Duh: What Happened To Theoretical AI?

by David Gelernter, 06.22.09, 06:00 PM EDT, Forbes

The two major mystery-boxes of mind-science (each decorated with an intriguing question mark) are "consciousness" and "thought." Both mysteries are notoriously hard to unravel, but computing ought to help us understand thought, which is (on one level) a process or series of actions--like computing itself. Consciousness, on the other hand, is a state of being--and, despite the best efforts of theoretical AI, there is no reason to believe that a computer will ever achieve this state, or that software can bring it about.

As far as we know, consciousness can only be created by a mind, and a mind can only be realized by a human's (or some other advanced creature's) brain and body working together. If, in the near future, a grinning robot should walk up to you at a party and say, "Hi, my name is Bob; pleased to meet you," you'd be apt to cut it some slack and assume that it really is--on some level and in some way--"pleased." But in fact there's no reason to believe that any robot is pleased to meet you or ever will be, has ever been pleased to meet anybody, or has ever experienced the state of mind we call "pleasure" under any circumstances at all. So far as we know, software cannot re-create the sort of inner mental world human beings inhabit.

___________

Unbelievably, Gelernter is on the right track, up to the point where he writes "there's no reason to believe that any robot is pleased to meet you or ever will be, has ever been pleased."

Why is there "no reason"? What makes this proposition -- and many others like it -- so high a wall?

Well, because it's the highest wall of that box everybody's trying to think outside of -- where science is concerned, at any rate. It is the single greatest unexamined premise of our traditional worldview or paradigm (to use a word now out of fashion, a victim of its own success).

That wall is the dusty barrier between primary and secondary qualities, bequeathed to us by Democritus and the other atomists of Greek antiquity. Reinforced by Galileo, Newton and several of their most famous philosophical contemporaries, this division is so much a part of our thinking that we are typically unconscious of it.

Locke gets the credit for having named the distinction:

These I call original or primary qualities of the body, which I think we may observe to produce simple ideas in us, viz., solidity, extension, figure, motion or rest, and number.

Secondly, such qualities which in truth are nothing in the objects themselves, but powers to produce various sensations in us by their primary qualities, i.e. by the bulk, figure, texture, and motion of their insensible parts, as colour, sounds, tastes, etc., these I call secondary qualities.

And yet colors behave like vectors and so do the photons which bring them to us: SPECTRA

But I have gone into all that at length on too many occasions and I am not eager to rehearse my ideas here, but will be content to point the way.

History teaches us

Quanta & Consciousness

Field Effect Technologies

Spooky computers closer to reality

Solid-state quantum processing demonstrated.

Katharine Sanderson

The computers of tomorrow could be quantum not classical, using the quantum world's strange properties to vastly increase memory and speed up information processing. But making quantum computing parts from standard kit has proved difficult so far.

Now physicist Leonardo DiCarlo of Yale University, New Haven, and his colleagues have made the first solid-state quantum processor, using similar techniques to the silicon chip industry. The processor has used programs called quantum algorithms to solve two different problems. The work is published in Nature¹.

Reverse-Engineering the Quantum Compass of Birds

"Still, no quantum mind connection," say most idiots.

Scientists are coming ever closer to understanding the cellular navigation tools that guide birds in their unerring, globe-spanning migrations.

The latest piece of the puzzle is superoxide, an oxygen molecule that may combine with light-sensitive proteins to form an in-eye compass, allowing birds to see Earth’s magnetic field.

“It connects from the subatomic world to a whole bird flying,” said Michael Edidin, an editor of Biphysical Journal, which published the study last week. “That’s exciting!”

The superoxide theory is proposed by Biophysicist Klaus Schulten of the University of Illinois at Urbana-Champaign, lead author of the study and a pioneer in avian magnetoreception. Schulten first hypothesized in 1978 that some sort of biochemical reaction took place in birds’ eyes, most likely producing electrons whose spin was affected by subtle magnetic gradients.

Wired Science

Visual system that detects movement, colors & textures created

Mimicking the way in which a retina works is a hard as it sounds. Scientists from Stanford University, in the United States, have spent the past two years working on imitating the way in which information is processed in biological systems, in other words through the transmission of events in specifically connected networks (where information is captured and transmitted at the same time).

