Jeff Dean (Google Senior Fellow) – Deep Learning for Solving Challenging Problems (Jun 2019)
Chapters
00:00:03 Machine Learning's Potential for Solving Societal Problems
Introduction: The field of machine learning is experiencing a surge of interest and research, with the number of machine learning papers posted on Archive growing faster than the old Moore’s law exponential growth rate of computational power.
Deep Learning: Deep learning is a rebranding of older ideas, such as neural networks, which have been around for some time. Neural networks are an exciting abstraction that enables the modeling of hierarchical pattern recognition systems. They can learn patterns of patterns and build up features automatically through learning algorithms.
Computational Power: In the 1980s and 1990s, neural networks showed promise for toy-sized problems, but they lacked the computational power to scale up to real-world problems. Around 2008-2010, computational devices became powerful enough to apply neural networks to real problems.
Applications of Neural Networks: Neural networks can learn functions from data, including input-output relationships. They have shown significant improvements in various fields, such as computer vision, audio processing, and natural language processing. Neural networks can take in raw data, such as pixels or audio waveforms, and produce meaningful outputs, such as image captions or speech transcripts.
ImageNet Challenge: The ImageNet challenge is a yearly competition to evaluate the accuracy of computer models in classifying images into one of a thousand categories. In 2011, before neural networks were widely used, the winning entry had a 26% error rate. By 2016, the error rate had dropped to 3%, demonstrating the dramatic improvement in computer vision capabilities.
Conclusion: The advancements in machine learning, particularly deep learning, have enabled computers to perform tasks that were previously challenging, such as image recognition and speech transcription. These advancements have fundamental consequences for various fields and industries, as computers can now perceive and understand the world in new ways.
00:05:36 Grand Engineering Challenges and Machine Learning
Introduction: Machine learning has the potential to contribute to solving grand engineering challenges identified by the U.S. National Academy of Engineering, aiming to improve society’s health, knowledge, and sustainability.
Autonomous Vehicles: Autonomous vehicles are close to commercial deployment due to advancements in deep learning and computer vision. They can process various sensor data, including cameras, LIDAR, and radar, to create a comprehensive model of the surrounding environment. Waymo, a subsidiary of Alphabet, is conducting trials in Phoenix, Arizona, with 100 self-driving cars carrying real passengers without safety drivers.
Robotics: Computer vision advancements enable robots to learn from perceptual inputs and perform various actions. Grasping is a key task in robotics, and machine learning has significantly improved grasp success rates. By pooling experiences from multiple robots, grasping performance can be further enhanced.
00:10:15 Robotics Developments in Grasping, Skill Acquisition, and Health Informatics
Robotics: In 2016, Google’s robotics research group achieved a 78% grasping success rate for unseen objects using a combination of robotic experimentation and supervised learning. By 2018, with continued improvements and the use of reinforcement learning, the grasping success rate increased to 96%, demonstrating significant progress in robotic grasping capabilities. This improvement has implications for building robotic systems that can perform more complex tasks, as grasping is a fundamental aspect of robotic functionality. Google’s AI residents collect training videos for robots to emulate, enabling robots to learn new skills by observing human actions through video. With reinforcement learning, robots can learn to pour liquid into different cups or mugs after watching just 15 short video clips and practicing for 15 trials. This method allows robots to acquire new skills by learning from data about the world and human actions, making them more flexible and adaptable.
Advanced Health Informatics: Advanced health informatics is another area where machine learning has the potential for significant impact. Google is excited about the potential of machine learning to improve healthcare and is actively exploring its applications in this field.
00:12:40 AI in Medical Diagnosis and Healthcare Prediction
Diagnosing Diabetic Retinopathy with AI: AI models trained on fundus imagery can detect diabetic retinopathy as accurately as board-certified ophthalmologists. By leveraging retinal specialist annotations, the model’s accuracy can match the gold standard of care. AI models can also detect cardiovascular health factors, such as biological sex, from retinal images.
Predicting Patient Outcomes with EHR Data: AI models trained on EHR data can make predictions about a patient’s condition and future medical needs. This includes predicting hospital admissions, medical expenses, diabetes risk, and diagnoses. Collaboration with healthcare systems has allowed for the development and testing of these models using de-identified medical records. AI models can predict mortality risk 24 hours earlier than current methods, providing physicians with more time to attend to critically ill patients.
