Jeff Dean (Google Senior Fellow) – Deep Learning for Solving Important Problems (May 2019)
Chapters
00:00:15 Machine Learning's Impact on Computer Vision
Growth of Machine Learning Research: The output of machine learning research has experienced a surge in recent years, outpacing the exponential growth rate of computing. This growth is attributed to the rise of deep learning, a subfield of machine learning that utilizes artificial neural networks to learn from raw data.
Neural Networks and Their Capabilities: Neural networks can learn complex functions from raw data, such as recognizing objects in images, transcribing speech, and translating languages. These systems are trained end-to-end, eliminating the need for manually engineered components. Simple code (about 500 lines) can be used to train translation systems that outperform traditional models.
Image Captioning and Computer Vision Advancements: Deep neural networks have enabled significant progress in computer vision. The ImageNet Challenge, a competition focused on image categorization, saw a remarkable reduction in error rates from 26% in 2011 to 3% in 2016. This achievement indicates that computers can now “see” and interpret visual information with remarkable accuracy.
Human-Level Performance and Beyond: While computers may not yet achieve general human vision, they have made significant strides in various tasks. In certain image recognition tasks, computers can outperform humans with extensive training. This progress highlights the potential for computers to enhance human capabilities in various domains.
00:05:11 Machine Learning in Healthcare: Using Computer Vision to Improve Diagnosis and Treatment of Diabetic Ret
Advances in Machine Learning: The National Academy of Engineering released a list of grand engineering challenges for the 21st century, focusing on improving healthcare, education, and the planet. Google’s research teams are working on projects that address some of these challenges, particularly in advanced health informatics.
Diabetic Retinopathy: Diabetic retinopathy is the fastest growing cause of blindness worldwide, affecting about 400 million people with diabetes. Early detection and treatment are crucial to prevent vision loss, but there is a shortage of ophthalmologists, especially in developing countries.
Machine Learning for Diabetic Retinopathy Diagnosis: Machine learning models can be trained to classify retinal images into different stages of diabetic retinopathy. Off-the-shelf computer vision models can be used with labeled images from ophthalmologists. A machine learning model can achieve accuracy comparable to or even exceeding that of the average US board-certified ophthalmologist.
Adjudicated Protocol: To further improve accuracy, retinal specialists can provide a single adjudicated diagnosis for each image. A machine learning model trained on this adjudicated data can achieve accuracy on par with retinal specialists, representing the gold standard of care.
Significance: Machine learning can help address the shortage of ophthalmologists and enable more people to access timely and accurate diabetic retinopathy diagnosis. This approach can be applied to other medical imaging problems with careful data collection, machine learning, and consultation with experts.
00:10:44 Advanced Machine Learning Applications in Medical Fields
Medical Insights from Retinal Images: AI can extract information from retinal images that ophthalmologists may miss, leading to discoveries about cardiovascular health. A single retinal image can provide an accurate assessment of cardiovascular risk, comparable to a five-year MACE score. Longitudinal retinal images could provide valuable insights into overall health.
Pathology Image Analysis: AI can detect cancer metastases in pathology images with pixel-level accuracy, outperforming pathologists. Prototype augmented reality microscope overlays predictive information onto pathology images in real-time.
Predicting Future Medical Records: Sequential prediction methods, such as seq2seq models, can predict future aspects of medical records, including events and abstract factors. AI can predict mortality risk earlier and more accurately than traditional methods using all data in a patient’s medical record.
Transformer Model for Text Understanding: The transformer model consumes entire sequences in parallel, using attention mechanisms for predictions. Transformer models achieve higher translation accuracies with significantly reduced compute compared to previous state-of-the-art models. Bidirectional encoder representations from transformers (BERT) builds on the transformer model for advanced text understanding.
00:20:13 Bidirectional Pre-training of Transformers for Language Understanding
BERT Overview: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model developed by Google AI. BERT is based on transformer architecture, a powerful deep learning model for processing sequential data.
BERT’s Training Process: BERT is trained using a masked language modeling technique. A certain percentage of words (10-20%) in a sequence are masked, and the model is trained to predict the masked words based on the context. This training method helps BERT develop a deep understanding of language and its context.
Benefits of Pre-training and Fine-tuning: Pre-training BERT on a large corpus of text enables it to learn general language representations. Subsequent fine-tuning on specific language tasks (e.g., sentiment analysis, question answering) with small datasets often leads to excellent results.
