How big is AI's carbon footprint? | BBC News

BBC News
9 May 202421:53

Summary

TLDR本期《AI解码》节目深入探讨了人工智能(AI)与能源消耗之间的关系。节目指出,AI的发展可能导致全球碳排放量增加80%,引发了对可持续性的关注。AI模型训练和运行需要大量能源,这与气候变化问题紧密相连。节目邀请了多位专家,包括气候研究员Sasha Loney和教育科技公司Century的CEO Pri Lani,讨论了AI模型的能源效率问题,并介绍了Hugging Face公司在AI模型能效标签方面的创新。此外,节目还涉及了AI在艺术创作、生物识别以及与动物沟通等领域的潜在应用,同时提出了相关的伦理和法律问题。

Takeaways

  • 🌍 人工智能(AI)的发展可能导致全球碳排放量增加80%,这引起了气候专家的担忧。
  • 💻 AI模型运行在由金属和塑料构成的云服务器上,每次查询AI模型都会对地球造成一定的成本。
  • ⚡ 根据Vox的报告,AI已经消耗相当于一个小国的能源量,而我们仍处于AI发展的初期阶段。
  • 📈 AI和机器学习对数据存储的需求不断增长,这直接影响了能源消耗和环境影响。
  • 🚗 训练大型语言模型的能耗巨大,每次查询也会产生能耗,这可能导致相当于数百万辆汽车一年的碳排放量。
  • 🔋 AI模型,如聊天机器人和多模态模型(涉及图像和视频),消耗的电量非常巨大。
  • 🌐 尽管存在能源瓶颈问题,但大型科技公司如Google和Microsoft正在投资建立自己的能源系统,以支持AI的发展。
  • 🌿 AI公司Hugging Face正在开发一个系统,为AI模型提供能源效率评级,帮助用户在选择模型时考虑能效。
  • ⚖️ 随着AI的发展,国家主权和对能源控制的需求也在增长,各国都在寻求建立自己的云基础设施。
  • 🤖 AI技术的进步,如深度伪造(Deepfakes)和AI生成的内容,引发了关于形象权、现实与虚构界限以及伦理问题的讨论。
  • 🐋 AI还有潜力帮助我们解码动物语言,例如通过分析鲸鱼的叫声和点击声,这可能有助于我们更好地理解动物行为。

Q & A

  • AI在战场上的进步上周讨论了哪些主题?

    -上周的节目中讨论了人工智能在战场上的进展,特别是AI在军事应用方面的发展。

  • 为什么AI的发展可能会导致全球碳排放量增加80%?

    -AI的发展需要大量的能源,特别是用于训练大型AI模型,如语言模型。这些模型运行在数据中心,消耗大量电力,如果这些电力来源于非可再生能源,将导致巨大的碳排放。

  • Sasha Luch是哪家公司的首席气候研究员?

    -Sasha Luch是AI公司Hugging Face的首席气候研究员。

  • 根据Vox的报告,AI目前消耗的能量相当于哪个国家的能源消耗量?

    -根据Vox的报告,AI目前消耗的能量相当于一个小国的能源消耗量。

  • AI模型训练和查询对环境有何影响?

    -AI模型的训练和每次查询都会消耗能量,尤其是大型语言模型,它们比以往的模型更加耗能。这种能源消耗与数据中心的电力来源有关,如果使用非可再生能源,将导致潜在的大量碳排放。

  • 为什么说AI的发展可能加剧气候变化问题?

    -AI的发展需要大量的计算资源,这通常意味着需要更多的电力。如果这些电力主要来自化石燃料等非可再生能源,那么AI的发展将增加全球的碳排放,从而加剧气候变化问题。

  • Chris Starky是哪家公司的CEO,他们的主要业务是什么?

    -Chris Starky是NextGen Cloud的CEO,他们的主要业务是构建大规模的GPU集群,专注于提供高密度的加速计算服务。

  • 如何优化AI模型以减少能源消耗?

    -优化AI模型以减少能源消耗可以通过提高基础设施效率、改进冷却技术、使用优化的硬件平台等方式实现。例如,使用Nvidia的Blackwell平台可以显著降低成本和能源消耗。

  • 为什么各国都希望拥有自己的主权云服务?

    -各国希望拥有自己的主权云服务是为了控制自己的数据和计算资源,确保数据安全和隐私,同时减少对外国服务的依赖。

  • 为什么AI在艺术创作中的应用引起了好莱坞创意产业的敏感反应?

    -AI在艺术创作中的应用引起了好莱坞创意产业的敏感反应,因为人们担心AI可能会取代人类艺术家的工作,破坏艺术创作中的人类体验,并且可能对艺术和文化媒介产生负面影响。

  • AI生成的普京传记电影引发了哪些讨论?

