1 / 8

Sustainable Generative AI: Balancing Innovation and Impact

Discover how Generative AI training and Agentic AI courses are addressing the environmental impact of AI development through sustainable frameworks and certified programs.

Sunita28
Download Presentation

Sustainable Generative AI: Balancing Innovation and Impact

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sustainability in Generative AI: Balancing Innovation and Impact Generative AI models have exploded in growth since 2023, widely impacting many sectors. This rise demands balancing rapid innovation with urgent global sustainability challenges.

  2. The Promise and Proliferation of Generative AI • Expanded application in text, images, and agentic AI GPT-4 and similar models now have billions of parameters, automation Key driver of productivity and creative industries pushing AI capabilities far beyond before. • • Accelerates scientific breakthroughs and enterprise solutions

  3. Hidden Costs: Energy and Emissions Training Demands Ongoing Energy Use Carbon Impacts Training large models consumes Deployment and fine-tuning Data centers risk soaring carbon huge amounts of electricity. increase long-term energy footprints, often undisclosed consumption. publicly.

  4. Water and Hardware: Beyond the Carbon Footprint Data centers consume vast water Hardware manufacture and transport Growing AI infrastructure affects volumes for cooling, straining local add hidden emissions and ecological ecosystems and local resource supplies. disruption. balances.

  5. Agentic AI: Scaling the Environmental Impact Self-Directed AI Resource Intensive Rising Concerns Agentic AI acts independently, Continuous computation chains Agentic AI's rapid growth intensifies planning and iterating on tasks. multiply resource consumption. sustainability challenges.

  6. Transparency and Ethics: The Information Gap AI providers rarely disclose full Energy use, water consumption, and Calls grow for ethical transparency by environmental impacts of their computing power remain largely industry, academia, and regulators. systems. opaque.

  7. Emerging Solutions and the Path Forward Renewable Energy AI firms test clean energy for sustainable data center power. Smart Scheduling Optimizing workloads reduces peak energy demand and waste. Policy & Awareness User and regulator engagement vital for responsible AI development.

  8. Conclusion: Building a Sustainable Future for Generative and Agentic AI Environmental Responsibility Sustainability depends on 2 transparency and responsible deployment. Enormous Benefits 1 Generative and agentic AI unlock creativity and innovation potential. Collective Action Developers, users, and regulators 3 must collaborate to protect our future.

More Related