Jeff Dean (Google Senior Fellow) – Deep Learning for Solving Important Problems (May 2019)


Chapters

00:00:15 Machine Learning's Impact on Computer Vision
00:05:11 Machine Learning in Healthcare: Using Computer Vision to Improve Diagnosis and Treatment of Diabetic Ret
00:10:44 Advanced Machine Learning Applications in Medical Fields
00:20:13 Bidirectional Pre-training of Transformers for Language Understanding
00:24:27 Machine Learning Infrastructure and Its Applications for Global Impact
00:30:56 Automating Machine Learning Problem Solving
00:38:38 Hardware Innovations for Efficient Deep Learning
00:41:12 Large Scale Multitask Learning and Building Fair Machine Learning Systems
00:51:48 Artificial Intelligence Regulation and Reasoning

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.


Notes by: QuantumQuest