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

ORIGINAL: The Register
By Jack Clark,
29 Mar 2014

 

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.

Jeff Hawkins
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 machinesadvanced “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.
Illustration: A comparison between biological neurons and HTM cells
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. ®

How D-Wave Built Quantum Computing Hardware for the Next Generation

ORIGINAL: IEEE Spectrum
By Jeremy Hsu
11 Jul 2014

Photo: D-Wave Systems

One second is here and gone before most of us can think about it. But a delay of one second can seem like an eternity in a quantum computer capable of running calculations in millionths of a second. That’s why engineers at D-Wave Systems worked hard to eliminate the one-second computing delay that existed in the D-Wave One—the first-generation version of what the company describes as the world’s first commercial quantum computer.

Such lessons learned from operating D-Wave One helped shape the hardware design of D-Wave Two, a second-generation machine that has already been leased by customers such as Google, NASA, and Lockheed Martin. Such machines have not yet proven that they can definitely outperform classical computers in a way that would support D-Wave’s particular approach to building quantum computers. But the hardware design philosophy behind D-Wave’s quantum computing architecture points to how researchers could build increasingly more powerful quantum computers in the future.

We have room for increasing the complexity of the D-Wave chip,” says Jeremy Hilton, vice president of processor development at D-Wave Systems. “If we can fix the number of control lines per processor regardless of size, we can call it truly scalable quantum computing technology.

D-Wave recently explained the hardware design choices it made in going from D-Wave One to D-Wave Two in the June 2014 issue of the journal IEEE Transactions on Applied Superconductivity. Such details illustrate the engineering challenges that researchers still face in building a practical quantum computer capable of surpassing classical computers. (See IEEE Spectrum’s overview of the D-Wave machines’ performance from the December 2013 issue.)

  

Photo: D-Wave SystemsD-Wave’s Year of Computing Dangerously

Quantum computing holds the promise of speedily solving tough problems that ordinary computers would take practically forever to crack. Unlike classical computing that represents information as bits of either a 1 or 0, quantum computers take advantage of quantum bits (qubits) that can exist as both a 1 and 0 at the same time, enabling them to perform many simultaneous calculations.

Classical computer hardware has relied upon silicon transistors that can switch between “on” and “off” to represent the 1 or 0 in digital information. By comparison, D-Wave’s quantum computing hardware relies on metal loops of niobium that have tiny electrical currents running through them. A current running counterclockwise through the loop creates a tiny magnetic field pointing up, whereas a clockwise current leads to a magnetic field pointing down. Those two magnetic field states represent the equivalent of 1 or 0.

The niobium loops become superconductors when chilled to frigid temperatures of 20 millikelvin (-273 degrees C). At such low temperatures, the currents and magnetic fields can enter the strange quantum state known as “superposition” that allows them to represent both 1 and 0 states simultaneously. That allows D-Wave to use these “superconducting qubits” as the building blocks for making a quantum computing machine. Each loop also contains a number of Josephson junctions—two layers of superconductor separated by a thin insulating layer—that act as a framework of switches for routing magnetic pulses to the correct locations.

But a bunch of superconducting qubits and their connecting couplers—separate superconducting loops that allow qubits to exchange information—won’t do any computing all by themselves. D-Wave initially thought it would rely on analog control lines that could apply a magnetic field to the superconducting qubits and control their quantum states in that manner. However, the company realized early on in development that it would need at least six or seven control lines per qubit, for a programmable computer. The dream of eventually building more powerful machines with thousands of qubits would become an “impossible engineering challenge” with such design requirements, Hilton says.

The solution came in the form of digital-to-analog flux converters (DAC)—each about the size of a human red blood cell at 10 micrometers in width— that act as control devices and sit directly on the quantum computer chip. Such devices can replace control lines by acting as a form of programmable magnetic memory that produces a static magnetic field to affect nearby qubits. D-Wave can reprogram the DACs digitally to change the “bias” of their magnetic fields, which in turn affects the quantum computing operations.

