Jeff Dean (Google Senior Fellow) – Deep Learning to Build Intellgent Systems (Apr 2018)
Chapters
00:00:02 Machine Learning Frontiers in Solving Grand Challenges
ML Research in Solving Grand Challenges: Jeff Dean presents research from Google on utilizing machine learning (ML) to solve various grand challenges identified by the US National Academy of Engineering. ML is expected to contribute to addressing these challenges, with the focus on those highlighted in red.
Performance Improvement: General-purpose computer performance has significantly increased since 1970, but the rate of progress has slowed down after 2008. Single-thread CPU performance has plateaued despite the growing number of transistors.
ML’s Computational Requirements: The development of large and complex ML models, such as AutoML, demands substantial computational resources. Large training data sets (terabytes or petabytes) require computationally intensive techniques.
Challenges in Deploying ML Systems: ML systems deployed at scale face challenges, including: Expensive models that need to be served. Hundreds of thousands of requests per second. Strict latency requirements of tens or hundreds of milliseconds. Serving billions of users.
ML Research at Google: Google Research explores various approaches to address the challenges in deploying ML systems. Research focuses on improving efficiency and reducing computational requirements.
Conclusion: By applying ML to grand challenges, Google aims to positively impact society. Advances in ML efficiency and performance are crucial for addressing these challenges effectively.
00:02:40 Machine Learning Innovations in Healthcare and Infrastructure
Urban Infrastructure and Self-Driving Cars: Self-driving cars will transform cities by eliminating the need for parking lots. Machine learning plays a crucial role in understanding raw perceptual inputs from sensors and building models of the world for safe navigation.
Advancements in Health Informatics: Machine vision enables automated medical imaging systems to aid diagnosis. Computer vision models can grade diabetic retinopathy on par with retinal specialists. Consensus decisions from retinal specialists improve the quality of training data and the resulting model. New discoveries from retinal images reveal links between cardiovascular health and eye health.
Predictive Tasks for Health Care: Sequence-to-sequence models have achieved significant accuracy in translation tasks. These techniques can be applied to medical records to predict future outcomes. Predictive models can answer questions like readmission risk, length of stay, and likely diagnoses.
00:09:55 Machine Learning for Chemistry, Material Science, and Brain Research
Machine Learning in Healthcare: Machine learning techniques surpass baseline methods in predicting patient deterioration, allowing physicians to focus attention on at-risk patients earlier. Accuracy in predicting patient deterioration is significantly higher 24 hours earlier using machine learning.
Machine Learning in Quantum Chemistry: Neural networks trained on computationally expensive quantum chemistry simulators can predict molecular properties much faster. Message passing neural nets, specifically designed for chemical configurations, enable 300,000 times faster predictions compared to traditional high-performance computing simulations. This faster tool allows chemists to screen millions of molecules, identify interesting ones, and dive deeper into their details.
Machine Learning in Connectomics: Connectomics aims to understand the static connectivity map of neurons in a brain. Flood-filling networks, a technique developed by Google, use raw images and previous predictions to accurately trace neural tissue in 3D stacks of scanned images. This technique has led to a 1,000-fold increase in the run length before an error occurs, allowing for the reconstruction of larger volumes of neural tissue. Static connectivity is crucial for understanding the dynamic properties of how brains work.
TensorFlow and the Grand Challenges: TensorFlow is an open-source machine learning system for large-scale training and inference tasks. Its accessible Apache 2.0 license allows users to adapt and modify it as needed. The vibrant TensorFlow community actively contributes to its improvement and expansion.
Real-World Applications of TensorFlow: Connectera’s fitness sensors for cows detect health issues through fitness data. Tensorflow models detect chainsaw sounds to prevent illegal logging. Penn State and the International Institute of Tropical Agriculture’s office in Tanzania use TensorFlow for disease detection in cassava plants, providing healthcare for plants.
Challenges in Expanding Machine Learning: Current approaches to solving machine learning problems rely heavily on specialized expertise. This limits the accessibility of machine learning to a small number of organizations with the necessary expertise. There are many organizations with data and problems that machine learning could address but lack the expertise to implement solutions.
Goal: Democratizing Machine Learning: Aim to make machine learning accessible to a wider range of organizations and individuals. Develop tools and techniques that allow non-experts to easily apply machine learning to solve their problems. Broaden the scope of machine learning’s impact in the world.
