Going On the Offensive Against Dementia

This article is re-posted from the Al Fin Longevity blog


Rather than fighting a rear-guard action against advancing dementia, some researchers are taking an offensive approach -- a "procognitive" approach. This means that instead of trying to slow down the inevitable decline of dementia, these researchers aim to rebuild brain tissues and brain function. They aim to push back against the decline and reverse it.

Scientists have developed a small peptide that they say can reverse some of the cognitive repercussions of neurodegenerative and potentially trauma-related brain disorders such as Alzheimer’s disease, by increasing synaptogenesis. The peptide, called dihexa, is a stabilized derivative of angiotensin IV (AngIV)...

...Washington State University’s Joseph W. Harding, Ph.D., Alene T. McCoy, Ph.D., and colleagues based their development on prior work demonstrating that the three terminal amino acids of AngIV and its analog Norleucine1-angiotensin IV (Nle1-AngIV) are central to the precognitive activities of the peptides. The team thus set out to develop much smaller, more stable derivatives of Nle1-AngIV that retained the active structure but could be administered orally and cross the blood-brain barrier.

The resulting lead compound, dihexia (N-hexanoic-Tyr-Ile-(6) aminohexanoic amide) not only fulfilled these requirements, but proved to be active at picomolar concentrations, and led to dramatic improvements in the cognitive abilities of a scopolamine-treated rat model of learning deficits, and also aged Sprague-Dawley rats.

Importantly, the treatment was effective whether the animals received it directly into the brain, via injection, or orally. Further analyses indicated that dihexia’s precognitive activity was associated with a drug-induced stimulation of dendritic spinogenesis in the hippocampal brain region, and that the newly formed dendritic spins were creating functional synapses. Encouragingly, dihexia was even more potent than brain-derived neurotrophic factor (BDNF), a growth-promoting protein that is used to create neuronal connections, but which hasn’t yet been developed for therapeutic use.

“At its core dementia results from a combination of diminished synaptic connectivity among neurons and neuronal death in the entorhinal cortex, hippocampus, and neocortex,” the researchers note in their published paper in the Journal of Pharmacology and Experimental Therapeutics. However, previous attempts to develop protein neurotrophic factors as therapeutics has been limited by their inability to cross the blood-brain barrier, and the need to manufacture such agents by recombinant methods, which is costly. “The development of dihexa has seemingly overcome these impediments by virtue of its oral activity, demonstrated pro-cognitive/anti-dementia activity, and anticipated low manufacturing costs.” _Gen Engineering News

Another interesting candidate for procognitive therapy is ABT-288, an H3 receptor antagonist.

Another very interesting class of procognitive drugs is the Ampakines. Ampakine CX546 is a recent drug candidate undergoing study.

Here are two different approaches to procognition in the context of developing novel anti-psychotic drugs:

Egis 11150 which has broad spectrum adrenergic, serotenergic, and dopaminergic antagonist activity
New anti-psychotics with 5HT2C receptor agonist and 5HT6 receptor antagonist activity

It is important for neuropharmacologists to take this fateful step, of taking the offensive in the fight against dementia. Repairing and rebuilding the damaged tissues in the brain will likely get better results than a piecemeal promotion of this neurotransmitter, or combating this protein or another. A procognitive approach holds out the possibility of a return to normal function, rather than a mere slowing of inevitable decline.

The technology of the early diagnosis of future dementia is advancing on multiple fronts, from better brain scanners to genetic testing to two recent interesting developments:

Virtual Reality Functional Capacity Assessment Tool (VRFCAT). Not only does VRFCAT promise to provide early diagnosis of cognitive problems, but it should also prove useful for monitoring of different therapeutic regimens.

New mathematical approaches to model the brain as a complex network of interconnected processing nodes Such mathematical modeling approaches should provide new ways of diagnosing normal vs. impaired brain network activity, as well as new ways to monitor effects of therapies on brain network function.

Eventually, what we will want from procognitive treatments, is for all of us to be able to become capable of clearer and deeper thought. For now, we will be happy to watch dementia sufferers coming back into themselves.

But once we see that beginning to happen, we are likely to want to "spread the wealth around" to include more and more people, maybe even ourselves.

Saturday Morning Cartoon for the Cognitively Boosted

This video by Eric Schadt gives us a look at the incredible complexity of dynamic gene expression in humans, primarily in the brain. If you pay attention, you will begin to understand the challenge of understanding brain disease and intervening pharmacologically in brain gene expression.

Other videos from the same conference as this presentation was given. Videos at this symposium tend to be under 30 minutes in length.

Other videos from conferences on cognitive science that you may find interesting: 2011 MIT Brains, Minds, and Machines Several panel discussions, with many famous cognitive scientists participating, if you want to attach a face and voice to a cogsci author you may have read.

2006 IBM Almaden Conference on Cognitive Computing
You will find several classic and useful presentations in this group of videos.

2004 Columbia University Brain and Mind

H/T Brian Wang

Militaries Look at Neuroscience and Cognitive Enhancement

The militaries of the advanced world, in anticipation of the inevitable future war or conflict, are constantly looking for ways to give their forces an advantage on the field (or sea) of battle. The Royal Society has recently released a report on the use of neuroscience to enhance military performance, and to subdue opposing forces.

Transcranial direct current stimulation (tDCS)/Transcranial Magnetic Stimulation (TMS)
These technologies transiently disrupt or enhance brain function through brain stimulation. TMS induces weak electrical currents in the brain using a rapidly changing magnetic field; it is administered by placing a coil of wire, with current passing through, close to the scalp. tDCS passes weak electrical currents through the skull by attaching electrodes directly to the scalp.

Because TMS and tDCS can suppress as well as stimulate neural activity they are powerful tools to complement neuroimaging as they can be used to investigate whether activity of neurons in a particular brain region is necessary or causal for a particular function. If transient simulation impairs a particular mental process one can infer that the area is necessary.

Additionally, the timing of mental processes can also be investigated with these techniques, by showing if stimulation at only one particular time during a process is effective at causing disruption. Enhancement of brain activity in terms of driving brain plasticity via tDCS approaches is in its infancy but growing rapidly within the neuroscience community _Royal Society PDF Download: Neuroscience Conflict Security Brain Waves

Modern militaries cannot ignore the pivotal role of the mind in military conflict. Further, militaries cannot assume that their adversaries in future conflicts will ignore the central role of neuroscience. In other words, military planners must consider both offensive and defensive uses of neuroscience for future wars.

In terms of cognitive enhancement for soldiers, seamen, and marines, militaries have been looking at a number of key issues. First, they want to start with cognitively competent, resilient, and flexible warriors. In addition, they want to be able to train these fighters to the optimum levels possible, for their specific range of tasks. Finally, they want to develop better ways to repair and rehabilitate service members who have been wounded and injured.

Some techniques used widely in neuroscience are on the brink of being adopted by the military to improve the training of soldiers, pilots and other personnel.

A growing body of research suggests that passing weak electrical signals through the skull, using transcranial direct current stimulation (tDCS), can improve people's performance in some tasks.

One study cited by the report described how US neuroscientists employed tDCS to improve people's ability to spot roadside bombs, snipers and other hidden threats in a virtual reality training programme used by US troops bound for the Middle East.

"Those who had tDCS learned to spot the targets much quicker," said Vince Clark, a cognitive neuroscientist and lead author on the study at the University of New Mexico. "Their accuracy increased twice as fast as those who had minimal brain stimulation. I was shocked that the effect was so large."

Clark, whose wider research on tDCS could lead to radical therapies for those with dementia, psychiatric disorders and learning difficulties, admits to a tension in knowing that neuroscience will be used by the military.

"As a scientist I dislike that someone might be hurt by my work. I want to reduce suffering, to make the world a better place, but there are people in the world with different intentions, and I don't know how to deal with that.

"If I stop my work, the people who might be helped won't be helped. Almost any technology has a defence application."