Engineering Tools for Scientific Discovery: AI can be used to analyze large datasets and identify patterns that would be difficult or impossible for humans to find. This can lead to new insights and discoveries in various fields, including medicine, materials science, and climate change. AI can also be used to automate tasks and free up researchers to focus on more creative and challenging problems.
00:18:03 TensorFlow: Open-Source Machine Learning Platform for All
TensorFlow: A Catalyst for Machine Learning Development: TensorFlow emerged as the second-generation system developed by Google for internal research and deployments. Open-sourced in November 2015 under the Apache 2.0 license, encouraging adoption and modifications. TensorFlow’s popularity is evident in its GitHub stars, indicating a thriving community of users and contributors.
Impact of TensorFlow on the Machine Learning Community: TensorFlow has fostered a vibrant community actively improving the system and expanding its applications. With over 36 million downloads, it has reached a significant user base, including individuals and organizations.
Examples of TensorFlow Applications: A Netherlands-based company utilizes TensorFlow to analyze sensor data for fitness monitoring in cows, identifying abnormal behaviors. A collaboration between Penn State University and the International Institute of Tropical Agriculture leverages TensorFlow for deep learning-based detection of cassava disease, a major threat to global food security. TensorFlow enables offline disease detection through mobile applications, addressing connectivity limitations in certain regions.
Focus on Simplifying Machine Learning: Recognizing the limited expertise in machine learning model training, Google aims to make the process more accessible. Efforts are underway to reduce the specialized knowledge required for effective machine learning model development.
00:20:47 Automating Machine Learning Experiments and Architecture Search
AutoML: Machine learning is currently a complex process requiring extensive expertise and experimentation. AutoML aims to simplify this process by automating many tasks, reducing the need for specialized knowledge.
Neural Architecture Search: Neural architecture search is a method of automating the design of neural networks. It uses a meta model to propose model architectures, trains them, and provides feedback to the meta model to refine future proposals. This approach has been successful in achieving state-of-the-art results on challenging tasks like ImageNet.
Benefits of AutoML: AutoML can perform more experiments than human-supervised experiments, leading to better results. It can optimize models for specific constraints, such as computational cost or latency. AutoML can explore multiple architectures simultaneously, reducing the overall cost of experimentation.
Ongoing Research: Researchers are exploring various approaches to improve AutoML, including: Using evolutionary algorithms instead of reinforcement learning. Incorporating accuracy and computational cost into the reward function. Learning augmentation policies. Exploring multiple architectures simultaneously to reduce cost.
00:23:46 Machine Learning Principles and Specialized Hardware
Machine Learning and Specialized Hardware: Machine learning is driving demand for more computational power. Deep learning algorithms benefit from reduced precision and specific linear algebra operations. Tensor Processing Units (TPUs) are specialized hardware designed for efficient reduced-precision linear algebra.
TPU Features and Availability: TPU version 3 delivers 420 teraflops of compute per board. TPUs are accessible through Google Cloud products and can be configured in large pods for petaflop-scale computation. TensorFlow is used for programming TPUs, ensuring ease of programming and scalability.
TPU Access for Research: Google offers free access to 1,000 TPUs for researchers with interesting ideas, promoting open publishing and collaboration. Cloud TPUs are also available through a browser-based Colab implementation.
Responsible Use of AI in Society: Google has published a set of principles for responsible AI, addressing issues such as fairness, transparency, accountability, and safety. The principles guide Google’s internal uses of machine learning and contribute to broader discussions on AI ethics. Active research is ongoing to address challenges like removing unfair bias in machine learning models.
00:27:59 Machine Learning and Google's Journey to AI
Jeff Dean’s Long-Standing Involvement in Machine Learning at Google: Google has been utilizing machine learning for around 15-16 years, particularly training large logistic regression models for various purposes.
The Realization of Machine Learning’s Significance: The defining moment for Jeff Dean came when he explored the potential of training large-scale neural networks.
Collaboration with Andrew Ng and Scaling Up Neural Networks: Jeff Dean’s encounter with Andrew Ng led to the development of a system capable of training neural networks across thousands of computers concurrently, yielding impressive results.
