Digital with Analog override/rev II

Rarely has an advertising image been hailed as a pop culture icon. In that rarified company of Marlboro Man and Benetto Pieta belongs this 1978 photograph by Steven Steigman, which would later be known as the Blown-away Man. The ad for Hitachi Maxell, the Japanese manufacture of stereos has since been parodied from Family Guy to P.Diddy, and to this day, has been recycled and reused by Maxell is its ad campaigns.

The ads showed hair and tie of a man sitting in a Le Corbusier chair — along with the lampshade and martini glass next to him — being blown back by the tremendous sound from speakers in front of him. Who actually modeled for the ad is unclear. Steigman wanted a model with long hair (for obvious reasons), but when a model could not easily be found, Steigman used a makeup man working for his ad agency Scali, McCabe, Sloves. The model is identified only as Jack. To achieve the wind-blown position, Steigman put tonnes of hairspay on the model’s hair, and tied some hair strands to the ceiling with fishing lines. The lampshade, tie and martini glass were also likewise tied to fishing lines.

The photo was instantaneously a hit, a powerful statement that music has power and force to move the mind and the soul. It was so popular that it was expended into a TV ad campaign. In the television versions, either Wagner’s Ride of the Valkyries or Mussorgsky’s Night on Bald Mountain was the music responsible for those powerful waves.

But this isn't about the Blown-away Man and Maxell but rather about digital with analog overrode and how it applies to the uncanny valley and AI. 

1. Is this face of a real person or artificially synthesized? (See answer below.)

You might be confident in your ability to tell a real face from one created using artificial intelligence. But a new study has found that your chance of choosing accurately would be slightly better if you just flipped a coin—and you are more likely to trust the fake face over the real one.

Farid has been exploring synthetic images—and how well people can tell them apart from the real ones—for years. He initially focused on the rise of computer-generated imagery. But the medium’s path has accelerated in recent years as deep-learning-based neural networks known as GANs (generative adversarial networks) have become more sophisticated at generating truly realistic synthetic images. “If you look at the rate of improvement of deep fakes and [GANs], it’s an order of magnitude faster than CGI,” he says. “We would argue that we are through the uncanny valley for still faces.

As corollary, the impedance to ubiquitous AI has been the amount of power said tech uses as neural nets are analog, taking measurements of the outside world while computationally, the backend evaluates the data input crunching bits, thus creating a computational mismatch requiring enormous power to overcome, a problem considered to be almost impossible to solve until now.

Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.

Today’s most successful artificial intelligence algorithms, artificial neural networks, are loosely based on the intricate webs of real neural networks in our brains. But unlike our highly efficient brains, running these algorithms on computers guzzles shocking amounts of energy: The biggest models consume nearly as much power as five cars over their lifetimes.

Enter neuromorphic computing, a closer match to the design principles and physics of our brains that could become the energy-saving future of AI. Instead of shuttling data over long distances between a central processing unit and memory chips, neuromorphic designs imitate the architecture of the jelly-like mass in our heads, with computing units (neurons) placed next to memory (stored in the synapses that connect neurons). To make them even more brain-like, researchers combine neuromorphic chips with analog computing, which can process continuous signals, just like real neurons. The resulting chips are vastly different from the current architecture and computing mode of digital-only computers that rely on binary signal processing of 0s and 1s.

The BrainScaleS-2 neuromorphic chip, developed by neuromorphic engineers at Heidelberg University, uses tiny circuits that mimic the analog computing of the real neurons in our brains.

2. Is this face of a real person or artificially synthesized? (See answer below.)

Wth this being said, everything's different now as AI's a reality not to be denied and man is not ready for it. Bill Joy, in 2010 wrote

What we do suggest is that the human race might easily permit itself to drift into a position of such dependence on the machines that it would have no practical choice but to accept all of the machines' decisions. As society and the problems that face it become more and more complex and machines become more and more intelligent, people will let machines make more of their decisions for them, simply because machine-made decisions will bring better results than man-made ones. Eventually a stage may be reached at which the decisions necessary to keep the system running will be so complex that human beings will be incapable of making them intelligently. At that stage the machines will be in effective control. People won't be able to just turn the machines off, because they will be so dependent on them that turning them off would amount to suicide.

Answers

1. Fake

2. Real