Google creates 'artificial brain' - and it immediately starts watching cat videos because of course it does.
dailymail.co.ukGoogle has created an ‘artificial brain’ from 16,000 computer processors, and sat it down with an internet connection.
There’s a certain grim inevitability to the fact that the YouTube company’s creation began watching stills from cat videos.
The team, led by Google’s Dr Jeff Dean, used the 16,000 processor array to create a brain-style ‘neural network’ with more than a billion connections.
The team then fed it random images culled from 10 million YouTube videos - and let it ‘learn’ by itself.
Unsurprisingly, the machine focused in on cats.
‘We never told it during the training, ‘This is a cat,” said Dr. Dean. ‘It basically invented the concept of a cat.’
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Neural network gets an idea of number without counting
newscientist.comAN ARTIFICIAL brain has taught itself to estimate the number of objects in an image without actually counting them, emulating abilities displayed by some animals including lions and fish, as well as humans.
Because the model was not preprogrammed with numerical capabilities, the feat suggests that this skill emerges due to general learning processes rather than number-specific mechanisms. “It answers the question of how numerosity emerges without teaching anything about numbers in the first place,” says Marco Zorzi at the University of Padua in Italy, who led the work.
The finding may also help us to understand dyscalculia - where people find it nearly impossible to acquire basic number and arithmetic skills - and enhance robotics and computer vision.
The skill in question is known as approximate number sense. A simple test of ANS involves looking at two groups of dots on a page and intuitively knowing which has more dots, even though you have not counted them. Fish use ANS to pick the larger, and therefore safer, shoal to swim in.
To investigate ANS, Zorzi and colleague Ivilin Stoianov used a computerised neural network that responds to images and generates new “fantasy” ones based on rules that it deduces from the original images. The software models a retina-like layer of neurons that fire in response to the raw pixels, plus two deeper layers that do more sophisticated processing based on signals from layers above.
The pair fed the network 51,800 images, each containing up to 32 rectangles of varying sizes. In response to each image, the program strengthened or weakened connections between neurons so that its image generation model was refined by the pattern it had just “seen”. Zorzi likens it to “learning how to visualise what it has just experienced”.
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“Google researchers and Stanford scientists have discovered that if you show a large enough computing system millions of images from random YouTube videos for three days, the computer will teach itself to recognize ... cats.”
—Google’s artificial brain watches YouTube, learns to see cats
[Science News] Do you believe scientists can build an artificial brain in 10 years?
At least Henry Markram does. Henry Markram is the director of Blue Brain project, which attempted to bring engineering to neuroscience to the largest extent, building up an artificial brain in 10 years.
Is it completely insane? Absolutely not. We have witnessed how much progress supercomputer has made during the past decades, how many human brains are successfully challenged by those robots. We should believe a possibility that those computer can simulate the brain activity.
Wait, it’s kind of tricky. They didn’t say it was necessarily a human brain. That’s right, what they started off is rat’s neocortical column, which contains roughly 10,000 neurons, a lot less complicated than others.
However, during a talk given in TED conference in 2009, Henry confirmed that the long-term goal was to build a detailed, functional stimulation of the physiological processes in the human brain.
“It’s far more about running the model, looking at a phenomenon, looking at where the vulnerabilities are and looking for diseases rather than running a model that somehow is going to be magically intelligent,” he said.
That’s ambitious!
One of their key selling points is that their project can help improve our use of existing knowledge. There are currently about 200,000 neuroscientists worldwide, who have produced millions of scientific articles. “The knowledge is fragmented,” says Markram. “It’s time to bring these pieces together.”
If we are positive about this, we can actually take use of the artificial brain to accelerate and expand our current understanding of human brain and untangle some neurological diseases. However, not long ago, it was the human genome project, which also raises high expectation — turns out to be a disappointment to patients. Alzheimer’s continues to plague mankind, as do Parkinson’s and amyotrophic lateral sclerosis, also known as Lou Gehrig’s disease.
At this point, science makes no difference with entertainment. You come up with an idea, everyone buys it. Then you can carry it on. It’s always good to have an ambitious goal and some tempting selling points. The truth is, this project has already raised almost 1.5 billion dollars for 10 years, and will collaborate with geneticist, molecule biologist, electrical and computer engineer, and neuroscientists in 9 universities. Apparently, there are enough talents that believe they can achieve such a goal and plenty of resources available. Just like what Henry said, we are just 6 seconds in the history of evolution.
What will happen in the next second?
Ratte mit Robotergehirn
golem.deForscher in Israel haben einen Chip entwickelt, der als ein künstliches Kleinhirn fungiert. Diesen haben sie einer Ratte implantiert.
