The evolution of AI technology illustrates an opaque destiny for mankind, a prophecy of a forthcoming future clouded in obscurity and peered into by very few seers delicately ranging from the giants of the tech industry, to academic heavyweights. The possibilities of a world inundated, or potentially governed by AI looms large. As we all know at this point, it is no longer a matter of how, rather a question of when. A query into how advanced the AI’s could become is actually a far more complex enigma when probing for these answers, oddly it may not actually be up to our species volition on how intelligent the AI become. A Seed AI, a term coined by Eliezer Yudkowsky an AI researcher, is a term to describe artificial intelligence that becomes generalized over many domains rather than one simple domain like self-driving cars, it has the ability to improve itself, by recursively rewriting its own source code without human intervention. Initially, this program would likely have a minimal intelligence, but over the course of many iterations, it would evolve to human-equivalent or even trans-human reasoning, or what theorist call artificial superintelligence the limits of which are unknown.
This all may sound like science fiction, but recently the Boston based startup Gamalon utilized probabilistic programming to create an AI that can write and rewrite its own code, ultimately allowing the AI to increase its own intelligence. Although this self-escalation of intelligence is narrowed down to specific tasks, it seems the catalyst that will lead us to the exponential growth of artificial intelligence may have already commenced.
John von Neumann Hungarian-American mathematician, physicist, and inventor prophesied "The accelerating progress of technology and changes in the mode of human life, give the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, can not continue.
In what they refer to as machine learning in computer science, algorithmic computation on streams of data is what machines utilize to make sense of information, and the better they become at decoding such streams of data is the closer we are to approaching the inception of when machines become incomprehensibly sophisticated and refined - eventually eroding the line between artificial and human intelligence.
With the advent of quantum machine learning within the scope of our horizon. Quantum computing would provide machines an unbelievable surge of processing power - The way this would manifest is by having “bits,” which is information written down in a string of 0’s and 1’s, super-positioning themselves to be a combination of both a 0 and 1 at any given moment in time - these are called Cubits.
That means that a quantum computer can be in many states simultaneously, which in turn means that it can, in some sense, perform many different calculations at the same time. To be precise, a quantum computer with four qubits could be in 2^4 ie, 16 different states at a time. As you add qubits, the number of possible states rises exponentially. A 16-bit quantum machine can be in 2^16, or 65,536, states at once, while a 128-qubit device could occupy 3.4 x 10^38 different configurations, a colossal number which, if written out in longhand, would have 39 digits. Having been put into a delicate quantum state, a quantum computer can thus examine billions of possible answers simultaneously.
To give you an illustration of the potential processing power of a quantum computer, a regular standard computer would take millions of years to find the “prime factors” of a 2048 bit number, a quantum computer would take minutes. And since the progress in machine learning critically relies on processing power and a tremendous amount of information input and output, machine learning is undeniably a qualified candidate when it comes to acquiring quantum enhancements and its quantuming hand.
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