1 00:00:00,050 --> 00:00:03,050 Lessen environmental and ecosystem impacts of AI. 2 00:00:03,080 --> 00:00:08,930 Artificial intelligence has seen exponential growth and integration into various facets of modern life, 3 00:00:08,930 --> 00:00:11,810 presenting both opportunities and challenges. 4 00:00:12,380 --> 00:00:18,050 One of the critical areas requiring attention is the environmental and ecosystem impacts of AI. 5 00:00:18,740 --> 00:00:24,710 This lesson explores the multifaceted environmental consequences of AI, emphasizing the importance 6 00:00:24,710 --> 00:00:29,270 of responsible AI development and deployment to mitigate adverse effects. 7 00:00:30,260 --> 00:00:36,770 AI systems, particularly those involving machine learning and deep learning, are computationally intensive. 8 00:00:37,430 --> 00:00:43,400 The training of a single AI model can consume significant amounts of energy, with estimates suggesting 9 00:00:43,400 --> 00:00:49,520 that training a large natural language processing model can emit as much carbon as five cars over their 10 00:00:49,520 --> 00:00:50,450 lifetimes. 11 00:00:52,130 --> 00:00:58,010 This high energy consumption primarily stems from the extensive use of data centers, which house thousands 12 00:00:58,010 --> 00:00:59,840 of servers operating around the clock. 13 00:00:59,870 --> 00:01:06,280 These data centers require substantial electricity, often derived from non-renewable resources, thereby 14 00:01:06,280 --> 00:01:08,560 contributing to greenhouse gas emissions. 15 00:01:09,640 --> 00:01:16,390 To illustrate, Google's data centers, which support its AI operations, consumed approximately 12.4 16 00:01:16,390 --> 00:01:23,530 terawatt hours of electricity in 2019, equivalent to the energy consumption of nearly 1.1 million US 17 00:01:23,530 --> 00:01:24,850 homes in a year. 18 00:01:25,570 --> 00:01:30,790 While tech companies like Google have made strides toward using renewable energy sources, the overall 19 00:01:30,790 --> 00:01:33,760 carbon footprint of AI remains substantial. 20 00:01:33,790 --> 00:01:39,760 This energy demand is not just a matter of operational costs, but represents a significant environmental 21 00:01:39,790 --> 00:01:45,670 burden that needs addressing through both technological innovation and policy regulation. 22 00:01:46,780 --> 00:01:52,690 The environmental impact of AI extends beyond energy consumption to include the lifecycle of hardware 23 00:01:52,690 --> 00:01:54,430 used in AI systems. 24 00:01:54,790 --> 00:02:00,520 Manufacturing AI hardware involves mining and processing rare earth elements which are environmentally 25 00:02:00,520 --> 00:02:02,170 destructive processes. 26 00:02:02,590 --> 00:02:08,050 For instance, the extraction of lithium, a critical component in batteries for AI hardware, can lead 27 00:02:08,080 --> 00:02:11,460 to water scarcity, soil degradation, and pollution. 28 00:02:11,670 --> 00:02:18,150 These activities disrupt local ecosystems and can have long lasting detrimental effects on biodiversity 29 00:02:18,150 --> 00:02:19,350 and human health. 30 00:02:20,190 --> 00:02:24,600 Moreover, the disposal of AI hardware poses another environmental challenge. 31 00:02:25,410 --> 00:02:30,900 Electronic waste is one of the fastest growing waste streams globally, and AI hardware contributes 32 00:02:30,900 --> 00:02:31,950 to this problem. 33 00:02:32,760 --> 00:02:38,310 E-waste contains hazardous materials like lead and mercury, which can leach into the soil and water, 34 00:02:38,310 --> 00:02:40,530 causing environmental and health issues. 35 00:02:41,280 --> 00:02:48,840 The United Nations estimated that in 2019 alone, the world generated 53.6 million metric tons of e-waste, 36 00:02:48,840 --> 00:02:52,890 with only 17.4% being recycled properly. 37 00:02:53,430 --> 00:02:58,560 This highlights the urgent need for sustainable practices in the production, use and disposal of AI 38 00:02:58,590 --> 00:02:59,610 technologies. 39 00:03:00,120 --> 00:03:04,770 The environmental footprint of AI also includes its impact on water resources. 40 00:03:05,220 --> 00:03:10,890 Data centers require significant amounts of water for cooling purposes to prevent overheating of servers. 41 00:03:11,430 --> 00:03:18,010 This water usage can strain local water supplies, particularly in regions already facing water scarcity. 42 00:03:18,550 --> 00:03:24,310 For instance, data centers in areas like California which experience frequent droughts exacerbate the 43 00:03:24,310 --> 00:03:26,110 stress on water resources. 44 00:03:26,470 --> 00:03:32,200 This underscores the necessity for water efficient cooling technologies and sustainable water management 45 00:03:32,200 --> 00:03:34,390 practices in the AI industry. 46 00:03:35,470 --> 00:03:41,080 While the environmental impacts of AI are considerable, there are also potential positive contributions 47 00:03:41,080 --> 00:03:43,540 of AI to environmental sustainability. 