Research with tDCS is in its infancy, but work so far suggests it might help people by boosting their attention and memory. According to the Royal Society report, when used with brain imaging systems, tDCS "may prove to be the much sought-after tool to enhance learning in a military context".

One of the report's most striking scenarios involves the use of devices called brain-machine interfaces (BMIs) to connect people's brains directly to military technology, including drones and other weapons systems.

The work builds on research that has enabled people to control cursors and artificial limbs through BMIs that read their brain signals.

"Since the human brain can process images, such as targets, much faster than the subject is consciously aware of, a neurally interfaced weapons system could provide significant advantages over other system control methods in terms of speed and accuracy," the report states. _Guardian

More:

Building a homemade tDCS system

Should brain enhancement choices be left to governments and elite overlords?

Turning every brain into Spock's brain

Waiting for superbrain?

Cognitive enhancers

The Royal Society report discusses ways of screening potential recruits which amount to advanced, high tech IQ tests, which are essentially bias-free and culture independent. You will not hear very much in the news about this aspect of cognitive and neuroscience research, but for an elite military, being able to select the best of the best -- for your purposes -- is paramount. Advanced cognitive and brain-machine training is the icing on the cake, for future oriented strategists and tacticians.

How Long Before We Develop a Human Superbrain?

Pharmacological enhancers of cognition promise a bright new future for humankind: more focus, more willpower, and better memory, with applications ranging from education to military combat. Underlying such promises is a linear, more-is-better vision of cognition that makes intuitive sense. This vision is at odds, however, with our understanding of cognition’s evolutionary origins. The mind has evolved under various constraints and consequently represents a delicate balance among these constraints. Evidence of the trade-offs that have shaped cognition include (a) inverted U-shaped performance curves commonly found in response to pharmacological interventions and (b) unintended side effects of enhancement on other traits. Taking an evolutionary perspective, we frame the above two sets of findings in terms of within-task (exemplified by optimal-control problems) and between-task (associated with a gain/loss asymmetry) trade-offs, respectively. With this framework, psychological science can provide much-needed guidance to enhancement development, a field that still lacks a theoretical foundation. _Thomas Hills

The above is the abstract from a recent paper published in Current Directions in Psychological Science, a journal of the Association for Psychological Science, titled: Why Aren’t We Smarter Already: Evolutionary Trade-Offs and Cognitive Enhancements. The authors suggest that we are not likely to develop enhanced intelligence for humans anytime soon, for a variety of reasons. More:

Just as there are evolutionary tradeoffs for physical traits, Hills says, there are tradeoffs for intelligence. A baby’s brain size is thought to be limited by, among other things, the size of the mother’s pelvis; bigger brains could mean more deaths in childbirth, and the pelvis can’t change substantially without changing the way we stand and walk.

Drugs like Ritalin and amphetamines help people pay better attention. But they often only help people with lower baseline abilities; people who don’t have trouble paying attention in the first place can actually perform worse when they take attention-enhancing drugs. That suggests there is some kind of upper limit to how much people can or should pay attention. “This makes sense if you think about a focused task like driving,” Hills says, “where you have to pay attention, but to the right things—which may be changing all the time. If your attention is focused on a shiny billboard or changing the channel on the radio, you’re going to have problems.”

It may seem like a good thing to have a better memory, but people with excessively vivid memories have a difficult life. “Memory is a double-edged sword,” Hills says. In post-traumatic stress disorder, for example, a person can’t stop remembering some awful episode. “If something bad happens, you want to be able to forget it, to move on.”

Even increasing general intelligence can cause problems. Hills and Hertwig cite a study of Ashkenazi Jews, who have an average IQ much higher than the general European population. This is apparently because of evolutionary selection for intelligence in the last 2,000 years. But, at the same time, Ashkenazi Jews have been plagued by inherited diseases like Tay-Sachs disease that affect the nervous system. It may be that the increase in brain power has caused an increase in disease.

Given all of these tradeoffs that emerge when you make people better at thinking, Hills says, it’s unlikely that there will ever be a supermind. “If you have a specific task that requires more memory or more speed or more accuracy or whatever, then you could potentially take an enhancer that increases your capacity for that task,” he says. “But it would be wrong to think that this is going to improve your abilities all across the board.” _MedXpress

Very disappointing, if true. But is it possible that the authors overlooked something? After all, a few million years ago, chimpanzee psychologists and philosophers must have been thinking and saying much the same about the prospects for superior chimp brains, yes?

But in fact, a chimpanzee superbrain did develop, which we call the "human brain."

Despite the minute genetic differences between human brains and their primate relatives, Homo sapiens cognitive ability is significantly more advanced, enabling us to “make complicated tools, come up with complicated culture and colonize the world,” said lead author Mehmet Somel, a postdoc studying human evolutionary genomics at the University of California, Berkeley. Because humans spend more than a decade developing into adults and learning, far more than the two or three years of chimpanzee adolescence, researchers have long suspected that developmental genes are involved in human brain evolution. “And the idea that brain gene expression profiles might be different between species was proposed 40 years ago,” Somel added. _Scientist

We are just beginning to learn the genetic and epigenetic specifics which led to the divergence of the human brain from the brain of the common ape ancestor. Fascinating changes in the details of gene expression in the brain created a whole new level of cognitive functioning. There is no reason to doubt that similar genetic and epigenetic changes could lead to even newer and higher levels of cognition.

The human brain has borrowed various hacks and kludges from brain and nerve evolution all the way back down the evolutionary tree. Some of these hacks and kludges are potentially limiting in terms of other, concurrent hacks and kludges that might otherwise be utilised. But there are potential hacks and kludges which might replace the limiting hacks, and some of these potential hacks might very well allow an entire train of further, enhancing hacks to follow.

That is a possibility that most mainstream psychologists and philosophers fail to understand -- generally because they have adopted groupthink as their modus operandi. This is a common failure of academics from the inbred world of the university culture. Perhaps that is why so many of the world-changing visionaries and billionaires of our day have been high school and college dropouts. They escaped before their brains could be gelded.

There are a number of ways in which we might approach the human superbrain. Simple pharmacologic cognitive enhancers, such as stimulants, are not likely to provide the broad spectrum enhancement we will need. But there are a number of prosthetic enhancements for the human brain which would give us near quasi-superbrain status, over time. Certainly the things that humans can do when empowered by modern computing and telecommunications tools would astound most humans of past eras.

But what we really want, are superbrains that continue working even if the power goes out or the batteries run down. For that, we will need genetic and epigenetic change. So how can we go about inducing these genetic changes without running into the problems that so many highly intelligent persons and breeding groups have run into?

That will be a topic of future articles.

Cross-published from Al Fin the Next Level

A Look at Cognitive Enhancers of Today and Tomorrow

Cognitive enhancer	Neuromodulatory mechanism	Cognitive functions improved	Known brain systems most affected	Currently recommended clinical use
Methylphenidate, amphetamine	Dopamine and noradrenaline reuptake inhibitors	Response inhibition, working memory, attention, vigilance	Frontoparietal attentional systems, striatum, default mode networks	ADHD, wake-promoting agent
Caffeine	Non-selective adenosine receptor antagonist	Vigilance, working memory, incidental learning	Frontal lobe attentional systems	–
Nicotine	Nicotinic cholinergic receptor agonist	Working memory, episodic memory, attention	Fronto-parietal attentional systems, medial temporal lobe, default mode networks	–
Modafinil	Unknown, but effects on dopamine, noradrenaline and orexin systems proposed	Working memory, episodic memory, attention	Frontal lobe attentional systems	Wake-promoting agent
Atomoxetine, reboxetine	Noradrenaline reuptake inhibitors	Response inhibition, working memory, attention	Frontoparietal attentional systems	ADHD, depression
Donepezil, galantamine, rivastigmine (AChEI)	Blocks enzymatic breakdown of acetylcholine	Episodic memory, attention	Frontal lobe attentional systems	Alzheimer's disease, PDD, DLB
Memantine	Noncompetitive, low-affinity, open channel blocker of the NMDA receptor	Episodic memory, attention	Frontal and parietal lobe	Alzheimer's disease

Table SourceThe ongoing process of ageing in all advanced societies around the world presents the unhappy prospect of a veritable global epidemic of Alzheimer's and other neurodegenerative conditions. Such an ominous prospect makes the quest for cognitive enhancers somewhat urgent, for all modern nations. We will look at the nature of current cognitive enhancers, and consider the prospects for future enhancers of cognition. The focus will be on long-term enhancement and neuroprotection, rather than the short-term performance enhancers which are popular on college campuses.

