Presumably, when general-purpose AI is finally accomplished, this will also be just another algorithm? By a similar token, Google's work in driverless cars, search technologies, etc are just more algorithms...
Sheesh. We must have become so saturated with technology that we are jaded; immune to any surprise and forgetful of how seemingly magical this stuff is.
RE: portrait - I assume that you mean the presence of a portrait reduces the credibility of the software. It would be disturbing and reflect poorly upon you if your intended meaning was concerned with aesthetics.
"Just" conceals a heck of a lot of work. When microbiologists simulate the natural process of protein folding on a classical computer, is this just normal computing? Consider fluid dynamics and turbulance problems encountered over and over in aerodynamics which are solved using clusters. Is this just normal coding? Yes, these simulations are subject to all the same constraints as in normal computing, but the real novelties and science are contained in the work which overcomes or works around such limitations.
Writing off such sofware as just normal computing is an ill-educated point of view. One which denegrates the many years of science and effort spent by people pushing the cutting-edge through seemingly resistant boundaries.
Yes, perhaps it's a decent algorithm for finding near-optimal solutions to the traveling salesman problem, but the quantum computing thing seems to be mostly marketing. Emulating quantum computing on non-quantum hardware sounds like... just normal computing.
If anything is dubious here it is D-Wave's claims. I've heard a lot of people claim that their machine isn't a proper quantum computer and cannot perform the same kinds of operations. I also remember prior claims that the, at the time, state of the art D-Wave machine had worse performance than a well coded software implementation. But I don't know how fast their current hardware is versus a software implementation.
The algorithm has already been proven by ServicePower's customers. It was first used back in 2012 for an academic problem:
"Quantum annealing is a combinatorial optimization technique inspired by quantum mechanics. Here we show that a spin model for the k-coloring of large dense random graphs can be field tuned so that its acceptance ratio diverges during Monte Carlo quantum annealing, until a ground state is reached. We also find that simulations exhibiting such a diverging acceptance ratio are generally more effective than those tuned to the more conventional pattern of a declining and/or stagnating acceptance ratio. This observation facilitates the discovery of solutions to several well-known benchmark k-coloring instances, some of which have been open for almost two decades."