Most researchers have focused on building quantum computers using the traditional logic-gate model of computing. But D-Wave has focused on a more specialized approach known as “quantum annealing —a method of tackling optimization problems. Solving optimization problems means finding the lowest “valley” that represents the best solution in a problem “landscape” with peaks and valleys. In practical terms, D-Wave starts a group of qubits in their lowest energy state and then gradually turns on interactions between the qubits, which encodes a quantum algorithm. When the qubits settle back down in their new lowest-energy state, D-Wave can read out the qubits to get the results.

Both the D-Wave One (128 qubits) and D-Wave Two (512 qubits) processors have DACs. But the circuitry setup of D-Wave One created some problems between the programming DAC phase and the quantum annealing operations phase. Specifically, the D-Wave One programming phase temporarily raised the temperature to as much as 500 millikelvin, which only dropped back down to the 20 millikelvin temperature necessary for quantum annealing after one second. That’s a significant delay for a machine that can perform quantum annealing in just 20 microseconds (20 millionths of a second).

By simplifying the hardware architecture and adding some more control lines, D-Wave managed to largely eliminate the temperature rise. That in turn reduced the post-programming delay to about 10 milliseconds (10 thousandths of a second)— a “factor of 100 improvement achieved within one processor generation,” Hilton says. D-Wave also managed to reduce the physical size of the DAC “footprint” by about 50 percent in D-Wave Two.

Building ever-larger arrays of qubits continues to challenge D-Wave’s engineers. They must always be aware of how their hardware design—packed with many classical computing components—can affect the fragile quantum states and lead to errors or noise that overwhelms the quantum annealing operations.

We were nervous about going down this path,” Hilton says. “This architecture requires the qubits and the quantum devices to be intermingled with all these big classical objects. The threat you worry about is noise and impact of all this stuff hanging around the qubits. Traditional experiments in quantum computing have qubits in almost perfect isolation. But if you want quantum computing to be scalable, it will have to be immersed in a sea of computing complexity.”

Still, D-Wave’s current hardware architecture, code-named “Chimera,” should be capable of building quantum computing machines of up to 8000 qubits, Hilton says. The company is also working on building a larger processor containing 1000 qubits.

The architecture isn’t necessarily going to stay the same, because we’re constantly learning about performance and other factors,” Hilton says. “But each time we implement a generation, we try to give it some legs so we know it’s extendable.”

Can The Human Brain Project Succeed?

ORIGINAL: Spectrum
By Rachel Courtland
Posted 9 Jul 2014 | 17:00 GMT

Image: Getty Images

An ambitious effort to build human brain simulation capability is meeting with some very human resistance. On Monday, a group of researchers sent an open letter to the European Commission protesting the management of the Human Brain Project, one of two Flagship initiatives selected last year to receive as much as €1 billion over the course of 10 years (the other award went to a far less controversy-courting project devoted to graphene).

The letter, which now has more than 450 signatories, questions the direction of the project and calls for a careful, unbiased review. Although he’s not mentioned by name in the letter, news reports cited resistance to the path chosen by project leader Henry Markram of the Swiss Federal Institute of Technology in Lausanne. One particularly polarizing change was the recent elimination of a subproject, called Cognitive Architectures, as the project made its bid for the next round of funding.

According to Markram, the fuss all comes down to differences in scientific culture. He has described the project, which aims to build six different computing platforms for use by researchers, as an attempt to build a kind of CERN for brain research, a means by which disparate disciplines and vast amounts of data can be brought together. This is a “methodological paradigm shift” for neuroscientists accustomed to individual research grants, Markram told Science, and that’s what he says the letter signers are having trouble with.

But some question the main goals of the project, and whether we’re actually capable of achieving them at this point. The program’s Brain Simulation Platform aims to build the technology needed to reconstruct the mouse brain and eventually the human brain in a supercomputer. Part of the challenge there is technological. Markram has said that an exascale-level machine (one capable of executing 1000 or more petaflops) would be needed to “get a first draft of the human brain”, and the energy requirements of such machines are daunting.

