ARTIFICIAL INTELLIGENCE TODAY IS STILL JUST AUTOMATION SPUN INTO CLICKBAIT AND SNAKE-OIL
Does “machine learning” operate outside of its objectively defined and/or predicted criteria boundary?
It doesn’t matter what is the end-result of the machine learning insofar as it may seem to resemble some aspect of real life like playing the game Go. AI may look impressive, in a lay sense, AI may be useful too but this is not fundamental. It isn’t an intelligence breakthrough in a human-transcending sense.
What matters, for AI and for machine learning, is how the particular swathe of reality has been broken down, its meta accurately distilled data, into coherent binary e.g. computer code variables and functions. The data is then parsed through coded procedures to drive responses. This parsing must be have a sufficiently high fidelity in its YES/NO ON/OFF ruleset model. None of this describes anything new.
Go is a useful example. Media reports "computer learns to play Go better than the best human players" adding "computer uses machine learning to self-evolve from beginner to beyond expert, at Go, and not even its programmers know how it does so." Is that so? These statements are deliberately mysterious, resembling the journalistic techniques used often in tabloids to equate astrology as somehow an equivalent of astronomy.
After a little research it turns out the parameters of the Go game and the rules of Go must be comprehensively and systematically broken down into a complete set of variables, conditions, and functions. The meta-reality we would describe as ‘to be playing Go’ - how to win, for instance - must be coded, instance by instance, to build the environment of the Go "machine learning" program layer. These steps could be developed to generalise a diversity of simpatico rulesets and thus it might be said the artificial intelligence program is able to “learn” how to play a thousand board games. Is it learning, though? It’s still limited to a learning within precoded parameters, whichever games might happen to fall in range of extant code’s ability to resolve and parse.
Once this much of the AI program is complete things become simple. The "machine learning" comes into play. The AI can play out a million games of Go or Chess or whatever, starting out as a beginner and applying with perfect memory the improvements it "learns" from each game. It doesn't need lateral thinking or intuition or human intelligence to best us because it runs at speeds magnitudes faster than we do, gathers data from each game-experience, and recalls each and every improvement and sorts outcome possibilities perfectly. This soon builds superior gameplay to any human champion.
None of this amounts to a generalized intelligence or something we might recognize as approaching four dimensional human intelligence. No doubt we'll continue to progress this field and more slices of reality (and its functions) will be broken down and codified and parsed by custom-built hardware operated by artificial intelligence using machine learning. It would be no surprise to see robots and AI in every aspect of everyday life.
This isn't true artificial intelligence. There's a red herring debate the ethics of using robots as "slaves" by presuming at some point in an extant development evolution, consciousness may emerge once the functional intelligence becomes sufficiently complex. This would certainly be a concern if artificial intelligence and machine learning meant what most presume them to mean. If they did, we'd conceivably start creating intelligence like a snail or a fish, then move up the ladder of intelligence until we reached humans and beyond. As it is, however, what we're making is automatic toolsets of increasing complexity of mechanism and action operating in a wider range of conditions; but it still boils down to representing reality through integer variable arrays parsed by mechanisms as mathematical functions generating binary conditions that drive action.
If a human being can’t define the reality to enough ‘decimal places’ its fidelity passes objective reciprocity the AI gets constructed handicapped. Objective reciprocity could be defined as the potential to parse back and forth between “reality” and the complete coded detail of the “representation”. In the case of a game things are easier: its rules must be broken down into variables and functions and conditions. The complexity is finite. Without this distillation, though, there’s no starting point for "artificial intelligence". Machine learning can’t initiate it.
At best it's a case of the human brain being able to distill the meta of whatever piece of reality is being automated, in detail that amounts to complete understanding; including the parameters of AI choice. Then and only then can this machine learning become meaningful.
The human brain is slower at calculation than a computer so while the design, codification and implementation of the artificial intelligence is slow. It takes the human brain a long time to work through myriad possibilities and analyse then represent these as precisely distilled variables and mathematical functions. The ‘learning curve’ a human coder must follow is a creative challenge from the outset.
Once the code is running and the bugs are fixed, the AI can run through with perfect patience. The computer can take up the task. The AI is able to perform its functions at exponentially faster rate than any human being. Add machine learning to the mix and you put in another layer of design, codification and implementation but whose addition is a toolset for the computer AI to create, parse and resolve its own data; and follow predefined rulesets to use this parsed data to improve subsequent iterations of its designated function.
In a way, the Go player against the Go artificial intelligence (with machine learning as its backstory) is an unfair contest by default. It plays entirely to the AI's strengths and the human being's weaknesses. The only unknown factor in such an encounter is the skill and attention to detail of the AI's coders. In a way, the surprise should be if a human player EVER beats a well-coded artificial intelligence and this will be a short-lived triumph as the AI code can make sure the loss is never repeated, with 100% clarity and recall.
Humans will have to surrender supremacy at board games - and indeed most feats of dexterity and processes susceptible to being codified accurately. Driving vehicles, most blue collar jobs, many white collar professions, aspects of almost every job there is: distill it, code it, artificial intelligence and machine learning will execute it better. At best jobs will use AI to replace many human 'parts' and extend the efficiency of the rest. Society will continue to be transformed.
This does NOT mean we have created intelligence, though. Crudely, all of the above - all current artificial intelligence and machine learning - it amounts to no more intelligence than a pocket calculator. However impressive the scale, however physically splendid the application, however complex the calculations: nothing in today's world nor anything currently in development truly deserves to be called "artificial intelligence". There is no example of "machine learning" that transcends human analysis, abstraction, creativity and imagination. Not yet. Don't be dulled by the hype.
Bulllshit Exhibit A: This is what A.I. looks like, according to A.I.
