Geoffrey Hinton (Google Scientific Advisor) – Turing Award Winners Keynote Event (Feb 2020)


Chapters

00:00:00 Challenges of Convolutional Neural Networks in Object Recognition
00:03:54 Architectural Problems with Convolutional Neural Networks
00:10:27 Unsupervised Learning of Linear Structures in Vision
00:20:28 Transformers for Image Parsing
00:24:50 Generative Modeling of MNIST Digits with Parts and Holes
00:29:50 Capsule Networks for Visual Perception
00:31:53 Inverse Rendering for Vision as Inverse Graphics
00:34:21 Deep Learning: Beyond Supervised Learning and Neural Networks
00:36:50 Deep Learning: Beyond Neural Networks
00:43:13 Artificial Intelligence Challenges and Inspirations From Human and Animal Learning
00:50:20 Self-Supervised Learning: Overcoming Challenges in Image and Video Prediction
00:54:14 Self-Supervised Learning: A New Paradigm for Artificial Intelligence
00:57:10 Energy-Based Training and Learning in a Stochastic Environment
01:03:52 Self-Supervised Learning for the Next Generation of AI Systems
01:11:14 Understanding System 2 Processing in Deep Learning
01:16:18 Systematic Generalization for Machine Learning
01:27:39 Consciousness Priors and Causal Reasoning for Efficient Learning
01:38:37 Vector Representations and System 2-Inspired Machine Learning
01:41:05 Technical and Meta Discussions on AI and Machine Learning
01:50:23 Surveying the Landscape of AI Research: Challenges, Opportunities, and Ethical Considerations
02:00:00 Long-Term Research in AI: Balancing Short-Term Gains and Structural Changes
02:04:40 Questions and Discussion on the Nature of AI and Its Relationship to Science

Abstract

The Evolution and Future of Artificial Intelligence: From Convolutional Neural Networks to Self-Supervised Learning and Beyond

Abstract:

The evolution of artificial intelligence (AI) has taken a significant leap, shifting from convolutional neural networks (CNNs) to self-supervised learning. Key pioneers like Geoff Hinton, Yann LeCun, and Yoshua Bengio have made substantial contributions, transforming computer vision, while addressing limitations and exploring new horizons, including self-supervised learning and deep learning concepts.

Introduction:

The field of AI has seen remarkable strides, with key figures like Geoff Hinton, Yann LeCun, and Yoshua Bengio playing pivotal roles in shaping its evolution. This article offers a comprehensive analysis of their contributions, the advancements and challenges in convolutional neural networks (CNNs), and the emerging landscape of AI, focusing on self-supervised learning and deep learning concepts.

The Pioneering Work of Hinton, LeCun, and Bengio:

The trio’s groundbreaking work laid the groundwork for major AI applications in computer vision, natural language processing, and speech recognition. Their contributions are integral to the progress witnessed in diverse fields such as mobile robotics, computational neuroscience, and computer science. Initially met with skepticism, they persevered, demonstrating unwavering grit and determination, eventually bringing neural networks to the forefront of modern AI. Their story stands as an inspiration for scientific exploration beyond prevailing trends.

Limitations of CNNs:

Despite their success, CNNs face challenges in handling viewpoint changes, such as rotation and scaling. Achieving viewpoint invariance remains inefficient, and their lack of human-like perception poses a significant limitation. CNNs excel at handling translations but struggle with rotations and scaling. Training CNNs on multiple viewpoints is inefficient, and ideal neural nets should effortlessly generalize to new viewpoints.

Equivariance over Invariance:

In contrast to the conventional emphasis on invariance, equivariance in AI models offers a unique approach that preserves essential information while enabling representation changes with viewpoint. This approach aligns more closely with human perception, where representations change with viewpoint while invariant representations remain constant. Hinton believes that perceptual systems possess equivariant representations of percepts and invariant representations of labels.