    -AI生成的普京传记电影引发了关于形象权、现实与虚构之间的界限模糊、以及深度伪造技术(deepfakes)的伦理问题的讨论。

  • 科学家如何使用AI来研究鲸鱼的交流?

    -科学家通过记录鲸鱼的叫声和点击声,寻找其中的模式,并将这些模式与鲸鱼的行为(如深潜、上浮或觅食)相关联。然后,他们尝试将这些模式与英语语言进行对比,创建一种语言模型,以预测鲸鱼的行为,并尝试与鲸鱼进行交流。

Outlines

00:00

🌏 AI与能源消耗:全球碳排放的潜在增长

本段落讨论了人工智能(AI)在能源消耗方面的增长及其对全球碳排放的潜在影响。提到了AI技术进步可能带来的全球碳排放量80%的增长,以及AI模型运行和训练所需的巨大能源消耗。强调了AI对可持续性的影响,包括数据中心的物理组成和它们所消耗的大量能源。提到了气候专家Sasha Luch的观点,以及AI公司Hugging Face的相关工作,讨论了AI模型对环境成本的影响。

05:01

🔍 AI发展与环境影响:挑战与机遇

这一段落深入探讨了AI对环境的影响,特别是大型语言模型的能源消耗。讨论了AI在不同行业的广泛应用,以及政府在监管AI环境影响方面的挑战。提到了训练大型语言模型的能源强度,以及每个查询所消耗的能源。还提到了微软投资数十亿美元建造超级计算机Stargate的计划,以及这可能对能源系统产生的影响。此外,还讨论了Hugging Face上AI模型的能源效率问题,以及如何通过提供能源使用信息来帮助用户做出更环保的选择。

10:02

🚀 数据中心的能源优化与未来挑战

本段落讨论了数据中心在能源使用和冷却技术方面的挑战,特别是随着AI和高性能计算需求的增长。提到了数据中心功率密度的增长,以及传统的空气冷却方法可能不再可持续。介绍了液冷直接到芯片等新的冷却方法,以及这些方法如何提高能效。同时,也提到了国家对主权云的需求,以及如何在全球范围内实现数据中心的可持续能源供应。

15:06

🎨 AI在艺术和娱乐行业的影响

这一段落讨论了AI在艺术和娱乐行业中的应用,以及它对人类艺术家和创作过程的潜在影响。提到了苹果公司发布的新iPad广告,以及它在好莱坞创意产业中引发的争议。讨论了AI在音乐、电影制作中的应用,以及它可能对艺术和文化领域构成的威胁。同时,也提到了AI生成的普京传记电影,以及它在法律和伦理上的问题。

20:08

🐋 AI与动物交流:未来的可能性

本段落探讨了AI在动物交流研究中的潜在应用,特别是科学家如何使用AI来解码和理解鲸鱼的语言。讨论了鲸鱼使用摩尔斯电码节奏的可能性,以及AI如何帮助创建这种语言的模型。提到了通过AI模型预测鲸鱼行为的实验,以及未来可能实现与动物交流的前景。同时,也提出了关于人类与动物互动的伦理问题。

Mindmap

每周主题探讨
上周主题:AI在战场的应用
本周主题:AI与能源消耗
AI解码节目
节目介绍
AI模型的能源成本
AI与全球碳排放增加的关联
气候专家的警告
AI的能源消耗
云服务的物理组成
AI查询的环境成本
可持续性问题
数据中心的电力消耗
非可再生能源的碳排放问题
AI模型训练与部署
不同AI模型的能源消耗比较
生成性AI模型的高能耗问题
AI模型的能源效率
AI与能源消耗
AI增长与气候危机的交织
AI帮助解决气候问题的可能性
AI与气候变化
大型语言模型的能源消耗
AI查询对环境的影响
Sasha Lney的见解
数据中心的可再生能源使用
优化AI模型以减少能源消耗
Chris Starky的见解
专家观点
AI发展与环境影响
大型技术公司建设自己的能源系统
对全球能源去碳化的潜在影响
技术公司的能源系统
Hugging Face的AI模型效率评价
能源之星评级系统的开发
AI模型的效率与性能
各国对主权云服务的需求
可持续基础设施的挑战
国家主权与云服务
AI的未来与挑战
Apple iPad广告的争议
AI对艺术和文化领域的威胁
AI在艺术创作中的应用
AI生成的普京传记电影
形象权和真实性的问题
AI生成的内容与法律问题
科学家使用AI研究鲸鱼语言
AI在动物交流中的潜力与伦理问题
AI与动物交流
AI的其他应用与争议
人工智能与环境影响
Alert

