I had a great conversation with Ed Horley (@EHorley) and Patrick Hubbard (@FerventGeek) last night around new technologies. We were waxing intellectual about all things related to advances in analytics and intelligence. There’s been more than a few questions here at VMworld 2016 about the roles that machine learning and artificial intelligence will play in the future of IT. But during the conversation with Ed and Patrick, I finally hit on the perfect analogy for machine learning and artificial intelligence (AI). It’s pretty easy to follow along, so don’t panic.
Machine learning is an amazing technology. It can extrapolate patterns in large data sets and provide insight from seemingly random things. It can also teach machines to think about problems and find solutions. Rather than go back to the tired Target big data example, I much prefer this example of a computer learning to play Super Mario World:
You can see how the algorithms learn how to play the game and find newer, better paths throughout the level. One of the things that’s always struck me about the computer’s decision skills is how early it learned that spin jumps provide more benefit than regular jumps for a given input. You can see the point in the video when this is figured out by the system, whereafter all jumps become spinning for maximum effect.
Machine learning appears to be insightful and amazing. But the weakness of machine learning is be exemplified by Deep Thought from The Hitchhiker’s Guide to the Galaxy. Deep Thought was created to find the answer to the ultimate question of life, the universe, and everything. It was programmed with an enormous dataset – literally every piece of knowledge in the known universe. After seven million years, it finally produces The Answer (42, if you’re curious). Which leads to the plot of the book and other hijinks.
Machine learning is capable of great leaps of logic, but it operates on a fundamental truth: all inputs are a bounded data set. Whether you are performing a simple test on a small data set or combing all the information in the universe for answers you are still operating on finite information. Machine learning can do things very fast and find pieces of data that are not immediately obvious. But it can’t operate outside the bounds of the data set without additional input. Even the largest analytics clusters won’t produce additional output without more data being ingested. Machine learning is capable of doing amazing things. But it won’t ever create new information outside of what it is operating on.
Artificial Intelligence (AI), on the other hand, is more like the question in The Hitchhiker’s Guide. Deep Thought admonishes the users of the system that rather than looking for the answer to Life, The Universe, and Everything, they should have been looking for The Question instead. That involves creating a completely different computer to find the Question that matches the Answer that Deep Thought has already provided.
AI can provide insight above and beyond a given data set input. It can provide context where none exists. It can make leaps of logic similarly to those that humans are capable of doing. AI doesn’t simply stop when it faces an incomplete data set. Even though we are seeing AI in infancy today, the most advanced systems are capable of “filling in the blanks” to cover missing information. As the algorithms learn more and more how to extrapolate they’ll become better at making incomplete decisions.
The reason why computers are so good at making quick decisions is because they don’t operate outside the bounds of the possible. If the entire universe for a decision is a data set, they won’t try to look around that. That ability to look beyond and try to create new data where none exists is the hallmark of intelligence. Using tools to create is a uniquely biologic function. Computers can create subsets of data with tools but they can’t do a complete transformation.
AI is pushing those boundaries. Given enough time and the proper input, AI can make the leaps outside of bounds to come up with new ideas. Today it’s all about context. Tomorrow may find AI providing true creativity. AI will eventually pass a Turing Test because it can look outside the script and provide the pseudorandom type of conversation that people are capable of.
Computers are smart. They think faster than we do. They can do math better than we can. They can produce results in a fraction of the time at a scale that boggles the mind. But machine learning is still working from a known set. No matter how much work we pour into that aspect of things, we are still going to hit a limit of the ability of the system to break out of it’s bounds.
True AI will behave like we do. It will look for answers when their are none. It will imagine when confronted with the impossible. It will learn from mistakes and create solutions to them. That’s the power of intelligence versus learning. Even the most power computer in the universe couldn’t break out of its programming. It needed something more to question the answers it created. Just like us, it needed to sit back and let its mind wander.