It would probably be fair to say that we are still in the first generation of studies to examine the potential for cognitive enhancement in humans. In both healthy individuals and many patient groups, the overall effects of drugs generally seem to be modest. However, there is evidence that there might be more significant effects in subgroups, such as those whose baseline performance is poorest or individuals with a particular genotype. Moreover, new drugs aimed at enhancing the phasic response of neurotransmitter systems, such as direct nicotinic agonists for the cholinergic system [34], might prove to have greater effects than existing modulators that globally increase levels of a neurotransmitter in a tonic fashion. The neurobiology underpinning the effects of cognitive enhancers and the mechanisms that determine responsiveness across individuals promise to be the focus of research in health and brain disorders in the future. _Source

The ongoing study of current cognitive enhancers such as those in the table above, have given us scattered hints as to what future therapies might offer. Here is a short list of possible future targets for cognitive therapies:

Among targets under investigation, cholinergic receptors have received much attention with several nicotinic agonists (α7 and α4β2) actively in clinical trials for the treatment of AD, CIAS and attention deficit hyperactivity disorder (ADHD). Both glutamatergic and serotonergic (5-HT) agonists and antagonists have profound effects on neurotransmission and improve cognitive function in preclinical experiments with animals; some of these compounds are now in proof-of-concept studies in humans. Several histamine H3 receptor antagonists are in clinical development not only for cognitive enhancement, but also for the treatment of narcolepsy and cognitive deficits due to sleep deprivation because of their expression in brain sleep centers. Compounds that dampen inhibitory tone (e.g., GABAA α5 inverse agonists) or elevate excitatory tone (e.g., glycine transporter inhibitors) offer novel approaches for treating diseases such as schizophrenia, AD and Down syndrome. In addition to cell surface receptors, intracellular drug targets such as the phosphodiesterases (PDEs) are known to impact signaling pathways that affect long-term memory formation and working memory. Overall, there is a genuine need to treat cognitive deficits associated with many neuropsychiatric conditions as well as an increasingly aging population. _Source

It is important for us, at the outset, to take as realistic a viewpoint toward the possibility of meaningful cognitive enhancement as possible. The Likelihood of Cognitive Enhancement (Lynch et al 2011 PDF) is a useful introduction to many of the practical issues that need to be faced from the very beginning of this enterprise. Cognitive Enhacement: Promises and Perils (Hyman 2011 PDF) is a less technical introduction to the topic, perhaps more accessible to most laymen.

Cognitive Enhancement as a Pharmacotherapy Target for Stimulant Addiction (Sofuoglu 2010) looks at the use of cognitive enhancers as possible treatments for cocaine and methamphetamine addictions. Long term and heavy use of these drugs leads to cognitive deficits which make it even more difficult for a person to stop using these drugs and lead a "normal" life. The restoration of cognitive function is likely to provide a certain amount of "mental fortification" to allow at least some addicts to turn away from the dead end lifestyle. Similarly, restoration of cognitive function in persons suffering from age-related neurodegeneration is more likely to allow the person to participate in normal social interaction, and to undertake some level of responsibility, and perhaps productive activity.

Emerging Pharmacotherapies for Neurodevelopmental Disorders (Wetmore et Garner 2010) looks at the use of cognitive enhancers for persons who suffer from neurodevelopmental disorders such as Down's Syndrome, Fragile X, autism, etc. Given the overlap of mechanisms between some of the cognitive deficits in developmental disorders and ageing-related cognitive deficits, some of the coming developments in this area of pharmacotherapy should also prove quite helpful for treating age-related dementias.

As more is learned about the time-course of dysfunction in NDDs [neurodevelopmental disorders], targeting of therapies to the existing brain state may be improved. Moreover, individuals with NDDs have multiple cognitive and behavioral disabilities, and a particular drug therapy may improve only a subset of cognitive functions. Thus, a combination of complementary drugs may offer the most benefit by addressing deficits in attention, arousal, information processing, or depression.
...
The NDDs discussed here are phenotypically diverse yet linked by common mechanisms of dysfunction, including abnormal gene dosage, imbalance among neurotransmitter systems, and local protein translation (Fig. 2). A particular NDD can be caused by mutations in multiple genes, underscoring the convergence of dysfunction in key biochemical pathways. _Source

Finally, I would like to append to this entry some material from an earlier Al Fin article, which provides a few hints of future drug targets, as well as links to related material:

AMPAkines
CREB
PDE Inhibitors(4,10)
Nicotinic Alpha-7 agonists
mGluR antagonists
5HT6 antagonists

Frontrunners in the pharmaceutical race for smarter, better memory drugs include Memory Pharmaceuticals, Cortex Pharmaceuticals, Saegis Pharmaceuticals, Helicon, Lilly, Pfizer, Wyeth, Merck, Sention and many others. The precedent of approving drugs for erectile dysfunction (ED)--a lifestyle drug--suggests that smart drugs will eventually be approved for drooping memories as well.

Further Reading:

Molecules for Memory

Nootropics

Smart Drugs: What Are the Prospects?

Shaping the Brain with Smart Drugs PDF (Gazzaniga)

CREB and Memory PDF (basic neuroscience)

CREB, Synapses, and Memory Disorders PDF

Hat tip Advanced Nano and Kurzweilai.net

Cross-posted from an earlier article at Al Fin Longevity

Check back for updated information on coming cognitive enhancers in the not-too-distant future.

Al Fin cognitivists have always believed that true, lasting cognitive enhancement will require genetic modification in conjunction with cell replacement and in situ cell rejuvenation. But there will be a wide range of stopgap measures between here and there.

In the meantime, use it or lose.

Objective Testing of Individual Learning Capacity

Researchers collected brain imaging data from people performing a motor task, and then analysed this data using new computational techniques. They found evidence that the 'flexibility' of a person's brain - how much different areas of the brain link up in different combinations; essentially 'swapping partners' - can be used to predict how fast someone will learn. _PO

PO
Scientists are developing better tools for testing human cognitive capacity objectively. In a recent study featuring an international scientific team from Oxford University, UC Santa Barbara, and UNC Chapel Hill, sophisticated brain imaging techniques were used to predict how quickly individuals could learn particular motor tasks.

The new study uses computational methods developed to analyze what the researchers call multilayer networks, in which each layer might represent a network at one snapshot in time, or a different set of connections between the same set of brain regions. These layers are combined into a larger mathematical object, which can contain a potentially huge amount of data and is difficult to analyze. Previous methods could only deal with each layer separately.

..."Parts of the brain communicate with one another very strongly, so they form a sort of module of intercommunicating regions of the brain," said first author Danielle S. Bassett, postdoctoral fellow in physics at UC Santa Barbara. "In this way, brain activity can segregate into multiple functional modules. What we wanted to measure is how fluid those modules are."

Bassett explained that there are flexible brain regions with allegiances that change through time. "That flexibility seems to be the factor that predicts learning," said Bassett. "So, if you are very flexible, then you will end up learning better on the second day, and if you are not very flexible, then you learn less." _PO

The ability to objectively measure cognitive capacity using brain imaging, bypasses most of the problems identified with IQ testing. During a brain imaging session, the person does not necessarily know what is being measured, and could not likely influence the measured outcome even if he tried -- short of withholding cooperation entirely. And that would be quite obvious to observers.