Keywords

💡人工智能

人工智能(AI)是指由计算机系统执行的模仿人类智能的技术,它可以包括学习、推理、解决问题、感知、理解语言等能力。在视频中,人工智能是核心主题,讨论了其在战场上的应用、对全球碳排放的影响以及其在能源消耗方面的可持续性问题。

💡气候变化

气候变化是指由于全球碳排放增加导致的全球平均温度上升现象。视频中提到,气候专家警告人工智能的发展可能会导致全球碳排放量增加80%,这强调了AI发展与气候变化之间的紧密联系。

💡可持续性

可持续性通常指的是在不损害未来代际满足自身需求的能力的情况下满足当前需求。视频中讨论了AI模型运行的云基础设施是由金属、塑料构成,并且需要大量能源来驱动,这引发了对AI发展可持续性的关注。

💡数据中心

数据中心是存储、处理和分发大量信息的设施,通常包含许多服务器和数据存储系统。视频中提到,AI模型训练和部署在数据中心,这些中心消耗大量电力,如果使用非可再生能源,则可能导致巨大的碳排放。

💡能源消耗

能源消耗指的是在执行某项活动或过程中所使用的能量总量。视频中特别提到了AI模型,尤其是大型语言模型,如Chat GPT,其训练和每次查询都涉及显著的能源消耗,对环境有重大影响。

💡再生能源

再生能源是指来自自然过程中不断补充的能源,如太阳能、风能、水能等。视频中提到,如果数据中心能够完全使用再生能源供电,那么可以减少AI对环境的负面影响。

💡大型语言模型

大型语言模型是指能够处理和生成自然语言文本的复杂计算模型,如Chat GPT。视频中讨论了这些模型的能源密集型训练过程,以及它们在日常使用中的能源消耗。

💡多模态模型

多模态模型是指能够处理并整合来自多种感官输入(如图像、声音、文本)的AI模型。视频中提到,除了语言模型外,还有处理图像和视频的多模态模型,它们同样在能源消耗方面具有显著影响。

💡冷却技术

冷却技术是用于降低设备温度的技术,特别是在数据中心中,冷却技术对于维持服务器和其他硬件的适宜工作温度至关重要。视频中提到了液体冷却直接到芯片的技术,这是一种提高冷却效率的新方法。

💡数字主权

数字主权指的是一个国家或地区对其数据和网络空间的控制权。视频中讨论了各国希望拥有自己的云基础设施和数据中心,这涉及到数字主权的问题,以及如何在全球范围内实现AI的可持续和道德发展。

💡AI生成内容

AI生成内容是指利用人工智能技术自动创建的文本、图像、音频或视频内容。视频中提到了AI生成的电影和生物特征模拟,如AI生成的普京传记片,这引发了关于AI在艺术和娱乐行业中角色的讨论。

