Join top executives in San Francisco on July 11-12 to hear how leaders are integrating and optimizing AI investments for success. Learn more
Hardly a week goes by without another dramatic report of humanity and the planet reaching a climate change tipping point. The latest reports were one heartbreaking analysis of the World Meteorological Organization and arresting criticism of the UN Secretary-General. Both were shared in the last days of April.
Artificial intelligence will determine whether we blow through the tipping point or row back from the edge.
AI is one of the most important tools left in the fight against climate change. AI has turned its hand risk predictionthe prevention of harmful weather conditionssuch as forest fires and carbon offsets. It’s described as essential to ensure that companies meet their ESG objectives.
Yet it is also an accelerator. AI requires enormous computing power, which consumes energy when designing algorithms and training models. And just as software has eaten the world, AI will follow.
AI will contribute as much as $15.7 trillion to the global economy by 2030, which is greater than the GDP of Japan, Germany, India and the UK. That’s a lot of people using AI as ubiquitously as the internet, from using ChatGPT to create emails and write code to using text-to-image platforms to create art.
The power that AI deploys has been increasing for years. For example, the power needed to train the largest AI models doubled approximately every 3.4 months300,000 times as large between 2012 and 2018.
The cost of high energy
Computing goes hand in hand with high energy costs and a larger carbon footprint, which accelerates global climate change.
This is especially true for AI. The sheer number of GPUs running machine learning algorithms get hot and need cooling; otherwise they melt. Training even one large language model (LLM) requires a dazzling amount of energy with a large environmental footprint.
As we enter the GPT4 era and the models get bigger, the energy required to train them grows. GPT-3 was 100 times larger than its predecessor GPT and GPT-4 was 10 times larger than GPT-3. All the while, larger models are released faster. GPT-4 arrived in March 2023, almost four months after ChatGPT (powered by GPT-3.5) was released at the end of November 2022.
For balance, we shouldn’t assume that as new models and companies emerge in space, AI’s environmental footprint will continue to grow. Geeta Chauhan, an AI engineer at Meta, uses open-source software to reduce the operational carbon footprint of LLMs. Her latest work shows a 24-fold reduction in carbon emissions compared to GPT-3.
However, the popularity of AI and its exponential power is undermining much of the climate action currently in place and casting doubt on its potential to be part of the solution.
We need a solution that allows AI to flourish while reducing its environmental footprint. So what do we do?
Tempering the carbon addiction
As always, technology will get us out of this predicament.
For the explosion of AI to be sustainable, advanced computing must emerge and do the heavy lifting for many 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 much, much less energy.
In short, advanced computing is the most effective tool we have for tempering AI’s carbon addiction. This will allow us to slow down creeping climate change.
There are a number of different advanced computing technologies emerging that could solve some of the problems AI is currently addressing.
Quantum computing, for example, is superior to AI drug discovery. As people live longer, they are confronted in increasing numbers with new diseases that are complex and untreatable. This has been called the “better than The Beatles” problem, where new drugs make modest improvements over already successful therapies.
Until now, drug development has focused on rare events within a data set and making informed estimates to design the right drugs that target and bind to the proteins that cause disease. LLMs can be efficiently used to assist in this task.
LLMs are remarkably good at predicting which words in our vocabulary would fit best in a sentence to accurately convey meaning. Drug discovery is not vastly different because the problem is identifying the best fit or configuration of molecules in a compound to get a therapeutic result.
However, molecules are quantum elements, so quantum computing is much better at tackling this problem. Quantum computing has the capacity to quickly simulate large numbers of binding sites in drugs to create the right configuration for the treatment of currently incurable diseases.
Advanced Computing: Quantum and Beyond
Thanks to the capabilities of Quantum, these can be solved much faster and with much less energy consumption.
Another development with a real possibility of being an improvement on AI is photonics, or so-called optical computing, which uses laser-generated light instead of electricity to transmit information.
Some companies are building computers using this technology, which is much more energy efficient than most other computer technologies and is increasingly recognized as a way to reaching Net Zero.
Elsewhere we have neuromorphic computers. This is a form of computer engineering in which elements of the computer system are modeled after those in the human brain and nervous system. They perform calculations to mimic the analog nature of our neural system. Trials of this technology include projects from mythical And Semron. Neuromorphic is another greener option that requires further investment. The hardware has the potential to run large deep learning networks that are more energy efficient than comparable classical computer systems.
For example, processing information by its hundred billion neurons is consumed only 20 wattscomparable to an energy-saving lamp in the house.
The development and application of these innovations is necessary if we want to slow down climate change.
Advanced computer leaders
There are many startups (and investors) around the world that are obsessed with advanced computers, but only a handful of companies focus on so-called impact areas such as healthcare, the environment and climate change.
Within quantum computing, those are the most exciting companies developing use cases for energy and drug discovery Pascal (his co-founder Was rewarded the 2022 Nobel Prize in Physics), Qubit Pharmaceuticals And IBM. When it comes to photonics, we consider the leaders with global impact Light Matter And Luminouswhile in neuromorphic computing we track the progress of Groq, Semron And Intel.
Advanced computers are essential to achieving the energy efficiency we need to fight climate change. It simply takes too long and is too energy intensive to run artificial neural networks on a GPU.
By using advanced computing methods as an alternative to AI, companies can significantly reduce the impact of AI on the environment, while still ensuring that its immense power can mitigate some of the effects of climate change, such as anticipating wildfires or extreme weather .
The existential end point is approaching for our environment. But the situation is not hopeless.
The use of advanced computing is a credible and powerful way to counter the problem. We must invest in these technologies now to solve the greatest challenge facing humanity.
Francesco Ricciuti is a VC at Runa Capital.
Data decision makers
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovation.
To read about advanced ideas and up-to-date information, best practices and the future of data and data technology, join DataDecisionMakers.
You might even consider contributing an article yourself!
Read more from DataDecisionMakers