With the ability to objectively measure cognitive capacity, we are faced with a "put up or shut up" moment. If scientists seriously want to accurately measure group differences in IQ and cognitive ability, they now have the tools to do so. But if they only want to shout down opposing viewpoints without doing the work to get to the heart of the matter -- they will continue to shout, grouse, whine, and grumble. Which is it to be?

Understanding the Brain / Mind: Key Concept Videos

Understanding how the human mind works is one of the key steps to self-liberation. Exciting discoveries in cognitive science, neuroscience, and machine "cognition" are paving the way to a powerful comprehension of who we are and what we can do. The following links go to different cognitive science and neural science conferences on video. Think of them as hidden gold mines of mental treasure.

Almaden Institue 2006 Cognitive Computing

2007 Brain Network Dynamics Conference at UCB

Almaden 2007 Cognitive Computing at UCB

2008 Holiday Lectures Making Your Mind: Molecules, Motion, and Memory

NIH Neuroscience Videos and Podcasts

A very good set of links to Online Neuroscience Lectures (Kilgard UT Dallas)

A good set of links to mind / brain lectures from 2006

Free Online Cognitive Psychology Courses from Top Universities

A lecture is only a tiny bite of knowledge, needing to be fleshed out by careful study, using several sources. Contradictory sources are best because they force one to think in order to resolve the contradictions, if possible.

We are fortunate that politics has largely ignored the subjects of cognitive science and neuroscience. Freedom from political correctness and political restraints in funding allows a science to progress more or less naturally over time.

In other areas of study, such as gender and ethnic differences in cognitive abilities, or climate science, the political constraints prevent the necessary open free for all, the give-and-take that good science requires.

Getting past political correctness is one significant goal of the next level project. To do that, we must understand the human mind in isolation and in groups.

Another Prediction for Human-Level AI: 2025!

Today's AI prediction (via Josh Hall) comes from Shane Legg, whose PhD thesis is entitled Machine Superintelligence (PDF).   Shane predicts the arrival of human level Artificial Intelligence (HL-AI) around the year 2025 to 2028.  His 90% confidence interval for HL-AI is between 2018 and 2036.

Shane begins his discussion by predicting that computers capable of speeds of 10^18 flops will be commonplace by 2020. Then he says something interesting on the topic of quantum brain and on the relationship between computer power and AGI:

I had a chat to a quantum physicist here at UCL about the recent claims that there is some evidence for this. He’d gone through the papers making these claims with some interest as they touch on topics close to his area of research. His conclusion was that it’s a lot of bull as they make assumptions (not backed up with new evidence) in their analysis that essentially everybody in the field believes to be false, among other problems.

Conclusion: computer power is unlikely to be the issue anymore in terms of AGI being possible. _VettaProject

Just as I was beginning to think that Shane may have the beginnings of a grip on the AGI problem, he says this:

The main question is whether we can find the right algorithms.

The "algorithm mindset" is a curse that computer science oriented AGI researchers are particularly prone to. It is one reason it has taken so long to begin to understand intelligence, and also one of the main reasons that the flock of optimistic predictions for AI (by 2020, 2025, etc) are likely to be wrong. Shane then has some interesting comments on the idea of the brain as an AGI machine:

At a high level what we are seeing in the brain is a fairly sensible looking AGI design. You’ve got hierarchical temporal abstraction formed for perception and action combined with more precise timing motor control, with an underlying system for reinforcement learning. The reinforcement learning system is essentially a type of temporal difference learning though unfortunately at the moment there is evidence in favour of actor-critic, Q-learning and also Sarsa type mechanisms — this picture should clear up in the next year or so. The system contains a long list of features that you might expect to see in a sophisticated reinforcement learner such as pseudo rewards for informative queues, inverse reward computations, uncertainty and environmental change modelling, dual model based and model free modes of operation, things to monitor context, it even seems to have mechanisms that reward the development of conceptual knowledge.

This is an example of trying to view the brain as a type of computer, optimised to run machine learning algorithms. Although wrong, there may well be some fruitful ideas to come from the enterprise of holding the brain up on one side, and an advanced machine learning platform facing it -- like two large mirrors facing each other -- and seeing what sorts of iterative and recursive reflections bounce out into the real world.

Here is the best part of Shane's article:

The really tough nut to crack will be how the cortical system works. There is a lot of effort going into this, but based on what I’ve seen, it’s hard to say just how much real progress is being made. From the experimental neuroscience side of things we will soon have much more detailed wiring information, though this information by itself is not all that enlightening. What would be more useful is to be able to observe the cortex in action and at the moment our ability to do this is limited. Moreover, even if we could, we would still most likely have a major challenge ahead of us to try to come up with a useful conceptual understanding of what is going on. Thus I suspect that for the next 5 years, and probably longer, neuroscientists working on understanding cortex aren’t going to be of much use to AGI efforts. My guess is that sometime in the next 10 years developments in deep belief networks, temporal graphical models, liquid computation models, slow feature analysis etc. will produce sufficiently powerful hierarchical temporal generative models to essentially fill the role of cortex within an AGI.

The cortical brain is difficult for computer scientists to understand, because it is not algorithmic -- it is complex and often chaotic (much worse than merely "fuzzy" or "probabilistic"). Some aspects of cortical function are important for AGI researchers to learn. Other aspects of cortical function are irrelevant to AGI. Which is which? And what about subcortical brain centers and modules? What about the neuroendocrine system, the glial and vascular systems of the brain? What is important enough to be copied, and what can be ignored or vigorously abstracted? Read the whole thing and consider the problem for yourself.

Al Fin AGI researchers are fascinated by the rapid development of ever more powerful computing platforms. Better algorithms -- particularly ones that optimise massively parallel processing platforms -- are desperately needed for all areas of computing. But human intelligence is not algorithmic. So the development of human level AI will not automatically follow from the construction of highly complex and speedy computing platforms. Some new paradigms are clearly called for.

Machine intelligence may find an algorithmic path to human-level and beyond. But since human intelligence is not algorithmic, such a project would have to proceed without a solid conceptual template. Generally, such innovation takes longer than when building from a working proof of concept. It should be fun to watch.

For other essential reading, I suggest a trip to NextBigFuture, where Brian considers fascinating recent developments in technology and science. I discovered the link to the article discussed above by following a link from this NextBigFuture story about climate control machines.

The Brain Is Far More Complex Than Believed

We have been bombarded with predictions that human-level artificial intelligence will be developed "within ten years." These predictions inevitably come from persons with a computer science, engineering, or artificial intelligence background. Such  prognosticators understand algorithms and / or electrical circuits, but do they understand how consciousness is created? Do they comprehend the basis for the only human-level intelligence that exists: the human brain? Clearly not.

This respected Harvard team of neuroscientists has its hands full studying a nematode nerve network of 4 measly neurons! They are hoping to expand their study to include more than 4 neurons soon.

Meanwhile, there is the problem of "The Other Brain", the glial network.

Glia communicate by broadcasting chemical messages. Moreover, glia can sense information flowing through neural circuits and alter the communications between neurons at synapses! Glia, we now know, have receptors for detecting the flow of ions generated by neurons firing electrical impulses and for sensing the neurotransmitters neurons release at synapses. Glia intercept these signals and act upon them to increase or decrease the transmission of information across synapses and speed or slow the transmission of electrical information through axons.


These recent discoveries open an entirely new dimension into brain function. Glia are involved in all aspects of nervous system health and disease. They can control neuronal communication, development of the fetal brain, generation of new neurons in the adult brain, participate in epilepsy, Alzheimer's disease, mental illnesses such as depression and schizophrenia, and they provide a new mechanism of learning that operates beyond synapses. _RDouglasFields

Henry Markram's Blue Brain project is the one supercomputer project that seems to be taking into account much (but not all) of the brain's complexity.   But Markram's project is not trying to create human-level AI.  It is trying to create a simulated brain that can be used to better understand brain function and diseases of the brain.   Markram is a neuroscientist, so his goals are focused more on the realities of the brain than on the goal of "human-level AI."