Highlights

节目探讨了人工智能(AI)在战场上的进步,以及AI消耗的能源问题。

气候专家警告,AI的发展可能导致全球碳排放量增加80%。

AI模型运行在由金属和塑料构成的云上,需要大量能源。

每次AI模型的查询都会对地球造成代价。

Vox报告指出,AI的能源消耗量已相当于一个小国的能源消耗。

AI和语言模型,如Chat GPT,比以前的模型更加耗能。

2019年的数据显示,流媒体视频的碳排放量相当于开车160米。

Hugging Face发布的Llama 3模型产生了约2290吨的二氧化碳排放。

Mid Journey平台半小时内产生的10张独特图像的能耗相当于手机电池充电2.5次。

AI的能源瓶颈问题和对可再生能源的需求。

Hugging Face的Sasha讨论了大型语言模型的能源使用情况和环境影响。

微软计划投资1000亿美元用于名为Stargate的超级计算机,由多个核电站供电。

Hugging Face正在开发AI模型的能效评级系统。

NextGen Cloud的Chris Starky讨论了如何通过可再生能源为数据中心提供动力。

讨论了数据中心的优化方法,包括冷却技术和硬件优化。

对AI在艺术领域的影响进行了讨论,包括对创意产业的敏感性和AI在艺术创作中的潜力。

AI生成的普京传记电影预告片引发关于形象权和现实与虚构界限的讨论。

科学家使用AI来解码和理解鲸鱼的语言,可能有助于与动物沟通。

节目最后提醒观众,所有节目内容均可在BBC的YouTube频道上观看。

Transcripts

00:00

you're watching the context it's time

00:01

for our weekly regular segment AI

00:08

decoded welcome to Aid decoded every

00:10

week in this program we take you deep

00:12

into the world of artificial

00:14

intelligence and what we've tried to do

00:17

is focus these programs on one

00:19

particular theme so last week we looked

00:21

at the advance of AI on the battlefield

00:23

tonight we're going to consider

00:25

something a number of you have raised

00:27

and that is AI and energy or more

00:30

specifically the energy that AI consumes

00:33

there are climate experts who are

00:35

warning that the advance of artificial

00:37

intelligence could lead to an 80%

00:40

increase in our Global carbon emissions

00:43

well let's start with sustainability

00:45

because that cloud that AI models live

00:48

on is actually made out of metal plastic

00:51

and powered by vast amounts of energy

00:53

and each time you query an AI model it

00:56

comes with a cost to the planet that is

00:59

Sasha luch lead climate researcher at

01:01

the AI company hugging face she's going

01:03

to be joining us from Montreal in just a

01:05

moment how much energy are we using

01:08

while according to this report from Vox

01:09

AI is already consuming as much energy

01:12

as a small country and we are only at

01:15

the beginning the next web says this is

01:18

where our two existential crises collide

01:21

with one another climate crisis and the

01:24

exponential growth of AI can one help

01:27

solve the other or will it exac acate

01:30

the problem here in the studio our

01:31

regular AI contributor PRI Lani CEO of

01:34

the AI powered education company Century

01:37

T welcome good to see you right look

01:40

when it comes to digital everybody knows

01:42

there is a cost there's the wiring

01:44

there's the chips there's the precious

01:45

metals there's the water that cools the

01:47

data processing centers what we often

01:50

don't don't talk about is the energy

01:52

that goes into producing Ai and

01:54

specifically the training of AI language

01:58

models like chat GPT yeah why because

02:01

they're far more hungry than what we've

02:03

seen before so you have these AI models

02:05

that you and I have talked about we've

02:07

shown we've played with right these air

02:09

models are trained and they're deployed

02:11

in data centers and the data centers

02:13

consume vast amounts of electricity so

02:16

if you are powering the data center with

02:19

non-renewable sources then essentially

02:21

you have potentially huge carbon

02:24

emissions and those particular models

02:26

that we're talking about these

02:26

generative AI models not just language

02:28

models right not just llm in terms of

02:30

chat models but also the multimodal

02:32

models images videos consume an enormous

02:35

amount so if you remember in about 2019

02:37

we used to say if you stream an hour of

02:39

video right it was 36 grams of CO2 just

02:42

to put that into context for everyone

02:44

because that's why we're here okay

02:45

that's driving a car typical kind of

02:47

petrol car about 160 m now meta they

02:51

just released llama 3 on hugging face

02:53

one of my favorite platforms okay and

02:56

that model they said um emitted about 2,

02:59

290 metric tons of CO2 and so you're

03:03

going to say put that into context

03:05

that's about 500 average cars and what

03:08

they Adit in an entire year so you start

03:11

to get what we had with digital which is

03:13

what you asked all the way to these

03:16

really huge AI models and I just want to

03:18

show you uh Christian what I looked at a

03:20

little bit earlier before I came in so I

03:22

just spent I just looked at half an hour

03:24

of the images produced on Mid Journey so

03:26

this is mid Journey that you're seeing

03:27

now okay so you've got about 10 unique

03:29

images that have been produced by this

03:31

particular platform and that was about

03:33

half an hour about 4:30 p.m. UK time

03:35

today okay just those images and that's

03:38

just on one channel of where you can

