How AI Can Help and Hurt the Environment | WSJ Tech News Briefing
Summary
TLDRIn this Tech News Briefing, Zoe Thomas discusses the dual impact of AI on climate change. AI has the potential to reduce climate change by improving efficiency in sectors like aviation and flood forecasting. However, it also contributes significantly to greenhouse gas emissions and water consumption, with models like Bloom and GPT-3 having substantial carbon footprints. The discussion highlights the importance of considering AI's location and integration to minimize its environmental impact, with companies like Google aiming to replenish water usage and suggesting more mindful AI deployment.
Takeaways
- 🌐 Technology, particularly data centers, has a significant impact on the climate, emitting greenhouse gases comparable to the aviation industry and using substantial amounts of water.
- 🤖 Artificial Intelligence (AI) can be utilized to improve efficiency and reduce climate change impacts, such as by optimizing flight paths and predicting floods.
- 🛫 Alphabet's Google and American Airlines have used AI to reduce the creation of vapor trails in planes, which contribute to global warming, and to forecast river floods.
- 💧 AI startups, especially in San Francisco, are simplifying the process for companies to access clean power, which is also being adopted by everyday people.
- 🖼️ Image generators are using AI to depict what a warmer world might look like, including scenarios like ocean encroachment and wildfires.
- 🔍 The environmental impact of AI itself is a growing area of research, with studies beginning to assess the lifetime carbon footprint of machine learning models.
- 🌿 A study by AI app developer Hugging Face revealed that the carbon footprint of the Bloom model, with 176 billion parameters, was so large that it doubled the total emissions of the model.
- 🔧 Manufacturing hardware like GPUs, which are used in deep machine learning, involves a significant amount of pure water and rare metals, adding to the climate cost.
- ⚡️ OpenAI's GPT-3 model had a carbon footprint over 20 times higher than Bloom and consumed three times as much power, highlighting the scale of energy use in AI.
- 💧 AI models like GPT-3 require water for cooling, with estimates suggesting a basic conversation could use up to a 500 milliliter bottle of water.
- 🌊 Google's large language model Lambda used around a million liters of water for training, and the company's data center water consumption increased by 20% in 2022.
- 🌳 Google has a 2030 target to replenish 120% of the water it consumes and is focusing on using non-fresh water sources like wastewater or seawater.
- 📍 The location of AI model training can significantly affect carbon emissions, with states like California having more renewable energy sources than Virginia, which relies more on fossil fuels.
- 🌍 Internationally, Microsoft noted that its Asian data centers had three times worse water use effectiveness compared to those in the U.S., due to warmer climates and different energy sources.
- 🔄 To reduce AI's climate impact, one practical step is to avoid integrating AI into areas where it is not necessary, as this can increase the climate cost of everyday actions.
Q & A
What is the potential impact of technology on climate change?
-Technology, particularly data centers, can have a significant impact on climate change by emitting greenhouse gases comparable to the aviation industry and consuming large amounts of water. However, artificial intelligence (AI) can also help mitigate these effects by improving efficiency and understanding climate impacts better.
How can AI be utilized to reduce the impact of climate change?
-AI can improve efficiency in various sectors. For example, Google and American Airlines have used AI to reduce vapor trails in planes, which contribute to global warming. AI is also used to forecast river floods and recommend eco-friendly routes, and startups are using it to simplify the process of obtaining clean power.
What are some of the ways AI is being used in everyday life to address climate change?
-AI is being used in image generators to show what warmer worlds might look like, including ocean encroachment and the appearance of the world on fire if temperatures rise significantly. This helps to raise awareness and potentially drive action on climate change.
What studies have been conducted on the energy use of AI and its impact on climate change?
-Studies by organizations such as Hugging Face have explored the lifetime carbon footprint of machine learning models, revealing that factors beyond just the energy used in training, such as manufacturing hardware, can significantly contribute to a model's total emissions.
How does the carbon footprint of the AI model Bloom compare to other models?
-Bloom, a machine learning model with 176 billion parameters, was found to have a carbon footprint that doubled when considering factors outside of just the energy used in training. This includes the manufacturing of GPUs and other hardware.
What is the scale of energy use for models like OpenAI's GPT-3 during training?
-OpenAI's GPT-3 has a significantly higher carbon footprint, more than 20 times higher than Bloom, and consumes three times as much power. It also uses enough energy over its training period to power an average American home for over four decades.
How does the water use of AI models compare to traditional data centers?
-Research indicates that AI models like GPT-3 require a considerable amount of water for cooling, with GPT-3 needing the equivalent of a 500 milliliter bottle of water for a basic conversation. Google's large language model, Lambda, used around a million liters of water for training alone.