The best of the "brain simulations" by AI workers is the simulation by Dharmendra Modha's IBM team. It barely simulated one of the most basic functions of the visual cortex, at average speeds less than 1 / 100th that of a mammalian brain. It required over $1 million a year to power its processors, and much more to cool the supercomputer. For something with the same number of neurons as a cat brain, Modha's simulation was a hugely inefficient use of energy -- compared to a cat.

There are some interesting new hardware tools coming along, such as Chua's memristors, meminductors, and memcapacitors. Alice Parker's BioRC project at USC may hold some future promise.

The point is not that a human-level AI would have to emulate the human brain in great detail. That would be a rather pointless extravagance, when crafty abstraction can save space, time, energy, and effort. But new hardware and software tools are needed, obviously. One cannot build a slow-functioning silicon retard occupying a skyscraper-sized building requiring a nuclear reactor to power and mega-tons of refrigeration to cool, and call it a human-level brain.

Anyone wanting to build a human-level machine intelligence will need to understand much of what consciousness entails, and how the human brain achieves it.

It is possible that an insect-level machine brain of appropriate size and energy-consumption might be developed within 5 to 10 years. A rodent-level machine brain of appropriate dimensions might be developed within 10 to 20 years, if we are lucky. A human-level brain may not take more than 5 years to achieve after the rodent-level brain, but shrinking it to the size and energy-efficiency of a human brain may take longer.

Humans are stupid. But they are also the most powerful general-purpose intelligence known to us. Machine intelligence of human-level or better would generate significant changes to human societies. Modern conventional human governments do not want to lose control of this particular revolution. If they do, it might be the last mistake they ever make.

Fascinating Recent History of NOS1 Variant

In January of 2009, Neuroskeptic blog was reporting on a study in Archives of General Psychiatry that painted NOS1 Ex1f variant as "an impulsivity gene." Apparently the January 2009 study included brain imaging, which suggested that normal prefrontal inhibition of activity was somehow impaired in persons with the NOS1 variant studied. Pairing the January 2009 AGP study with the more recent October 2009 AGP study suggests a tangible link between impulsivity and cognition -- via the NOS1 gene.

NOS1 codes for the enzyme nitric oxide synthase 1, which is expressed in neurones and makes nitric oxide (Nitrogen monoxide, NO). NO is a small molecule with various roles in animals, most famously the ability to induce erections - Viagra works by enhancing this effect. NO is also known to act as a neurotransmitter, with widespread but poorly understood functions in the brain. It's therefore plausible that altered nitric oxide synthase function could affect behaviour, and several animal studies suggest that indeed it does.

...In two separate control samples of normal German adults (most of whom) were screened to exclude psychiatric disorders (n=640 and 1314), 21 and 20% carried two copies of the short allele (SS). I've helpfully highlighted that in green above. Then, in samples of German people who displayed various forms of impulsive behaviour, the SS genotype was more common: in 383 adults with ADHD (28% SS), 189 adults who had attempted suicide (25% SS), and adults with "Cluster B" personality disorders 26% SS, but not those with "Cluster C" disorders representing anxious traits. Also, in a sample of 182 criminals referred to a forensic psychiatry unit, those who had been assessed as "violent" were more likely to carry the SS genotype than those not (p=0.04). In a nutshell, SS is bad. _Neuroskeptic

A lot of researchers are likely to make their careers on this one gene variant. NOS1 may prove to be a very important gene for both cognition and behaviour. It is unlikely to be the most important gene involved in thinking, feeling, and acting, but it seems to be important enough.

In the 12 years since this polymorphism has attracted more attention than any other in the field of behavioural genetics with several hundred papers at last count. So if that's anything to go by, we'll be hearing a lot more about NOS1. Stay tuned. Neuroskeptic

The Self-Organising Brain

...the brain of a newborn itself seems to organise its own development. The electrical activity of the waking brain triggers the series of events... _SD

We are not born knowing how to improvise jazz riffs on the saxophone. A newborn child is not able to prove mathematical theorems or argue political economics. But the infant child is born with the potential to do those things. How does the brain bootstrap upon itself to create the sophisticated organ of thought, action, and communication that adult humans possess?

Chaos brews in the brains of newborns: the nerve cells are still bound only loosely to each other. Under the leadership of Academy Research Fellow Sari Lauri, a team of researchers at the University of Helsinki has been studying for years how a neural network capable of processing information effectively is created out of chaos. The team has now found a new kind of mechanism that adjusts the functional development of nerve cell contacts.

The results were published in early September as the leading article of the Journal of Neuroscience.

The work carried out by Lauri's team and its partners at the Viikki campus sheds light on a development path that results in some of the large number of early synapses becoming stronger. The researchers found out that the BDNF growth factor of nerve cells triggers a functional chain which promotes the release of the neurotransmitter glutamate. BDNF enables the release of glutamate by prohibiting the function of kainate receptors which slow down the development of the preforms of the synapses. The activity of the kainate receptors restricts the release of glutamate and the development of synapses into functional nerve cell contacts. _SD

Until brain cells begin communicating via bioelectric signalling, true organisation of the brain cannot take place. The "waking brain" triggers the bioelectric signalling. What creates the "waking brain?"

During early phases of brain development gene expression and postranslational modifications of gene expression are controlled by biochemical signals which are produced in a cellular microenvironment. Later in brain development there is a difference from the development of other organs because electrical signals are added to biochemical messengers as a further signaling in the self-organizing between genes and their respective environments. It must be considered that these electrical signals are capable in influencing gene expression and postranslational modifications. Electrical signals are transported by neuronal processes over distances and with highly topological selectively. This enlarges the range and complexity of the "environment" available to self-organization process. The "environment" relevant to brain self-organization includes all domains with which the evolving brain is capable to interact and from which it receives messages. The same electrical signals which convey messages are used by the brain as information carriers for computational process .... results in the replacement of sensory stimuli by self-organization activity patterns that are contingent on past experience, present motivational state and expectancy of the future. _Turbes1993

Something has to "wake up" the developing brain to initiate this highly complex process of autopoietic development. While the child remains warm and wet within the womb, it has little reason to wake. But when squeezed and thrust into the cold and open brightness, little eyes instinctively open in reaction to the disturbance. This tiny opening sets up a chain of events leading to the jazz riff or the discovery of a new scientific principle.

Far from being a mere local phenomenon, clusters of nerves organise themselves both locally and over distances within the brain, and between the brain and peripheral organs of the body. How is it done?

A considerable amount of evidence and theory suggests that transient, short-lived phase-coupled oscillations within and between specialized areas of the brain provide a mechanism for neural integration. The idea is that these oscillations are coupled or “bound” together into a coherent network when people attend to a stimulus, perceive, remember, think and act. __ScottKelso

In the brain, traditional biochemical signaling and transduction is combined with neuroelectrical stimulation in a complex brew of cognitive magic. Current understanding of the brain is not yet at a fine enough resolution to trace back to first principles of how mind emerges from brain. But we are getting there.

A Brain Balanced on the Razor's Edge of Chaos

The human brain is made up of 100 billion neurons — live wires that must be kept in delicate balance to stabilize the world’s most magnificent computing organ. Too much excitement and the network will slip into an apoplectic, uncomprehending chaos. Too much inhibition and it will flatline. RockefellerUniversity

One of the problems with past and present efforts to reverse-engineer the brain, is the lack of a deep understanding of how the brain manages to balance its own activity so well -- like a surfer on a big wave. Researchers at Rockefeller University have discovered that some previous conceptions of how the brain operates, may be in error. If so, then correcting the error may bring cognitive scientists a little closer to reverse-engineering a functioning brain.

