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The rollercoaster ride of expectations and apprehensions surrounding artificial intelligence (AI) continues to accelerate as businesses draw closer to grasping the rapidly evolving technology and its potential applications in their operations. Recently, advanced AI platforms such as generative AI and large language models (LLMs) have come under scrutiny due to their substantial energy consumption and subsequent environmental footprint. Some researchers even suggest that LLMs consume hundreds of liters of freshwater annually and emit greenhouse gases equivalent to that of a small country.
With global temperatures surpassing the 1.5-degree mark for the first time over the course of an entire year, stakeholders worldwide are questioning where the primary responsibility lies in mitigating the worsening climate crisis. Climate change remains a pressing concern for both consumers and corporations amidst concerted efforts to reduce CO2 emissions. This raises concerns about the public perception of companies utilizing AI tools with significant environmental impacts, particularly as unchecked AI usage could potentially counteract global progress in combating climate change, according to research from MIT.
Despite recent legislation such as the EU AI Act and President Biden’s executive order, which primarily address other aspects of AI responsibility, some major players in the AI industry have begun proactively self-regulating and working towards sustainable AI practices. Here are some approaches that AI leaders are adopting to incorporate ecological consciousness while preserving the profound business value of AI technology.
- Purpose-built AI: One strategy involves pivoting away from general AI tools towards purpose-built AI tailored for specific tasks and objectives. For instance, ABBYY has developed machine learning and natural language processing models specifically designed to read and understand documents processed within enterprise systems. This focused approach not only enhances accuracy and privacy but also reduces energy consumption associated with broader AI models.
- Empowering developers: AI companies are empowering developers to create specialized AI platforms tailored to their unique needs. OpenAI, for example, allows developers to customize “GPT” platforms for specific purposes, enabling them to trim unnecessary features and data that contribute to ecological damage. By narrowing the scope of AI applications, developers can enhance both ecological responsibility and business outcomes.
- Sustainable business practices: Beyond AI technology itself, companies can adopt sustainable practices internally and leverage AI to explore other areas of sustainability within their operations. Microsoft, for instance, runs its AI-supporting hardware exclusively on clean energy, reducing operational emissions. Additionally, AI can aid in measuring, reporting, and visualizing data to drive sustainability initiatives across various business functions.
- Enterprises’ accountability: While early AI legislation has primarily focused on privacy and ethics, enterprises must take accountability for the environmental implications of their AI usage. As enterprises mature in their understanding of AI, they must make informed and responsible decisions regarding its deployment. Scrutinizing large organizations’ carbon footprint and environmental impacts is essential, encouraging transparency and ecological responsibility in AI usage.
In summary, while the hype and misconceptions surrounding AI persist, enterprises have matured in their approach to AI adoption, enabling them to navigate its complexities responsibly. However, ongoing scrutiny and accountability are crucial to ensuring that AI technologies contribute positively to sustainability efforts while delivering valuable business outcomes.