The roller coaster of AI expectations and concerns continues to evolve at breakneck speed as businesses get closer and closer to understanding the technology’s rapid evolution and its possible functions within their activity. More recently, advanced artificial intelligence platforms such as generative AI and large language models (LLM) have come under scrutiny due to their voracious energy consumption and ecological impact This results in some researchers hypothesizing that LLMs consume hundreds of liters of fresh water and produce annual emissions equivalent to that of a small country.
As global warming exceeds 1.5 degrees in an entire year for the first time, global stakeholders are questioning where the bulk of responsibility should lie to prevent the climate crisis from worsening. Climate change remains an issue of critical importance to consumers and businesses amid global efforts to reduce CO2 emissions, which bodes poorly for the public image of any company that uses products of consumption. AI tools without controlling their carbon footprint. More importantly, widespread and uncontrolled use of AI could have disastrous consequences for the environment – MIT research suggests the training could potentially significantly thwart global progress in tackling climate change .
Despite the apparent ecological apathy of recent legislation like the EU AI law and President Biden’s executive order, which largely focus on other facets of AI accountability, some major players in AI AI have begun to proactively self-regulate and work towards the sustainable use of AI. Here’s how artificial intelligence leaders are approaching AI with an ecological conscience, while preserving the profound challenges of artificial intelligence. business value of technology.
Senior Director of AI Strategy, ABBYY.
Purpose-built AI
Many of the downsides of generative AI and LLMs come from the massive reserves of data you have to navigate it to generate value. Not only does this raise risks in terms of ethics, accuracy and confidentialitybut this greatly exacerbates the amount of energy needed to use the tools.
Instead of very general AI tools, companies have started moving towards more targeted AI, specialized for specific tasks and goals. For example, ABBYY has taken this approach by training its machine learning and natural language processing models to specifically read and understand documents running in enterprise systems, just like a human. With AI skills pre-trained to process very specific document types with 95% accuracy, organizations can save trees by eliminating the use of paper while reducing the amount of carbon emitted by tedious processes. document management process.
Empowering developers
AI companies don’t need to shoulder the burden of sustainable AI alone: some are proactively putting the ball in the developers’ court.
OpenAIthe artificial intelligence pioneer responsible for the very popular ChatGPT, recently announced that developers can create their own “GPT” platforms for specialized purposes. This allows developers and organizations to restrict their use of AI with a high degree of customization, removing excessive features and data that amplify ecological damage. For example, developers could design GPTs for purposes limited to creative writing advice, culinary information, technical support, or some other niche purpose.
Given the increased risks of inaccuracy and privacy violations associated with very general AI models, developers will likely be motivated to leverage these narrower, more specialized GPT platforms not only for their ecological responsibility , but also for better business results.
Sustainable business practices
Businesses should also take a step back from the technology itself and look within their organization for other ways to sustainably leverage AI. For example, Microsoft revealed that their AI-enabled hardware runs exclusively on clean energy, thus exempting them from creating so-called “operational emissions.”
Additionally, companies can use AI as a tool to explore other facets of their business where sustainability might be a priority. Forrester highlights measurement, reporting and data visualization capabilities of artificial intelligence to suggest it could fuel its own climate revolution.
While objectively important, emissions are not the only metric used to encompass ecological impacts: studies have shown that a combination of robotics and AI has reduced herbicide use in some contexts by 90%. As businesses continue to explore the utility and implications of AI, they must explore the full extent of its ability to improve and contribute to sustainable development.
Businesses take over
So far, early AI legislation has largely failed to come to grips with the ecological implications of artificial intelligence, focusing instead on privacy and other ethical areas. While these areas are also crucial for responsible use of AI, businesses must remain accountable for how they leverage artificial intelligence to drive business value.
2023 may have been a year of hype, noise, expectations and misconceptions around artificial intelligence, but the maturity businesses have gained over the past year has empowered them to make informed and responsible decisions regarding the use of AI. However, it is wise to examine, challenge and hold large organizations accountable for their carbon footprint and other environmental impacts: those who prioritize ecological responsibility should have nothing to hide.
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