Now a research team from the UGR has evaluated the degree of precision of different models in estimating movement, and have combined the responses of four movement detection cells, two of which are static (on and off), and two transitory (decrease and increase). "One of our developments is a multimodal attention operator, which can detect movement in objects of different colours and textures", Fran Barranco, one of the researchers involved in this project, tells SINC.

The objective of this study, which has been published in the latest issue of the journal IEEE Transactions on Systems. Man and Cybernetics, was to combine movement and attention based on information provided by the artificial retina, a visual system capable of selectively capturing moving objects in real time.

The use of an event-driven model, which makes it possible to focus only on areas of activity, has been fundamental, both in the movement processing model as well as in the multimodal selective attention model created in Granada.

Synchronized Brain Waves Focus Our Attention

Weird Science

Separate brain regions firing in unison may be what keeps us focused on important things while we ignore distractions.

A deluge of visual information hits our eyes every second, yet we’re able to focus on the minuscule fraction that’s relevant to our goals. When we try to find our way through an unfamiliar area of town, for example, we manage to ignore the foliage, litter and strolling pedestrians, and focus our attention on the street signs.

Now, researchers at the Massachusetts Institute of Technology have discovered that the brain’s control center syncs up to its visual center with high-frequency brain waves, directing attention to select features of the visual world.

“It’s been known that the prefrontal cortex plays an important role in focusing our attention, but the mystery was how,” said neuroscientist Robert Desimone, who led the study, published in Science Friday. “Now we have some insight into how it has that focusing role — through this synchrony with our sensory systems.”

_______________________

Let me just point out that I've been arguing for years that this kind of synchrony would be essential to preserving the phase relations of incident waves/vectors -- and necessary for building up conscious representations of the environment, as is most easily 'seen' in vision.

Quantum Theory May Explain Wishful Thinking

Slashdot.org

"Humans don't always make the most rational decisions. As studies have shown, even when logic and reasoning point in one direction, sometimes we chose the opposite route, motivated by personal bias or simply 'wishful thinking.' This paradoxical human behavior has resisted explanation by classical decision theory for over a decade. But now, scientists have shown that a quantum probability model can provide a simple explanation for human decision-making — and may eventually help explain the success of human cognition overall."

Robots begin to evolve

New Scientist

04 February 2009

by Paul Marks

LIVING creatures took millions of years to evolve from amphibians to four-legged mammals - with larger, more complex brains to match. Now an evolving robot has performed a similar trick in hours, thanks to a software "brain" that automatically grows in size and complexity as its physical body develops.

Existing robots cannot usually cope with physical changes - the addition of a sensor or new type of limb, say - without a complete redesign of their control software, which can be time-consuming and expensive.

So artificial intelligence engineer Christopher MacLeod and his colleagues at the Robert Gordon University in Aberdeen, UK, created a robot that adapts to such changes by mimicking biological evolution. "If we want to make really complex humanoid robots with ever more sensors and more complex behaviours, it is critical that they are able to grow in complexity over time - just like biological creatures did," he says.

As animals evolved, additions of small groups of neurons on top of existing neural structures are thought to have allowed their brain complexity to increase steadily, he says, keeping pace with the development of new limbs and senses. In the same way, Macleod's robot's brain assigns new clusters of "neurons" to adapt to new additions to its body.

The robot is controlled by a neural network - software that mimics the brain's learning process. This comprises a set of interconnected processing nodes which can be trained to produce desired actions. For example, if the goal is to remain balanced and the robot receives inputs from sensors that it is tipping over, it will move its limbs in an attempt to right itself. Such actions are shaped by adjusting the importance, or weighting, of the input signals to each node. Certain combinations of these sensor inputs cause the node to fire a signal - to drive a motor, for example. If this action works, the combination is kept. If it fails, and the robot falls over, the robot will make adjustments and try something different next time.

Finding the best combinations is not easy - so roboticists often use an evolutionary algorithm to "evolve" the optimal control system. The EA randomly creates large numbers of control "genomes" for the robot. These behaviour patterns are tested in training sessions, and the most successful genomes are "bred" together to create still better versions - until the best control system is arrived at.