BERT’s Impact on Language Understanding: BERT has achieved significant improvements in various language understanding tasks, as demonstrated by the General Language Understanding Evaluation (GLUE) benchmark. These improvements highlight the effectiveness of pre-trained language models and their transferability to various NLP tasks.
Natural Questions Data Set: Google AI has released the Natural Questions Data Set, which contains 300,000 training examples for question answering. The data set includes complex questions and answers that require a deep understanding of language and context. This data set serves as a valuable resource for researchers and practitioners working on question answering systems.
BERT’s Influence on Question Answering: BERT has had a significant impact on question answering systems, with many leading models incorporating BERT in various ways. BERT’s ability to understand language context and extract relevant information makes it a powerful tool for question answering.
Conclusion: BERT, with its pre-trained language representations and transfer learning capabilities, has revolutionized the field of natural language processing. It has paved the way for advancements in various language understanding tasks, including sentiment analysis, question answering, and more.
00:24:27 Machine Learning Infrastructure and Its Applications for Global Impact
Importance of Machine Learning Tools: Machine learning needs tools to simplify expressing machine learning ideas and deploying models in various applications. Open-source tools like TensorFlow promote standardisation, community development, and wider usage of machine learning.
TensorFlow’s Success: TensorFlow’s second generation is open-sourced to encourage a community of contributors and users. It offers both research-friendly expression and production-ready deployment of machine learning models. Diverse use cases range from fitness sensors for cows to detecting cassava disease in the field. The community has grown, with a significant portion of contributors from outside Google.
Applying Machine Learning: Flood forecasting using machine learning enables accurate predictions, leading to focused alerts for potentially affected individuals. Use cases like these demonstrate the need for tools that support machine learning applications beyond data centres.
Efficient Large Models: Machine learning models can benefit from having large capacities but may not require the full capacity for every example. Human and other real nervous systems use only a small fraction of their capacity at any given moment. Incorporating this inspiration into machine learning models can lead to more power-efficient models.
Mixture of Experts Layer: A mixture of experts layer consists of multiple “experts” (neural networks) with different expertise. A gating network learns which expert is suitable for a given context or example. Experts develop different specialisations, such as understanding scientific research, playing a critical role, or using fast adverbs. This sparse activation approach allows for more efficient models compared to traditional fully-activated models.
00:30:56 Automating Machine Learning Problem Solving
AutoML and the Future of Machine Learning: AutoML aims to automate aspects of solving machine learning problems, addressing the shortage of ML expertise and making ML accessible to a broader range of organizations. AutoML tackles tasks like selecting model structures, determining training parameters, and optimizing update rules, enabling faster and more efficient problem-solving.
Model Generation and Reinforcement Learning: AutoML employs a model-generating model that creates a diverse set of models, which are then evaluated on the target problem. The accuracy of these generated models serves as a reinforcement learning signal, guiding the model-generating model towards promising regions of the model space.
AutoML Results: AutoML has demonstrated superior performance compared to human-designed models, achieving both higher accuracy and lower computational costs. AutoML excels at both high-end models, where accuracy is prioritized, and low-end models, where computational efficiency is crucial.
Product Development and Applications: Google has developed a Cloud AutoML product that extends AutoML capabilities beyond vision to include language, translation, and relational table data. AutoML has shown promise in various domains, including customer behavior prediction and autonomous vehicle development.
Research Frontiers in AutoML: Exploration of evolutionary algorithms as an alternative to reinforcement learning for search. Optimization of update rules and inference latency within the reward function. Learning data augmentation policies to enhance model performance. Simultaneous exploration of multiple architectures for computational efficiency.
Conclusion: AutoML is a rapidly evolving field with the potential to democratize machine learning and make it accessible to a wider range of users. Ongoing research continues to push the boundaries of AutoML, promising further advancements in automating machine learning tasks and unlocking new possibilities in problem-solving.
00:38:38 Hardware Innovations for Efficient Deep Learning
Computational Power and Model Accuracy: Increased computational power leads to larger models and generally higher accuracy. Deep learning is transforming how we think about computational devices. GPUs are particularly well-suited for machine learning computations.
Reduced Precision and Specialized Computers: Deep learning algorithms and models are tolerant of reduced precision, making them suitable for specialized computers. Low-precision linear algebra operations form the core of these computations. Specialized computers can be designed to excel at these operations.
Early Success and Thought Exercise: Early successes in speech, image, and language recognition motivated further exploration. Concerns arose about the computational demands of deploying better speech models. Calculations showed that doubling the number of data center computers would be needed to handle the increased workload.
