There isn't just one definition of general intelligence. In this video, learn about the difficulty in characterizing intelligence.
- Back in 1955, Dartmouth Professor John McCarthy coined the term artificial intelligence. He created the term as part of an academic grant to fund the first AI workshop. This workshop was to see if early computers could behave in ways that everyday people would identify as intelligent. In the early 1950s, single computers were taking up entire floors. Even with their enormous size, they had much less processing power than modern smartphones.
So they didn't make much progress creating a fully intelligent artificial person. What they did do was create a new term that ignited everyone's imagination. The term, artificial intelligence, inspired a new generation of journalists, writers, academics, and computer scientists. It opened the door to large grants that early computer scientists used to build out a whole new area of research. In fact, there's a good chance that if Professor McCarthy had come up with a different name, this 1955 workshop would have faded into memory.
Artificial intelligence as a technology is any system that exhibits behavior that could be interpreted as human intelligence. But this simple definition cuts to the very heart of one of the big challenges in AI. How do you define human intelligence? There are many different forms of human intelligence. Many great artists are terrible mathematicians. On the flip side, many great mathematicians are terrible artists. Yet, they can each be super intelligent in their own field. There's no one standard for human intelligence.
That makes it very difficult to point to a computer and say that it's intelligent. There are certainly some things that computers are very good at. In fact, there are many tasks where they're much better than humans. A computer has been able to beat humans in chess for decades. IBM Watson beat some of the best champions in the game show Jeopardy. Google's DeepMind has beaten the best players in the 2,500-year-old Chinese game called Go, a game that's so complex that there's thought to be more than twice as many possible game scenarios as there are atoms in the universe.
As good as they are, none of these computers understand the purpose of a game or even why they're playing. They're simply flexing their special talent of following rules and pattern matching. So how can a system that's so intelligent also be equally unaware of the concept of a game or even its own ability? The main challenge is that computer intelligence and human intelligence act in very different ways. This is seen as intelligent behavior, only because computers can be so much better than any human in identifying and matching different patterns.
That means when a computer is doing something that it's really good at, it's much easier to think of this skill as intelligence. In many ways, a game is a perfect environment for a computer. It has set rules with a certain amount of possibilities. When IBM's Watson played Jeopardy, it used natural language processing to understand the question. Then it used pattern matching to check its database for a possible answer. Think about the chess program that can beat even the best humans.
It was only a few years after the first AI workshop that computer scientists played their first version of a chess program. Even those early computers could thrive in a world of set rules and possibilities. They could match players' moves with patterns of possible countermoves. A computer could play out many thousands of scenarios before its opponent even reached across the board. That's why artificial intelligence will always seem more impressive when computers are on their home turf.
The first organization that will get the most benefit from AI will be the ones that work within a well-defined space with set rules. It's no surprise that organizations, like Google, are fully embracing artificial intelligence. Their whole business is about pattern matching. They match your questions against their massive database of possible answers. Because we're humans, we don't think about certain tasks the same way that a computer would. So if you're considering whether AI will have an impact on your organization, try to think about the things that computers are really good at.
Do you have a lot of pattern matching in your organization? Does a lot of your work have set rules and possibilities? It'll be this work that will be the first to benefit from artificial intelligence.
This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
- The history of AI
- Machine learning
- Technical approaches to AI
- AI in robotics
- Integrating AI with big data
- Avoiding pitfalls