Vision, Accuracy, and the Right Brain

In a TED talk I can't seem to forget, Iain McGilchrist, in RSA fashion, animates the two sides of ourselves. These are the two sides of our brains, the two hemispheres. I don't think I need to explain the Left Brain - Right Brain distinction on account of its general popularity. I will only say that this is not an absolute thing; it's a heuristic for understanding how our complicated heads run all that bugged out code up there.

The part of McGilchrist's talk I can't forget lies in his premonition that humans have been heading in the Left direction since as far back as we can remember, but it may soon be time for the pendulum to turn the other direction. It's hard to believe. We can't have megacities without the Left Brain. No spaceships, no biodomes, no nanofabricated body modifications.

Or can we? If you read about contemporary advances in artificial intelligence, you might be thinking that it's quite possible.

Much of the new things happening in this field (which are actually old ideas running on new hardware) are based on a pretty revolutionary paradigm.  I'm referring here to deep learning neural nets and more generally the idea of machine learning. This semantically refers to an approach to computing that uses a kind of brute force instead of a superintelligent program. It's kind of like a Wisdom of the Crowds thing plus computers; instead of one thousand guesses, there's 100 trillion guesses. (I say 'a kind of brute force' because in other instances, some would could the traditional method a brute force of computation.)

So the answer they come up with isn't exact, it's approximate. But when you have to query petabytes of data, you can no longer expect an exact answer.

Check out for example this new thing where scientists Frankenstein a neural net and a cell phone together to makeshift a microscope as powerful as one in a high grade laboratory. We no longer need to see every micropixel to get a clear picture of what we're looking at. Instead we teach an algorithm to see, and it does something that in theory is similar to what our brain does when we see. We make stuff up, we fill in the dots, we approximate. (In actuality, the algorithm is taught not how to see, but how to learn to see.)

We live in this post-truth world, right? Facts don't matter; belief matters. Being right is not as important as convincing people that you're right. That's a tangent. But we do live in a world of Big Data, so big we really don't know what to do with it all. We simply can't make computers powerful enough to handle all of it. But we have this new approach that can scale-up. It approximates, which is not what we're used to, but it does reach the scale of Big Data.

Have we reached this inflection point where our technology is starting to act more like our brain (the whole brain, not just the left side)? Is there where we find out, after hundreds of years (after the Enlightenment / Scientific Revolution) that absolute certainty is not the ultimate goal in all knowledge-gathering endeavors? Just speculating here, but it sure seems like we're headed for a future that looks more like a wet biological mess than a crystallized spreadsheet.

Notes

Deep learning transforms smartphone microscopes into laboratory-grade devices
May 2018, phys.org

Iain McGilchrist, The Divided Brain, 2011
this is a TED talk based on his book

Bicameralism 
Julian Jaynes, The Origin of Consciousness and the Breakdown of the Bicameral Mind, 1976

Natural vs Artificial

So if we enslave mountains of bacteria to be forcefed glucose while we reap their colorful shit, is that natural or artificial?

Four strains of bacteria work together to produce pigment for food and cosmetics industry
Jul 2017, phys.org

Researchers at Rensselaer Polytechnic Institute have shown that four strains of E. coli bacteria working together can convert sugar into the natural red anthocyanin pigment found in strawberries, opening the door to economical natural colors for industrial applications.

"We feed the bacteria glucose and they do the rest."
-Mattheos Koffas, a professor of chemical and biological engineering at Rensselaer, and member of the Center for Biotechnology and Interdisciplinary Studies

image credits: Air bubbles formed from melted ascorbic acid (vitamin C) crystals (50x), Marek Miś, 2016 Nikon Small World Micrography Competition

Photonic Cognition

Researchers investigate decision-making by physical phenomena
Jun 2017, phys.org

The Illusion of Control is a common theme here on Network Address. So every once in a while we see something  about how things inhuman, and things not even alive, are making decisions just like us. This forces us to consider whether "we" make decisions at all.

From the article:

"Decision-making is typically thought of as something done by intelligent living things and, in modern times, computers. But over the past several years, researchers have demonstrated that physical objects such as a metal bar, liquids, and lasers can also "make decisions" by responding to feedback from their environments. And they have shown that, in some cases, physical objects can potentially make decisions faster and more accurately than what both humans and computers are capable of.

...
"In a new study, a team of researchers from Japan has demonstrated that the ultrafast, chaotic oscillatory dynamics in lasers makes these devices capable of decision making and reinforcement learning, which is one of the major components of machine learning. To the best of the researchers' knowledge, this is the first demonstration of ultrafast photonic decision making or reinforcement learning, and it opens the doors to future research on "photonic intelligence."

"In our demonstration, we utilize the computational power inherent in physical phenomena," coauthor Makoto Naruse at the National Institute of Information and Communications Technology in Tokyo told Phys.org.

...
[and by the way, when we take this further, like to the inevitable AI overlord conclusion...]

"Such systems provide huge potential for our future intelligence-oriented society. We call such systems 'natural Intelligence' in contrast to artificial intelligence."

"In experiments, the researchers demonstrated that the optimal rate at which laser chaos can make decisions is 1 decision per 50 picoseconds (or about 20 decisions per nanosecond)—a speed that is unachievable by other mechanisms. With this fast speed, decision making based on laser chaos has potential applications in areas such as high-frequency trading, data center infrastructure management, and other high-end uses.

The Beginnings of the Mass Transference Device

AKA Metabots, Mindborgs, and Organic Computing

source

This now makes three-people babies the second scariest thing I've ever heard.

Wiring Monkey Brains Together Has a Point, Say Scientists
WIRED, July 2015

"Today, researchers at Duke University announced they have done nearly that, wiring animal brains together so they could collaborate on simple tasks. Network monkeys displayed motor skills, and networked rats performed computations.

"That’s right. They made a botnet out of brains."

"To build the monkey network, Nicolelis’ team first implanted electrodes in rhesus macaque brains, positioned to pick up signals from a few hundred neurons. Then they connected two or three of the macaques to a computer with a display showing a CG monkey arm. The monkeys were supposed to control the arm, directing it toward a target like a boat crew rows forward. When the monkeys got the arm to hit the target, the researchers rewarded them with juice. (“Each monkey had different juice preference,” says Nicolelis. “We had to do a preference test beforehand.”) To be clear, the monkeys don’t think “move my arm” and the arm moves—they learn what kind of thinking makes the arm move and keep doing that—because monkeys love juice.

"The rat study was even weirder. For this one, the neuroscientists directly wired four rats’ brains together—using the implants to both collect and transmit information about neural activity—so one rat that responded to touch, for example, could pass on their knowledge of that stimulus to another rat. Then the researchers set the rats to a bunch of different abstract tasks—guessing whether it might rain from temperature and air pressure data, for example, or telling the difference between different kinds of touch-stimuli. The brain collectives always did at least as well on those tests as an individual rat would have, and sometimes even better. And in a successful effort to squidge people out, the researchers called these rat-borg collectives “organic computers” or, even worse, “brainets.”"

Animal brains connected up to make mind-melded computer
New Scientist, July 2015

Building an organic computing device with multiple interconnected brains
Nature

Computing Arm Movements with a Monkey Brainet
Nature