Economic and Practical Considerations: Even if economically feasible, building more data centers takes time and resources. There is a need for specialized hardware to support deep learning applications.
00:41:12 Large Scale Multitask Learning and Building Fair Machine Learning Systems
TPU Chips and Systems: Google’s Tensor Processing Unit (TPU) is a custom ASIC designed for low-precision linear algebra operations. TPUv1 was used for inference tasks, achieving high performance in applications like speech recognition and image recognition. TPUv2 and TPUv3 are optimized for both training and inference, with water cooling for enhanced performance. TPU pods connect multiple chips together to solve larger problems, offering high-speed connectivity and simple programming.
Benefits of TPUs: Significantly faster training times compared to GPUs, enabling more efficient experimentation and research. Open-source reference implementations for various models, including BERT and image recognition models.
Data Set Search: Data set search indexes 10 million data sets from different providers, helping researchers find relevant data for their projects.
Multitask Learning Systems: Proposal for large-scale multitask learning systems that can activate only a small portion of the model for specific tasks. Reinforcement learning algorithms can guide the system to find useful pathways and representations across tasks. Components within the system can adapt using architecture search processes.
Ethical Considerations in Machine Learning: Google’s principles for using machine learning in its products and services. Importance of having definitive principles to guide ethical uses of machine learning. Research focus on eliminating unfair bias while preserving beneficial bias in models.
00:51:48 Artificial Intelligence Regulation and Reasoning
Ethics and Regulation of Machine Learning: The speaker highlights the importance of considering ethical and regulatory aspects of machine learning, especially when it is used in sensitive domains such as healthcare and self-driving vehicles.
Certification of Predictive Models as Medical Devices: The speaker suggests that clear scientific studies demonstrating the benefits of machine learning approaches can help convince regulators to certify predictive models as medical devices.
Integrating Human Knowledge into Machine Learning Models: Incorporating explicit human knowledge, such as knowledge graphs and reasoning, can improve the performance of machine learning models.
Limitations of Feedforward Prediction: Current machine learning models often rely on feedforward prediction, which limits their ability to make complex predictions and consider multiple possibilities.
Need for Iterative Reasoning: The speaker emphasizes the need for developing machine learning models that can engage in iterative reasoning, similar to human cognition.
Advantages of Human Intuition and Creativity: The speaker acknowledges that human intuition and creativity are valuable in machine learning research, especially in generating new ideas and approaches.
AutoML for Efficient Experimentation: Automated machine learning (AutoML) can assist researchers in running numerous experiments efficiently, freeing up time for creative endeavors.
Addressing the Reasoning Question: The speaker recognizes the challenge of building machine learning models with advanced reasoning capabilities and encourages researchers to explore this area.
Abstract
Revolutionizing the World: The Accelerated Growth and Impact of Machine Learning and AI
The recent advancements in machine learning (ML) and artificial intelligence (AI) have marked a significant era in technological evolution, demonstrating an exponential growth that surpasses even the famed Moore’s Law. At the forefront of this evolution is deep learning, a refined form of artificial neural networks, reshaping our approach to AI with its ability to learn from raw data. This article aims to dissect the core advancements and impacts of ML and AI, emphasizing the profound changes they bring to various sectors, particularly healthcare and computational efficiency.
Core Developments in Machine Learning and AI
Machine Learning’s Exponential Rise
Machine learning research output has shown a remarkable growth trajectory, moving from mere computational advancements to spearheading research innovation. This shift underscores a fundamental change in the way we approach technology development, moving towards innovation-based progress. The exponential growth rate of computing in recent years has been outpaced by the output of machine learning research. This growth is attributed to the rise of deep learning, a subfield of machine learning that utilizes artificial neural networks to learn from raw data.
The Deep Learning Revolution
Deep learning has revitalized the concept of artificial neural networks, marking a significant leap in machine learning capabilities. Its inherent ability to learn from unprocessed data and execute complex tasks has been a game-changer, setting a new standard in the field. Neural networks can learn complex functions from raw data, such as recognizing objects in images, transcribing speech, and translating languages. These systems are trained end-to-end, eliminating the need for manually engineered components. Simple code (about 500 lines) can be used to train translation systems that outperform traditional models.
Seamless End-to-End Training
Modern machine learning models benefit from end-to-end training. This process removes the need for hand-engineered components, leading to more sophisticated and effective systems.
