Part II of The Completely Non-Technical Guide to Machine Learning and AI
My previous post raised the question “what is machine learning and artificial intelligence (AI)?” and answered with a functional definition: computer systems that combine measurements and math to make decisions so complicated that until recently only humans could make them.
Now a new question: “What’s the difference between machine learning and AI?”
Machine learning is usually considered a subcategory of artificial intelligence. It’s artificial, because we’re talking about machines (computers) instead of people. And intelligence, because unlike ordinary computer systems which can only follow exact instructions, with machine learning computers can learn from examples (they can be trained) to do things we associate with intelligent behavior, like translating words from one language to another, recognizing people and animals from photographs, driving cars, and having conversations (via chat bots). Machine learning can not only mimic human behavior, to some extent it attempts to simulate the human brain: neural networks—one of the workhorse methodologies of machine learning—were “inspired by” and named after brain neurons…even though they work in quite a different way.
Probably the most exciting recent example of machine learning “intelligence” is an advanced game program called AlphaZero developed by Google’s DeepMind team. AlphaZero used machine learning to rapidly teach itself how to play the complex strategy games chess, Go, and shogi (a Japanese version of chess). Older computer programs designed to play games like these (for example, IBM’s famous Deep Blue, the first to beat a world champion human chess master) were mostly limited to choosing from pre-designed strategies already spelled out by human experts. But AlphaZero learned to play by playing against itself, without mimicking human players. In a matter of weeks AlphaZero became skilled enough to beat the best human players and the best computer chess programs. Along the way it invented winning strategies that humans hadn’t discovered despite thousands of years of game play.
And unlike previous generation computer games programs which by necessity must specialize in just one strategy game, because it’s learning-driven rather than instruction-driven AlphaZero was able to become expert in multiple games with only a few tweaks.
Having said all of this, machine learning and AI are not completely interchangeable. AI is much broader than just machine learning. (Pro tip: using the term AI to describe machine learning actually annoys some data scientists, who dislike the distractions that come with the hype and fiction surrounding AI.) So, how are they different? Let’s look at a rough diagram:
Machine learning rests at the intersection where data analytics—the computer software for number-crunching, from Excel spreadsheets on up—overlaps AI. Machine learning stands out as a current success story at that intersection. But not everything involving computers that people have described as AI also involves machine learning. Deep Blue, for example, does not use machine learning.
AI is a broad label. Stories and speculation about artificial intelligence go back at least as far as Greek Mythology (Talos, for instance, was supposed to be an “automaton” constructed of brass). In recent times, the 60+ year history of scientific explorations of AI didn’t start with what we now call machine learning, and might not end with it.
By now you’ve noticed an additional term, “AGI”, in a dotted circle on the chart, also at the intersection of data analytics and AI. AGI stands for “artificial general intelligence.” Researchers, futurists, and storytellers focused on AGI are looking forward to a human-like intelligence, an AI capable of self-awareness, rapid, independent learning and the ability to transfer knowledge between situations like humans do. It’s important not to confuse AGI with currently available machine learning. Although an impressive amount of advanced research is in progress to expand the range of AI, AGI doesn’t actually exist at present except in fiction (and arguably we aren’t even close…yet). Many people think that current machine learning techniques such as neural networks, as powerful as they are, can not be used to create an AGI, but rather that as-yet-unperfected or unimagined techniques will be used instead.
However, the present is an excellent time to start talking seriously about AI ethics. For starters, can we allow AI (whether or not it’s an AGI) to make life or death decisions for us without direct oversight—such as decisions made by self-driving cars and automated weapons? This will be the topic of a future post in this series.