Sebastian Thrun (Stanford Adjunct Professor) – Artificial Intelligence Q&A (Mar 2017)

Chapters

Abstract

Machine Learning and AI: Transforming Decision-Making and Challenging Traditional Notions

In an era where technology is rapidly advancing, Artificial Intelligence (AI) and Machine Learning (ML) have become pivotal in shaping various aspects of our lives. This comprehensive exploration delves into the intricacies of AI and ML, discussing their advancements, limitations, and real-world applications, including their impact on decision-making, adversarial search, planning in self-driving cars, and the challenges in representing knowledge.

The Evolution and Limitations of AI and ML

AI, with machine learning as a subset, has made significant strides in various domains like chess, Go, and self-driving cars. However, AI still grapples with achieving general intelligence and transferring knowledge across different skills. Machine learning, particularly deep learning, has been at the forefront of learning from data, sharing similarities with data mining, analysis, and statistics.

While experts have often underestimated AI’s capabilities, its successes have often been limited to highly specialized and repetitive tasks, such as playing Go or driving cars. AI lacks the universal capabilities of humans, such as transferring knowledge across different skills or demonstrating broad-minded intelligence.

The traditional approach in machine learning has been to focus on extracting rules from data, particularly in the early days of the field. Algorithms like decision trees were developed to extract rules from small data spaces with limited dimensions. However, modern deep networks are massively complex, making it challenging to extract rules from them.

Extracting rules from deep networks would allow for easier communication of learned knowledge to other people and AI systems. This could potentially lead to more efficient teaching and reasoning in AI systems. However, designing systems that are agnostic of explicit rules but embody rules through their architecture is a challenging open question.

Humans have accelerated their progress compared to chimpanzees due to speech and the ability to reason and think through hypotheticals. Discovering structure in deep learning and harvesting that structure could potentially lead to more human-like thinking in AI networks.

The Human-AI Interaction

The interplay between human decision-making and AI is complex. Humans often rely on emotional and subconscious decisions, later rationalized through language, contrasting early AI’s focus on logic. Machine learning now plays a significant role in shaping human perception and decision-making.

Sebastian Thrun argues that human decision-making often involves subconscious emotional decisions defended by logical reasoning. Early AI research focused on reasoning and logic, but now machine learning is recognized as equally important.

Practical Advice for AI Students

Students entering the AI field are advised to focus on building and testing systems in real-world scenarios, emphasizing end-to-end systems rather than component-based debates. This hands-on approach is essential for understanding and solving actual problems.

Thrun recommends building and testing AI systems to gain practical experience and encounter unexpected challenges. Debating algorithms and components is less effective than focusing on end-to-end systems that solve real-world problems.

Adversarial Search and Machine Learning

Adversarial search, particularly in machine learning, is an area of growing interest. Training two machine learning modules to generate and distinguish fake data has led to the production of highly accurate fake data processors. This technique has implications in various fields, including stock market analysis, where predicting investor reactions can be more effective than traditional methods.

The stock market is a complex system influenced by the behavior of multiple players. Predicting how investors react to news and events can be a better strategy than solely assessing a company’s long-term worth.

Reinforcement Learning and Multiplayer Alpha-Beta Pruning

The correlation between adversarial search, reinforcement learning, and multiplayer alpha-beta pruning is significant. Both fields involve evaluation and reward functions, with a focus on expected and unexpected future rewards. Understanding these concepts, through practical implementation and thorough study, is crucial.

Shuming noted similarities between adversarial search evaluation functions and reinforcement learning reward functions. Sebastian Thrun acknowledged the separation of these communities and the potential for valuable research at their intersection.

Simplifying Complex Concepts

There’s a growing need for initiatives that make research papers and algorithms more accessible, using visuals, animations, and interactive tools. These efforts, however, often lack recognition within the academic system.

Sebastian Thrun praised Peter Norvig and Stuart Russell’s book on machine learning as a good example, but acknowledged the lack of a central repository for such resources. He highlighted the challenge that researchers face in getting rewarded for creating user-friendly resources, as the focus often lies on publishing novel papers.

Dynamic Learning and Rule Extraction

AI agents dynamically learn game or problem rules through reinforcement learning, adjusting their behavior based on feedback. Extracting rules from deep learning networks remains a challenge due to their complexity, yet it holds the potential to make AI systems more versatile and human-like.

Enel asked how an AI agent can learn rules dynamically when not provided with knowledge about the problem domain in advance. The discussion on this topic was not included in the provided transcript.

The Future of AI and Search Algorithms

Visionaries like Sebastian Thrun believe search algorithms are still in their infancy, with potential to evolve beyond simple keyword queries to more complex, assistant-like functionalities across various devices.

The Wright brothers’ invention of controlled motorized flight was a breakthrough that changed the perception of flight. The focus on wing shape and flapping wings was misleading, as controllability proved to be the key innovation.

The pendulum between rule-based and non-rule-based AI approaches has shifted towards no rules. However, symbolic information remains crucial, suggesting the need for a return to rules. Sebastian Thrun believes that search algorithms are in their infancy and that there’s an opportunity to reinvent the entire business of search. The goal is to create a meaningful partnership with an assistant that can perform various tasks, including accessing information, opening windows, and managing calendars.

Planning in Self-Driving Cars and Knowledge Representation

Planning applications in self-driving cars, using algorithms like ASTAR, demonstrate the practical implications of AI in real-world scenarios. However, representing knowledge using formal logic, as attempted in projects like Cyc, poses significant challenges due to the complexity and diversity of world knowledge. Machine learning offers an alternative, learning from data to derive rules.

Cyc aimed to represent all knowledge in the world using formal logic. However, it failed due to the inability to link different entities and concepts meaningfully, resulting in a lack of coherent reasoning. Machine learning offers a promising approach to AI due to its ability to learn from fuzzy rules and improve over time.

Planning is extensively used in self-driving cars, from incremental approaches to more complex scenarios like multi-agent planning. Self-driving cars can navigate intersections without traffic lights, demonstrating advanced planning capabilities.

Integrating Different AI Generations

The integration of various AI generations requires a problem-centric approach, identifying issues that necessitate both logical reasoning and extensive learning.

Cognitive robotics initially struggled with limited robot capabilities, highlighting the need for a problem-centric approach in AI research, focusing on solving specific problems that require a blend of reasoning and learning.

Arpan Chakraborty suggests focusing on problems that demand a combination of logic and planning from early AI systems and intensive learning algorithms from contemporary AI. Sebastian Thrun mentions cognitive robotics, led by Ray Reiter, which aimed to apply logic to robot thinking. The field faced challenges in achieving significant results due to the limited capabilities of robots at the time. Thrun criticizes researchers who prioritize integrating specific mechanisms over finding suitable problems. He emphasizes the importance of identifying problems where the integration of different AI generations is essential for a solution. Thrun suggests seeking problems that are solvable with the combination of desired AI mechanisms. Successfully solving such problems with the required mechanisms would yield valuable research results.

In summary, the field of AI and ML is marked by significant advancements, challenges, and opportunities for future growth. Understanding these dynamics is crucial for students, researchers, and practitioners in the field.

Notes by: Alkaid