Jeff Dean’s Role in Pioneering Big Data Technologies: Jeff Dean played a pivotal role in creating core big data technologies like MapReduce and BigTable, as well as being at the forefront of TensorFlow’s development.
Current Focus and Future Developments: Despite his extensive speaking engagements, Jeff Dean continues to actively contribute to technological advancements.
General Note: The provided text is a segment of a longer presentation, and this summary focuses on the specific information contained within this segment.
00:30:21 Machine Learning Models for Multitasking and General AI
Multitask Learning and Transfer Learning: Jeff Dean is working on a project that aims to develop a machine learning system capable of training models to perform thousands or tens of thousands of tasks simultaneously. Multitask and transfer learning have shown promising results on a small scale, but Dean believes that training a single model for multiple tasks can lead to significant improvements.
Benefits of Multitask Learning: Training a single model for multiple tasks can result in faster learning of new tasks with less data. The model can leverage its expertise in previously learned tasks to adapt and solve new tasks more efficiently.
Challenges in Multitask Learning: Machine learning models are typically trained to perform specific tasks and may struggle to generalize to unrelated tasks. Training a single model for multiple tasks requires careful management of the learning process to avoid interference between tasks.
General AI and Leveraging Expertise: Dean’s research aims to address the limitations of current machine learning models and enable them to perform a wide range of tasks. By training a model to perform multiple tasks, the system can leverage its expertise in one task to learn new tasks more effectively.
Future Directions: Dean emphasizes the need to develop machine learning models that can learn to solve new tasks by leveraging their existing knowledge and expertise. The goal is to create a system that can continuously learn and adapt to new challenges, moving towards general AI capabilities.
Abstract
The Evolution of Machine Learning and Its Impacts on Society and Technology
Engaging the Future: Machine Learning’s Revolution in Science and Engineering
Machine learning (ML) and deep learning (DL) have become the cornerstones of modern technological and scientific advancements. This article delves into the remarkable journey of these technologies, exploring how they are reshaping various facets of our society, including healthcare, engineering, and scientific discovery.
The Exponential Growth of Machine Learning and Deep Learning
ML’s evolution is characterized by an exponential increase in research, fueled by advancements in computational power. Deep learning, essentially a rebranding of neural networks, has gained prominence with the surge in powerful computational resources. The function-learning ability of neural networks stands out, particularly in tasks like image and speech recognition, and language translation. Machine learning and specialized hardware are intertwined. The increasing demand for computational power is pushing the development of specialized hardware like Tensor Processing Units (TPUs), designed for efficient deep learning computations. TPUs provide reduced precision and specific linear algebra operations, enabling faster and more efficient training of deep learning models. Google offers free access to TPUs for researchers and makes them accessible through Google Cloud products, promoting open publishing and collaboration.
Breakthroughs in Image Recognition and Speech Models
Significant progress in neural network-based image recognition has enabled computers to categorize images with astonishing accuracy, even identifying specific subcategories like distinct monkey species. Furthermore, state-of-the-art results in speech recognition through end-to-end models mark another milestone, transforming raw audio waveforms into accurate transcripts.
Neural Networks in Language Translation and Image Captioning
Neural networks have also revolutionized language translation, maintaining the intended meaning across languages. In the field of image captioning, these networks can generate simple sentences describing images, showcasing their understanding of visual information.
ImageNet Challenge and Computers’ Enhanced Visual Perception
The ImageNet challenge highlights the rapid advancement in image classification accuracy, with a dramatic reduction in error rates from 26% in 2011 to just 3% in 2016, surpassing human error rates. This advancement has endowed computers with remarkable visual perception capabilities.
Addressing Grand Engineering Challenges through Machine Learning
Machine learning is pivotal in tackling the grand engineering challenges identified by the U.S. National Academy of Engineering in 2008. Google’s research in autonomous vehicles, robotics, and energy solutions exemplifies ML’s role in addressing these challenges.
Autonomous Vehicles and Robotics: A Leap Forward
Deep learning and computer vision have catalyzed significant progress in autonomous vehicle technology, with trials like Waymo’s in Phoenix demonstrating their near-term viability. In robotics, advancements in computer vision have enabled robots to learn from perceptual inputs, significantly improving their grasping success rates and overall capabilities.
