AI model compression techniques optimise large language models for faster, cheaper predictions whilst maintaining performance. Key methods include model pruning, quantization, and knowledge distillation. These enable deployment in resource-constrained environments, reduce latency, lower costs, and improve energy efficiency, particularly for real-time AI applications and edge devices.
Waymo has introduced EMMA, a new AI research model for self-driving that combines visual and textual data processing with language model knowledge. While still in research, EMMA aims to enhance autonomous driving decision-making. Waymo recently raised $5.6 billion, valuing it at over $45 billion, and is developing new vehicles based on the Hyundai Ioniq 5.
A Chinese autonomous driving company has raised $100 million and aims to have its system in 200,000 cars by 2025, up from 20,000 now. The system is comparable to Tesla's and could enable affordable smart EVs in China. The company's mapless approach gives it a cost advantage.
Tesla aims to develop fully self-driving cars by 2026 using 'end-to-end artificial intelligence'. They plan to train AI systems with video from existing vehicles, provide self-driving capabilities to current owners next year, and introduce robotaxis by 2026. This strategy aims to deliver the technology faster and more cost-effectively than competitors.
Hyundai has unveiled a hydrogen-powered concept SUV called the Initium, set to debut at auto shows in November. Production is planned for next year. The vehicle has a 404-mile range on hydrogen and includes a backup electric system. Hyundai is investing £4 billion in hydrogen technology as part of its carbon neutrality goal. Market availability is yet to be confirmed.
