Although the computer program is built to play only "Go," this new development suggests computers might train themselves better than humans can. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Although the computer program is built to play only "Go," this new development suggests computers might train themselves better than humans can.
A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. Artificial intelligence, which is bringing us everything from self-driving cars to personalized ads on the web, is also invading the world of medicine.
Google just expanded its free software for creating smart, self-learning computers. The company added the ability to run this software, known as TensorFlow, across a network of many computers -- the same way that Google uses it for its own operations. This means that anyone with access to a bunch of computer servers can create their own smart computer that can basically program itself. Set the computer program up with whatever it is you want it to learn. Give it a bunch of data to study and then the computer knows how to do things that, until now, only humans could do like talk, recognize pictures, draw, etc.
Peering through wire-rim glasses, he places the black stone on the board, in a mostly empty zone, just below and to the left of a single white stone. In Go parlance it is a "shoulder hit," in from the side, far away from most of the game's other action. Across the table, Lee Sedol, the best Go player of the past decade, freezes. He looks at the 37 stones fanned out across the board, then stands up and leaves. In the commentary room, about 50 feet away, Michael Redmond is watching the game via closed-circuit.
First of all, more labeled data sets to learn from. Training a machine learning program can lead to better results if you improve the algorithms being used, but often just throwing more data at it will help it improve its accuracy greatly. In the internet and big data age, an enormous amount of data is being collected every day, from the tweets you send, to the pictures you publish, or the articles you're shopping online. Your own picture tagging helps figure out that a cat is indeed present in a picture, or what a given customer might buy next, considering it bought X and Y before, mimicking the behavior of other shoppers online. Big quality data sets, with labels and outcomes, help improving the learning ability of your neural networks.