Revolutionizing Health Informatics and Medical Imaging
In healthcare, machine learning promises transformative changes. DeepMind Health’s application of AI in tackling diseases and enhancing patient care, such as early sepsis detection and personalized cancer treatments, is noteworthy. Similarly, machine learning models trained on fundus images can diagnose diabetic retinopathy, showcasing accuracy on par with or better than specialists. Google AI/ML Innovations in Medical Imaging and EHRs: Diagnosing Diabetic Retinopathy with AI AI models trained on fundus imagery can detect diabetic retinopathy as accurately as board-certified ophthalmologists. By leveraging retinal specialist annotations, the model’s accuracy can match the gold standard of care. AI models can also detect cardiovascular health factors, such as biological sex, from retinal images. Predicting Patient Outcomes with EHR Data AI models trained on EHR data can make predictions about a patient’s condition and future medical needs. This includes predicting hospital admissions, medical expenses, diabetes risk, and diagnoses. Collaboration with healthcare systems has allowed for the development and testing of these models using de-identified medical records. AI models can predict mortality risk 24 hours earlier than current methods, providing physicians with more time to attend to critically ill patients. Engineering Tools for Scientific Discovery AI can be used to analyze large datasets and identify patterns that would be difficult or impossible for humans to find. This can lead to new insights and discoveries in various fields, including medicine, materials science, and climate change. AI can also be used to automate tasks and free up researchers to focus on more creative and challenging problems.
Electronic Medical Records (EMR) Analysis: A New Frontier
Machine learning’s ability to sequentially analyze EMR data for predicting various outcomes like hospital admissions and medical expenses underscores its potential in healthcare. Sequential prediction models can even predict mortality risk earlier than current methods, revolutionizing critical care.
TensorFlow’s Contribution to Democratizing Machine Learning
Google’s TensorFlow, an open-source machine learning platform, has played a crucial role in popularizing machine learning. With 36 million downloads, TensorFlow’s ease of use has facilitated diverse applications, from disease detection in plants to fitness sensors for cows. Expanding Access to Machine Learning through TensorFlow and Simplifying its Usage TensorFlow: A Catalyst for Machine Learning Development TensorFlow emerged as the second-generation system developed by Google for internal research and deployments. Open-sourced in November 2015 under the Apache 2.0 license, encouraging adoption and modifications. TensorFlow’s popularity is evident in its GitHub stars, indicating a thriving community of users and contributors. Impact of TensorFlow on the Machine Learning Community TensorFlow has fostered a vibrant community actively improving the system and expanding its applications. With over 36 million downloads, it has reached a significant user base, including individuals and organizations. Examples of TensorFlow Applications A Netherlands-based company utilizes TensorFlow to analyze sensor data for fitness monitoring in cows, identifying abnormal behaviors. A collaboration between Penn State University and the International Institute of Tropical Agriculture leverages TensorFlow for deep learning-based detection of cassava disease, a major threat to global food security. TensorFlow enables offline disease detection through mobile applications, addressing connectivity limitations in certain regions. Focus on Simplifying Machine Learning Recognizing the limited expertise in machine learning model training, Google aims to make the process more accessible. Efforts are underway to reduce the specialized knowledge required for effective machine learning model development.
Automating Machine Learning with Neural Architecture Search
The advent of neural architecture search, part of AutoML, has automated the design of deep learning models, often outperforming human-designed systems. This automation has optimized models for specific constraints, enhancing computational efficiency. AutoML: Automating Machine Learning Machine learning is currently a complex process requiring extensive expertise and experimentation. AutoML aims to simplify this process by automating many tasks, reducing the need for specialized knowledge. Neural Architecture Search Neural architecture search is a method of automating the design of neural networks. It uses a meta model to propose model architectures, trains them, and provides feedback to the meta model to refine future proposals. This approach has been successful in achieving state-of-the-art results on challenging tasks like ImageNet. Benefits of AutoML AutoML can perform more experiments than human-supervised experiments, leading to better results. It can optimize models for specific constraints, such as computational cost or latency. AutoML can explore multiple architectures simultaneously, reducing the overall cost of experimentation. Ongoing Research Researchers are exploring various approaches to improve AutoML, including: Using evolutionary algorithms instead of reinforcement learning. Incorporating accuracy and computational cost into the reward function. Learning augmentation policies. Exploring multiple architectures simultaneously to reduce cost.