Crucially, some experts say that even if we had the computational might to simulate the brain, we’re not ready to. “The main apparent goal of building the capacity to construct a larger-scale simulation of the human brain is radically premature,” signatory Peter Dayan, who directs a computational neuroscience department at University College London, told the Guardian. He called the project a “waste of money” that “can’t but fail from a scientific perspective“. To Science, he saidthe notion that we know enough about the brain to know what we should simulate is crazy, quite frankly.”

This last comment resonated with me, as it reminded me of a feature that Steve Furber of the University of Manchester wrote for IEEE Spectrum a few years ago. Furber, one of the co-founders of the mobile chip design powerhouse ARM, is now in the process of stringing a million or so of the low-power processors together to build a massively parallel computer capable of simulating 1 billion neurons, about 1% as many as are contained in the human brain.

Furber and his collaborators designed their computing architecture quite carefully in order to take into account the fact that there are still a host of open questions when it comes to basic brain operation. General-purpose computers are power-hungry and slow when it comes to brain simulation. Analog circuitry, which is also on the Human Brain Project‘s list, might better mimic the way neurons actually operate, but, he wrote,

as speedy and efficient as analog circuits are, they’re not very flexible; their basic behavior is pretty much baked right into them. And that’s unfortunate, because neuroscientists still don’t know for sure which biological details are crucial to the brain’s ability to process information and which can safely be abstracted away

The Human Brain Project’s website admits that exascale computing will be hard to reach: “even in 2020, we expect that supercomputers will have no more than 200 petabytes.” To make up for the shortfall, it says, “what we plan to do is build fast storage random-access storage systems next to the supercomputer, store the complete detailed model there, and then allow our multi-scale simulation software to call in a mix of detailed or simplified models (models of neurons, synapses, circuits, and brain regions) that matches the needs of the research and the available computing power. This is a pragmatic strategy that allows us to keep build ever more detailed models, while keeping our simulations to the level of detail we can support with our current supercomputers.

This does sound like a flexible approach. But, as is par for the course with any ambitious research project, particularly one that involves a great amount of synthesis of disparate fields, it’s not yet clear whether it will pay off.

And any big changes in direction may take a while. Although the proposal for the second round of funding will be reviewed this year, according to Science, which reached out to the European Commission, the first review of the project itself won’t begin until January 2015.

Rachel Courtland can be found on Twitter at @rcourt.

DARPA Wants a Memory Prosthetic for Injured Vets—and Wants It Now

ORIGINAL: Spectrum
By Eliza Strickland
9 Jul 2014

Photo: Getty Images
No one will ever fault DARPA, the Defense Department’s mad science wing, for not being ambitious enough. Over the next four years, the first grantees in its Restoring Active Memory (RAM) program are expected to develop and test prosthetic memory devices that can be implanted in the human brain. 

 

It’s hoped that such synthetic devices can help veterans with traumatic brain injuries, and other people whose natural memory function is impaired. The two teams, led by researchers Itzhak Fried at UCLA and Mike Kahana at the University of Pennsylvania, will start with the fundamentals.
They’ll look for neural signals associated with the formation and recall of memories, and they’ll work on computational models to describe how neurons carry out these processes, and to determine how an artificial device can replicate them. They’ll also work with partners to develop real hardware suitable for the human brain. Such devices should ultimately be capable of recording the electrical activity of neurons, processing the information, and then stimulating other neurons as needed.The RAM research derives from an engineering approach to memory that’s gaining traction. (Spectrum covered the work of one of its leading proponents, Ted Berger, in the recent article The End of Disability.) If the brain is essentially a collection of circuits, the thinking goes, a memory is formed by the sequential actions of many neurons. If a person has a brain injury that knocks out some of those neurons, the whole circuit may malfunction, and the person will experience memory problems. But if electrodes can pick up the signal in the neurons upstream from the problem spot, and then convey that signal around the damage to intact neurons downstream, then the memory should function as normal.
In a press briefing yesterday, program manager Justin Sanchez said that the first human experiments will be conducted with hospitalized epilepsy patients who have electrodes implanted in their brains as they await surgery (this is done so their doctors can pinpoint the origin of their seizures). Since epilepsy patients often experience memory loss as well, Sanchez said they’re a natural fit for the research. Eventually trials would include military servicemembers who suffer the aftereffects of traumatic brain injuries, and finally civilians with similar injuries.
DARPA recently decided to beef up its research in biological technologies, spurred in part by the needs of veterans returning from Iraq and Afghanistan. But it seems likely that the agency’s increased attention to programs like RAM was also prompted by the recognition that neural engineering is one of the most exciting frontiers in science, with the neural technologies advancing faster than the science that guides it.

