Robert Munro, author, expert in human and machine intelligence, discusses his work, and the intersection between artificial intelligence (AI) and human intelligence.

Munro discusses his background and what drove him to dig into AI, detailing the data side of AI and machine learning algorithms. He cites the example of autonomous vehicles, in that thousands of hours of driving data have been created by people, critical driving data that provides valuable and necessary information, such as 'this is the middle of the street,' this is a stop sign,' etc.

Therefore, the quality of the data is often times more important than the algorithm itself. And it was this construct that motivated Munro to devote his time and research into the nexus between artificial intelligence (AI) and human intelligence. 

The AI data guru discusses active learning, the analysis of data and algorithms to determine how accurate and up to date they are, to suss what is missing, etc. It is the process of trying to decide which is the best data for humans to review, and is a major topic in Munro's latest book.

Munro goes on to discuss languages and algorithmic functioning from a historical perspective, touching on bad data and large volumes of data, and the impact of both. As he states, sometimes it is best to use a smaller amount of data, a batch of data that is carefully curated, as opposed to simply a large data set that is more generic. 

Munro furthers the conversation, discussing the differences between various languages and how adaptable systems are created. Munro provides information on raw data and the methods to utilize it, discussing the specifics in regard to natural language processing and machine learning. He discusses task creation and models for prediction in AI.

Wrapping up, Munro provides further information on computer vision models for autonomous driving, and other issues such as predictive maintenance, identifying cracks in machinery, the integration of robotics and more.

And Munro provides his thoughts on the future of artificial intelligence, where we are currently, and the prospects for the coming years.