illuminem summarizes for you the essential news of the day. Read the full piece on The Conversation or enjoy below:

🗞️ Driving the news: AI's potential to tackle the climate crisis is significant, yet its large energy requirements, notably during the training and inference phases of large language models like GPT-3, contribute to carbon emissions
• Innovative approaches such as spiking neural networks (SNNs) and lifelong learning (L2) technologies are being developed to reduce AI's carbon footprint

🔭 The context: The energy demands of AI models, particularly those involving artificial neural networks (ANNs), have skyrocketed, making AI a notable contributor to CO₂ emissions
• ANNs, which are fundamental to most AI systems, require extensive computing power and memory due to their complex calculations
• In contrast, SNNs offer an energy-efficient alternative by mimicking the brain's energy-efficient processing through the use of spikes for information transmission, leading to potentially lower energy requirements and emissions

🌍 Why it matters for the planet: The advancement of SNNs and L2 technologies represents a promising direction in making AI more sustainable
• By significantly reducing the energy consumption of AI systems, these technologies can help mitigate the environmental impact of AI's expanding carbon footprint, contributing to efforts against the climate crisis

⏭️ What's next: Ongoing research and development in SNNs and L2 algorithms aim to bring these technologies closer to practical application, offering a pathway to more energy-efficient AI systems
• This progress, alongside other advancements like quantum computing, holds the potential to reduce the environmental impact of AI significantly

💬 One quote: "SNNs can be up to 280 times more energy efficient than ANNs," highlights the significant potential for energy savings in AI operations.

📈 One stat: Training GPT-3 generated 502 metric tonnes of carbon, equivalent to the annual emissions from 112 petrol-powered cars.

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