5 Ways to Reduce Your AI and ChatGPT Footprint
We’ve shared the environmental impact of AI and what companies are doing to reduce their environmental footprint. But did you know you can take actions to reduce your footprint as well?
Researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide which is equivalent to approximately five times the lifetime emissions of an average American car (includes manufacturing of the car itself). One assessment suggests that ChatGPT is already consuming the energy of 33,000 homes.
Be Thoughtful of Re-running Your Prompt
A survey by Pew Research found that 55% of Americans said they regularly use AI, while 44% believe they do not regularly use AI.
According to estimates, ChatGPT emits 8.4 tons of carbon dioxide per year, more than twice the amount that is emitted by an individual, which is 4 tons per year.
For a simple conversation of 20-50 questions, the water consumed is equivalent to a 500ml bottle, making the total water footprint for inference substantial considering its billions of users.
The most common ways consumers use AI are by answering texts or emails, answering financial questions, and making travel plans. The top ways consumers use AI include:
Respond to people via text/email: 45%
Answer financial questions: 43%
Plan travel itinerary: 38%
Craft an email: 31%
Prepare for a job interview: 30%
Write a social media post: 25%
Summarize complex or long copy: 19%
Before modifying your prompt to run again or using AI haphazardly, consider the environmental impact. You can also schedule computational tasks during periods when renewable energy is more readily available can further decrease emissions.
Use existing large generative models, don’t generate your own
There are already many providers of large language and image models, and there will be more. Creating and training them requires enormous amounts of energy.
There is little need for companies other than large vendors or cloud providers to generate their own large models from scratch. They already have access to the needed training data and massive volumes of computing capability in the cloud, so they don’t need to acquire it.
So, as fun as it is to create and run your own models, unless you are an AI genius, consider using one of the many existing models. If a company wants a generative model trained on its own content, it shouldn’t start from scratch to train a model but rather refine an existing model.
Choose the Company Taking Sustainability Seriously
Nowadays 77% of companies are either using or exploring the use of AI in their businesses, and 83% of companies claim that AI is a top priority in their business plans. And 97% of business owners think using ChatGPT will help their business.
Tech and commerce companies are rapidly testing out approaches to reduce their carbon footprint. Take a look at the efforts being taken by big companies such as Microsoft, Google, and Amazon. While we are not there yet, transparency in reporting emissions will be required and that will be one way to select which AI model you should use.
A model trained and operating in the U.S. may use energy from fossil fuels, but the same model can be run in Quebec where the primary energy source is hydroelectric. Google offers a “Carbon Sense Suite” to help companies reduce energy consumption in their cloud workloads. Users of cloud providers can monitor the companies’ announcements about when and how they have deployed carbon-neutral or zero-carbon energy sources.
Advocate for greater transparency in the development and operation of machine learning systems
One way to address these issues is to advocate for greater transparency in the development and operation of machine learning systems while also advocating for laws that wil reduce the environmental footprint of data centers.
Scholars have developed frameworks to assist researchers in reporting their energy and carbon usage, in the hopes of promoting accountability and responsible practices in the field. To aid researchers in benchmarking their energy usage, some have made public online tools which encourage teams to conduct trials in eco-friendly areas, provide consistent updates on energy and carbon measurements, and actively assess the trade-offs between energy usage and performance before deploying energy-intensive models.
There are several packages and online tools available like CodeCarbon, Green algorithms, and ML CO2 Impact, which can be included in your code at runtime to estimate your emissions.
Be Creative!
Okay AI is new and fun, but do you remember the days where you sat outside writing that article or sat by the beach while painting your latest artwork?
I have always been my most creative when in nature. We know that spending time in nature reduces anxiety, increases creativity, and reduces our environmental footprint. So, before you use AI to generate that email, take some time to enjoy nature.
