An article in the NY Times, by Kenneth Miller, addresses the question of whether or not we will one day be able to upload a brain, that is, to simulate in a computer the complete behaviour of a human brain.
The author, a neuroscientist from Columbia University, addresses carefully the challenges involved in mind uploading and whole brain emulation.
The author’s (wild) guess is that it will take centuries to determine a connectome that is detailed enough to enable us to try brain uploading.
However, he also recognises that we may not need to reconstruct all the fine details of a brain, with its billions of neurons and trillions of synapses, whose structure varies in time and space. Still, a level of detail incommensurable with existing technology would be required to even have a shot of creating a model that would reproduce actual brain behaviour.
It seems the singularity may not be over the corner, after all…
(Image by Thomas Schultz, avaliable at Wikimedia commons).
In a paper recently published in the journal Astrobiology, Aditya Chopra and Charles Lineweaver, from the Australian National University, argue that the reason we have not met intelligent aliens is because, in general, life does not evolve fast enough to become a regulating force on planet ecologies.
If this explanation holds true or if it is, at least, one of the possible explanations, then many planets may have developed life, but in few or none of them has life lasted long enough to be able to regulate greenhouse gases and albedo, thus maintaining surface temperatures compatible with life. If this is true, then extinction is the default destiny for the majority of life that has ever emerged on planets in the galaxy and the universe. Furthermore, only planets where life develops rapidly enough to become a regulating force in the planet ecology remain habitable and may, eventually, develop intelligent life.
(Photo by By Ian Norman, via Wikimedia Commons).
Narayanan Kasthuri and a team of researchers from Harvard, MIT, Duke, and John Hopkins universities, fully reconstructed all the neuron sections and many sub-cellular objects, including synapses and synapse vesicles, in a volume of 1500 µm3 (which is just a little more than one millionth of a cubic millimeter) using 3×3×30 nm voxels.
The results, published in an article in the journal Cell, in July 2015, describe the experimental procedure and the conclusions. The data was obtained by collecting 2,250 brain slices, each roughly 30 nm thick, obtained with a tape-collecting ultramicrotome that slices brain sections using a diamond knife.The slices were imaged using serial electron microscopy and the images processed in order to reconstruct a number of volumes. In this volume, the authors have reconstructed the 3D structure of the 1500 µm3 of neural tissue, which included hundreds of dendrites, more than 1400 neuron axons and 1700 synapses, which corresponds to about one synapse per cubic micron.
(Rendering by the authors, used with permission)
Go is a beautiful game, with a very large branching factor that makes it extremely hard for computers. For decades, playing this game well was outside the reach of existing programs.
We just learned that computers finally mastered Go, in a paper published in the journal Nature. By using machine learning techniques and, in particular, deep learning, the program AlphaGo, created by Google’s company DeepMind, managed to beat Fan Hui, the European Go champion, five times out of five. Whether AlphaGo is sufficiently strong to beat the best players in the world, remains to be seen. However, it already represents a very significant advance of the state of the art.
What was maybe the last bastion in table games still unconquered by computers is no more. Computers are now better than humans at all table games invented by humanity.