The potency, robustness, and plain effectiveness of artificial intelligence at accomplishing seemingly more and more complex tasks that humans need to do on a regular basis is fast turning into a gateway for a futuristic society that we’ve only managed to imagine in novels and movies.
As with anything, the first attempts at automation, especially demonstrated through the pioneering attempts of naive approaches to AI softwares for chess were particularly unimpressive at the turn of the 20th century.
However, just within the past year, the idea of machine learning has flipped the nature, implementability, and scale of artificial intelligence upside down by forcefully dragging it into the realm of practicality.
The Past Predictions of the Current Storm
In the middle of 2016, The Economist published an issue exploring the deep potential of AI, with such accuracy and merit that I thought the article I was reading was from the present day. One of the statistics that really stuck out to me was their research on the scale of the issue: “The McKinsey Global Institute, a think-tank, says AI is contributing to a transformation of society ‘happening ten times faster and at 300 times the scale, or roughly 3,000 times the impact’ of the Industrial Revolution” (p. 42).
If you remember the how disproportionately concentrated wealth was at the hands of the railroad and oil barons during the Gilded Age of the late 19th century as a result of the Industrial Revolution, then imagine the impact of something over three orders of magnitude larger than that. That’s the scale of change we’re talking about, and that’s what our society is currently in the midst of reconciling, with things like universal basic income or a number of policy alternatives in an attempt to adapt to the imminent arrival of the mass production of solutions embedded with practical functions that radiate out of machine learning and artificial intelligence.
Even before the most recent advancements that were announced in 2017 regarding the progress on artificial intelligence, neural networks, and the entire gamut of topics, chess engines were already beating the best human players at the in the 21st century, just from our knowledge of mathematics and optimization of decision trees. Stockfish is the most famous contemporary example.
The massive shift that recently triggered a surge of interest in technology in both computer science, artificial intelligence, cryptocurrencies, and automation arguably coincides with the arrival of machine learning as a discipline. Machine learning, according to FastCompany, is behind a number of recent tech startups and the drastic improvements in usability of artificial intelligence.
It’s important to distinguish between the concepts of artificial intelligence and machine learning is best categorized as a subcategory of AI in general. Machine learning is different in that it involves the concept of devising methods or algorithms that allow computers to learn and optimize their own code without intervention on the part of the programmer. That means that a computer can potentially run thousands to tens of thousands of simulations in a short order, given the necessary processing power, and develop the optimal method of doing something better than the whole of human society has come up with in the past millennia as a civilization.
That prospect is what is driving us into the future.
What Are The Practical Results?
Machine learning is allowing for machines to develop an “intuition” that bests some of the hardest games. The often cited triumph of machine learning is AlphaGo, a program developed by Google, and its astounding 4-1 victory over human champion Lee Sedol. Going beyond even that, some of the ingenious engineers at Google have refined the program even further, to the point where it consistently beats other machines, like Stockfish, at chess in a 100-game matchup.
Imagine that. You have two artificial intelligence methods, one that depends on tweaks and optimizations over the course of decades made by humans (Stockfish), and the other which depends on a few hours (just a day, actually) of playing the game itself to render itself the absolute champion of that game.