http://www.dailymail.co.uk/news/article-2703103/Its-Paddington-Scare-Creepy-reworkings-childhood-favourite-wake-new-films-CGI-version.html

And the Tumblr…

http://creepypaddington.tumblr.com

I posit that something looking sort-of-conscious, that emerges from banking (or as likely other commercial) technology, will have an uncanny quality.

The theory matters because it shows they won’t succeed in making artificial consciousness. They won’t attempt it directly because they don’t know how and nothing they’re trying to do will attempt it accidentally, and therefore the theory predicts the continual failure of their expectations. Without consciousness, machines will never be able to compete with human brains.

That is: Deep Blue did not defeat Kasparov. A team of computer programmers, through the medium of Deep Blue, defeated Kasparov. Essentially they made it possible to stretch chess turns across several hours and several people. The only amazing thing is how far they had to stretch it before the non-grandmasters could, sometimes, defeat a grandmaster.

It also shows they’re not in the habit of questioning their assumptions, which mean they are trapped in their current paradigm unless serendipity frees them. (See: floating soap, chocolate chips.) While what they’re able to catallactically wring from their paradigm is impressive, it is ultimately self-limited.

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From another angle: been reading researchers who think they understand human cognition and don’t. The thing about chimps being better at game theory than humans. It is to laugh.

Most likely, it is unfeasible for a human brain to model a human brain, for the obvious overhead reason. This equally means it is impossible for a human to make a machine model a human. The idea that machines can enhance intelligence per se and not intellectual productivity is probably just false. Which would mean the singularity is not a possible outcome before advanced genetic engineering. if even then.

The only thing that even threatens to outstrip human ingenuity is the same thing that created human ingenuity: evolution. But evolutionary algorithms – I call it chaos tech – are not used to design much of anything. (I suspect due to liability: by definition they wouldn’t be fully predictable and would be hard to service.)

Can you imagine the status wizardry that’s necessary? “Eh, screw deep networks. We’re going with the ‘fucked-if-I-know’ theory. Oh and by the way, there’s no objective measure for ‘able to learn’ so it’s all selected by human judgment!” It’ll never happen. The paradigm is explicitly opposed to real progress.

From a more abstract perspective, homeostatis is a universal aspect or property of thermodynamic/information systems. It’s something that is unambiguosly defined in mathematical terms–you can measure and identify in the real world.

Since artificial minds are information systems, they can be programmed or embued with homeostatic mechanisms. Similar to how evolution has programmed us to have such primordial emotions or driving motivations.

Conciousness is a continuity. Things can be more or less concious.

The reason present-day attempts at building anthropomorphic robots result in failed, lifeless automatons without much conciousness isn’t because they lack homeostatic behaviors–oh, they have them, quite rigid ones in fact. It’s that their simplistic minds lack the complexity of our own.

If you were to compare the complexity of the most advanced software systems available today to our own brains, the distance is many orders of magnitude. It’s astronomical. In fact, it’s near impossible to ever approach the complexity of our minds with conventional register machines based off of the Von Neumann architecture, without consuming vast amounts of computational resources.

The Human Brain Project, and the related effort in the US, are attempting to simulate a human brain using such conventional computers. But what the academics that run those projects aren’t telling the masses is that in order to scale the simulation upwards, they’re using simpler models of how the mind performs computation at each progressively higher level of scale–they’re compressing the model and losing information in the process.

Now that said, there are alternative computers architectures on the horizon that will be able to do it. And I’m not talking about quantum computers. There are more powerful architectures than that. And they aren’t far off either.