"We're not going to replicate the neocortex, we're not going to simulate the neocortex, we just need to understand how it works in sufficient detail so we can say 'A-ha!' and build things like it," Hawkins says. "There is an incredible amount of unassimilated data that exists. Fifty years of papers. Thousands of papers a year. It's unbelievable, and it's always the next set of papers that people think is going to do it. ... it's not true that you have to wait for that stuff."

The root of the problems Hawkins faces may be his approach, which stems more from biology than from mathematics. His old colleague and cofounder of Numenta, Dileep George, confirms this.

"I think Jeff is largely right in what he wrote in On Intelligence," George told us. "There are different approaches on how to bring those ideas. Jeff has an angle on it; we have a different angle on it; the rest of the community have another perspective on it."

These ideas are echoed by Google's Norvig. "Hawkins, at least in his general-public-facing-persona, seems to be more driven by duplicating what the brain does, while the deep learning researchers take some concepts from the brain, but then mostly are trying to optimize mathematical equations," he told us via email.

"I live in the middle," Hawkins explains. "Where I know the neuroscience details very very well, and I have a theoretical framework, and I bounce back and forth between these over and over again."

The future

Hawkins reckons that what he is doing today "is maybe 5 per cent of how humans learn," Hawkins says.

He believes that during the coming year he will begin work on the next major area of development for his technology: action.

For Hawkins' machines to gain independence – the ability, say, to not only recognize and classify patterns, but actively tune themselves to hunt for specific bits of information – the motor component needs to be integrated, he explains.

"What we've proven so far – I say built and tested and put into a product – is pure sensor. It's like an ear listening to sounds that doesn't have a chance to move," he tells us.

If you can add in the motor component, "an entire world opens up," he says.

"For example, I could have something like a web bot – an internet crawler. Today's web crawlers are really stupid, they're like wall-following rats. They just go up and down the length up and down the length," he says.

"If I wanted to look and understand the web, I could have a virtual system that is basically moving through cyberspace thinking about 'What is the structure here? How do I model this?' And so that's an example of a behavioral system that has no physical presence. It basically says, 'OK, I'm looking at this data, now where do I go next to look? Oh, I'm going to follow this link and do that in an intelligent way'."

By creating this technology, Hawkins hopes to dramatically accelerate the speed with which generally applicable artificial intelligence is developed and integrated into our world.

It's taken a lot to get here, and the older Hawkins gets and the more rival companies spend, the bigger the stakes get. As of 2014, he is still betting his life on the fact that he is right and they are wrong. ®

ORIGINAL: The Register

By Jack Clark,

29 Mar 2014