Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care

Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy
Showing posts with label Neurons. Show all posts
Showing posts with label Neurons. Show all posts

Sunday, June 20, 2021

Artificial intelligence research may have hit a dead end

Thomas Nail
salon.com
Originally published 30 April 21

Here is an excerpt:

If it's true that cognitive fluctuations are requisite for consciousness, it would also take time for stable frequencies to emerge and then synchronize with one another in resting states. And indeed, this is precisely what we see in children's brains when they develop higher and more nested neural frequencies over time.

Thus, a general AI would probably not be brilliant in the beginning. Intelligence evolved through the mobility of organisms trying to synchronize their fluctuations with the world. It takes time to move through the world and learn to sync up with it. As the science fiction author Ted Chiang writes, "experience is algorithmically incompressible." 

This is also why dreaming is so important. Experimental research confirms that dreams help consolidate memories and facilitate learning. Dreaming is also a state of exceptionally playful and freely associated cognitive fluctuations. If this is true, why should we expect human-level intelligence to emerge without dreams? This is why newborns dream twice as much as adults, if they dream during REM sleep. They have a lot to learn, as would androids.

In my view, there will be no progress toward human-level AI until researchers stop trying to design computational slaves for capitalism and start taking the genuine source of intelligence seriously: fluctuating electric sheep.

Monday, April 9, 2018

Use Your Brain: Artificial Intelligence Isn't Close to Replacing It

Leonid Bershidsky
Bloomberg.com
Originally posted March 19, 2018

Nectome promises to preserve the brains of terminally ill people in order to turn them into computer simulations -- at some point in the future when such a thing is possible. It's a startup that's easy to mock. 1  Just beyond the mockery, however, lies an important reminder to remain skeptical of modern artificial intelligence technology.

The idea behind Nectome is known to mind uploading enthusiasts (yes, there's an entire culture around the idea, with a number of wealthy foundations backing the research) as "destructive uploading": A brain must be killed to map it. That macabre proposition has resulted in lots of publicity for Nectome, which predictably got lumped together with earlier efforts to deep-freeze millionaires' bodies so they could be revived when technology allows it. Nectome's biggest problem, however, isn't primarily ethical.

The company has developed a way to embalm the brain in a way that keeps all its synapses visible with an electronic microscope. That makes it possible to create a map of all of the brain's neuron connections, a "connectome." Nectome's founders believe that map is the most important element of the reconstructed human brain and that preserving it should keep all of a person's memories intact. But even these mind uploading optimists only expect the first 10,000-neuron network to be reconstructed sometime between 2021 and 2024.

The information is here.

Tuesday, February 27, 2018

Artificial neurons compute faster than the human brain

Sara Reardon
Nature
Originally published January 26, 2018

Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. That achievement, described in Science Advances on 26 January, is a key benchmark in the development of advanced computing devices designed to mimic biological systems. And it could open the door to more natural machine-learning software, although many hurdles remain before it could be used commercially.

Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google’s automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. But because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does.

“There must be a better way to do this, because nature has figured out a better way to do this,” says Michael Schneider, a physicist at the US National Institute of Standards and Technology (NIST) in Boulder, Colorado, and a co-author of the study.

The article is here.