人工智能英文版论文

人工智能英文版论文

人工智能

(AI)

< br>系统被认为是神经网络，可以识别图片，翻译，甚

至精通古老的游戏。以下是

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人工智能英文版论文篇一

Google's AI Reasons Its Way

around the London

Underground

谷歌人工智能推导出环绕

伦敦地铁系统的路线

DeepMind‟s

latest

technique

uses

external

memory to solve tasks that require logic and reasoning;a

step toward more humanlike AI

维和推理能力的任务

By

Elizabeth

Gibney,

Nature

magazine

on

October

14,

2016

伊丽莎白

.

吉布尼

2016

年

10

月

14

日发表于《自然》杂志

深度思维最新技术使用了外部存储来解决需要逻辑思

Artificial-intelligence

(AI)

systems

known

as

neural

networks can recognize images, translate languages and even

master

the

ancient

game

of

Go.

But

their

limited

ability

to

1

represent complex relationships between data or variables has

prevented them from conquering tasks that require logic and

reasoning.

人工智能

(A I)

系统被认为是神经网络，可以识别图片，翻译，甚

至精通古 老的游戏。

但他们描绘数据或变量之间的复杂关系的能力有

限， 这妨碍了他们克服需要逻辑思维和推理能力的任务。

In a paper published in Nature on October 12, the

Google-owned

company

DeepMind

in

London

reveals

that

it

has

taken

a

step

towards

overcoming

this

hurdle

by

creating a neural

network with an external memory. The combination allows

the

neural

network

not

only

to

learn,

but to

use memory to

store

and

recall

facts

to

make

inferences

like

a

conventional

algorithm.

This

in

turn

enables

it

to

tackle problems

such as

navigating

the

London

Underground

without

any

prior

knowledge

and

solving

logic

puzzles.

Though

solving

these

problems

would

not

be

impressive

for

an

algorithm

programmed

to

do

so,

the

hybrid

system

manages

to

accomplish this without any predefined rules.

在

10

月

12

日《自然》杂志中发表的一篇论文中，谷歌在伦敦

的子公司深度思维展示了他们通过结合外部存储创造了一个神经网

络，

来进一步克服这些障碍。

这种和外部存储的结合不仅允许神经网

2

络学习，

还可以通过存 储器来存储和回忆事件，

并以此来像正常情况

那样做推断。

这反过来能够让它解决难题，

比如在没有任何经验的情

况下操控伦敦地铁，

比如解决逻辑谜题。

尽管对于一 个算法程序来说

做到这点并不会令人印象深刻，

但这个混合系统 在没有任何先决条件

的情况下做到了这点。

Although

the

approach

is

not

entirely

new;DeepMind

itself reported attempting a similar feat in a preprint in 2014;

“

the progress made in this paper is remarkable

”

, says Yoshua

Bengio, a computer scientist at the University of Montreal in

Canada.

虽然这个方法不是一个全新的技术

深度思维自己就在

2014

< br>年

报告过他们尝试了一种相似的技术

;;

但“在论文中的这个进步是非凡

的”

，加拿大蒙特利尔 的计算机学家本吉奥

.

本希奥赞叹道。

MEMORY MAGIC

记忆魔法

A

neural

network

learns

by

strengthening

connections

between virtual neuron-like units. Without a memory, such a

network

might

need

to

see

a

specific

London

Undeground

map thousands of times to learn the best way to navigate the

tube.

< br>神经网络通过加强虚拟神经元之间的联系来学习。

如果没有存储

< br>器，

这样一个网络可能需要看一副特定的伦敦地铁地图数千次来学习

3

最佳路线。

DeepMind's

'differentiable

neural

computer';can

make

sense

of

a

map

it

has

never

seen

before.

It

first

trains

its

neural

network

on

randomly

generated

map-like

structures

(which

could

represent stations connected by lines, or

other relationships), in the process learning how to store

descriptions of

these relationships in

its

external

memory as

well as answer questions about them. Confronted with a new

map,

the

DeepMind

system

can

write

these

new

relationships;connections

between

Underground

stations,

in

one example from the paper;to memory, and recall it to plan a

route.

深度思维的新系统

;;

他们称它为微分神经计算机

;;

可以理解它从

未见过的地图。

第一次训练神经网络是在 随机生成的类似结构的地图

上

(

被铁路 线链接的车站，或者其他关系

)

，在这个过程中学习如何将

这些关系的描述存储在它的外部存储器并且回答问题。

面对一个新的

地图，深度思维的系统可以把这些新关系

;;

< br>按照一个图纸上例子来连

接各地铁站之间的关系

;;

写到存储器，并能够回忆这些关系然后计划

路线。

< br>

DeepMind‟s

AI

system

used

the

same

technique

4

new

system;which

they

call

a