Artificial Intelligence and Energy Consumption: The Hidden Cost of Smart Technology

 

You can think of artificial intelligence (AI) like a human brain, but a super powerful one! Like a child learning something new, AI processes large amounts of data, analyzes it and learns certain patterns. There’s a lot of math, statistics and complex algorithms involved. In particular, what we call deep learning improves itself by repeatedly performing calculations on huge data sets. This process requires very large and powerful computers.

For example, when you ask an AI model, “Is this a cat?”, the model has analyzed thousands or even millions of cat photos and learned every detail. It guesses by looking at things like the shape of an eye, an ear, the length of a whisker. But here’s the magic: It takes a lot of computing power to do this!

Artificial intelligence (AI) is entering more and more into our lives every day. From voice assistants on phones to the shows Netflix recommends to us, from Google’s search results to Tesla’s self-driving cars, AI is everywhere. But does having this incredible technology come at a price? Yes, it comes at a big price: Energy consumption!

Although we don’t realize it in everyday life, AI models consume huge amounts of energy when they run. But why is there such a huge energy demand? Where does it come from and what steps are being taken to reduce consumption? Let’s explore this topic in its simplest yet detailed form.

How Does Artificial Intelligence Work?

I mentioned that we can think of AI as a human brain, but it’s actually a much faster version that can do big calculations. AI is actually a set of algorithms that analyze data and try to learn certain patterns. This is done through methods such as machine learning and deep learning.

• Machine learning: The process of teaching computers to make decisions by showing them examples.
• Deep learning: A more sophisticated version uses artificial neural networks that mimic the neural networks in the human brain.

Real Life Examples:

• Google Photos automatically separates “tree” photos from the images in the album.
• Spotify gives you new recommendations based on the songs you listen to.
• Tesla’s autopilot system makes instant decisions by detecting traffic signs and pedestrians.

Why does it consume so much energy?

AI’s energy consumption is based on several reasons:

1. Big Data and Big Computing: AI models process millions or even billions of parameters. To do this, specialized hardware such as GPUs (Graphics Processors) or TPUs (Tensor Processors) are used. But these devices consume significant power while continuously computing.
2. Model Training is Very Difficult: Training an AI model is a process that sometimes takes thousands of hours. Google’s GPT-3, a language model, consumed about 10 gigawatt hours of energy to train. This is equivalent to the electricity consumed by a European city for several days!
3. Data Centers: There are huge data centers running behind AI. The huge server farms of companies such as Facebook and Google run millions of computers that consume significant amounts of electricity. And because these machines overheat, extra energy is needed to cool them down.

Where, How and Through What Means Is This Energy Sourced?

So, where does AI get this energy?

1. Fossil Fuels: Unfortunately, most of the world’s electricity still comes from fossil fuels such as coal, natural gas and oil. These emit carbon, contributing to climate change.
2. Renewable Energy: Giant companies such as Google and Microsoft have started to power their data centers with wind and solar energy. However, this is not yet enough to power all systems.
3. Private Electricity Infrastructures: Big tech companies sometimes build their own private power plants or make deals directly with green energy suppliers.
4. Alternative Cooling Technologies: Data centers are sometimes built near the poles or underwater to reduce cooling costs.

What is being done to reduce energy consumption?

The answer to this question actually depends on work in several different areas:

1. More Efficient Artificial Intelligence Algorithms: Scientists are looking for ways to make AI work with less computation. Building small but smart models is an important goal.
2. Hardware Improvements: A new generation of chips (e.g. Apple’s M-series chips or Google’s specialized TPUs) can do the same job with less energy consumption.
3. Green Data Centers: Data centers are trying to use less energy by improving cooling technologies. Microsoft even tested the idea of placing its data centers under the ocean! Because water has a natural cooling effect.
4. Edge Computing: Normally, for an AI process, data has to travel to and from large data centers. But now solutions are being developed for AI to work on the device. For example, some commands from Apple’s Siri or Google Assistant are run entirely on the phone, so there is no need to go back and forth to the data center.

Who is Taking What Steps?

Big tech companies, universities and researchers are working to solve this problem. Here are a few examples:

• Google: Since 2017, it has been running the “AI for Good” program to make its AI models more efficient and is working towards 100% renewable energy in its data centers.
• Microsoft: aims to be carbon negative by 2030, meaning it will remove more carbon from the atmosphere than it produces.
• Tesla: Autonomous vehicles drive using artificial intelligence. Tesla has developed its special “Dojo” chip to make these processes more efficient.
• Research Institutes: Universities such as MIT and Stanford are working on AI models that consume less energy.

The Most Common Artificial Intelligence Models and Their Uses

There are many models of AI used in different fields. Here are some of the most common ones and their uses:

GPT (Generative Pre-trained Transformer) Series

• Where Used: Chatbots, copywriting, translation, code generation
• Example: ChatGPT, Google Bard

CNN (Convolutional Neural Networks)

• Where Used: Image recognition, face recognition, x-ray analysis in healthcare
• Example: Facebook’s face recognition system, anomaly detection in MRI scans

RNN (Recurrent Neural Networks) & LSTM (Long Short-Term Memory)

• Where Used: Voice recognition, language modeling, prediction systems
• Example: Siri, Google Assistant, music recommendation systems

DQN (Deep Q-Networks) and Reinforcement Learning

• Where Used: Game playing AIs, robotic systems
• Example: AlphaGo, Tesla’s self-driving car system

Transformers

• Where Used: Natural language processing, large-scale data analytics
• Example: Google Translate, text summarization systems

Artificial intelligence is a tremendous technology, but energy consumption is a big problem. Fortunately, thanks to more efficient algorithms, renewable energy and hardware advancements, we are working to overcome these problems. It is very likely that we will see greener, more energy-efficient AI systems in the coming years. But still, like any technology, AI comes at a price — both in terms of energy and environmental impact!

So, do you think AI is really worth spending so much energy on? I’d love to hear your opinion in the comments.

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