Jeff Dean (Google Senior Fellow) – Data Science Career AMA (Mar 2019)
Chapters
00:00:00 From Academia to Industry: Jeff Dean's Journey
Childhood and Background: Jeff Dean had an unusual childhood, moving around the world due to his parents’ work in public health research and anthropology. He attended 11 schools in 12 years, living in various locations, including Hawaii, Uganda, Minnesota, and Somalia.
Interest in Computers: Jeff’s interest in computers began at age nine when his father introduced him to a kit computer, the IMSI 8080. He started programming in BASIC, writing computer games, and modifying existing games.
Education and Career Choices: Jeff pursued a double major in computer science and economics during his undergraduate studies. He took a parallel and distributed computing class that sparked his interest in research and neural networks. After graduating, Jeff worked for a year at the World Health Organization in Geneva before pursuing a PhD in computer science.
Advice for College Students: Jeff emphasizes the importance of exploring different subjects and trying out new things during college. He suggests taking classes outside of a fixed curriculum to discover new interests and broaden perspectives. He also stresses the value of seeking out opportunities to learn from people with diverse backgrounds and expertise.
Academia vs. Industry: Jeff initially considered a career in academia but only received one academic job offer after graduate school. He decided to pursue industrial research, interviewing at various labs before accepting a position at Google. Jeff’s decision was influenced by factors such as his wife’s career aspirations and the desire to work in the Bay Area.
Current Role and Impact: Jeff Dean is currently the Senior Vice President of Google Brain, a leading AI and ML research group within Google. He has made significant contributions to Google’s engineering developments, including MapReduce, BigTable, and TensorFlow. Jeff’s work has had a profound impact on the field of AI and ML, driving advancements in various applications and industries.
00:10:14 Jeff Dean's Transition from Academia to Industrial Research
Choosing Industrial Research over Academia: Jeff Dean preferred the industrial research setting because he enjoyed building things himself. He felt that as a professor, he would have less hands-on involvement due to the demands of teaching and administrative duties.
DEC’s Western Research Lab: Jeff Dean joined DEC’s Western Research Lab in Palo Alto, which was a small lab with a diverse range of research projects. He appreciated the intimate setting and the opportunity to explore various research directions.
Landing the Job at DEC: Jeff Dean’s published papers in graduate school likely played a role in attracting DEC’s attention. He gave a one-hour talk about his research to the entire research lab. He had numerous two-on-one interviews, where he discussed his research, answered questions, and engaged in technical conversations.
Interviewing Style: Jeff Dean’s interviewing style is influenced by his experience at DEC. He prefers free-form technical discussions, asking open-ended questions, and allowing conversations to take interesting directions.
Early Projects at DEC: One of Jeff Dean’s early projects at DEC involved building a distributed file system. The project aimed to provide high performance and reliability by replicating data across multiple servers. This project laid the foundation for his future work on distributed systems.
00:13:32 Evolution of a Computer Scientist's Career from DEC to Google
Building a Low Overhead Continuous Profiler: Jeff Dean and his team developed a low overhead continuous profiler for computer systems. The profiler periodically wakes up and samples the program counter to record where time is being spent. Collected samples are aggregated in a hash table and periodically written to disk. Tools were built to analyze the profiles and provide insights into cache misses, branch mispredicts, and other performance issues.
Transition to Google: Jeff Dean was motivated to leave DECC’s research lab due to the slow pace of getting research into production. He sought a startup environment where he could have a more direct impact on users.
Working in a Startup vs. a Big Tech Giant: In a startup, roles are less clearly defined, requiring a versatile mindset and the ability to tackle various problems. In a big tech company, roles are more specialized, allowing for deeper focus and comprehensive problem-solving.
Balancing Individual Contribution and Collaboration: Jeff Dean spent a significant portion of his career as an individual contributor, working on problems with a small group of people. In recent years, he transitioned to a management role, leading the Google Brain project.