Marcelo O. Magnasco, head of the Laboratory of Mathematical Physics at The Rockefeller University, and his colleagues developed the model to address how such a massively complex and responsive network such as the brain can balance the opposing forces of excitation and inhibition. His model’s key assumption: Neurons function together in localized groups to preserve stability. “The defining characteristic of our system is that the unit of behavior is not the individual neuron or a local neural circuit but rather groups of neurons that can oscillate in synchrony,” Magnasco says. “The result is that the system is much more tolerant to faults: Individual neurons may or may not fire, individual connections may or may not transmit information to the next neuron, but the system keeps going.”

Magnasco’s model differs from traditional models of neural networks, which assume that each time a neuron fires and stimulates an adjoining neuron, the strength of the connection between the two increases. This is called the Hebbian theory of synaptic plasticity and is the classical model for learning. “But our system is anti-Hebbian,” Magnasco says. “If the connections among any groups of neurons are strongly oscillating together, they are weakened because they threaten homeostasis. Instead of trying to learn, our neurons are trying to forget.” One advantage of this anti-Hebbian model is that it balances a network with a much larger number of degrees of freedom than classical models can accommodate, a flexibility that is likely required by a computer as complex as the brain.

In work published this summer in Physical Review Letters, Magnasco theorizes that the connections that balance excitation and inhibition are continually flirting with instability. He likens the behavior to an indefinitely large number of public address systems tweaked to that critical point at which a flick of the microphone brings on a screech of feedback that then fades to quiet with time.

This model of a balanced neural network is abstract — it does not try to recreate any specific neural function such as learning. But it requires only half of the network connections to establish the homeostatic balance of exhibition and inhibition crucial to all other brain activity. The other half of the network could be used for other functions that may be compatible with more traditional models of neural networks, including Hebbian learning, Magnasco says. _RU

The next time your brain tells you "it's not easy being me", perhaps you should believe it. We all would like better brains, no doubt. But first we need to understand the things that work well vs. the things that could work better, in the brains we already have.

Clearly the emerging view of the dynamic brain goes far beyond a mere mapping of all synaptic connections. Because as we have been learning for the past few decades, synaptic mappings in the cortex change frequently -- sometimes not so subtly. The complex dynamic neural codes that underpin our knowledge, memories, speculations, loves, hates, and dreams, will not be cracked easily or soon.

The Connections Behind Concept Formation

The human brain never rests. It constantly compares patterns and attempts to determine the relevance of sense clusters. We like to think we understand "the difference that makes a difference." Since we think in terms of concepts, metaphors, models, and emotion-tinged intuitions, it is important that they be laid down as soundly as possible -- from top to bottom.

...while there is little doubt that humans form and use concepts all the time, very little is known about how conceptual knowledge is created in the brain or how it allows us to make efficient choices.

Now, Dr Kumaran and colleagues have used behavioural and neuroimaging techniques to reveal how this knowledge emerges in the human brain, and how it is used to guide decision making. The results of the study, which was funded by the Wellcome Trust, are published in the journal 'Neuron'.

In the study, 25 volunteers were asked to predict whether the weather would be sunny or raining based on the appearance of the night sky. The night sky was represented by patterns on a computer screen. If correct, the participants could win money.

Initially, participants tended to memorise the outcome associated with specific patterns. However, it soon became clear to the participants that groups of patterns were conceptually related - by applying this information, participants were able to solve the task in a different setting where the concepts were similar but the patterns themselves new.

By applying fMRI scanning techniques, which measure changes in blood flow in the brain to identify areas of activity, Dr Kumaran and colleagues were able to show that the emergence of conceptual knowledge was underpinned by a coupled circuit involving the hippocampus (an area of the brain responsible for learning and memory) and the ventromedial prefrontal cortex (used in decision-making).

Most significantly, however, the researchers found that activity in the hippocampus alone predicted which participants would be able to successfully apply the concepts they had learned in a new setting. This suggests that the hippocampus creates and stores these concepts, and passes this information to the prefrontal cortex where it can be put to use, for example in making choices where financial reward is at stake.

The results highlight the role of the hippocampus in acquiring new concepts, possibly through its unique networking capacities, which allow multiple memories to be related to one another. _WellcomeTrust

Of course, just because concepts are well formed and accessible, doesn't mean the brain will make use of them at the proper time and place, and in the proper way. A great deal depends upon whether the person is paying attention.

The findings of the Salk researchers, published in the September 24, 2009 issue of the journal Neuron, reveal that the uptick in the firing rate is only a small part of the story. "What we found is that attention also reduces background activity," says postdoctoral researcher and first author Jude Mitchell, Ph.D. "We estimate that this noise reduction increases the fidelity of the neural signal by a factor that is as much as four times as large as the improvement caused by attention-dependent increases in firing rate. This reduction in noise may account for as much as 80% of the attention story." _SD

A very interesting finding. As one of the authors (Reynolds) remarks: we need a much better understanding of the brain's attentional mechanisms.

Some brains are much better at all of this than others, of course. And very few brains are actually trained to reach their potential, in this age of dumbed down curricula and politically correct indoctrination. Yet, it is good for researchers to focus on these critically important areas, so that when society decides that it is serious about meeting the challenges of the future, a new generation of more enlightened educators and scientists can find ways to optimise the brain function of its young persons.

How is Higher Reasoning Like a Bowel Movement?

Or, Why Computers Can Never Think, Since They Have No Guts

The crux of the matter is the type of reasoning that people use, compared to the type of reasoning that computers use. Computers use deductive reasoning almost exclusively. Humans, on the other hand, use a wide array of reasoning strategies -- including deduction. Humans unconsciously use inference and induction to build their beliefs, prejudices, and modes of operation. Humans use their entire bodies to think, not just their brains.

From their first breath (and earlier), humans use their body sensations to build inferences about the world around them. At first, the child is aware only of internal sensations and the feeling of body functions.

Lakoff and Nunez (1997, 2000) proposed a theory of embodied learning in mathematics where cognition is situated in the mind and developed through psychological and biological processes. Grounding and linking metaphors support the development of schema. These are influenced both by the body and the environment and develop understanding of mathematical ideas..... Mathematics is viewed as human imagination where mathematical reasoning is based on bodily experiences (Johnson, 1987). _CarolMurphy U of Exeter PDF

Human thought is "grounded" to body sensations and "gut feelings" via "grounding metaphors." These metaphors are not based upon language, since they are laid down long before the infant has begun to learn language. Later, observations of his own and others' movements are integrated into the child's inferential data base. Then, as he learns language, the unconscious metaphors take on a more formal aspect which can be analysed using linguistic theory.

You may be starting to see how radically different basic human reasoning is from the formal deductive logic that computer codes and machines incorporate and use.

Let's look at inductive reasoning.

How do humans reason in situations that are complicated or ill-defined? Modern psychology tells us that as humans we are only moderately good at deductive logic, and we make only moderate use of it. But we are superb at seeing or recognizing or matching patterns—behaviors that confer obvious evolutionary benefits. In problems of complication then, we look for patterns; and we simplify the problem by using these to construct temporary internal models or hypotheses or schemata to work with.1 We carry out localized deductions based on our current hypotheses and act on them. And, as feedback from the environment comes in, we may strengthen or weaken our beliefs in our current hypotheses, discarding some when they cease to perform, and replacing them as needed with new ones. In other words, where we cannot fully reason or lack full definition of the problem, we use simple models to fill the gaps in our understanding. Such behavior is inductive. _ Inductive Reasoning Arthur Santa Fe InstPDF _ via _SimoleonSense

Some modern efforts to emulate human cognition in machines are making use of "pattern matching" algorithms and neural nets, with some limited success. Pattern matching is very basic thinking strategy, that is utilised in a closely related strategy -- analogy. Computers can use these primitive strategies to "self-expand" and "self-organise" a data base.

These simple strategies can be augmented with Bayesian inference modules, in an attempt to maintain inferential discipline, as it were.

But in the absence of "grounding metaphors", imaginative and intuitive computers will simply not know when to stop. Available memory will fill up with junk, and the system will crash. Computer programmers and designers simply cannot take that chance, for now.

