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Artificial Intelligence UPSC NOTE

  Potential environmental impact of AI

  • Given the huge problem-solving potential of artificial intelligence.

  • It wouldn’t be far-fetched to think that AI could also help us in tackling the climate crisis.

  • When we consider the energy needs of AI models, it becomes clear that the technology is as much a part of the climate problem as a solution.

  • The emissions come from the infrastructure associated with AI, such as building and running the data centres that handle the large amounts of information required to sustain these systems.

  • But different technological approaches to how we build AI systems could help reduce its carbon footprint

  • Two technologies in particular hold promise for doing this,

  1.  Spiking neural networks 

  2. Lifelong learning.

  • The lifetime of an AI system can be split into two phases: 

  1. Training 

  2. Inference. 

  • During training, a relevant dataset is used to build and tune, improve, the system. 

  • In inference, the trained system generates predictions on previously unseen data.

  • Training an AI that’s to be used in self-driving cars would require a dataset of many different driving scenarios and decisions taken by human drivers.

  • After the training phase, the AI system will predict effective manoeuvres for a self-driving car. 

  • Artificial neural networks, are an underlying technology used in most current AI systems.

  • They have many different elements to them, called parameters, whose values are adjusted during the training phase of the AI system.

  • These parameters can run to more than 100 billion in total.

  • While large numbers of parameters improve the capabilities of ANNs, they also make training and inference resource-intensive processes

  • To put things in perspective, training GPT-3 (the precursor AI system to the current ChatGPT) generated 502 metric tonnes of carbon, which is equivalent to driving 112 petrol powered cars for a year.

  • GPT-3 further emits 8.4 tonnes of CO₂ annually due to inference

  • Since the AI boom started in the early 2010s, the energy requirements of AI systems known as large language models — the type of technology that’s behind ChatGPT — have gone up by a factor of 300,000.

  • With the increasing ubiquity and complexity of AI models, this trend is going to continue, potentially making AI a significant contributor of CO₂ emissions

  • In fact, our current estimates could be lower than AI’s actual carbon footprint due to a lack of standard and accurate techniques for measuring AI-related emissions.

Ways to make AI more sustainable 

  • L2 is another strategy for reducing the overall energy requirements of ANNs over the course of their lifetime that we are also working on.

  • Training ANNs sequentially (where the systems learn from sequences of data) on new problems causes them to forget their previous knowledge while learning new tasks.

  • ANNs require retraining from scratch when their operating environment changes, further increasing AI-related emissions.

  • L2 is a collection of algorithms that enable AI models to be trained sequentially on multiple tasks with little or no forgetting.

  • L2 enables models to learn throughout their lifetime by building on their existing knowledge without having to retrain them from scratch.

  • The field of AI is growing fast and other potential advancements are emerging that can mitigate the energy demands of this technology.

  • For instance, building smaller AI models that exhibit the same predictive capabilities as that of a larger model.

  • Advances in quantum computing — a different approach to building computers that harnesses phenomena from the world of quantum physics — would also enable faster training and inference using ANNs and SNNs.

  • The superior computing capabilities offered by quantum computing could allow us to find energy-efficient solutions for AI at a much larger scale.

  • The climate change challenge requires that we try to find solutions for rapidly advancing areas such as AI before their carbon footprint becomes too large.

Spiking Neural Networks (SNNs) - Artificial neural networks (ANN)

Artificial Neural Networks (ANNs)

  • Inspired by the structure and function of the brain, but do not necessarily mimic them closely.

  • Information is processed through a series of interconnected nodes called artificial neurons.

  • These neurons use mathematical functions to process and transmit information.

  • Widely used in various applications, including image recognition, natural language processing, and self-driving cars.

Spiking Neural Networks (SNNs)

  • More closely mimic the biological neural networks found in the brain.

  • Neurons in SNNs communicate with each other using short electrical pulses called spikes.

  • The timing and frequency of these spikes encode information.

  • SNNs are theoretically more powerful than ANNs and have the potential to be more energy-efficient.

  • However, they are still under development and face challenges in training and hardware implementation.

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Learnerz IAS | Concept oriented UPSC Classes in Malayalam: Artificial Intelligence UPSC NOTE
Artificial Intelligence UPSC NOTE
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