00:20:42 Automating Machine Learning: Neural Architecture Search and the Future of AI
Neural Architecture Search: Machine learning experts make decisions about model structure, such as the number and size of layers. Neural architecture search automates this process by training a model generating model to generate good machine learning models. The model generating model is steered towards promising architectures and away from less promising ones using a reward signal from a trained objective function.
AutoML Outperforms Human Experts: AutoML can generate model architectures that achieve higher accuracy than those designed by human experts. AutoML can achieve better accuracy with lower computational cost, making it useful for resource-constrained applications.
AutoML as a Google Cloud Product: AutoML for vision problems is available as a Google Cloud product. Google is working on extending AutoML to other domains as products.
Deep Learning’s Impact on Computer Design: The need for more computational power to train larger, more accurate models and perform tasks like AutoML is transforming computer design.
00:24:18 TPUs: Fast and Scalable Hardware for Machine Learning
TPU Properties and Architecture: Deep learning models exhibit two key properties: reduced precision tolerance and reliance on linear algebra operations. TPUs are customized hardware designed for these models, optimizing speed and efficiency for matrix multiplies and related computations. A single TPU chip features a large matrix multiply unit, scalar and vector units, and high-speed memory. TPU pods comprise 64 TPUs, delivering 11.5 petaflops of computation with reduced precision.
TPU Performance and Benefits: TPUs offer substantial performance improvements, enabling tasks that previously took days to complete in hours. TensorFlow compatibility allows for easy scaling of programs across CPUs, GPUs, and TPUs. Using TPUs has boosted productivity by simplifying processes and reducing required time.
Reference Models and Success Stories: Google provides reference models for various problems, open-sourced on GitHub and regularly tested for accuracy. Internal search ranking models trained on a quarter TPU pod showed a significant speed improvement. Large image models experienced a 10x speedup in convergence time. WaveNet production model for text-to-speech conversion runs on TPUs, generating speech at 20x real-time speed.
Software Improvements and Scaling: Continuous software improvements have enhanced TPU performance by 45%. Training an ImageNet model on a TPU pod takes only 12 minutes, achieving an epoch every eight seconds. This rapid turnaround time allows for more experimentation and faster identification of effective approaches.
Cost-Effectiveness and Benchmarks: Cost comparison benchmarks show that training on TPUs is roughly 4x more cost-effective than the second-best option. With TPUs, researchers can conduct more experiments at a lower cost while achieving faster results.
Introduction: Jeff Dean discusses the potential of machine learning to enhance computer systems, shifting from building systems for machine learning to exploring machine learning for systems.
B-Trees and Neural Networks: Traditional database systems use B-trees for sorted data retrieval on disk, predicting the block containing a key. Dean suggests replacing B-trees with neural networks that directly predict the key’s location based on its characters. This approach outperforms B-trees in terms of speed and size, offering a 60% speedup and a 20-fold reduction in size.
Hash Tables and Bloom Filters: Machine learning can also improve hash tables by utilizing the key space’s properties. Neural networks can replace traditional hash functions, leading to better bucket utilization and reduced empty space. Applying machine learning to Bloom filters, which predict set elements, can yield a 36% space improvement.
Heuristics and Pattern Recognition: Computer systems are filled with heuristics and data structures that must work well in general cases, limiting their ability to adapt to specific usage patterns. Machine learning can take advantage of these patterns, improving the effectiveness of heuristics.
Applications in Various Domains: Dean identifies numerous potential applications of machine learning in computer systems, including compilers, networking, and resource management. By leveraging machine learning, systems can make informed decisions based on learned patterns, leading to improved performance and efficiency.
Conclusion: Machine learning offers significant opportunities for enhancing computer systems by replacing heuristics with learned models. This approach can result in substantial improvements in speed, size, and space utilization, transforming core system components.
00:34:37 Automating Machine Learning for Enhanced Performance and Adaptability
Key Points: Automated Performance Optimization: Complex programs with user-configurable performance options can be challenging for users. Machine learning can be used to automate these settings, eliminating the need for manual tuning. Meta-Learning in Machine Learning: Machine learning can be used to learn various aspects of machine learning problems, such as computation placement, kernel implementation, optimization update rules, and input preprocessing steps. Reinforcement Learning and Numeric Metrics: Reinforcement learning-based approaches are effective for optimizing machine learning systems. The key to success is having a numeric metric to measure and optimize, enabling the system to learn and adapt. APIs for Integrating Learned Decisions: To facilitate the integration of learned decisions into existing code, it is important to develop clean APIs that make it easy to incorporate learned decisions into operating system code or compiler code, replacing hand-coded heuristics.