What steps is Google taking to address its water consumption in data centers?
-Google is attempting to use non-fresh water sources such as waste water, industrial water, or seawater for cooling in their data centers. They have also set a 2030 target to replenish 120% of the water they consume.
What are some practical steps that can be taken to reduce the climate impact of AI?
-One practical step is to use AI less when it is not necessary, such as in search engines where existing software works efficiently without AI integration. This can help reduce the climate cost of basic actions that have been performed efficiently without AI.
How does the location of AI models affect their carbon emissions?
-The location can significantly impact carbon emissions because the energy sources used in different states or countries vary. For instance, training models in California, where there is a lot of wind power, can result in lower emissions compared to states that rely on fossil fuels.
What is the significance of the water use effectiveness of Microsoft's Asian data centers compared to their U.S. locations?
-Microsoft's Asian data centers were found to have three times worse water use effectiveness than their U.S. locations, indicating that the geographical location of AI models can have a substantial impact on water consumption and overall environmental impact.
Outlines
🌐 AI's Role in Climate Change Mitigation
The first paragraph of the Tech News Briefing discusses the significant impact technology, particularly data centers and artificial intelligence (AI), has on climate change. It highlights that data centers emit greenhouse gases equivalent to the aviation industry and consume substantial water resources. However, AI has the potential to mitigate these effects by improving efficiency in various sectors. For instance, Google and American Airlines have utilized AI to reduce vapor trails that contribute to global warming. AI is also being used to forecast floods and recommend eco-friendly routes. Startups are leveraging AI to facilitate the transition to clean energy. Additionally, AI-powered image generators are visualizing the effects of climate change, such as ocean encroachment and wildfires, to raise awareness.
💧 The Environmental Footprint of AI Development
The second paragraph delves into the environmental implications of AI's own development and operation. Recent studies have begun to assess the energy consumption and carbon footprint of AI models. One study by Hugging Face examined the lifetime carbon footprint of the Bloom AI model, revealing that factors beyond training, such as manufacturing hardware like GPUs, significantly contribute to the model's overall emissions. The energy and water consumption during the training of large AI models like OpenAI's GPT-3 and Google's Lambda are staggering, with GPT-3 consuming enough energy for an American home for over 40 years and Lambda using a million liters of water in training alone. The paragraph also touches on Google's efforts to use non-fresh water sources and its commitment to water replenishment by 2030. It concludes by suggesting ways to reduce AI's climate impact, such as integrating AI judiciously and considering the environmental cost of training AI models in different geographic locations.
Mindmap
Keywords
💡Climate Impact
💡Artificial Intelligence (AI)
💡Data Centers
💡Greenhouse Gas
💡Efficiency
💡Alphabet's Google
💡Machine Learning Model
💡GPU (Graphics Processing Unit)
💡Water Use
💡Sustainability
💡Eco-friendly Routes
💡AI's Own Climate Impact
Highlights
Technology, particularly data centers, contributes significantly to greenhouse gas emissions, comparable to the aviation industry, and consumes a lot of water.
Artificial intelligence (AI) can help mitigate the climate impact by improving efficiency in various ways.
Alphabet's Google and American Airlines have utilized AI to reduce vapor trails in planes, which contribute to global warming.
AI is also being used to forecast river floods and recommend eco-friendly routes.
Startups, especially in San Francisco, are employing AI to simplify the process for companies to obtain clean power.
AI-powered image generators are visualizing the effects of a warmer world, such as ocean encroachment and wildfires.
AI's own climate impact is a growing area of research, with studies beginning to assess its energy use and carbon footprint.
Hugging Face's study on the AI model Bloom revealed that factors beyond training, like manufacturing hardware, significantly contribute to the model's lifetime carbon footprint.
The manufacturing of GPUs for deep machine learning involves substantial use of pure water and rare metals, adding to the climate cost.
Open AI's GPT-3 model has a carbon footprint over 20 times higher than Bloom and consumes three times as much power.
Bloom's energy use for training is equivalent to powering an average American home for over 40 years.
A single training run of Bloom has 25 times the emissions of a round-trip flight from New York to San Francisco for one passenger.
Research from the University of California Riverside indicates that AI models like Chat GPT-3 require substantial water usage for cooling.
Google's large language model Lambda used around a million liters of water just for training.
Google has a 2030 target to replenish 120% of the water it consumes and uses non-fresh water sources when possible.
Reducing AI's climate impact can be achieved by using it judiciously and avoiding unnecessary integration into systems that already function well without it.
The location of AI model training can significantly impact carbon emissions due to differences in energy sources between states and countries.
Microsoft found that its Asian data centers' water use effectiveness was three times worse than in the U.S locations.
