Machines Like Us: A Deep Dive into AI and Common Sense
Ronald J. Brachman and Hector J. Levesque’s Machines Like Us: Toward AI with Common Sense is a thought-provoking exploration of one of artificial intelligence’s biggest challenges—developing machines that can think with common sense rather than just specialized expertise. This book provides a compelling argument for why AI systems struggle with reasoning beyond predefined rules and how researchers might overcome these limitations.
Understanding Common Sense in AI
One of the book’s central themes is the distinction between artificial intelligence and human cognition. While AI has made significant strides in areas like natural language processing and image recognition, it still lacks the ability to handle unpredictable situations with the same intuition as humans. The authors illustrate this gap with relatable examples, such as a self-driving car stuck at a red light indefinitely because it lacks the reasoning to determine an appropriate workaround [A].
Brachman and Levesque argue that common sense is essential for AI systems to function effectively in real-world environments. They explore how AI has historically focused on expertise rather than broad reasoning, leading to systems that excel in specific domains but fail in general problem-solving.
The Challenge of Encoding Common Sense
A major obstacle in AI development is the difficulty of encoding common sense reasoning into machines. The book discusses various approaches, including symbolic AI, expert systems, and deep learning. While deep learning has dominated AI research in recent years, the authors advocate revisiting symbolic AI as a means to represent and manipulate commonsensical knowledge [B].
The authors highlight the limitations of current AI models, emphasizing that while they can process vast amounts of data, they lack the ability to infer meaning in the way humans do. They propose that AI systems should be designed to accept advice and provide reasons for their decisions—two fundamental requirements for trustworthy autonomous machines [C].
Key Takeaways for AI Enthusiasts and Researchers
For those interested in AI’s future, Machines Like Us offers valuable insights into the ongoing debate about artificial general intelligence (AGI). The book provides a well-structured overview of AI’s current capabilities and the hurdles that must be overcome to create truly intelligent systems.
The authors’ discussion on knowledge representation is particularly enlightening. They explain how AI can benefit from integrating multiple approaches—combining deep learning with rule-based systems to enhance reasoning capabilities. This hybrid approach could pave the way for AI models that not only recognize patterns but also understand context and make informed decisions.
Final Thoughts: A Must-Read for AI Enthusiasts
Machines Like Us is an essential read for anyone interested in AI, whether they are researchers, developers, or simply curious about the future of intelligent machines. The book’s accessible writing style makes complex concepts easy to grasp, while its real-world examples provide a compelling case for why common sense is the missing piece in AI development.
By addressing the limitations of current AI models and proposing innovative solutions, Brachman and Levesque offer a roadmap for creating AI systems that can think more like humans. Their insights into AI reasoning, knowledge representation, and trustworthy AI make this book a valuable resource for understanding the next frontier in artificial intelligence.
Sources:
A. https://www.forbes.com/sites/davidteich/2023/07/11/book-review-machines-like-us-the-difficulty-of-coding-ai-for-common-sense/
B. https://www.goodreads.com/book/show/58839208-machines-like-us
