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Barely a week goes by without another dramatic report about humanity and the planet approaching a climate change tipping point. The latest reports were heart-stopping analysis from the World Meteorological Organization and arresting criticism from the UN Secretary-General. Both were shared in the last days of April.
Artificial intelligence will determine whether we blow past the tipping point or step back from the brink.
AI is one of the important tools left in the fight against climate change. AI has turned its hand to risk prediction, prevention of harmful weather events such as wildfires and carbon offsets. It has been cited as important for ensuring that companies meet their ESG goals.
Yet, it is also an accelerator. AI requires enormous computing power, which churns through energy when designing algorithms and training models. And just as software has eaten the world, AI is set to follow.
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AI will contribute $15.7 trillion to the global economy by 2030, which is more than the GDP of Japan, Germany, India and the UK combined. From using ChatGPT to craft emails and using text-to-image platforms to create art, many people are using AI as the Internet.
The power used by AI has been increasing for years now. For example, the power required to train the largest AI models doubled roughly every 3.4 months, growing 300,000 times between 2012 and 2018.
This expansion brings opportunities to solve major real-world problems in everything from security and medicine to hunger and farming. It will also have a punitive effect on climate change.
high energy costs
Computing goes hand in hand with high energy costs and a large carbon footprint, tapping the accelerator pedal on the world’s climate change.
This is especially true for AI. The vast majority of GPUs running machine learning algorithms get hot and need to be cooled; Otherwise, they melt. Training even a large language model (LLM) requires a surprising amount of energy with a large carbon footprint.
For example:
As we move into the GPT4 era and the models get larger, the energy required to train them increases. GPT-3 was 100 times larger than its predecessor GPT, and GPT-4 was ten times the size of GPT-3. All the time, larger models are increasingly being released. GPT-4 arrives in March 2023, about four months after ChatGPT (powered by GPT-3.5) is released in late November 2022.
For balance, we should not assume that AI’s carbon footprint will continue to grow as new models and companies emerge in the space. Geeta Chauhan, an AI engineer at META, is using open-source software to reduce the LLM’s operational carbon footprint. Their latest work shows a 24-fold reduction in carbon emissions compared to GPT-3.
However, the popularity of AI and its exponential power undermine much of the climate action being implemented today and call into question its ability to be part of the solution.
We need a solution that allows AI to flourish while curbing its carbon footprint. So what should we do?
reducing carbon addiction
As always technology will get us out of this predicament.
For the explosion of AI to be sustainable, advanced computing must come to the fore and do the heavy lifting for many of the tasks currently performed by AI. The good news is that we already have advanced computing technologies ready to perform these tasks more efficiently and quickly than AI, with the added benefit of using far less energy.
In short, advanced computing is the most effective tool we have for reducing AI’s carbon addiction. With it, we can slow the creep of climate change.
There are many different technologies emerging in advanced computing that can solve some of the problems AI currently tackles.
For example, quantum computing is better than AI in drug discovery. As humans live longer, they are facing a greater number of new diseases that are complex and untreatable. This has been called the “better than The Beatles” problem, where new drugs are marginal improvements on an already successful therapeutic.
Until now, drug development has focused on rare events within a dataset and made educated guesses to design the right drugs to target and bind to disease-causing proteins. LLM can be efficiently used to assist in this task.
LLMs are remarkably good at predicting which words in our vocabulary can fit into a sentence to accurately convey meaning. Drug discovery is not wildly different because the problem is identifying the best fit or configuration of molecules in a compound to achieve a therapeutic result.
However, molecules are quantum entities, so quantum computing is much better equipped to deal with this problem. Quantum computing has the potential to quickly simulate large numbers of binding sites in drugs to create the right configuration to treat currently incurable diseases.
Advanced Computing: Quantum and Beyond
The capabilities of quantum matter mean that they can be resolved very rapidly and using very little energy.
Another development with real potential to enhance AI is photonics, or so-called optical computing, which uses laser-generated light instead of electricity to transmit information.
Some companies are making computers that use this technology, which is much more energy-efficient than other computing technologies and is increasingly being recognized as a way to achieve net zero.
Elsewhere, we have neuromorphic computers. It is a type of computer engineering where elements of computer systems are modeled on elements of the human brain and nervous system. They compute to replicate the analogous nature of our nervous system. Trials of this technology include projects from Mythic and Semron. Neuromorphic is another green option that needs further investment. Its hardware has the potential to run large deep learning networks that are more energy efficient than comparable classical computing systems.
For example, processing information through its hundred billion neurons consumes only 20 watts, the same as an energy-saving light bulb in a home.
Developing and implementing these innovations is essential if we are to stop climate change.
advanced computing leader
Many startups (and investors) around the world are obsessed with advanced computing, but there are only a handful of companies focusing on so-called impact sectors such as healthcare, the environment, and climate change.
Within quantum computing, the most exciting companies that are developing use cases for energy and drug discovery are Pascal (its cofounder was awarded the Nobel Prize in Physics 2022), Qubit Pharmaceutical and IBM. When it comes to photonics, we look to global impact leaders such as Lightmatter and Luminous, while in neuromorphic computing, we’re tracking the progress of Grok, Semron, and Intel.
Advanced computing is key to achieving the energy efficiency needed to fight climate change. It takes too long and is too energy-intensive to run artificial neural networks on GPUs.
By adopting AI as an alternative to advanced computing methods, businesses can greatly reduce AI’s impact on the environment, while ensuring that its immense power can mitigate some of the effects of climate change, such as wildfires Fear of fire or extreme weather.
The existential endpoint for our environment is approaching. But the situation is not hopeless.
The deployment of advanced computing is a reliable and powerful resource to combat the problem. We need to invest in these technologies now to solve the biggest challenge facing humanity.
francesco ricetti VC at Runa Capital.
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