Breakthroughs in Computer Vision
The ImageNet Challenge serves as a testament to the advancements in computer vision. From a 26% error rate in 2011 to a mere 3% in 2016, this leap demonstrates the improved capabilities of computers in image recognition and understanding. Deep neural networks have enabled significant progress in computer vision. The ImageNet Challenge, a competition focused on image categorization, saw a remarkable reduction in error rates from 26% in 2011 to 3% in 2016. This achievement indicates that computers can now “see” and interpret visual information with remarkable accuracy.
Machine Learning in Healthcare
Addressing Grand Engineering Challenges
Google’s research teams, in response to the National Academy of Engineering’s grand challenges, are leveraging machine learning to enhance healthcare, education, and overall societal well-being. The National Academy of Engineering released a list of grand engineering challenges for the 21st century, focusing on improving healthcare, education, and the planet. Google’s research teams are working on projects that address some of these challenges, particularly in advanced health informatics.
Transformative Health Informatics
Machine learning’s integration into healthcare decision-making is revolutionizing patient care, offering more precise and effective medical interventions. Advances in Machine Learning: Diabetic Retinopathy: Diabetic retinopathy is the fastest growing cause of blindness worldwide, affecting about 400 million people with diabetes. Early detection and treatment are crucial to prevent vision loss, but there is a shortage of ophthalmologists, especially in developing countries. Machine learning models can be trained to classify retinal images into different stages of diabetic retinopathy. Off-the-shelf computer vision models can be used with labeled images from ophthalmologists. A machine learning model can achieve accuracy comparable to or even exceeding that of the average US board-certified ophthalmologist. To further improve accuracy, retinal specialists can provide a single adjudicated diagnosis for each image. A machine learning model trained on this adjudicated data can achieve accuracy on par with retinal specialists, representing the gold standard of care. Machine learning can help address the shortage of ophthalmologists and enable more people to access timely and accurate diabetic retinopathy diagnosis. This approach can be applied to other medical imaging problems with careful data collection, machine learning, and consultation with experts.
Tackling Diabetic Retinopathy
The challenge of diabetic retinopathy diagnosis, especially in regions like India with a shortage of eye doctors, highlights the potential of machine learning in healthcare. Computer vision models trained to classify retinal images are proving to be as accurate, if not more so, than human experts.
Broadening Medical Image Analysis
AI’s ability to detect health risks and predict factors like age and gender from retinal images is surpassing the capabilities of traditional methods. This advancement extends to pathology, where AI can identify cancerous cells with higher accuracy than human pathologists. Medical Insights from Retinal Images: AI can extract information from retinal images that ophthalmologists may miss, leading to discoveries about cardiovascular health. A single retinal image can provide an accurate assessment of cardiovascular risk, comparable to a five-year MACE score. Longitudinal retinal images could provide valuable insights into overall health. Pathology Image Analysis: AI can detect cancer metastases in pathology images with pixel-level accuracy, outperforming pathologists. Prototype augmented reality microscope overlays predictive information onto pathology images in real-time.
Advancements in Sequential Prediction
AI’s role in predicting future medical events showcases its potential in enhancing patient care and optimizing healthcare management. Predicting Future Medical Records: Sequential prediction methods, such as seq2seq models, can predict future aspects of medical records, including events and abstract factors. AI can predict mortality risk earlier and more accurately than traditional methods using all data in a patient’s medical record. Transformer Model for Text Understanding: The transformer model consumes entire sequences in parallel, using attention mechanisms for predictions. Transformer models achieve higher translation accuracies with significantly reduced compute compared to previous state-of-the-art models. Bidirectional encoder representations from transformers (BERT) builds on the transformer model for advanced text understanding.
The Emergence of BERT and AutoML
BERT’s Language Understanding Breakthrough
BERT, with its unique bidirectional training and pre-training on extensive language tasks, has revolutionized natural language processing. Its ability to comprehend context and fill in missing information has broadened its application scope. BERT Overview: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model developed by Google AI. BERT is based on transformer architecture, a powerful deep learning model for processing sequential data. BERT’s Training Process: BERT is trained using a masked language modeling technique. A certain percentage of words (10-20%) in a sequence are masked, and the model is trained to predict the masked words based on the context. This training method helps BERT develop a deep understanding of language and its context. Benefits of Pre-training and Fine-tuning: Pre-training BERT on a large corpus of text enables it to learn general language representations. Subsequent fine-tuning on specific language tasks (e.g., sentiment analysis, question answering) with small datasets often leads to excellent results. BERT’s Impact on Language Understanding: BERT has achieved significant improvements in various language understanding tasks, as demonstrated by the General Language Understanding Evaluation (GLUE) benchmark. These improvements highlight the effectiveness of pre-trained language models and their transferability to various NLP tasks.