03:41

find mid Journey okay one there are

03:43

there are lots hundreds and hundreds but

03:45

just those 10 alone would take about two

03:46

and a half times the battery charge of a

03:50

phone so this is a significant problem

03:52

and if anyone's interested there are

03:54

some great interviews actually with Mark

03:55

Zuckerberg and others that say that this

03:57

is there is going to be an energy Bott

03:59

neck right and obviously for activists

04:02

this is a significant problem so I'm

04:03

really looking forward to having Sasha

04:05

on to discuss this with her well let's

04:06

bring her in Sasha lney uh she is the

04:08

lead climate researcher at hugging face

04:11

uh which priet really likes and she

04:13

spent nearly a decade looking at data

04:15

storage and machine learning and how

04:17

this all contributes to our energy

04:19

consumption welcome to the

04:20

program thank you for having me right

04:23

before we before we get started maybe I

04:25

could just frame our conversation with

04:26

an image that you sent us actually that

04:28

that for me really underlines just how

04:31

expensive our digital usage has become

04:33

so here is Google's annual energy use

04:35

18.3 trillion Watts that's 10 or 15% of

04:39

that is going towards Ai and here's what

04:42

the Republic of Ireland uses in any one

04:45

year

04:46

29.3 trillion wat so one company Sasha

04:49

is now consuming about 2third of what a

04:51

small country uses each year and yet I

04:54

don't hear governments talking about

04:58

that problem

05:00

I think it's because uh AI is really a a

05:02

horizontal it's not like your typical

05:04

vertical like agriculture transportation

05:06

and it actually affects all Industries

05:09

anything that uses AI anything from

05:11

navigation to web search and so I think

05:14

governments don't really know what

05:15

bucket to put it in and when you don't

05:16

know what bucket it goes in you tend to

05:18

kind of let it slip through the cracks

05:21

and Sasha can you describe this in terms

05:23

of the scale of large language models so

05:26

what their usage is like and actually

05:28

tell us what therefore that means in

05:29

terms of energy consumption and the

05:31

impact on the

05:33

environment definitely language models

05:35

have become one of the most popular

05:37

usages of AI and they're being deployed

05:39

in everything nowadays you can talk to

05:41

to your stove or your fridge and uh in a

05:44

recent study we did we found that so

05:46

training a large language model is

05:48

definitely very uh energy intensive and

05:50

that's the the numbers that you gave but

05:52

actually each query also uses energy and

05:55

depending on the size of the model 200

05:57

to 500 million queries will equal the

06:00

amount of energy used for training so it

06:02

might seem like a lot but uh for chat

06:05

GPT it it it averages around 10 million

06:07

users a day so within a couple of weeks

06:09

you have this vast amount of energy that

06:12

you know is equivalent to all these cars

06:14

over a year but just with people using

06:16

the tool did you see this story this

06:18

week um that microsof Microsoft are

06:21

going to plow in about a hundred billion

06:24

dollars into this super computer called

06:26

Stargate um and it's going to be powered

06:28

by not one they say not one but several

06:32

nuclear power stations and and that got

06:34

me thinking because I I have heard Sam

06:36

Alman at at chat gbt talk about this

06:39

open AI he's talked about this and he he

06:41

says yeah that that's how we're going to

06:42

have to work we're going to have to

06:43

create our own Energy Systems is that is

06:45

that perhaps where new energy comes from

06:47

these the biggest companies in the world

06:49

driving the

06:50

investment well I mean it is it is a

06:52

problem because to what extent do you

06:54

want big tech companies to be building

06:55

their own nuclear reactors and um maybe

06:58

that energy can be better used used uh

07:00

for other things right because we should

07:01

be decarbonizing our energy globally and

07:04

currently if we're going to funnel all

07:05

that investment into the energy use for

07:08

AI maybe other sectors will get

07:09

overlooked and we should be focusing on

07:11

those if we really want to decarbonize

07:14

and Sasha what I'm quite excited about

07:15

is look when we talk about AI models and

07:17

I was on hugging face today so for those

07:19

who don't know what it is it's an AI

07:20

model repository and playing with llama

07:24

3 I like the fact that models now have a

07:26

description potentially of you know how

07:28

much energy they have used you've

07:30

produced something pretty novel at