Transcripts
[Music]
welcome to Tech news briefing it's
Monday October 2nd I'm Zoe Thomas for
The Wall Street Journal
technology can have a big impact on the
climate data centers admit about the
same greenhouse gas as the aviation
industry and consume a lot of water but
artificial intelligence can help us
better understand and cut down on that
if we can limit ai's own climate impact
here to talk about how to do that is
nuha Dolby who reported on this for the
wsj's sustainability pro team Newhall we
talk a lot on this show about the
potential of AI one thing it could do is
help reduce climate change impact how
could it do that yeah so there are a
number of ways but broadly it's about
improving efficiency so for instance
alphabets Google and American Airlines
have used artificial intelligence and
they've used that to help planes create
fewer Vapor Trails and those Vapor
Trails actually contribute to global
warming the companies also use it to
forecast river floods and they can use
it to recommend eco-friendly routes
there are startups too based out of
predominantly San Francisco
one's using AI to simplify the process
for companies to get clean power and
that's also being used by Everyday
People too so lots of people and image
generators that have become pretty
pretty popular have used it to generate
images of what warmer worlds will look
like so things like ocean encroachment
to looking at what the world would look
like on fire if it gets hot all right
let's talk about ai's own climate impact
have there been any studies about Ai and
its energy use yeah so this is kind of a
newer area that people are looking into
but there are a couple studies one came
out of hugging face which is an AI app
developer and the research scientist
there had decided to map the lifetime
carbon footprint of a machine learning
model with 176 billion parameters and
that model is called Bloom
so the factors outside of the energy
used just in training the model is
something that a lot of research in the
area hasn't really factored in wound up
being so large that they actually
doubled the total emissions of the
entire model so for instance
manufacturing a GPU or like a graphics
Processing Unit it's a piece of Hardware
that's in most computers and is also
used in this deep machine learning
manufacturing those involves a lot of
pure water and rare metals and that kind
of thing and that'll add to the climate
cost Bloom just alone used more of a
thousand of those gpus and that's just
one of many factors that this research
took into account so more impact than
just its energy use but if we were to
look at Bloom's energy use do we have a
sense of the scale there so for a model
of similar size that might be more
familiar open AIS chat GPT 3 had
significantly higher carbon emissions so
more than 20 times higher and it
consumed three times as much power as
bloom bloom also used enough energy she
ingested training over a number of
months to power the average American
home for just over four decades and the
training run also had 25 times the
emissions of just one passenger's
round-trip flight from New York to San
Francisco so if you're feeling guilty
about that transatlantic flight you took
recently here's some context for it what
about water use data centers typically
use a huge amount of water to cool
themselves what is the water use like
for AI so research out of the University
of California Riverside has shown that
about chat gpt3 needs to drink like a
500 milliliter bottle of water for just
a basic conversation of between 20 to 50
inquiries depending on where that
electricity is generated gpt4 probably
uses more the research out of the
University also did estimates for
Google's large language model known as
Lambda and that one used around a
million liters of water for its training
alone Google's on-site data center water
consumption overall in 2022 has also
gone up by around 20 compared with the
year before have the company said
anything about ways that they might
reduce that Google has said that when
they try to use water they try to use
things that aren't fresh water so things
like waste water industrial or even sea
water when Google is a 2030 Target to
replenish 120 of the water it consumes
are there ways to reduce ai's climate
impact things that companies or users
might be able to do one thing people can
do of course is is using less but a
practical step in limiting those
emissions is just to not integrate AI
into things that it doesn't need to be
in our search engines work now we have
lots of software that works just fine
and as companies try to integrate AI
into it they're increasing the climate
cost of all those basic actions that
everyone does and has done just fine
before and can continue to do just fine
without that integration what about in
terms of location for example should we
be thinking about where these AI models
are housed in the U.S for instance where
there's no Central Electric grid
training models in one state versus
another can have a pretty big impact on
carbon emissions and you don't actually
have to move to do that because all this
is done over the internet
in California where we have a lot of
wind power there's a good shot that the
energy is producing less emissions and
if you're using it in Virginia which has
lots of coal and other fossil fuels
internationally because it's typically
warmer in Asia Microsoft said last year
that it's Asian data centers actual
water use Effectiveness was three times
worse than that of their locations in
the U.S so if you're training an AI
model you could triple your water use
just by having that be in Asia all right
that was nuha Dolby who reported on this
for wsj pro sustainability
and that's it for Tech news briefing
Today's show was produced by Anthony
bansi with supervising producer Melanie
Roy I'm Zoe Thomas for The Wall Street
Journal we'll be back tomorrow thanks
for listening
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