Conclusion: Machine Learning Hardware in its Infancy: The field of machine learning hardware is still in its early stages, with the potential for significant breakthroughs as faster systems and wider deployment enable rapid experimentation and automation of the machine learning process. Learning in Computer Systems: Incorporating learning into the core of computer systems can enhance their adaptability and performance. There are vast possibilities to explore in this area.
Abstract
Revolutionizing Fields: The Transformative Impact of Machine Learning on Society and Technology
In a dynamic leap forward, machine learning (ML) is reshaping critical areas of society, from healthcare and urban infrastructure to computer systems and scientific discovery. Jeff Dean, a renowned figure in this field, highlights the pivotal role of ML in addressing grand challenges, like those identified by the US National Academy of Engineering. Dean’s insights, coupled with breakthroughs in health informatics, quantum chemistry, and connectomics, underscore ML’s expanding influence. Furthermore, advancements in ML tools, like TensorFlow and neural architecture search, democratize access to this technology, enabling even non-experts to leverage its capabilities. This article explores these transformative impacts of ML, detailing its applications, challenges, and the future landscape it is shaping.
Machine Learning’s Far-Reaching Impact:
Jeff Dean’s discussion on the plateauing of single-thread CPU performance contrasts starkly with the burgeoning demand for computational resources driven by ML models and large datasets. Central to his narrative is the potential of ML in addressing the 14 grand challenges laid out by the US National Academy of Engineering. The red-highlighted subset of these challenges particularly benefit from ML applications, suggesting a future where ML plays a critical role in societal progress.
Urban Infrastructure and Autonomous Vehicles:
The imminent public release of self-driving cars epitomizes ML’s transformative effect on urban infrastructure. By interpreting raw sensor data, these autonomous vehicles promise to reshape cityscapes, reducing the need for expansive parking lots and revolutionizing urban mobility. Machine learning plays a crucial role in understanding raw perceptual inputs from sensors and building models of the world for safe navigation.
Revolution in Health Informatics:
In healthcare, ML’s impact is profound. Automated systems, powered by computer vision and deep learning, are now aiding in the diagnosis of diseases like diabetic retinopathy. These ML models match the accuracy of specialized medical professionals and have even ventured into predicting broader health factors from retinal images, illustrating ML’s expanding scope in medical diagnostics. Machine vision enables automated medical imaging systems to aid diagnosis. Computer vision models can grade diabetic retinopathy on par with retinal specialists. Consensus decisions from retinal specialists improve the quality of training data and the resulting model. New discoveries from retinal images reveal links between cardiovascular health and eye health.
Predictive Healthcare and Personalized Medicine:
The application of deep learning in predictive tasks within healthcare is another notable advancement. By analyzing medical records, these models can foresee patient outcomes, from readmission risks to length of stay, showcasing a future of more personalized and effective healthcare strategies. Machine learning techniques surpass baseline methods in predicting patient deterioration, allowing physicians to focus attention on at-risk patients earlier. Accuracy in predicting patient deterioration is significantly higher 24 hours earlier using machine learning.
TensorFlow and Democratizing Machine Learning:
The release of TensorFlow, an open-source machine learning platform, marks a significant step towards democratizing ML. This tool has fostered a vibrant community, extending ML’s reach to diverse fields. Applications range from early health issue detection in livestock to aiding in illegal logging prevention and plant disease diagnosis, reflecting TensorFlow’s versatility and accessibility.
TensorFlow and the Grand Challenges:
TensorFlow is an open-source machine learning system for large-scale training and inference tasks. Its accessible Apache 2.0 license allows users to adapt and modify it as needed. The vibrant TensorFlow community actively contributes to its improvement and expansion.
Real-World Applications of TensorFlow:
– Connectera’s fitness sensors for cows detect health issues through fitness data.
– Tensorflow models detect chainsaw sounds to prevent illegal logging.
– Penn State and the International Institute of Tropical Agriculture’s office in Tanzania use TensorFlow for disease detection in cassava plants, providing healthcare for plants.
Neural Architecture Search and AutoML:
The development of AutoML, particularly through neural architecture search, represents a leap in automating ML model design. These systems, outperforming human experts, democratize ML, enabling non-specialists to solve complex problems and drive innovations.
Neural Architecture Search:
Machine learning experts make decisions about model structure, such as the number and size of layers. Neural architecture search automates this process by training a model generating model to generate good machine learning models. The model generating model is steered towards promising architectures and away from less promising ones using a reward signal from a trained objective function.
