Jeff Dean (Google Senior Fellow) – Deep Learning to Build Intellgent Systems (Apr 2018)


Chapters

00:00:02 Machine Learning Frontiers in Solving Grand Challenges
00:02:40 Machine Learning Innovations in Healthcare and Infrastructure
00:09:55 Machine Learning for Chemistry, Material Science, and Brain Research
00:16:30 Making Machine Learning Accessible
00:20:42 Automating Machine Learning: Neural Architecture Search and the Future of AI
00:24:18 TPUs: Fast and Scalable Hardware for Machine Learning
00:30:16 Machine Learning for Computer Systems
00:34:37 Automating Machine Learning for Enhanced Performance and Adaptability

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.


Notes by: QuantumQuest