The RAM program is part of the overarching federal BRAIN Initiative, announced with much fanfare by President Obama in 2013. With a first-year budget of $110 million parceled out to three agencies and considerable cooperation from deep-pocketed private institutions, you can expect this decade to be a brainy one.

 

The Most Ambitious Artificial Intelligence Project In The World Has Been Operating In Near-Secrecy For 30 Years

ORIGINAL: Business Insider
Dylan Love
Jul. 2, 2014

douglas lenat

Screenshot. Doug Lenat”We’ve been keeping a very low profile, mostly intentionally,” said Doug Lenat, President and CEO of Cycorp. “No outside investments, no debts. We don’t write very many articles or go to conferences, but for the first time, we’re close to having this be applicable enough that we want to talk to you.”IBM‘s Watson and Apple‘s Siri stirred up a hunger and awareness throughout the United States for something like a Star Trek computer that really worked — an artificially intelligent system that could receive instructions in plain, spoken language, make the appropriate inferences, and carry out its instructions without needing to have millions and millions of subroutines hard-coded into it.

As we’ve established, that stuff is very hard. But Cycorp’s goal is to codify general human knowledge and common sense so that computers might make use of it.

Cycorp charged itself with figuring out the tens of millions of pieces of data we rely on as humans — the knowledge that helps us understand the world — and to represent them in a formal way that machines can use to reason. The company’s been working continuously since 1984 and next month marks its 30th anniversary.

Many of the people are still here from 30 years ago — Mary Shepherd and I started [Cycorp] in August of 1984 and we’re both still working on it,” Lenat said. “It’s the most important project one could work on, which is why this is what we’re doing. It will amplify human intelligence.

It’s only a slight stretch to say Cycorp is building a brain out of software, and they’re doing it from scratch.

Any time you look at any kind of real life piece of text or utterance that one human wrote or said to another human, it’s filled with analogies, modal logic, belief, expectation, fear, nested modals, lots of variables and quantifiers,” Lenat said. “Everyone else is looking for a free-lunch way to finesse that. Shallow chatbots show a veneer of intelligence or statistical learning from large amounts of data. Amazon and Netflix recommend books and movies very well without understanding in any way what they’re doing or why someone might like something.

It’s the difference between someone who understands what they’re doing and someone going through the motions of performing something.”

Cycorp’s product, Cyc, isn’t “programmed” in the conventional sense. It’s much more accurate to say it’s being “taught.” Lenat told us that most people think of computer programs as “procedural, [like] a flowchart,” but building Cyc is “much more like educating a child.

We’re using a consistent language to build a model of the world,” he said.

This means Cyc can see “the white space rather than the black space in what everyone reads and writes to each other.” An author might explicitly choose certain words and sentences as he’s writing, but in between the sentences are all sorts of things you expect the reader to infer; Cyc aims to make these inferences.

Consider the sentence, “John Smith robbed First National Bank and was sentenced to thirty years in prison.” It leaves out the details surrounding his being caught, arrested, put on trial, and found guilty. A human would never actually go through all that detail because it’s alternately boring, confusing, or insulting. You can safely assume other people know what you’re talking about. It’s like pronoun use — he, she, it — one assumes people can figure out the referent. This stuff is very hard for computers to understand and get right, but Cyc does both.