Google Brain Project: Jeff Dean co-founded the Google Brain project with Andrew Ng and Greg Corrado. The project investigates large-scale neural networks for computer vision, speech recognition, and language understanding. Unlike other Google X projects, Google Brain focused on software relevant to core Google teams.
00:21:08 Journey from Researcher to Manager: Balancing Hands-On Work and Leadership
Jeff Dean’s Career Evolution: Jeff Dean’s career took an unexpected managerial turn when he was tasked with managing a team of seven people at Google X, which grew to hundreds of employees over time. As his responsibilities expanded, he eventually assumed the role of leading all of Google’s research division, while still dedicating two days a week to hands-on coding projects.
Balancing Management and Hands-On Work: Dean divides his time between managing his large organization and engaging in hands-on coding projects. He allocates two days a week to work on projects he finds interesting and important, while the rest of his time is dedicated to steering the organization, ensuring impactful research, and fostering collaboration.
Mentorship and Inspiration: Dean acknowledges the importance of mentorship and having someone experienced to learn from. He mentions that he has been inspired and mentored by various individuals throughout his career, but the specific names of these mentors are not mentioned in this segment of the transcript.
00:23:49 Influences on a Career in Machine Learning
Background: Jeff Dean recalls teachers who supported his interests in math and computing throughout his educational journey. His undergraduate advisor influenced his decision to pursue graduate studies. His Ph.D. advisor guided his research in optimizing compilers.
Collaboration with Sanjay Gemmawat: Jeff Dean and Sanjay Gemmawat had a long working relationship, complementing each other’s strengths. They often pair-programmed together, combining their logical and frenetic approaches.
Learning from Experts in Machine Learning: Dean acknowledges the depth of knowledge in machine learning within Google. He highlights the importance of collaborative learning, especially with those possessing expertise beyond his own. He mentions Andrew Ng, Geoffrey Hinton, and their contributions to his understanding of machine learning.
Advice for Aspiring Data Scientists: Dean emphasizes the need for a strong fundamental understanding of machine learning concepts. He stresses the importance of practical skills, such as data cleaning and feature engineering. He suggests that new data scientists consider specializing in a particular domain.
Challenges in the Field of Machine Learning: Dean acknowledges the challenges of dealing with large datasets and complex models. He highlights the need for careful evaluation to prevent bias and ensure fairness. He expresses concern about the potential for misuse of machine learning technology.
Future Directions: Dean expects the field of machine learning to continue evolving rapidly. He anticipates new breakthroughs in areas such as natural language processing and reinforcement learning. He emphasizes the importance of ethical considerations and responsible use of machine learning.
Tools, Knowledge, and Skills: Programming, machine learning algorithms, and computer architecture are essential for understanding computation at a high level of abstraction. Knowledge of cache and branch predictors, as well as the strengths and weaknesses of different accelerators, is beneficial. Interdisciplinary skills, such as healthcare, neuroscience, or quantum chemistry, can be valuable when applying machine learning to various problems.
Rapid Evolution of Machine Learning: Machine learning techniques are evolving rapidly, making it crucial for data scientists to stay updated. AutoML tools can automate many aspects of the machine learning process, potentially changing the skill sets required for data scientists.
Impact of AutoML on Skill Sets: AutoML can reduce the need for extensive experimentation and fine-tuning, allowing data scientists to focus on higher-level aspects of machine learning. However, data scientists still need to understand the underlying principles of machine learning to make informed decisions and interpret results. AutoML can democratize machine learning, making it accessible to a broader range of users.
AutoML Tools and the Human Role: AutoML techniques can automate the iterative process of running experiments, analyzing results, and designing new experiments in machine learning. AutoML tools can enhance human capabilities by automating repetitive tasks and enabling higher-level insights. Humans should focus on guiding AutoML tools, interpreting results, and making strategic decisions rather than performing manual, repetitive tasks.