07:32

hugging face haven't you so um when

07:34

you're when you're building these AI

07:35

models we're focused on the latency the

07:37

speed of the model and how performant

07:39

the model is but you're potentially

07:41

creating a like a little trip advisor of

07:43

how efficient it is so tell us about

07:45

that yeah currently when people go on

07:48

hugging phas they tend to shop around a

07:49

little bit for models that work for

07:51

their for the task that they want to do

07:52

it could be language it could be it

07:54

could be audio it could be image

07:56

generation now we're even looking at

07:57

video and typically they'll look at

07:59

things like performance or latency but

08:01

I'm calculating the energy usage across

08:03

all different tasks and models on on the

08:06

Hub on the hugging hugging face website

08:08

and I want to provide that information

08:09

to people so that they can Factor it in

08:11

so maybe this model uh is not only

08:13

faster but it's more efficient maybe

08:14

this model is slightly less performant

08:16

but it's vastly more efficient so I'm

08:18

I'm develop en I'm developing energy

08:20

star ratings for AI models well listen

08:22

since we're talking about how we

08:23

mitigate the problem let me introduce

08:24

you to Chris starky because he is the

08:26

CEO of the London Bay startup next Jen

08:28

cloud and they been in business since

08:30

2020 and they Source data centers that

08:33

are entirely powered by renewable energy

08:36

welcome Chris to the program tell us

08:38

what you do and how your clients would

08:40

typically

08:41

work well I mean so I mean we're on the

08:45

other side of the fence where our

08:47

business is all about building you know

08:48

large scale um GPU clusters basically H

08:52

we've got a core focus on building high

08:54

density accelerate accelerated compute

08:57

basically this is typically what

08:59

companies um maybe that use hugging face

09:02

or companies that are using uh or

09:04

building their their own foundational

09:06

models this is the type of

09:07

infrastructure that they would be uh

09:10

consuming um and our mission effectively

09:12

is to deliver this at scale 100%

09:14

renewably powered and Chris how do we

09:17

achieve this sort of optimization of

09:20

models because there's all sorts of

09:21

things that we could be looking at so

09:23

we're looking at infrastructure

09:24

efficiency uh that I know you look at in

09:27

terms of cooling Technologies I'd love

09:29

to hear more about that and then also

09:31

the optimized um Hardware the say for

09:34

example Nvidia brought out the black

09:35

wellpath platform and what they say with

09:37

Blackwell is that it reduced the costs

09:38

and energy consumption by about 25x for

09:42

tech companies so can you describe these

09:44

sorts of methodologies you know how

09:46

Reliant we are on these and when we talk

09:48

about cooling methodologies can you

09:49

explain that to us you know we know that

09:52

data centers and we know that these

09:54

racks need cooling but it would be

09:56

really great if you can walk the

09:57

audience of the process how long have we

09:59

got um I'm not too sure how how long

10:01

we've got but uh not long

10:05

so so I a short high level I mean you

10:09

know only a few years ago you know we

10:11

were building um high performance

10:14

environments that you know maybe 10 to

10:16

to 20 kilowatts um was was was deemed as

10:18

kind of high dense um and uh you know

10:22

now quite commonly we're building out um

10:24

you know environments that are 50 to 60

10:27

kilowatt per rack um and the new

10:30

iteration the next generation of chips

10:32

um and some of the infrastructure you

10:34

know that that we're bringing into play

10:35

for next year 2025 you know we're going

10:38

to be north of 120 kilowatts per act so

10:41

you know we're seeing a a clear you know

10:44

uh increase in exponential growth in

10:46

density um and you know that that's

10:49

great we can fit more power into Data

10:51

Centers but you know the the the data

10:52

centers will eventually be drawing a

10:54

huge amount of resource a huge amount of

10:56

energy so it's it's just unsustained

10:59

able to to have traditional techniques

11:01

of calling like air coing which is quite

11:04

common now um so you know we've got a

11:06

keen focus on on you know triing and

11:09

testing new ways to cool the chips um

11:11

one of the new ways um is something

11:13

called you know liquid cool direct to

11:15

chip um this brings a huge amount of

11:17

efficiency but at the same time you know

11:19

as chips get more powerful you know

11:22

we're we're obviously drawing a huge

11:23