48 00:03:44,230 --> 00:03:50,230 AI can enhance energy efficiency across various sectors by optimizing energy use, predicting maintenance 49 00:03:50,230 --> 00:03:54,070 needs, and integrating renewable energy sources more effectively. 50 00:03:54,790 --> 00:04:00,910 For example, AI driven smart grids can balance energy supply and demand, reducing waste and improving 51 00:04:00,910 --> 00:04:04,990 the reliability of renewable energy sources like wind and solar power. 52 00:04:05,470 --> 00:04:12,130 Such applications demonstrate that when used responsibly, AI has the potential to contribute to environmental 53 00:04:12,130 --> 00:04:14,380 conservation and sustainability. 54 00:04:15,520 --> 00:04:19,090 AI I can also aid in monitoring and protecting ecosystems. 55 00:04:19,090 --> 00:04:25,060 Machine learning algorithms can analyze satellite imagery to track deforestation, habitat destruction, 56 00:04:25,060 --> 00:04:27,850 and illegal fishing activities in real time. 57 00:04:28,330 --> 00:04:34,240 This information can help conservationists and policymakers take timely action to protect endangered 58 00:04:34,240 --> 00:04:36,700 species and preserve biodiversity. 59 00:04:36,730 --> 00:04:42,940 An example is the use of AI by the World Wildlife Fund to combat illegal wildlife trade by identifying 60 00:04:42,940 --> 00:04:45,400 and tracking online trafficking activities. 61 00:04:46,300 --> 00:04:52,120 These applications highlight the dual role of AI as both a potential environmental threat and a powerful 62 00:04:52,120 --> 00:04:54,070 tool for environmental protection. 63 00:04:55,870 --> 00:05:01,450 Addressing the environmental and ecosystem impacts of AI requires a multifaceted approach. 64 00:05:01,480 --> 00:05:07,570 Policymakers, technologists, and industry leaders must collaborate to develop and enforce regulations 65 00:05:07,570 --> 00:05:10,210 that promote sustainable AI practices. 66 00:05:10,240 --> 00:05:16,060 This includes setting standards for energy efficiency, incentivizing the use of renewable energy, 67 00:05:16,060 --> 00:05:19,450 and implementing robust e-waste management protocols. 68 00:05:19,920 --> 00:05:25,590 Additionally, there is a need for greater transparency and accountability in the AI industry regarding 69 00:05:25,590 --> 00:05:27,150 environmental impacts. 70 00:05:27,630 --> 00:05:32,760 Companies should be required to disclose their carbon footprints and water usage, enabling consumers 71 00:05:32,760 --> 00:05:35,430 and stakeholders to make informed decisions. 72 00:05:37,740 --> 00:05:42,870 Furthermore, research and development in AI should prioritize sustainability. 73 00:05:43,200 --> 00:05:48,960 This involves designing energy efficient algorithms, developing hardware with longer lifespans, and 74 00:05:48,960 --> 00:05:52,470 exploring alternative cooling methods that reduce water usage. 75 00:05:53,010 --> 00:05:58,560 The AI community must also engage in interdisciplinary collaborations with environmental scientists 76 00:05:58,560 --> 00:06:05,100 and ecologists to understand better and mitigate the environmental implications of AI technologies. 77 00:06:06,300 --> 00:06:09,720 Education and awareness are crucial components of this endeavor. 78 00:06:10,470 --> 00:06:15,930 Courses like the AI Governance Professional Certification can play a vital role in equipping professionals 79 00:06:15,930 --> 00:06:22,260 with the knowledge and skills needed to navigate the complex landscape of AI's environmental impacts. 80 00:06:22,830 --> 00:06:25,550 By fostering a deeper understanding of these issues. 81 00:06:25,550 --> 00:06:32,750 Such educational initiatives can empower individuals to advocate for and implement sustainable AI practices 82 00:06:32,750 --> 00:06:34,430 in their respective fields. 83 00:06:36,290 --> 00:06:41,960 In conclusion, while AI holds immense promise for advancing human capabilities and solving complex 84 00:06:41,960 --> 00:06:47,120 problems, it also poses significant environmental and ecosystem challenges. 85 00:06:47,630 --> 00:06:53,660 The high energy consumption, resource intensive hardware production, e-waste generation, and water 86 00:06:53,660 --> 00:06:59,570 usage associated with AI underscore the need for sustainable practices in its development and deployment. 87 00:06:59,900 --> 00:07:05,210 Simultaneously, AI has the potential to contribute positively to environmental sustainability through 88 00:07:05,210 --> 00:07:08,960 applications in energy efficiency and ecosystem monitoring. 89 00:07:09,230 --> 00:07:15,470 Balancing these dual roles requires concerted efforts from policymakers, industry leaders, technologists, 90 00:07:15,470 --> 00:07:16,610 and educators. 91 00:07:16,820 --> 00:07:22,940 By prioritizing sustainability in AI research, development, and governance, we can harness the benefits 92 00:07:22,940 --> 00:07:25,970 of AI while minimizing its environmental footprint.