If computers were human,” Lenat told us, “they’d present themselves as autistic, schizophrenic, or otherwise brittle. It would be unwise or dangerous for that person to take care of children and cook meals, but it’s on the horizon for home robots. That’s like saying, ‘We have an important job to do, but we’re going to hire dogs and cats to do it.’

If you consider the world’s current and imagined robots, it’s hard to imagine them not benefitting from Cyc-endowed abilities that grant them a more human-like understanding of the world.



Just like computers with operating systems, we might one day install Cyc on a home robot to make it incredibly knowledgable and useful to us. And because Cycorp started from zero and was built up with a knowledge of nearly everything, it could be used for a wide variety of applications. It’s already being used to teach math to sixth graders.

Cyc can pretend to be a confused sixth grader, and the user’s role is to help the AI agent understand and learn sixth grade math. There’s an emotional investment, a need to think about it, and so on. Our program of course understands the math, but is simply listening to what students say and diagnosing their confusion. It figures out what behavior can it carry out that would be most useful to help them understand things. It’s a possibility to revolutionize sixth grade math, but also other grade levels and subjects. There’s no reason couldn’t be used in common core curriculum as well.

We asked Lenat what famed author and thinker Douglas Hofstadter might think of Cyc:

[Hofstadter] might know what needs to be done for things to be intelligent, but it has taken someone, unfortunately me, the decades of time to drag that mattress out of the road so we can do the work. It’s not done by any means, but it’s useful.

Neuroscientists Join the Open-Source Hardware Movement

By Eliza Strickland
Posted 11 Jun 2014
Two MIT grad students offer up DIY brain-recording gear

Photo: Open Ephys

Graduate students Josh Siegle and Jakob Voigts were planning an ambitious series of experiments at their MIT neuroscience labs in 2011 when they ran into a problem. They needed to record complex brain signals from mice, but they couldn’t afford the right equipment: The recording systems cost upward of US $60,000 each, and they wanted at least four. So they decided to solve their dilemma by building their own gear on the cheap. And knowing that they wouldn’t be the last neuroscientists to encounter such a problem, they decided to give away their designs. Now their project, Open Ephys, is the hub of a nascent open-source hardware community for neural technology.



Siegle and Voigts weren’t knowledgeable about either circuit design or coding, but they learned as they went along. By July 2013, they were ready to manufacture 50 of their recording systems, which they gave to collaborators for beta testing. This spring they manufactured 100 improved units, which are now arriving in neuroscience labs around the world. They estimate that each system costs about $3,000 to produce.

Neuroscience has a history of hackers, Siegle says, with researchers cobbling together their own gear or customizing commercial systems to meet their particular needs. But those new tools rarely leave the labs they are built in. So scientists spend a lot of time reinventing the wheel. The goal of Open Ephys (which is short for open-source electrophysiology) is not just to distribute the tools that Siegle and Voigts have come up with so far but to encourage researchers to put resources into developing open-source tools for the benefit of the whole community. “In addition to changing the tools, we also want to change the culture,” Siegle says.

Photo: Open Ephys Open Ephys just distributed 100 of its acquisition boards to neuroscience labs around the world.

The flagship tool that Siegle and Voigts developed is an acquisition board, which makes sense of the electric signals from electrodes implanted in an animal’s brain. The board interfaces with up to eight headstages that amplify, filter, multiplex, and digitize signals from the brain, and then sends those signals to a computer for further processing. Commercial systems typically have individual ICs perform each of those four functions, but Siegle and Voigts’s system uses a single microchip for the four steps. The chip was recently developed by Intan Technologies, based in Los Angeles. “Once we realized these chips were available, it seemed kind of silly to keep buying the big systems,” Siegle says.