Limitations of AutoML Tools: Current AutoML tools are limited in their ability to generate completely new techniques or explore vast search spaces. AutoML tools excel in searching within defined architectural spaces, but struggle with excessively large search spaces.
Imagining a More Advanced AutoML System: A hypothetical AutoML system could rapidly evaluate new ideas by incorporating them into a search space across numerous problems. This system could provide valuable feedback on the effectiveness of new ideas, accelerating the spread and refinement of innovative approaches.
Emerging Opportunities in Machine Learning: Application of machine learning across various disciplines holds immense potential for advancements. New opportunities lie in exploring the intersection of machine learning with other fields, such as biology, chemistry, and materials science.
00:36:44 Advancing Machine Learning through Multitask Learning and Talent Development
Single Model for All Machine Learning Problems: Jeff Dean envisions training a single model with large capacity and sparse activation to solve all machine learning problems. Different parts of the model would be specialized for different tasks, and the routing would be learned to activate the relevant parts for a given task. This approach would allow for sharing of knowledge across related tasks and examples.
Value of MOOCs and Non-Traditional Education: MOOCs can provide a solid educational foundation for individuals interested in pursuing a career in machine learning. Self-motivated individuals can benefit from the rich educational content available online. MOOCs, combined with practical experience gained through Kaggle contests or relevant job roles, can pave the way for a successful career in machine learning.
Qualities Sought in Job Interviews: Jeff Dean values candidates who can engage in meaningful conversations and bring fresh perspectives to discussions. Open-ended questions are preferred to assess a candidate’s ability to think critically and explore various approaches to a problem. Technical skills are important, but the emphasis is on assessing a candidate’s overall fit as a colleague and their potential to contribute to the team.
00:43:54 Machine Learning: Opportunities, Challenges, and the Importance of Diversity
Machine Learning’s Potential and Significance: Jeff Dean believes there is great potential for machine learning to solve complex tasks and make a positive impact in areas such as healthcare and education. He sees opportunities for advancements in machine learning systems that can address real-world problems and drive meaningful outcomes.
Fostering Inclusivity and Diversity in Machine Learning: Dean expresses his concern about the lack of gender and racial diversity in the machine learning field, particularly among his daughter’s peers. He emphasizes the need to inspire and expose all individuals, regardless of background, to the possibilities and career paths available in computing. Dean stresses the importance of creating inclusive work environments that promote respect and inclusion for everyone, recognizing the value of diverse perspectives in shaping machine learning products and their impact on society.
Addressing Socioeconomic Disparities: Dean acknowledges the inherent advantages enjoyed by individuals with access to technology and resources at home, which can create disadvantages for those who lack such privileges. He advocates for initiatives and efforts aimed at reducing these disparities and ensuring equal opportunities for all individuals interested in pursuing a career in machine learning.
Conclusion: The conversation concludes with a note of gratitude to the audience and a preview of the final day of CareerCon 2019, focusing on “Getting the Job” with a lineup of engaging sessions and activities.
Abstract
Exploring the Journey of Jeff Dean: From Childhood to AI Leadership
Abstract:
Jeff Dean’s remarkable journey, marked by a global childhood, a passion for computer science, and a transformative career in AI and machine learning (ML), serves as a beacon for aspiring professionals. His experiences, from the early days of programming in BASIC to leading Google’s AI initiatives, highlight the significance of embracing challenges, valuing diversity, and maintaining a balance between personal growth and professional responsibilities. This article delves into Dean’s life, exploring his influences, career decisions, and contributions to the field of AI, while also reflecting on the evolving landscape of machine learning, the impact of AutoML tools, and the importance of inclusivity in technology.
Childhood and Influences:
Jeff Dean’s childhood, characterized by frequent relocations due to his parents’ careers in tropical disease research and anthropology, instilled in him an adaptability and curiosity that would later define his professional life. Immersed in diverse cultures and exposed to a range of experiences, Dean’s early introduction to computers sparked a lifelong passion. This nomadic upbringing laid the foundation for his innovative approach to problem-solving and his interest in technology.