Stacked Capsule Autoencoders:

Hinton introduced a novel approach to computer vision using Stacked Capsule Autoencoders. This model, leveraging unsupervised learning and whole-part relationships, marks a significant shift towards building structure into neural networks. Stacked Capsule Autoencoders represent a new approach to 3D object recognition, focusing on unsupervised learning and whole-part relationships.

Transformer Mechanism in AI:

The integration of a multilayer transformer in capsule networks, exemplified by the Set Transformer, represents a leap forward in encoding relationships between capsules and handling complex inference problems. Unlike CNNs, transformers utilize coincidence activation, making them more effective filters.

Application to MNIST Digits:

The effectiveness of these new models is demonstrated through their application to the MNIST dataset, showcasing their capability in handling parts and wholes and reconstructing digits with remarkable accuracy. Capsule networks aim to model MNIST digits using parts and high-level capsules. The parts are learned to capture specific features, while high-level capsules learn to combine parts and model high-level concepts. The network reconstructs the image from the extracted parts and high-level capsules. The activation of high-level capsules indicates the parts present in the image.

Challenges and Vision for AI:

Despite these advancements, AI faces challenges in scalability, handling deformable parts, and learning efficiently from limited data. The vision component of AI continues to evolve, with the capsule model capturing figure perception and extending to real 3D images. Stacked Capsule Autoencoders serve as building blocks to capture more structure in neural networks, aiming to effectively capture intrinsic geometry.

New Insights on Inverse Graphics and Generative Models:

Inverse Graphics:

1. Understanding inverse graphics as the process of breaking down shapes into smaller parts until they resemble basic elements enables the extraction of sensible parts from an image.

2. It involves inverting the rendering process to recover meaningful object parts, akin to vision as inverse graphics.

Generative Models:

1. The complexity of generative models is more significant than recognition models in terms of model selection criteria.

2. It is advantageous to create a simple generative model with extensive wired instructions, delegating the challenging task of inversion to a large transformer network.

Ian Le Guin: A Godfather of AI and His Passion for Self-Supervision:

1. Ian Le Guin’s Contributions:

– Renowned professor specializing in various fields, including computer science, data science, and neural science.

– Significant contributions to machine learning, computer vision, mobile robotics, and computational neuroscience.

– Co-founder of the Partnership on AI, aiming to advance AI for beneficial purposes.

2. Ian Le Guin’s Personal Traits:

– Known for his positive attitude, passion for research, and love for life.

– Recognized as one of the “godfathers of AI” for his significant contributions to the field.

3. Ian Le Guin’s Perspective on Self-Supervision:

– Emphasizes self-supervision as a higher-level, inspirational approach to deep learning.

– Defines deep learning as building systems by assembling parameterized modules.

Deep Learning’s Definition, Applications, and Impact:

1. Definition of Deep Learning:

– A branch of machine learning involving optimizing computation graphs through gradient-based learning.

– Incorporates prior knowledge and inductive bias into architectures.

– Involves complex computations, such as minimizing energy functions, for inference.

– Applicable to supervised, reinforcement, self-supervised, and unsupervised learning paradigms.

2. Applications of Deep Learning:

– Highly successful in supervised learning tasks with large datasets, such as speech recognition, image recognition, natural language processing, and computer vision.

– Recent research has demonstrated its ability to perform symbolic manipulation, solving integrals and differential equations.

– Widely applied in various industries, including automotive, medical imaging, and social media, with significant societal impacts.

3. Challenges of Deep Learning:

– Learning with fewer labels, samples, or trials.

– Learning to reason and making reasoning compatible with gradient-based learning.

– Learning to plan complex action sequences and decompose tasks into subtasks.



The evolution of AI, from the foundational work of Hinton, LeCun, and Bengio to the latest advancements in self-supervised learning and deep learning concepts, illustrates the field’s dynamic nature. As AI continues to advance, addressing its limitations and integrating various approaches will be crucial for its continued success and broader application.


Notes by: QuantumQuest