If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude. It should be obvious then that driverless cars will require an immense amount of data gathering and analysis; they will also need to connect to cloud-based traffic and navigation services, and will draw on leading technologies in sensors, displays, on-board and off-board computing, in-vehicle operating systems, wireless and in-vehicle data communication, analytics, speech recognition and content management. The IoT and machine learning look set to fundamentally alter the way our world works – in a manner that is exactly the opposite of a killer robot from the future.
Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.
Creating a self-driving car should not be difficult, but it's taking a while. Autonomous vehicles have been making headlines for years now, yet few of us have ever been in one or even seen one. We know that flying planes is more difficult than driving cars, yet pilots have enjoyed autopilot for decades. The answer is clear, or more precisely, clear vision. Pilots have used autopilot for decades in clear, open skies.
Two recent accidents involving Tesla's Autopilot system may raise questions about how computer systems based on learning should be validated and investigated when something goes wrong. But machine learning techniques are increasingly used to train automotive systems, especially to recognize visual information. For example, a deep learning neural network can be trained to recognize dogs in photographs or video footage with remarkable accuracy provided it sees enough examples. A team at Princeton designed an automated driving system based largely on deep learning.
He drives through San Francisco's Potrero Hill neighborhood and then onto Interstate 280. The technology he's building represents an end run on much more expensive systems being designed by Google, Uber, the major automakers, and, if persistent rumors and numerous news reports are true, Apple. More short term, he thinks he can challenge Mobileye, the Israeli company that supplies Tesla Motors, BMW, Ford Motor, General Motors, and others with their current driver-assist technology. At 14, he was a finalist in the prestigious Intel International Science & Engineering Fair for building a robot that could scan a room and figure out its dimensions.