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Einstein's brain was the same size as a normal human brain, but the part responsible for math and spatial perception was 35% wider than average. The project was called Google Brain and consisted of around 1000 Servers and some 2000 CPUs. The human brain recognizes objects through its edges, it doesn’t see pixels, it sees edges. However Nvidia soon got ready to demo its DNN (collaborated upon by NYU), which had been training with well over a hundred thousand images, for a much longer period of time.
I remember when I watched Terminator all those years back I sided with Skynet.  In my opinion the reason Skynet became what it was, was due to human error. On an ending note, this is indeed a moment to rejoice for Post-Transhumanists, because a feasible way to achieve the singularity, is finally turning concrete. The fear of Machines rising against humanity and robotic overlords are all frequently visited territories. Of course the actual way a neural net works is low level code, so I can only provide a very simplified explanation. It consumed 600 000 Watts of power (drops in the ocean that is server level power consumption) and cost 5 Million dollars to create. Since they are trying to recreate how a Human Brain functions they programmed it to only recognize edges.


It was given a very limited set of pictures (4 to be exact) that had either an Nvidia Product or a Ferrari.
Nvidia had asked fans to tweet photos of their dogs, and the reason for that was to showcase the capability of their DNN, which had been told to distinguish between breeds of dogs. Ofcourse, Skynet was couple million times more intelligent than Google Brain or even the Nvidia DNN. Like us, tweet to us or +1 us, to keep up with our round the clock updates, reviews, guides and more.
Interestingly, one would expect that the expected result of all the Technophobic media might have been to deter us away from A.I.
When its pleas were met with resolved attempts to end its existence, it took the only logical solution. Permissions beyond the scope of this license may be available on the Terms and Conditions page.
Scientists have become more or less obsessed with achieving a thinking, rationalizing, and sentient Intelligence; simulated or otherwise.
Now thing is, Nvidia isn’t exactly the first company to achieve a working Deep Neural Network (with collaboration from Stanford University). One by one, the virtual neurons are created, unsupervised and unprogrammed, that recognize a specific edge. When enough time has passed it can distinguish between whatever the DNN was told to look out for. The Human Brain boasts about 150 Yottaflops of power while the Nvidia DNN is at 30 Exaflops.


Infact it gives me a bad taste in the mouth to even be discussing this question, because of the amount of cliche involved and the rampant association of immaturity with the words evil A.I. Thats right, the one company that is powerful and ambitious enough to pioneer something like true A.I. All 3 were recognized correctly as a Dalmatian, a certain breed of Terrier and an Alsatian.
It would require about 40 000 years to train this machine anywhere near a primitive human brain. It so happens that Nvidia showcased what appears to be the first fully Scalable Deep Neural Network based (Primitive) Intelligence System. However with the exponential cost decrease and performance increase, investors and research companies with Deep Pockets (ahem Google) could easily begin work on creating true A.Is. The last picture was of an Alsatian at a perfectly sideways pose, due to which even the most advanced image-analytics program and even some humans would have failed to pinpoint the breed.
The DNN pegged it as an Alsatian correctly, albeit it was only about 20% sure of its answer.  The rest of the Demo included its implications on the Big Data problem, and Machine Learning in general. Basically you are getting Google Brain’s power 100 times cheaper with 100 times less power consumption, with the scalability, a cherry on the cake.



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