The president and cofounder of Intan, Reid Harrison, says that shrinking and consolidating the gear wasn’t that complicated—it mostly required initiative. “It’s such a niche market that no one else had tried to miniaturize the technology,” he says. “It’s not exactly on the scale of CPUs and cellphones, which drive most IC technology.” However, Harrison says he recognized a need for his small, multipurpose chips. Neuroscientists are always trying to fit more electrodes into an animal’s brain to record more neural activity, he says, which requires ever tinier devices with the electronics close to the electrodes. “You could put 1,000 electrodes in the brain, but you don’t want 1,000 wires on an animal that’s supposed to be mobile,” he says. The Intan chips take information from up to 64 electrodes and turn it into one digital signal, eliminating the confusion of wiring.

The major neural technology companies have designed products that incorporate Intan’s chips, but they also swear by their larger, multichip systems. Keith Stengel, the founder of Neuralynx, in Bozeman, Mont., says that in his big systems, each component is optimized for peak performance. “A lot of our customers have said that you buy a Neuralynx system for the serious work that you’re going to publish, and then you get an Open Ephys system as a second system, for grad students to start their research on,” he says.

 

Illustration: Open Ephys Open Ephys offers building instructions for this head-mounted neural implant system for mice.

Andy Gotshalk, CEO of Blackrock Microsystems, in Salt Lake City, also argues that the commercial products will continue to be the gold standard. “You’re not going to be moving into FDA clinical trials using an Open Ephys system,” he says. The commercial products come with guarantees of quality and reliability, he says, as well as intensive customer support. Gotshalk says his customers are willing to pay a premium for that backing.

Both Stengel and Gotshalk say they welcome Open Ephys to the market and think that its systems can fill a niche. They’re also willing to work with the upstart to make sure their commercial software works with the Open Ephys hardware. Harrison agrees that the community is happy to have another option to work with, and he draws a parallel to the computing industry. “The existing tools are like the PCs and the Macs of the neuroscience world, but now we also have this Linux,” Harrison says. “It’s a lot less expensive, and you can hack it yourself, but it’s not for everyone.

ORIGINAL: IEEE Spectrum

Mathematical Model Of Consciousness Proves Human Experience Cannot Be Modelled On A Computer

ORIGINAL: Medium

A new mathematical model of consciousness implies that your PC will never be conscious in the way you are
One of the most profound advances in science in recent years is the way researchers from a variety of fields are beginning to think about consciousness. Until now, the c-word was been taboo for most scientists. Any suggestion that a researchers was interested in this area would be tantamount to professional suicide.
That has begun to change thanks to a new theory of consciousness developed in the last ten years or so by Giulio Tononi, a neuroscientist at the University of Wisconsin in Madison, and others. Tononi’s key idea is that consciousness is phenomenon in which information is integrated in the brain in a way that cannot be broken down.
So each instant of consciousness integrates the smells, sounds and sights of that moment of experience. And consciousness is simply the feeling of this integrated information experience.
What makes Tononi’s ideas different from other theories of consciousness is that it can be modelled mathematically using ideas from physics and information theory. That doesn’t mean this theory is correct. But it does mean that, for the first time, neuroscientists, biologists physicists and anybody else can all reason about consciousness using the universal language of science: mathematics.
This has led to an extraordinary blossoming of ideas about consciousness. A few months ago, for example, we looked at how physicists are beginning to formulate the problem consciousness in terms of quantum mechanics and information theory.
Today, Phil Maguire at the National University of Ireland and a few pals take this mathematical description even further. These guys make some reasonable assumptions about the way information can leak out of a consciousness system and show that this implies that consciousness is not computable. In other words, consciousness cannot be modelled on a computer.
Maguire and co begin with a couple of thought experiments that demonstrate the nature of integrated information in Tononi’s theory. They start by imagining the process of identifying chocolate by its smell. For a human, the conscious experience of smelling chocolate is unified with everything else that a person has smelled (or indeed seen, touched, heard and so on).
This is entirely different from the process of automatically identifying chocolate using an electronic nose, which measures many different smells and senses chocolate when it picks out the ones that match some predefined signature.
A key point here is that it would be straightforward to access the memory in an electronic nose and edit the information about its chocolate experience. You could delete this with the press of a button.
But ask a neuroscientist to do the same for your own experience of the smell of chocolate—to somehow delete this—and he or she would be faced with an impossible task since the experience is correlated with many different parts of the brain.
Indeed, the experience will be integrated with all kinds of other experiences. “According to Tononi, the information generated by such [an electronic nose] differs from that generated by a human insofar as it is not integrated,” say Maguire and co.
This process of integration is then crucial and Maguire and co focus on the mathematical properties it must have. For instance, they point out that the process of integrating information, of combining it with many other aspects of experience, can be thought of as a kind of information compression.
This compression allows the original experience to be constructed but does not keep all of the information it originally contained.
To better understand this, they give as an analogy the sequence of numbers: 4, 6, 8, 12, 14, 18, 20, 24…. This is an infinite series defined as: odd primes plus 1. This definition does not contain all the infinite numbers but it does allow it be reproduced. It is clearly a compression of the information in the original series.
The brain, say Maguire and co, must work like this when integrating information from a conscious experience. It must allow the reconstruction of the original experience but without storing all the parts.
That leads to a problem. This kind of compression inevitably discards information. And as more information is compressed, the loss becomes greater.
But if our memories were like that cannot be like that, they would be continually haemorrhaging meaningful content. “Memory functions must be vastly non-lossy, otherwise retrieving them repeatedly would cause them to gradually decay,” say Maguire and co.
The central part of their new work is to describe the mathematical properties of a system that can store integrated information in this way but without it leaking away. And this leads them to their central proof. “The implications of this proof are that we have to abandon either the idea that people enjoy genuinely [integrated] consciousness or that brain processes can be modelled computationally,” say Maguire and co.
Since Tononi’s main assumption is that consciousness is the experience of integrated information, it is the second idea that must be abandoned: brain processes cannot be modelled computationally.
 
