Reflection 3
In the article “AI has an environmental problem. Here’s what the world can do about that” published by the United Nations Environmental Programme, defining AI as a catch all term for a group of technologies that can be used to process and at a surface level superficially mimic human thinking. The history of AI goes back years with rudimentary forms if artificial intelligence existing since the 1950s. A notable example of this is the IBM 7094, being the earliest known recording of a computer-synthesized voice singing a song, “Daisy Bell,” the recording being made at Bell Labs in Murray Hill, New Jersey, on the IBM 7094 in 1961. However, as we have seen, this AI has continued to evolve as a fast pace in recent years due to advantages in computing power and the explosion of data. A lesser-known impact of AI advancement is its environmental cost, as the UN explains these large-scale AI systems rely on massive data centers that consume enormous amounts of electricity, spurring the emission of planet-warming greenhouse gases. This becomes a larger problem as more and more people begin to rely on artificial intelligence to give them information, as a request made through ChatGPT consumes 10 times the electricity of a Google search, as reported by the International Energy Agency. In places such as Ireland, AI-related data centers could soon account for nearly 35 percent of national energy use by 2026.
Beyond electricity, these data centers generate electronic waste, which contains hazardous substances such as mercury and lead, while relying heavily on critical minerals and rare elements, which are often mined unsustainably and consume vast amounts of water for cooling to put this into perspective AI-related infrastructure could soon consume six times more water than Denmark, a country with a population of 6 million people.
While taking this into account, at the same time, AI holds undeniable potential, having the ability to detect patterns and predict outcomes, which could make AI an invaluable resource for monitoring the environment and help governments, organizations and individuals make more planet-friendly decisions. Demonstrating how the same technology can help combat climate change while contributing to environmental strain at the same time.
This nature of AI relates to the discussion of Dr. Mariel Miller, whose work centres on the intersection of learning, teaching and technology. Discussing how AI can dynamically generate, refine and engage with human processes, having the capabilities to offer personalized learning support and adapt work based on individual needs. However, as discussed, there are many limitations to this technology. Generative AI can produce misinformation based on the prompts given, another key concern with AI can perpetuate human biases and reinforce existing inequalities. This belief is reinforces by the work of Lucas Wright, who notes that AI can work as a helpful tool that can allow its users to upload different documents as a base of knowledge and then handle custom instructions shaping outputs based on selected knowledge bases and questions.
The works reviewed highlight both the power and responsibility involved in the use of AI in the benefits it can provide when used as a tool to help further the learning journey, and the harmful impacts that can be caused by dependency on it.
