---

1.媒體來源: MIT Technology Review - Robotics


2.完整新聞標題:

Machines Can Now Recognize Something After Seeing It Once

現在只需看過一次，機器就可以辨識新事物

Algorithms usually need thousands of examples to learn something. Researchers
at Google DeepMind found a way around that.

演算法經常需要成千上萬個樣本才能學習事物。
Google DeepMind 研究員發現了一種方法可以繞過去。


3.完整新聞內文:

by Will Knight  November 3, 2016

Most of us can recognize an object after seeing it once or twice. But the
algorithms that power computer vision and voice recognition need thousands of
examples to become familiar with each new image or word.

大多數人只需看過一兩次就可以辨識一個物體。但是演算法驅動的電腦視覺與聲音辨識需
要上千個樣本才能熟悉一個新的圖片或單詞。

Researchers at Google DeepMind now have a way around this. They made a few
clever tweaks to a deep-learning algorithm that allows it to recognize
objects in images and other things from a single example—something known as
"one-shot learning." The team demonstrated the trick on a large database of
tagged images, as well as on handwriting and language.

https://arxiv.org/abs/1606.04080



The best algorithms can recognize things reliably, but their need for data
makes building them time-consuming and expensive. An algorithm trained to
spot cars on the road, for instance, needs to ingest many thousands of
examples to work reliably in a driverless car. Gathering so much data is
often impractical—a robot that needs to navigate an unfamiliar home, for
instance, can’t spend countless hours wandering around learning.



Oriol Vinyals, a research scientist at Google DeepMind, a U.K.-based
subsidiary of Alphabet that’s focused on artificial intelligence, added a
memory component to a deep-learning system—a type of large neural network
that’s trained to recognize things by adjusting the sensitivity of many
layers of interconnected components roughly analogous to the neurons in a
brain. Such systems need to see lots of images to fine-tune the connections
between these virtual neurons.



The team demonstrated the capabilities of the system on a database of labeled
photographs called ImageNet. The software still needs to analyze several
hundred categories of images, but after that it can learn to recognize new
objects—say, a dog—from just one picture. It effectively learns to
recognize the characteristics in images that make them unique. The algorithm
was able to recognize images of dogs with an accuracy close to that of a
conventional data-hungry system after seeing just one example.

http://image-net.org/



Vinyals says the work could be especially useful if it could quickly
recognize the meaning of a new word. This could be important for Google,
Vinyals says, since it could allow a system to quickly learn the meaning of a
new search term.

Vinyals 表示如果這個研究成果可以用來快速的辨識出新詞的意義，那會非常有用。這對
 Google 非常重要，因為它可讓系統很快地學習新的搜尋詞組的意義。

Others have developed one-shot learning systems, but these are usually not
compatible with deep-learning systems. An academic project last year used
probabilistic programming techniques to enable this kind of very efficient
learning (see "This Algorithm Learns Tasks As Fast As We Do").

https://goo.gl/4hRWmY



But deep-learning systems are becoming more capable, especially with the
addition of memory mechanisms. Another group at Google DeepMind recently
developed a network with a flexible kind of memory, making it capable of
performing simple reasoning tasks—for example, learning how to navigate a
subway system after analyzing several much simpler network diagrams (see
"What Happens When You Give a Computer a Working Memory?").

https://goo.gl/IMbG1q



"I think this is a very interesting approach, providing a novel way of doing
one-shot learning on such large-scale data sets," says Sang Wan Lee, who
leads the Laboratory for Brain and Machine Intelligence at the Korean
Advanced Institute for Science and Technology in Daejeon, South Korea. "This
is a technical contribution to the AI community, which is something that
computer vision researchers might fully appreciate."



Others are more skeptical about its usefulness, given how different it still
is from human learning. For one thing, says Sam Gershman, an assistant
professor in Harvard's Department for Brain Science, humans generally learn
by understanding the components that make up an image, which may require some
real-world, or commonsense, knowledge. For example, "a Segway might look very
different from a bicycle or motorcycle, but it can be composed from the same
parts."

其他人質疑它的實用性，並指出它與人類學習的差異。哈佛腦科學系助理教授 Sam


According to both Gershman and Wan Lee, it will be some time yet before
machines match human learning. "We still remain far from revealing humans’
 secret of performing one-shot learning," Wan Lee says, "but this proposal
clearly poses new challenges that merit further study."




4.完整新聞連結 (或短網址):
https://goo.gl/ywNfG8


5.備註:

one-shot learning


--
※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 36.227.0.122
※ 文章代碼(AID): #1O7FLEtz (Gossiping)
※ 文章網址: https://www.ptt.cc/bbs/Gossiping/M.1478292814.A.DFD.html