Humans have bodily needs and bodily functions. If these needs and functions are not accommodated, the system may crash. This is why body states are monitored so closely, and take on such a central importance from the earliest moment of a human being's existence. Human cognition must be grounded in the body, or the body may stop functioning. The "grounding metaphors" are hardwired into the system, and everything else is kludged around them.

Artificial Intelligence researchers sometimes imagine that they can dispense with all the "legacy microcode" and create a cognition from original principles. Back in the 1950s, they predicted human-level computation within a matter of a decade or two. By the 1980s and 1990s, AI people were beginning to acquire a bit of sensible caution -- although by and large they had still not figured out what was frustrating their goals. They figured out that pure logic computing, eg Prolog, wasn't going to work alone, but why in blazes couldn't they make more progress with LISP?

Then along came the connectionism and neural nets, and then genetic algorithms and fuzzy logic. All very useful, and all finding places in the overall strategy of understanding natural and artificial cognition. Neural nets could learn to match patterns, genetic algorithms could learn to optimise designs and strategies, and fuzzy logic could function effortlessly in environments that would crash formal deductive systems.

So what is the problem? If Henry Markram can say with a straight face that the human brain could be replicated within 10 years, given enough funding, why are investors not beating a path to the door of the Brain Mind Institute?

Because these "10 year" predictions are very easy to make. But most of them do not pan out. And because Markram is the only one making such grandiose claims. Jeff Hawkins thinks he can achieve great things within 10 years, but he hasn't claimed the ability to replicate the human brain. Of course, if Markram could "replicate" a rat brain within 10 years, his fame and fortune would be assured. Providing, of course, the resulting "brain" occupied a space no larger than an iPod. ;-)

Machine intelligence projects have created some amazing software. You can expect to experience much more amazement from that direction. But will computers actually think in the way we think of thinking?

Yes. But it will require a central grounding mechanism that forms a "sticky core" to which the different "thinking strategies" can link. The thinking machine will need better ways of keeping things real. It needs to have gut feelings.

The machine does not need a real body, but it can learn to feel as if it has a body. Its body might be an airplane or a train. Or it might be a neighborhood or an entire city. Or perhaps an electrical grid or a space elevator or space station. Any of these dynamic systems could play the part of an intelligent machine's body for the purposes of grounding. A complex simulation is the most logical starting point.

At that point, the thinking machine would have ways to test its models and inferences against "real" consequences.

Intelligence is Not an Algorithm

Algorithms involve several forms of abstraction. First, an algorithm consists of clear specifications for what should be performed in each step, but not necessarily clear specifications for how. In essence, an algorithm takes a problem specifying what should be achieved and breaks it into smaller problems with simpler requirements for what should be achieved. An algorithm should specify steps simple enough that what becomes identical to how as far as the person or machine executing the algorithm is concerned. _NewAtlantis

Anyone who has programmed computers understands the central role of algorithms to modern computing. An algorithm is a sequence (often involving iteration and/or recursion) of steps for converting an input into a useful output. Coming up with just the right algorithm to do a job often requires a high level of intelligence. Intelligence --> Good algorithm. Unfortunately, it does not work in reverse: Good algorithm --> Intelligence.

Properly understood, the first question underlying the AI debate is: Can the properties of the mind be completely described on their own terms as an algorithm? Recall that an algorithm has a definite start and end state and consists of a set of well-defined rules for transitioning from start state to end state. As we have already seen, it was the explicit early claim of AI proponents that the answer to this question was yes: the properties of the mind, they believed, could be expressed algorithmically (or “procedurally,” to use a more general term). But the AI project has thus far failed to prove this answer, and AI researchers seem to have understood this failure without acknowledging it.

...Once the unlikelihood of procedurally describing the mind at a high level is accepted, the issue becomes whether the mind can be replicated at some lower level in order to recreate the high level, raising the next important question: Are the layers of physical systems, and thus the layers of the mind and brain, separable in the same way as the layers of the computer?...it is correct to explain computers in terms of separable layers, since that is how they are designed. Physical systems, on the other hand, are not designed at all. They exist prior to human intent; we separate them into layers as a method of understanding their behavior. Psychology and physics, for example, can each be used to answer a distinct set of questions about a single physical system—the brain. We rely on hierarchies to explain physical systems, but we actually engineer hierarchies into computers.

...What is the basic functional unit of the mind? If the mind were a computer, it would be possible to completely describe its behavior as a procedure. That procedure would have to use certain basic operations, which are executed by some functional unit of the brain. The early hypothesis of AI was that this question was essentially irrelevant since the mind’s operations could be described at such a high level that the underlying hardware was inconsequential. Researchers today eschew such a large-scale approach, instead working under the assumption that the mind, like a computer program, might be a collection of modules, and so we can replicate the modules and eventually piece them back together—which is why research projects today focus on very specific subsystems of intelligence and learning.

...The unit of the mind typically targeted for replication is the neuron, and the assumption has thus been that the neuron is the basic functional unit of the mind. The neuron has been considered a prime candidate because it appears to have much in common with modules of computer systems: It has an electrical signal, and it has input and output for that signal in the form of dendrites and axons. It seems like it would be relatively straightforward, then, to replicate the neuron’s input-output function on a computer, scan the electrical signals of a person’s brain, and boot up that person’s mind on a suitably powerful computer.

...Every indication is that, rather than a neatly separable hierarchy like a computer, the mind is a tangled hierarchy of organization and causation. Changes in the mind cause changes in the brain, and vice versa. To successfully replicate the brain in order to simulate the mind, it will be necessary to replicate every level of the brain that affects and is affected by the mind.

...The fact that the mind is a machine just as much as anything else in the universe is a machine tells us nothing interesting about the mind. If the strong AI project is to be redefined as the task of duplicating the mind at a very low level, it may indeed prove possible—but the result will be something far short of the original goal of AI.

If we achieve artificial intelligence without really understanding anything about intelligence itself—without separating it into layers, decomposing it into modules and subsystems—then we will have no idea how to control it. We will not be able to shape it, improve upon it, or apply it to anything useful. Without having understood and replicated specific mental properties on their own terms, we will not be able to abstract them away—or, as the transhumanists hope, to digitize abilities and experiences, and thus upload and download them in order to transfer our consciousnesses into virtual worlds and enter into the minds of others. _NewAtlantis_via_SimoleonSense

Be sure to read the rest at the link above. I have been interested in artificial intelligence since the early 1990s, and was initially quite optimistic about the prospects for the creation of an intelligent machine brain. Over the years, as I learned more about both machines and the brain, I have modified my expectations quite significantly.

I have become far more interested in questions of human cognition -- and how cognition in humans might be improved -- than in the development of intelligent machines. Questions having to do with the difference between rationality and intelligence, or understanding what makes super-intelligent people tick, are far more engaging to me than hearing about which robot was best able to imitate human facial expression or body movement.

Needless to say, every aspect of human intelligence, from the genetic, tothe molecular, to the neuronal, to the cortical columnar level and up, will come under scrutiny repeatedly in the quest for a fuller understanding of how the mind works -- and how it might be made to work better.

You can be sure that politicians and their enablers want to know how to pull your strings. Some of us, on the other hand, would rather everyone learn to pull his own strings -- and what it means to do so.

Intelligence is not an algorithm, but good algorithms will prove extremely helpful in the quest for intelligence -- and everything complex that we attempt. It is likely that most of us will eventually carry algorithmic co-processors that function as cognitive aids and augments. We will probably find it difficult to function at our highest levels without them.

H/T SimoleonSense

Cognitive Enhancement: Smart Drugs Fast Track

We all know university students who are taking Ritalin, Provigil, or other cognitive enhancers in order to compete better on the academic battleground. Professionals from many backgrounds also try to give themselves a career advantage with such smart drugs. Using fMRI on healthy volunteers taking Provigil (modafinil), neuroscientists are beginning to zero in on one likely mechanism of action for cognitive enhancers, which will allow for the faster and more targeted development of nootropics.