They go on to discuss this in more detail. If a person’s behaviour cannot be analysed independently from the rest of their conscious experience, it implies that something is going on in their brain that is so complex it cannot feasibly be reversed, they say.
In other words, the difference between cognition and computation is that computation is reversible whereas cognition is not. And they say that is reflected in the inability of a neuroscientist to operate and remove a particular memory of the small of chocolate.
That’s an interesting approach but it is one that is likely to be controversial. The laws of physics are computable, as far as we know. So critics might ask how the process of consciousness can take place at all if it is non-computable. Critics might even say this is akin to saying that consciousness is in some way supernatural, like magic.
But Maguire and go counter this by saying that their theory doesn’t imply that consciousness is objectively non-computable only subjectively so. In other words, a God-like observer with perfect knowledge of the brain would not consider it non-computable. But for humans, with their imperfect knowledge of the universe, it is effectively non-computable.
There is something of a card trick about this argument. In mathematics, the idea of non-computability is not observer-dependent so it seems something of a stretch to introduce it as an explanation.
What’s more, critics might point to other weaknesses in the formulation of this problem. For example, the proof that conscious experience is non-computable depends critically on the assumption that our memories are non-lossy.
But everyday experience is surely the opposite—our brains lose most of the information that we experience consciously. And the process of repeatedly accessing memories can cause them to change and degrade. Isn’t the experience of forgetting a face of a known person well documented?
Then again, critics of Maguire and co’s formulation of the problem of consciousness must not lose sight of the bigger picture—that the debate about consciousness can occur on a mathematical footing at all. That’s indicative of a sea change in this most controversial of fields.
Of course, there are important steps ahead. Perhaps the most critical is that the process of mathematical modelling must lead to hypotheses that can be experimentally tested. That’s the process by which science distinguishes between one theory and another. Without a testable hypothesis, a mathematical model is not very useful.
For example, Maguire and co could use their model to make predictions about the limits in the way information can leak from a conscious system. These limits might be testable in experiments focusing on the nature of working memory or long-term memory in humans.
That’s the next challenge for this brave new field of consciousness.
Ref: arxiv.org/abs/1405.0126 : Is Consciousness Computable? Quantifying Integrated Information Using Algorithmic Information Theory
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