Those of us who work with data for a living will, at some point, find ourselves at the other end of a conversation about Artificial intelligence (AI). Likely, if you’re me, that’s at a bar, and you just heard someone say “AI,” “Machine Learning,” or “Skynet.” In my case, I can’t help but to engage and ask what they’re doing with AI. Then comes the dreaded…”AI is here, and it will replace all our jobs.” Well, my friends, here are five ways to argue the difference between AI and ML.
1) At some point, someone will say this “new” field of AI. Put on your glasses and say, “The pursuit of AI is old; it’s credited to guys like Alan Turing, Allen Newell, Herbert Simon, and John McCarthy from the 1950’s.” Walk away in silence. Kidding aside, this is an old pursuit. Alan Turing derived the Turing Test in 1950 and published Computing Machinery and Intelligence. But progress takes time. It would be several more years before Turing, Newell, Cliff Shaw created the logic theorist and presented it at John McCarthy’s Dartmouth Summer Research Project on Artificial Intelligence. The logic theorist is thought to be the first AI. From there, DARPA would fund AI, the first atonomous car would be built in 1986, Deep Blue would defeat Gary Kasparov in 1997, SIRI and Watson showed up in 2011, and then Microsoft introduced Tay, and the ethics challenge of AI began. These are all stories in themselves, but in the evolution of AI, they’re all part of the story.
People debate the difference between clever ML and real AI. It’s complex, but the easiest way to simplify it is to say Machine Learning (ML) is considered a branch of AI, but not all AI is ML, and ML is a subset of what is now considered the broad field of AI. It doesn’t matter; the problem and the approach to solving it matter. If you’re trying to automate resetting passwords in a call center, you might not need some superintelligence.
At some point, the topic of deep learning will come up as a way to create the newest human-like AI or Skynet. The truth is that deep learning has accelerated the progress of AI and ML, but it’s likely not the end state. While deep learning is powerful, its continued evolution drives many AI breakthroughs. The need for better deep learning models has pushed computing to the next level. Deep learning is easy to train, but it’s also easy to fool. There’s a lot of ongoing research to find new algorithms and approaches that can deal with some weaknesses in deep learning.
People also love to debate if it’s “really” AI. From an academic perspective, ML is a part of AI, and a clever chatbot is a function of AI. However, what your friends are debating is actually a functional area of AI. Artificial General Intelligence (AGI) refers to a true thinking machine. This area is still being explored, and there are no tremendous true thinking systems out there, not in the sense of AGI.
Many people think that google, amazon, the government, and others have made it past AGI, and into a form of intelligence beyond humans. Superintelligence, a state of AGI, has not yet been achieved. Outside of science-fiction, we aren’t even close, and we have decades before we might get there. AI’s best use is in helping humans with remedial tasks, helping us remember and do jobs that require a high degree of computation. Symbolic, conceptual, and other thinking areas are still the human brain’s realm.
Now, venture into data social gatherings, bars, and dinners. Be confident with your newfound debate skills. If you happen in an establishment where people aren’t talking about data, bring it up yourself!