The drug modafinil is approved by the Food and Drug Administration (FDA) for treating narcolepsy, shift-work related sleep disorders and obstructive sleep apnea, and indicated for conditions including ADHD, Parkinson's disease and depression. But how it works has not been well understood until now.

"We have shown that the way modafinil works is by quieting activity in the LC and increasing its connections with the frontal cortex," said Cameron Carter, a UC Davis professor of psychiatry and behavioral sciences and senior author of the study.

"Now that we know how it works, we can develop better cognitive enhancers that can treat more people suffering from a wider variety of neurodevelopmental disorders, like ADHD, autism and schizophrenia," he said. _ScienceDaily

The demand for cognitive enhancers goes far beyond the competition for better grades and job performance for healthy young adults. A significant proportion of middle aged and older persons suffer from cognitive disorders which lead to confusion, forgetfulness, and a loss of ability to perform everyday normal activities of work and home life. The treatment of such acquired and debilitating cognitive disorders will drive the financing and research goals of neuro-cognitive science labs. But the competition for better grades and better career performance will provide a secondary motivation researchers and bystanders alike.

The use of smart drugs amounts to a type of "bootstrapping" of human consciousness, when used by neurocognitive researchers themselves to develop better means of achieving heightened cognition for impaired and healthy humans. For the long term, smart drugs will not be enough. Augmentation and modification of genetic and epigenetic mechanisms will be necessary in order for between-levels humans to step up to the next level.

Underlying the applied research into smart drugs and cognitive enhancement, is a massive ongoing research into systems biology, synthetic biology, bioinformatics, development of new research tools of all types, and a slow but steady transformation of popular expectation of what science can and should provide in the way of life enhancement.

Update: Here is a short but intriguing GNXP.com piece suggesting that part of the blame for the ongoing financials crash is due to the competitive use of amphetamines by financial analysts and traders. "A man's got to know his limitations." The abuse of amphetamines has certainly become a blight across many parts of the western world. A significant part of the abuse may begin as performance enhancement, which progresses to abject dependency and personal ruin in many cases.

It makes you wonder: how many ultra-aggressive politicians and celebrities owe some of their success to pharmaceuticals? A dependency upon caffeine is expected in high achievers. Addiction to nicotine is common as well. No reason to believe it stops there.

What the Mind Knows, What It Thinks It Knows That Just Isn't So, And What May Yet Grow

Everything we know, everything we believe, everything we think and understand, comes from the function of our brains. A better understanding of brain function is opening a clearer window into how our brains create the worlds we inhabit.

Recently, researchers from St. Louis and Boca Raton, Florida have applied "Granger Causality" to fMRI studies in an attempt to more precisely trace the flow of information between brain centers, in the performance of a mental task. Sir Clive Granger was a co-recipient of the 2003 Nobel Prize in Economics for his method of comparing complex time series of economic data, to forecast economic changes. The researchers used Granger causality to overcome the poor time resolution (roughly 2 seconds) in the fMRI data in analysing information flow in the brain.

Chad Sylvester, an M.D./Ph.D. student at Washington University, gathered the data for the analysis. Researchers gave volunteers a cue that a visual stimulus would be appearing soon in a portion of a computer display screen, and asked them to report when the stimulus appeared and what they saw. Corbetta's group previously revealed that this task activated two brain areas: the frontoparietal cortex, which is involved in the direction of the attention, and the visual cortex, which became more active in the area where volunteers were cued to expect the stimulus to appear.

Scientists believed the frontoparietal cortex was influencing the visual cortex, but the brain scanning approach they were using, functional magnetic resonance imaging (fMRI), can only complete scans about once every two seconds, which was much too slow to catch that influence in action. When researchers applied Granger causality, though, they were able to show conclusively that as volunteers waited for the stimulus to appear, the frontoparietal cortex was influencing the visual cortex, not the reverse. _Source

The complex timing and sequencing involved in neural signaling between brain centers, has been a particularly difficult problem in understanding how the brain generates consciousness and knowledge. It is also a difficult challenge to AI researchers who are trying to simulate animal consciousness via artificial cortex architectures.

Chris Chatham's blog Developing Intelligence regularly looks at some of these neuro-cognitive research teasers. One of the most interesting topics Chris examines is the topic of "far transfer":cognitive training for one skill that also creates competence in other areas.

What if training ourselves on one task yielded improvements in all other tasks we perform? This is the promise of the cognitive training movement, which is increasingly showing that such "far transfer" of training is indeed possible, while short of being "universal transfer." Interestingly, this phenomenon might be most likely to occur for some of the most abstract and challenging cognitive functions. _DevelIntel

In general, acquiring expertise in a narrow specialty does not result in broader competence. People often exaggerate their own competence in multiple areas, based upon expertise in a specialised field. If they are lucky, their misplaced hubris results only in their making fools of themselves, rather than losing their fortunes, families, and sanity.

In fact, all human minds have their blind spots, their areas of false certainty.

LEHRER: In your book, you compare the "feeling of certainty" that accompanies things such as religious fundamentalism to the feeling that occurs when we have a word on the-tip-of-our-tongue. Could you explain?

BURTON: There are two separate aspects of a thought, namely the actual thought, and an independent involuntary assessment of the accuracy of that thought.

To get a feeling for this separation, look at the Muller-Lyer optical illusion.

Even when we consciously know and can accurately determine that these two horizontal lines are the same length, we experience the simultaneous disquieting sensation that this thought—the lines are of equal length—is not correct. This isn't a feeling that we can easily overcome through logic and reason; it simply happens to us.

This sensation is a manifestation of a separate category of mental activity—-unconscious calculations as to the accuracy of any given thought. On the positive side, such feelings can vary from a modest sense of being right, such as understanding that Christmas falls on December 25, to a profound a-ha, "Eureka" or sense of a spiritual epiphany. William James referred to the latter—the mystical experience—as "felt knowledge," a mental sensation that isn't a thought, but feels like a thought.

Once we realize that the brain has very powerful inbuilt involuntary mechanisms for assessing unconscious cognitive activity, it is easy to see how it can send into consciousness a message that we know something that we can't presently recall—the modest tip-of-the-tongue feeling. At the other end of the spectrum would be the profound "feeling of knowing" that accompanies unconsciously held beliefs—a major component of the unshakeable attachment to fundamentalist beliefs—both religious and otherwise—such as belief in UFOs or false memories.

LEHRER: Why do you think that the feeling of certainty feels so good?

BURTON: Stick brain electrodes in rat pleasure centers (the mesolimbic dopamine system primarily located in the upper brain stem). The rats continuously press the bar, to the exclusion of food and water, until they drop. In humans the same areas are activated with cocaine, amphetamines, alcohol, nicotine and gambling—to mention just a few behaviors to which one can become easily addicted.

It is quite likely that the same reward system provides the positive feedback necessary for us to learn and to continue wanting to learn. The pleasure of a thought is what propels us forward; imagine trying to write a novel or engage in a long-term scientific experiment without getting such rewards. Fortunately, the brain has provided us with a wide variety of subjective feelings of reward ranging from hunches, gut feelings, intuitions, suspicions that we are on the right track to a profound sense of certainty and utter conviction. And yes, these feelings are qualitatively as powerful as those involved in sex and gambling. One need only look at the self-satisfied smugness of a "know it all" to suspect that the feeling of certainty can approach the power of addiction. _SciAm

It feels good to be right. Of course, one is often wrong at the same time he feels himself right. It still feels good. Hence the attraction of violent jihad, lynch mobs, programming by college professors, witch hunts, cult indoctrination, and political rallies. Resonating with the emotions of the crowd only magnifies the sensation of "rightness."

As America contemplates electing its own "Dear Leader", it is good to keep in mind how fallible our mental world's creation truly is. If a politician makes you feel that he will make everything right, you have certainly been taken in by a skilled practitioner of world creation. Run away. Quickly.