AutoML Outperforms Human Experts:
AutoML can generate model architectures that achieve higher accuracy than those designed by human experts. AutoML can achieve better accuracy with lower computational cost, making it useful for resource-constrained applications.
AutoML as a Google Cloud Product:
AutoML for vision problems is available as a Google Cloud product. Google is working on extending AutoML to other domains as products.
Deep Learning Reshaping Computer Design:
Deep learning’s growing demands are transforming computer design, necessitating more powerful computational capabilities. This shift is evident in the development of specialized hardware like Tensor Processing Units (TPUs), which are optimized for ML tasks.
TPUs: Efficient Customized Hardware for Deep Learning Models:
TPU Properties and Architecture:
Deep learning models exhibit two key properties: reduced precision tolerance and reliance on linear algebra operations. TPUs are customized hardware designed for these models, optimizing speed and efficiency for matrix multiplies and related computations. A single TPU chip features a large matrix multiply unit, scalar and vector units, and high-speed memory. TPU pods comprise 64 TPUs, delivering 11.5 petaflops of computation with reduced precision.
TPU Performance and Benefits:
TPUs offer substantial performance improvements, enabling tasks that previously took days to complete in hours. TensorFlow compatibility allows for easy scaling of programs across CPUs, GPUs, and TPUs. Using TPUs has boosted productivity by simplifying processes and reducing required time.
Reference Models and Success Stories:
Google provides reference models for various problems, open-sourced on GitHub and regularly tested for accuracy. Internal search ranking models trained on a quarter TPU pod showed a significant speed improvement. Large image models experienced a 10x speedup in convergence time. WaveNet production model for text-to-speech conversion runs on TPUs, generating speech at 20x real-time speed.
Software Improvements and Scaling:
Continuous software improvements have enhanced TPU performance by 45%. Training an ImageNet model on a TPU pod takes only 12 minutes, achieving an epoch every eight seconds. This rapid turnaround time allows for more experimentation and faster identification of effective approaches.
Cost-Effectiveness and Benchmarks:
Cost comparison benchmarks show that training on TPUs is roughly 4x more cost-effective than the second-best option. With TPUs, researchers can conduct more experiments at a lower cost while achieving faster results.
Machine Learning in System Design:
A notable trend is the integration of ML into core computer systems, replacing traditional heuristics with data-driven models. From enhancing system performance to enabling adaptability, ML’s incorporation into system design holds the promise of more efficient and responsive technologies.
Machine Learning for Computer Systems:
– Dean discusses the potential of machine learning to enhance computer systems, shifting from building systems for machine learning to exploring machine learning for systems.
– Replacing traditional data structures like B-trees with neural networks can result in significant performance and size improvements.
– Machine learning can optimize hash tables and Bloom filters, improving space utilization and reducing empty space.
– By leveraging patterns, machine learning can improve the effectiveness of heuristics and data structures in computer systems.
Advancing the Field of Machine Learning Hardware:
– Automating performance optimization and other aspects of machine learning problems can simplify the process and improve results.
– Reinforcement learning-based approaches can be effective for optimizing machine learning systems, given a numeric metric to measure and optimize.
– Clean APIs facilitate the integration of learned decisions into existing code, enhancing system adaptability and performance.
– The field of machine learning hardware is in its early stages, with vast potential for breakthroughs and automation.
– Incorporating learning into the core of computer systems offers exciting opportunities for enhanced adaptability and performance.
Machine learning, with its expansive applications and groundbreaking advancements, is not just a technological revolution but a societal one. From healthcare to urban development, and from system design to scientific discovery, ML’s influence is pervasive and growing. As this field evolves, it brings forth new challenges and opportunities, promising a future where ML-driven solutions become integral to solving complex, real-world problems.
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....
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's impact on engineering and system challenges is profound, driving innovations in healthcare, materials science, and computational systems, while also introducing ethical considerations and challenges. The integration of machine learning into core computer systems promises adaptability and responsiveness, revolutionizing various fields and aiding in solving complex problems....
TensorFlow, a versatile machine learning platform, has revolutionized problem-solving approaches, while transfer learning reduces data requirements and accelerates model development for diverse applications....
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....
Machine learning has revolutionized various facets of society, including healthcare, engineering, and scientific discovery, and its potential continues to expand with advancements in computational power and specialized hardware. With the development of narrow AI to general AI, machine learning's transformative potential is expected to grow exponentially in the future....