Research indicated that previous models like GPT-3 were undertrained; despite their size, they hadn't been trained on enough text to reach their full potential. For instance, Chinchilla, a model smaller than GPT-3, outperformed larger models by being trained on four times more data.

The largest AI models, like GPT-4, require massive GPU clusters, with power consumption reaching unprecedented levels, highlighting the increasing scale of AI infrastructure.

Hassabis argues that while smaller, specialized models can be effective, the development of large, general models is essential for achieving significant advancements in AI capabilities. He also mentions that larger models are essential for training the smaller, more efficient models that we use extensively.

Large language models, like ChatGPT, are trained on vast amounts of text data, allowing them to generate human-like responses.

The principle 'bigger model, more data, better results' has held true for the last 12 to 15 years in AI development.

GPT-4.5 is OpenAI's largest and most humanlike model to date, representing a significant step in scaling up unsupervised learning and achieving a deeper understanding of the world and human experience.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.

Generative AI and large language models, like ChatGPT, are incredibly powerful technologies that will soon become even more powerful.

With the release of GPT-3, it seems the sky is the limit for this new paradigm of scaling large language models.