amount more power you know per square

11:25

feet or if you like or per square foot

11:27

in each dat Center so you know yeah St

11:31

no I'm just going to say because we're

11:32

really pressed for time but I just want

11:33

to get a really quick final answer from

11:34

both of you maybe you could chip in on

11:36

this um pardon the P um what about

11:39

sovereignty because everybody wants

11:41

control of their own computers and and

11:43

obviously some of that's going to come

11:44

down to where the cloud is what energy

11:46

they have what energy they can generate

11:49

if we're going to make this available to

11:50

everybody how concerned are you both by

11:52

that Sasha let me start with you first I

11:55

feel that AI is really uh slipping

11:57

through the cracks when it comes to

11:58

accounting for energy and carbon because

12:00

it's often companies in one country

12:02

using cloud comput in another country

12:05

and often the the for example every time

12:06

I talk to Cloud providers they like we

12:08

don't know what's running on our centers

12:09

we it could be streaming it could be AI

12:11

so it's really hard for them to count uh

12:13

to to account for this energy usage so

12:15

every time I'm like okay give me a

12:16

number they're like we don't have any

12:17

numbers so I've seen that uh it's

12:20

currently not being accounted for let's

12:21

say yeah

12:23

Chris yeah I mean well I mean if they if

12:25

they're trying to do it sustainably I

12:27

think a lot of you know countries will

12:28

will struggle you know they they they

12:30

absolutely will there's just not enough

12:32

infrastructure you know locally to to to

12:34

provide sustainable infrastructure not

12:36

at the scale of the demand that we're

12:37

seeing currently every country is going

12:39

to want a sovereign Cloud they're all

12:41

absolutely going for it right now

12:43

everyone wants their own Sovereign gbt

12:45

for example um they're not going to be

12:47

able to do it currently certainly not

12:48

here in the UK I do not think no I'm PR

12:51

was just saying everybody wants a super

12:52

computer every wants a super computer

12:54

also as well I mean you know I just

12:55

thinking back to G20 you know when when

12:57

you you know to to to the the climate

13:00

conferences and and and you know when

13:02

people talk about carbon Footprints and

13:04

and what belongs to that carbon

13:06

footprint we talk about emissions but we

13:08

never talk about Cloud power or or

13:11

generating comp you know computer

13:13

generating it's the length of time that

13:14

will take for Supply to to create these

13:17

renewable energy you know data centers

13:20

100% to the demand think about how quick

13:22

it was that chat GPT exploded right

13:25

amazing Sasha Lon Chris starky amazing

13:28

stuff thank you for being with us here

13:30

on AI deoda come back soon after the

13:32

break Pria will guide us through some of

13:33

the big stories of the week anyone seen

13:35

the ad for the new Apple iPad not

13:38

everyone's cup of tea and I'll show you

13:39

a biopic on President Putin made

13:42

entirely back by Ai and how long before

13:46

we can speak to the animals we'll be

13:48

right

13:51

back you're watching AI decoded now the

13:54

new ad for the AI powered iPad has spot

13:57

quite the backlash among Hollywood

13:59

creatives uh why we will find out

14:02

because it's uh probably something to do

14:04

with this the fact that you can create

14:06

an entire movie now without auditioning

14:07

someone who looks like the Russian

14:09

president this is the AI generated

14:11

President Putin a biopic the guardian

14:13

says will be out for release in

14:15

September uh if you can reproduce

14:17

Vladimir Putin then it stands to reason

14:19

you can recreate anyone there's a story

14:21

here from the times that reports there's

14:22

been a surge in dead Bots or grief Bots

14:24

families using AI to bring their loved

14:26

ones back to life but does it require

14:28

tighter regulation and how long before

14:30

we can understand what the animals are

14:32

saying scientists have spoken to Sky

14:33

News and they say they think AI will one

14:36

day help them communicate with a sperm

14:38

well uh Dr doitt I presume let's start

14:42

with uh this huge sensitivity forer that

14:45

there is within the creative arts

14:46

industry um you know about about what AI

14:50

is doing replacing Talent destroying The

14:53

Human Experience of playing an

14:55

instrument or writing a song and so with

14:56

that in mind let me just play you the

14:58

new ad from apple and we'll talk about

15:00

it off the

15:06

B when I'm down and all

15:12

alone All I Ever Need is

15:15

[Music]