Specialized Hardware for Deep Learning and TPU Accessibility
Specialized hardware like Tensor Processing Units (TPUs) v3 has been instrumental in efficient deep learning computations. Google’s offer of free TPUs to researchers exemplifies the commitment to advancing this field.
Responsible AI Principles and Jeff Dean’s Vision
Google’s principles for responsible AI usage highlight the ethical considerations in AI applications. Jeff Dean, instrumental in the development of big data technologies and TensorFlow, remains a driving force in machine learning, focusing on projects like multitask learning and the development of general AI systems. Jeff Dean’s Presentation on Machine Learning Advances Jeff Dean, a Google researcher and visionary in the field of machine learning, presented his insights on the progress of machine learning, especially deep learning. He emphasized the exponential growth of machine learning research and the resurgence of neural networks due to increased computational power. Dean highlighted neural networks’ ability to learn hierarchical patterns and build features automatically through learning algorithms. He also noted the historical limitations of neural networks due to insufficient computational resources and the recent surge in their applicability to real-world problems. Machine Learning in Engineering Challenges Machine learning has proven its potential to address grand engineering challenges. In autonomous vehicles, deep learning and computer vision have enabled self-driving cars to process sensor data and navigate complex environments. Waymo’s trials in Phoenix showcased the near-term viability of autonomous vehicles. In robotics, computer vision advancements have led to robots capable of learning from perceptual inputs and performing various actions. Additionally, Google’s research in grasping success rates demonstrates the progress in robotic capabilities. Robotics and Advanced Health Informatics In robotics, Google has achieved notable success in grasping unseen objects through a combination of robotic experimentation and supervised learning. Moreover, reinforcement learning enabled robots to acquire new skills, such as pouring liquids, by learning from data and human actions. In healthcare, machine learning has made strides in disease detection and personalized treatments. Advanced health informatics, utilizing machine learning, holds promise for enhancing patient care and optimizing healthcare systems.
The Future of Machine Learning: Narrow vs. General AI
The shift from narrow AI, proficient in specific tasks, to general AI, capable of transferring knowledge across diverse domains, marks the next frontier in machine learning. Dean’s work in training models on a wide range of tasks is a step towards this ambitious goal.
The Expanding Horizons of Machine Learning
Machine learning’s journey from a niche field to a cornerstone of modern technology and science reflects its transformative potential. As it continues to evolve, its applications across various sectors, from healthcare to autonomous vehicles, signify a paradigm shift in how we interact with and leverage technology for societal advancement. The ongoing challenges, such as ethical considerations and bias mitigation, require vigilant attention, but the prospects of machine learning in revolutionizing our world remain boundless.
TensorFlow, a versatile machine learning framework, evolved from Google's DistBelief to address computational demands and enable efficient deep learning model development. TensorFlow's graph-based architecture and mixed execution model optimize computation and distribution across various hardware and distributed environments....
TensorFlow, an open-source machine learning library, has revolutionized research in speech and image recognition thanks to its scalability, flexibility, and real-world applicability. The framework's distributed systems approach and data parallelism techniques enable faster training and execution of complex machine learning models....
TensorFlow and XLA's integration enhances machine learning research and development by offering flexibility, scalability, and performance optimizations for diverse hardware platforms. XLA's just-in-time compilation and TensorFlow's comprehensive capabilities empower users to explore complex ideas and create high-performance models effortlessly....
Deep learning revolutionizes technology by enabling tasks learning, computer vision, and research advancements, while TensorFlow serves as a versatile platform for developing machine learning models....
Deep learning revolutionizes NLP by unifying tasks under a single framework, enabling neural networks to learn end-to-end without explicit linguistic programming. Deep learning models excel in text generation, capturing long-range dependencies and producing fluent, coherent sentences, outshining traditional methods in machine translation and parsing....
Jeff Dean's innovations in machine learning and AI have led to transformative changes across various domains, including healthcare, robotics, and climate change. Google's commitment to AI for societal betterment balances technological progression with ethical considerations....
TensorFlow, a versatile machine learning platform, has revolutionized problem-solving approaches, while transfer learning reduces data requirements and accelerates model development for diverse applications....