The future of AI in vehicles is seen as crucial, with the potential for improved autonomous driving capabilities, better human-machine interaction, and increased productivity for developers. The author emphasizes that companies that quickly adopt these technologies may gain a first-mover advantage and the opportunity to set industry standards, while those that delay may fall behind due to lack of features compared to competitors.
Key takeaways:
- Recent advancements in AI models, data, and computing power have led to significant changes in the development of autonomous vehicles, ushering in the era of AV 2.0 where every component of the vehicle uses machine learning.
- The age of the 'foundation model' has begun in machine learning, where models are trained on simpler, more fundamental tasks using larger amounts of data, making them highly adaptable and versatile.
- Foundation models can handle multiple types of data at once, like images and text, and can learn from a few examples, making the creation of models for specific tasks much cheaper and more efficient.
- It's crucial for car manufacturers and the wider vehicle industry to adopt AI technologies in their development processes and products, as AI has the potential to revolutionize the industry, improve autonomous driving capabilities, and increase productivity for developers.