Peter Norvig (Google Director of Research) – AI talk at L.A.S.T. festival (Jun 2014)

Chapters

00:00:17 Historical Social Sciences and Robotics: Teaching Computers to Interact and Learn
00:04:07 Scaling Language Models with Vast Textual Data
00:09:48 Machine Translation: Breaking Down Language Barriers
00:15:27 Object Recognition as Inventory Management
00:18:52 Inventories of Visual Features for AI Image Recognition
00:22:09 Future of Automation and the Economy

Abstract

The Evolution of AI and Computing: From Manual Labor to Machine Learning

Abstract

The evolution of computing and artificial intelligence (AI) has greatly impacted various aspects of society, economy, and technology. This article explores the shift from manual labor to knowledge-based tasks in computing, the transition from explicit instructions to learning by observation in AI, advances in robotics, AI’s role in historical social science, challenges in machine translation, the innovative approaches in object recognition, and the societal implications of automation. This comprehensive review provides insights into the remarkable journey of AI and computing, emphasizing the significant breakthroughs and the challenges that lie ahead.

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Evolution of Computing and AI: The Foundation of Modern Technology

Computing has transitioned significantly from its early days of manual labor to its current state, where knowledge-based tasks dominate. This shift has been pivotal in the development of modern computers. Early programming demanded precise instructions, while contemporary AI focuses on interaction and learning through examples. This transformation signifies a fundamental change in how we approach problem-solving and technology development.

Customer-Uploaded Pictures and Custom Tiles: Initially, the company allowed customers to upload any picture and have it made into custom tiles. This approach was expensive and inefficient due to the need to produce custom tiles for each picture.

Inventory of Popular Tiles: To address the challenges of producing custom tiles, the company focused on maintaining an inventory of popular tiles to fulfill future orders efficiently.

Predicting Future Pictures: The key challenge in maintaining an inventory of popular tiles was predicting the types of pictures customers would upload in the future.

Object Recognition as Predicting Future Pictures: The analogy between recognizing objects in the world and predicting the types of pictures customers would upload highlights the fundamental goal of object recognition systems.

Tiles as Building Blocks of Pictures: Just as tiles can be used to assemble pictures, objects can be seen as building blocks of scenes. This analogy illustrates the importance of understanding the relationships between objects in a scene for effective object recognition.

Discovery of Lines as Building Blocks of Pictures: Early work in object recognition revealed that lines can be used to represent objects in images. This discovery laid the foundation for further advancements in object recognition.

Bruno Holzheimer’s Discovery: Bruno Holzheimer’s group at Berkeley demonstrated that pictures can be constructed using lines. This finding provided a deeper understanding of the relationship between lines and objects in images.

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Object Recognition and Predictive Analysis

Innovative approaches in object recognition, such as the tile analogy, highlight AI’s capability in predictive analysis and pattern recognition. Google’s investment in image recognition research, processing millions of YouTube video frames to create an object inventory, exemplifies this advancement. The system’s ability to recognize common objects without explicit teaching marks a significant milestone in AI development.

Multi-Level Inventory: Researchers explored a multi-level inventory approach to object recognition, where smaller tiles are assembled into larger ones to create more complex objects. This approach allowed the system to recognize a wider range of objects.

Discovery of Objects: Using the multi-level inventory approach, researchers discovered that the system could recognize objects such as eyes, noses, faces, doors, wheels, and cars. This demonstrated the system’s ability to identify and classify objects in complex scenes.

Google’s Approach: Google invested in this research, aiming to apply significantly more computing power and data to the task. This investment enabled Google to make significant advancements in object recognition.

Experiment with YouTube Videos: Google conducted an experiment using 10 million YouTube videos, selecting one frame from each video and analyzing it at a high resolution. This experiment provided a vast and diverse dataset for training the object recognition system.

Identification of Cats and Faces: The system identified cats and faces as prominent objects within the YouTube videos, even without being explicitly taught about these concepts. This demonstrates the system’s ability to learn and recognize objects without human supervision.

Clustering and Texture Recognition: The system demonstrated the ability to cluster objects based on their similarities, such as textures, diagonal lines, and circles. This clustering capability allowed the system to group similar objects together, making it easier to identify and classify them.

Importance of Coherence: The system recognized objects based on their coherence and similarity, rather than relying on labels or names. This approach allowed the system to identify objects even if they were not labeled or named explicitly.

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Automation and Its Societal Impact

The rapid advancement of automation poses both opportunities and challenges. While it streamlines processes and enhances efficiency, it also contributes to unemployment and income inequality. The need to address these consequences is urgent, as exemplified by Peter Norvig’s analogy from Douglas Adams’ “The Hitchhiker’s Guide to the Galaxy,” which symbolizes the displacement of middle-class jobs due to automation.

Recognizing a Variety of Objects: AI has advanced significantly, enabling the recognition of diverse objects such as flowers, animals, keyboards, wine bottles, pizza, and more. This demonstrates the system’s versatility and ability to handle a wide range of objects.

The Future of Automation: Automation is rapidly changing the job market, particularly affecting routine jobs. This trend is leading to economic problems, unemployment, and income inequality.

Douglas Adams’ Analogy: Peter Norvig shares an analogy from Douglas Adams’ “The Hitchhiker’s Guide to the Galaxy” to illustrate the impact of automation. In the story, people are evacuated from their planet due to an impending solar nova. Three arks are built: the A-Ark for scientists, artists, and thinkers, the C-Ark for workers, and the B-Ark for middlemen. The B-Ark is launched first, but loses contact, leaving the fate of the other arks uncertain.

Automation’s Impact on the Economy: Automation is eliminating routine jobs, leading to economic issues such as unemployment and income inequality. The ability to create zero-cost, marketable intellectual property goods that can be duplicated freely and sold globally exacerbates income inequality. In the past, physical limitations restricted the reach and profitability of businesses, but now, app makers can capture the entire market, leading to a winner-takes-all scenario.

Challenges and Questions: Peter Norvig concludes his presentation by highlighting the need to address wealth distribution and find meaningful work for those displaced by automation. The future of automation and its impact on society remain uncertain, and questions about how to deal with these challenges remain unanswered.

Notes by: MatrixKarma


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