AutoML: Democratizing Machine Learning
AutoML represents a significant stride in making machine learning accessible to a wider audience. By automating model generation and evaluation, it addresses the scarcity of ML expertise and opens up new possibilities for non-experts.
Neural Architecture Search (NAS)
NAS epitomizes the innovative spirit of AutoML, creating models that often surpass human-designed counterparts in efficiency and accuracy. Model Generation and Reinforcement Learning: AutoML employs a model-generating model that creates a diverse set of models, which are then evaluated on the target problem. The accuracy of these generated models serves as a reinforcement learning signal, guiding the model-generating model towards promising regions of the model space. AutoML Results: AutoML has demonstrated superior performance compared to human-designed models, achieving both higher accuracy and lower computational costs. AutoML excels at both high-end models, where accuracy is prioritized, and low-end models, where computational efficiency is crucial.
Computational Efficiency and Ethical Considerations
The Role of Computational Power
Increased computational power directly correlates with the accuracy of deep learning models. Technologies like GPUs and Google’s TPUs have been instrumental in this development, offering optimized environments for ML computations. The Role of Computational Power: Increased computational power directly correlates with the accuracy of deep learning models. Technologies like GPUs and Google’s TPUs have been instrumental in this development, offering optimized environments for ML computations. TPUs: Pioneers in Machine Learning Hardware: Google’s TPUs, particularly in their pod configurations, have dramatically enhanced the speed and efficiency of machine learning training, enabling rapid experimentation and increased productivity.
TPUs: Pioneers in Machine Learning Hardware
Google’s TPUs, particularly in their pod configurations, have dramatically enhanced the speed and efficiency of machine learning training, enabling rapid experimentation and increased productivity.
Ethical Principles in Machine Learning
Google’s principles for the ethical use of machine learning in its products emphasize the need for fairness, accountability, and transparency. This approach is crucial in addressing challenges like bias elimination in AI systems.
A Balanced Future for AI and Human Expertise
The advancements in machine learning and AI are undoubtedly transforming our approach to global challenges. From healthcare to computational efficiency, the impact is profound and far-reaching. However, the balance between automation and human creativity remains vital. As we embrace the potential of AI, we must also acknowledge the importance of human intuition and oversight in steering these technologies towards beneficial and ethical applications.
In summary, the exponential growth of machine learning and AI is not just a testament to technological advancement but a beacon of hope for solving complex global issues. With continued ethical considerations and a balanced approach to human-machine collaboration, the future of these technologies is both promising and exciting.
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....
Machine learning has seen exponential growth and deep learning has revolutionized various fields, from healthcare to robotics, by learning from raw data and handling diverse data types. AutoML and specialized accelerators like TPUs have accelerated machine learning advancements....
Machine learning is revolutionizing society and technology by addressing grand challenges and enabling transformative applications in healthcare, urban infrastructure, computer systems, and scientific discovery. Through open-source tools like TensorFlow, neural architecture search, and specialized hardware like TPUs, machine learning is becoming more accessible and driving significant advancements in various fields....
Jeff Dean's journey in AI and machine learning showcases the significance of embracing challenges, valuing diversity, and maintaining a balance between personal growth and professional responsibilities. He envisions a future where AI models can solve complex tasks and positively impact fields like healthcare and education, emphasizing the importance of inclusion...
Machine learning's rapid growth and versatility have led to advancements in computer vision, speech recognition, and autonomous vehicles, while addressing challenges like aging infrastructure and healthcare disparities. Google's emphasis on ethical AI principles ensures responsible usage and societal welfare....
Machine learning advancements revolutionize computer vision, speech recognition, healthcare, and engineering, while autonomous vehicles and improved robotic control demonstrate their potential impact on urban infrastructure and medical treatments. Ethical considerations and algorithm fairness are emphasized to ensure the technology's positive societal impact....
Deep learning, a subset of machine learning using neural networks, has revolutionized how machines learn from raw data, leading to groundbreaking performances in various fields. Advancements in neural networks, computer vision, and machine learning hold promise for solving complex issues like urban infrastructure restoration and expanding healthcare access....