15:18

You

15:19

wi

15:24

they and we watch the M SN

15:32

oh yeah it's called Crush um imagine all

15:35

the things that it'll uh be used to uh

15:38

to create Apple CEO Tim Cook said let me

15:40

just uh that it'll uh obviously

15:43

revolutionize uh the Arts industry but

15:45

let me just read you this from Asif

15:47

capadia who is a a writer and director

15:49

she said I don't know why anyone thought

15:51

this AB was a good idea it's the most

15:52

honest metaphor she says for what tech

15:54

companies do to the Arts to musicians

15:57

creators writers filmmakers squeeze them

15:59

use them not pay well take everything

16:01

and that's the point right we've talked

16:02

about this on the program before yeah a

16:05

bit insensitive isn't it it's

16:06

insensitive I think obviously there's a

16:07

lot of anger because of two reasons

16:09

there the threat that AI poses but then

16:11

also it's just this idea that Tech

16:13

really misunderstands the art and the

16:15

fact that you know what's Technology's

16:17

role going to be in diminishing artistic

16:19

and cultural mediums right but what's

16:22

what's really interesting and I think

16:23

there's a really funny video do you want

16:24

to play and just it would take literally

16:27

30 seconds on actually what one director

16:29

created in response to Apple's um

16:32

adverts and I don't know if we can get

16:33

that up right now but it was a filmmaker

16:35

who reversed it and essentially um

16:39

described that you know was it was it

16:40

was a very very good quality video and

16:42

it was essentially all of the Arts all

16:44

the instruments crushing the iPad and it

16:46

was the reverse of that but you know why

16:49

do I not think that this is an actual

16:50

threat okay because instruments are not

16:52

just tools right your guitar your piano

16:54

that's a tactile that's an immersive

16:56

experience I've got a nickon DSR

16:59

um DSLR it enables me to have that sort

17:02

of artistic creativity when I'm taking a

17:04

picture right which you know an iPad as

17:07

great as it is and I love my iPad but

17:09

it's not able to do and then when it

17:11

comes to painting and drawing for

17:12

example you've got these artists that

17:14

use you know different physical mediums

17:15

like oils and acrylics it's not just all

17:17

about that digital photo but it was

17:20

incredibly insensitive I think an apple

17:22

people are wondering what what the

17:23

movies of the future might look like uh

17:25

and what it might do to the Cinematic

17:28

Arts in particular um maybe you should

17:30

have a quick look at this this is the

17:31

new AI biopic it give you an idea

17:34

entirely created by artificial

17:39

intelligence the president has found

17:41

time for you after all this will

17:44

culminate Nuclear

17:47

Strike only I can save you and your

17:50

family from prison Vladimir Putin has

17:52

won with

17:54

5344 of the votes it's an embarrassing

17:57

result what would have been the

18:00

satisfactory result for you Mr President

18:03

100 you interesting thing about that and

18:06

this is what panics people in Hollywood

18:08

is that you don't need auditions for

18:09

people that look like Putin you can

18:11

create Putin they've got a Bill Clinton

18:12

in there looks well it's Bill Clinton I

18:15

mean then there might be an issue over

18:16

that I mean absolutely in terms of

18:18

obviously your image rights right and I

18:20

think that's obviously we talked about

18:21

the legal issues before but I mean you

18:23

can do this at low cost with this sort

18:25

of Technology now right you don't have

18:26

to deal with the egos necessarily right

18:29

that you might otherwise have to but

18:30

there are huge ethical concerns with

18:32

this not just because it's easy to talk

18:33

about that being Putin I think a lot of

18:35

the world would say oh that's fine but

18:37

actually you're starting to potentially

18:38

blur the lines between between reality

18:41

and fiction you can misrepresent in many

18:43

ways I mean you do question why they

18:44

didn't just have an actor in The biopic

18:47

this is clearly just about using that

18:49

technology but I know there's so much

18:51

more to come in the future when we talk

18:52

about deep FES um and whether they're a

18:54

good use or a bad use but you know

18:56

clearly if this was if you if was of

18:59

rishy sunak kissed armor then it it

19:02

would be people would be up in arms

19:03

about it yeah it takes on a much greater

19:05

effect doesn't it when it looks

19:06

specifically like them exactly um look

19:09

the best story of the week is this story

19:10

uh about sperm whales we've known for

19:13

some time of course that their calls and

19:15

their clicks are highly

19:17

sophisticated to coordinate and

19:19

communicate with one another but what if

19:21

there is actually a language and these

19:24

scientists that have been studying the

19:25

Welles in the Eastern Caribbean found

19:27

that they use mors code with a a rhythm

19:29

or sort of tempo that suggests that

19:31

there is some sort of meaning behind it

19:33

and that's where potentially AI comes in

19:37

yeah how how all right okay so when

19:40

you're looking at language okay we map

19:42

language we map the words like imagine

19:44

creating a map of all the words okay and

19:46

where they are on this map and then you

19:47

calculate essentially the statistical

19:49

difference between words okay and this

19:52

is how you end up with these large

19:53

language models and you look at

19:54

similarities between words okay and so

19:57

here what they're looking at is when

19:59

they've got these clicks they're looking

20:01

simplistically for patterns between

20:04

those clicks and then they're actually

20:05

recording what the whales are doing and

20:07

then finding how those patterns might

20:09

relate to diving really deep right or

20:12

coming up or feeding and then they're

20:15

attaching essentially those patterns in

20:18

and creating this map and this language

20:20

and then there will be an exercise where

20:22

they'll try and essentially overlay that

20:25

with the English language the test is

20:27

right when they hear these clicks

20:29

because they're really loud by the way

20:31

so if you watch videos on on these

20:32

whales they're they're super loud right

20:34

um but when they hear those clicks can

20:37

the AO model that they then create

20:40

predict what the whale is going to do

20:42

and then that's how you constantly then

20:44

test and if that works then you can

20:46

extend that a bit further and you can

20:47

then start to create the language and

20:50

talk back to it exactly what people want

20:51

to do is potentially talk to their pets

20:53

the this does raise huge ethical issues

20:55

in terms of how we are interacting with

20:59

animals and whether we should should you

21:02

I don't want to be asked for a biscuit

21:04

every five

21:06

minutes absolutely not but I've talk to

21:09

the whales I don't want my dog to tell

21:11

me that it prefers my husband to me

21:13

that's

21:14

I the worst one yeah exactly but the

21:17

thing is so so obviously I think there's

21:19

a lot we can learn as well um but

21:22

there's going to be huge questions about

21:23

this but lots of people working on bats

21:25

whales elephants and the Ft has done a

21:27

fantastic podcast on it and I suggest

21:29

you listen to it oh go have listen to

21:30

that it's a great story uh

21:32

congratulations to Sky News who actually

21:34

picked that up with the scientists uh

21:35

it's a really good one though um that's

21:37

it from us we're out of time we'll do

21:38

this again same time next week just

21:40

before we go let me remind you that all

21:41

the programs that we do are on uh the

21:44

BBC's YouTube channel uh all the big

21:47

interviews lots of good people coming on

21:48

this program now so if you want to look

21:50

back at previous episodes go and have a

21:52

look at that