Dr. Norvig stresses the pivotal role of large datasets in machine learning model training. He proposes that larger data sets often result in enhanced performance, even more than complex algorithms trained with less data.

The session references a paper by Halevy, Norvig, and Pereira (2009), which indicates how web-scale data significantly improved machine translation, speech recognition, and information retrieval. The 'unreasonable' term signifies that simple models with large data can surpass intricate models with less data.

Dr. Norvig highlights Google's strategy of utilizing large datasets to improve its services. Examples include Google's spelling correction feature driven by user search query data, and Google Translate, which uses bilingual text corpora to train its translation models.

The necessity of rigorous testing and experimentation in machine learning is underlined by Dr. Norvig. Google's practice of consistent A/B testing to gauge algorithm performance is discussed as an essential part of the machine learning process.

In concluding the session, Dr. Norvig offers an insightful forecast regarding the trajectory of AI and machine learning. His prediction centers on the correlation between the expansion of data collection and the enhancement of AI performance.

In a data-driven world, the volume, variety, and velocity of data are continually increasing. This growth isn't only due to the rise of internet users but also the surge of interconnected devices, often referred to as the Internet of Things (IoT). The proliferation of these devices alongside advances in data storage and processing capabilities ensures an ever-growing pool of data for training and refining machine learning models.

Dr. Norvig foresees this trend catalyzing continual performance improvements in AI. These improvements are expected to be seen across various applications of AI, including but not limited to natural language processing, image and speech recognition, and predictive analytics.

Additionally, the increasing data volume can enable the discovery of subtle patterns and correlations that may be imperceptible with smaller datasets. This will allow AI to generate more accurate and nuanced predictions and insights, which can lead to more effective decision-making in a wide range of domains, from business and finance to healthcare and environmental monitoring.

Moreover, with more data, models can potentially become more generalizable and robust, as they can be trained and validated on a diverse range of scenarios and edge cases. This could lead to AI systems that are better equipped to handle real-world complexity and variability, further enhancing their performance and utility.