“Have you heard the rumor about butter? … Never mind — I shouldn’t spread it.”
Go ahead and roll your eyes. We’ll wait.
This groaner is just one of the many, many terrible jokes that Amazon’s “personal assistant” software, Alexa, will tell you — if you ask. But Alexa can do a lot more than make bad puns. Many people start their mornings by asking Alexa for the weather forecast or the latest news. A device that houses the software can also play music from your favorite playlists, keep a shopping list, order takeout food, answer trivia questions, send voice messages and even run “smart” home controls like thermostats.
Alexa is a form of artificial intelligence, or AI for short. But this digital personal assistant is just one of many AI systems that have have become a part of modern life. Another well-known one is AlphaGo. It’s an AI system designed by Google that recently beat a human champion, Lee Sedol, at the strategy board game Go. Other examples of AI abound. A spam-filtering assistant can detect which messages are pure junk. Then there are all of those self-driving vehicles that have started taking to the road.
Training AI systems to respond to problems with human-like intelligence — and learn from their mistakes — can take months, or even years.
Consider Alexa and similar software, such as Apple’s Siri. To do the tasks its human overlords ask, these systems must make sense of and then respond to sentences such as, “Alexa, play my Ed Sheeran playlist” or “Siri, what is the capital of India?” Computers can’t understand language as it is spoken by people. So AI researchers must find a way to help humans communicate with computers. The technology used to get computers to “understand” human speech or text is known as natural language processing. By natural language, computer scientists refer to the way people naturally talk or write.
To teach an AI system a task like understanding a sentence in English or responding to a person’s last move in a board game, scientists need to feed it lots of examples. To train AlphaGo, Google had to show it 30 million Go moves that people had made while playing the game. Then AlphaGo used what it learned by analyzing those plays (such as what moves won and which lost) as it played against different versions of itself.
During this practice, the program became so skilled and clever that it came up with novel moves — ones never seen in games between people.
Computers, software and devices that are powered by AI can do much more, however, than just play board games and music. They can perform serious tasks, such as helping kids learn math or helping doctors decide how to treat cancer.
The doctor’s computer will see you now
Scientists generate new information about diseases and treatments each and every day. According to one study, 2.5 million scientific papers were published worldwide in 2014 alone. A great man of these appeared in medical journals. Can doctors keep tabs on every new development in their field? Not a chance. But computers can.
Take Watson. IBM built this computer for one purpose — to answer people’s questions. Watson uses the same natural-language-processing approach that gives Alexa and Siri their gift of the gab. In 2011, Watson demonstrated just how good it was when it beat two flesh-and-blood champions answering questions on the TV show Jeopardy! That win earned Watson more than $77,000.
Clearly, Watson was a champ at answering trivia. Now IBM has given this quiz whiz a more serious job: helping doctors find the right treatments for cancer patients. In its new role, the program is known as Watson for Oncology. (Oncology refers to the study or treatment of cancer.)
Doctors at Memorial Sloan Kettering Cancer Center in New York City trained the program. Now a physician can ask this Watson system to recommend a treatment for a particular patient. Before it answers, the AI assistant quickly pulls up the patient’s medical history and lab reports. It also can review notes from doctors that are written in plain English. And it can tap into data on other Memorial Sloan Kettering patients — and how well their treatments worked. That’s thousands of cancer patients a year. Watson for Oncology can even sift through millions of pages of medical research to learn what other researchers have been reporting about a particular drug’s safety or how well it works.
In the end, a doctor may choose not to follow Watson for Oncology’s recommendation. When that happens, the software will log that information too. “It continues to learn as it gathers more and more experience,” explains Andrew Norden. He is a doctor and was a senior officer at IBM.
In one study, researchers at Manipal Hospital in Bangalore, India, found that Watson’s recommendations for treating breast tumors matched with those of the experts’ nine out of every 10 times. In another study, researchers at Memorial Sloan Kettering found that Watson for Oncology agreed with doctors’ recommendations for breast cancer patients in 98 out of every 100 instances.
“It’s a very valuable assistant to the doctors,” says Amit Rauthan. He is a doctor at Manipal Hospital. He uses Watson for Oncology as part of his practice. Yet he and other doctors don’t always follow Watson’s advice. Why? Many patients in India, for example, cannot afford the treatments that were the system’s first choices.
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