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technology
kē jì
科技
·HSK 5

researchers
yánjiū rényuán
研究人员
to propose
tíchū
提出
new
xīn
method
fāngfǎ
方法
expected to
yǒuwàng
有望
(object marker)
jiāng
artificial intelligence
réngōng zhìnéng
人工智能
energy consumption
nénghào
能耗
to reduce
jiàngdī
降低
one hundredfold
yī bǎi bèi
一百倍

one
yī gè
一个
international
guójì
国际
research
yánjiū
研究
team
tuánduì
团队
recently
jìnrì
近日
published
fābiǎo
发表
one (item)
yī xiàng
一项
new
xīn
study
yánjiū
研究
claims
shēngchēng
声称
can
néng
(object marker)
jiāng
artificial intelligence
réngōng zhìnéng
人工智能
(possessive particle)
de
energy consumption
nénghào
能耗
at most
zuì duō
最多
to reduce
jiàngdī
降低
one hundredfold
yī bǎi bèi
一百倍
at the same time
tóngshí
同时
to improve
tígāo
提高
model
móxíng
模型
(possessive particle)
de
accuracy
zhǔnquèxìng
准确性

April 9, 2026 at 12:45 PM

according to
Science Daily
Kēxué Rìbào
《科学日报》
recently
jìnrì
近日
reported
bàodào
报道
one
yī gè
一个
international
guójì
国际
research
yánjiū
研究
team
tuánduì
团队
has proposed
tíchū
提出
(completed action marker)
le
a kind of
yī zhǒng
一种
expected to
yǒuwàng
有望
substantially
dàfú
大幅
to reduce
jiàngdī
降低
artificial intelligence
réngōng zhìnéng
人工智能
energy consumption
nénghào
能耗
(possessive particle)
de
new
xīn
method
fāngfǎ
方法
researchers
yánjiū rényuán
研究人员
stated
biǎoshì
表示
this
gāi
method
fāngfǎ
方法
in
zài
testing
shíyàn zhōng
实验中
(object marker)
jiāng
energy consumption
nénghào
能耗
reduced
jiàngdī
降低
(completed action marker)
le
up to
zuì duō
最多
one hundred times
yī bǎi bèi
一百倍
in addition
cǐwài
此外
this
gāi
method
fāngfǎ
方法
also
hái
improved
tígāo
提高
(completed action marker)
le
model
móxíng
模型
(possessive particle)
de
accuracy
zhǔnquèxìng
准确性
this kind of
zhè zhǒng
这种
method
fāngfǎ
方法
combines
jiéhé
结合
(completed action marker)
le
traditional
chuántǒng
传统
(possessive particle)
de
neural networks
shénjīng wǎngluò
神经网络
and
similar to
lèisì
类似
humans
rénlèi
人类
(possessive particle)
de
symbolic
fúhào
符号
reasoning
tuīlǐ
推理
analysts
fēnxī rénshì
分析人士
pointed out
zhǐchū
指出
artificial intelligence
réngōng zhìnéng
人工智能
data center
shùjù zhōngxīn
数据中心
(possessive particle)
de
electricity
diànlì
电力
consumption
xiāohào
消耗
has already
yǐjīng
已经
become
chéngwéi
成为
global
quánqiú
全球
attention / concern
guānzhù
关注
(possessive particle)
de
issue
wèntí
问题
as
suízhe
随着
large-scale
dàxíng
大型
models
móxíng
模型
scale
guīmó
规模
continually
bùduàn
不断
expanding
kuòdà
扩大
energy
néngyuán
能源
cost
chéngběn
成本
also
accordingly
suí zhī
随之
rising
shàngshēng
上升
if
rúguǒ
如果
this
zhè xiàng
这项
technology
jìshù
技术
can
nénggòu
能够
large-scale
dà guīmó
大规模
be applied
yìngyòng
应用
will
jiāng
be expected to
yǒuwàng
有望
significantly
xiǎnzhù
显著
reduce
jiàngdī
降低
operating
yùnyíng
运营
costs
chéngběn
成本
this
cǐ cì
此次
study
yánjiū
研究
for
wèi
sustainable
kě chíxù
可持续
artificial intelligence
réngōng zhìnéng
人工智能
development
fāzhǎn
发展
provides
tígōng
提供
(completed action marker)
le
new
xīn
(possessive particle)
de
line of thinking
sīlù
思路

New Words

réngōng zhìnéng
人工智能
artificial intelligence
nénghào
能耗
energy consumption
jiàngdī
降低
to reduce
zhǔnquèxìng
准确性
accuracy
shénjīng wǎngluò
神经网络
neural networks
tuīlǐ
推理
reasoning
chéngběn
成本
cost

News in English

Science & Technology

Researchers Propose New Method Expected to Cut AI Energy Use by 100 Times

An international research team has recently published a new study claiming a method that can cut AI energy consumption by up to 100 times while improving model accuracy.

According to Science Daily, an international research team has proposed a new method expected to substantially reduce artificial intelligence energy consumption. Researchers stated that this method reduced energy consumption by up to one hundred times in testing and also improved model accuracy. This method combines traditional neural networks with symbolic reasoning similar to human thinking. Analysts point out that artificial intelligence data center electricity consumption has already become a global concern. As large-scale model scales continue to expand, energy costs are also rising accordingly. If this technology can be widely applied, it is expected to significantly reduce operating costs. This research provides new thinking for sustainable artificial intelligence development.

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