Growing up, Dean moved around the world due to his parents’ work in public health research and anthropology. He attended 11 schools in 12 years, living in various locations, including Hawaii, Uganda, Minnesota, and Somalia. At the age of nine, his father introduced him to a kit computer, the IMSI 8080, sparking his interest in computers. He started programming in BASIC, writing computer games, and modifying existing games.
University and Career Decisions:
Dean’s academic journey, balancing computer science and economics, was pivotal in shaping his career path. His work with the World Health Organization and the choice to focus on computer science for his PhD reflect a commitment to practical impact and a keen understanding of his strengths. This period was crucial in solidifying his interest in research and setting the stage for his future contributions to AI and ML.
In college, Dean pursued a double major in computer science and economics. During his undergraduate studies, he took a parallel and distributed computing class that sparked his interest in research and neural networks. After graduating, Dean worked for a year at the World Health Organization in Geneva before pursuing a PhD in computer science.
Academia vs. Industry:
The decision to pursue a career in industrial research over academia was a defining moment for Dean. His tenure at Digital Equipment Corporation’s Western Research Lab, where he engaged in diverse projects and honed his technical skills, was a critical stepping stone. This choice underscored his preference for practical application over theoretical exploration.
After completing his PhD, Dean initially considered a career in academia but only received one academic job offer. Influenced by factors such as his wife’s career aspirations and the desire to work in the Bay Area, he decided to pursue industrial research. Dean interviewed at various labs before accepting a position at Google.
Jeff Dean’s Journey at Google:
Dean’s transition to Google marked a significant shift in his career. His roles evolved from hands-on problem-solving to leading large-scale projects like Google Brain. This transition from individual contributor to manager highlights the duality of his expertise: a deep technical understanding coupled with the ability to guide and inspire large teams.
At Google, Dean has made significant contributions to the company’s engineering developments, including MapReduce, BigTable, and TensorFlow. His work has had a profound impact on the field of AI and ML, driving advancements in various applications and industries. He co-founded the Google Brain project with Andrew Ng and Greg Corrado, which investigates large-scale neural networks for computer vision, speech recognition, and language understanding. Unlike other Google X projects, Google Brain focused on software relevant to core Google teams.
Jeff Dean’s Career Evolution: During his tenure at Google, Dean’s career took an unexpected managerial turn. Initially tasked with managing a team of seven people at Google X, he eventually assumed the role of leading all of Google’s research division. Throughout this journey, Dean has struck a balance between managing his large organization and engaging in hands-on coding projects, allocating two days a week to pursue his own research interests while steering the organization, ensuring impactful research, and fostering collaboration.
Mentorship and Influences:
Throughout his career, Dean has been influenced by a range of mentors, from teachers to colleagues like Sanjay Gemmawat. These relationships not only shaped his technical skills but also his approach to collaboration and problem-solving.
In addition to his formal education, Dean credits his mentors and colleagues for shaping his career. One of his most influential mentors was Sanjay Gemmawat, a Google engineer who worked with Dean on the design and implementation of MapReduce. Dean also acknowledges the contributions of his other colleagues at Google, who have helped him develop his skills and broaden his perspective.
Collaboration with Sanjay Gemmawat: Jeff Dean and Sanjay Gemmawat had a long working relationship, complementing each other’s strengths. They often pair-programmed together, combining their logical and frenetic approaches. Their collaboration led to significant contributions to the field, including the development of MapReduce, a framework for distributed processing of large data sets.
Skills for Aspiring Data Scientists and ML Practitioners:
Dean’s advice for aspiring data scientists and ML practitioners emphasizes a strong foundation in mathematics, programming, and an understanding of machine learning algorithms. He advocates for adaptability and interdisciplinary knowledge, highlighting the dynamic nature of the field.
Dean emphasizes the importance of a strong foundation in mathematics, programming, and machine learning algorithms for aspiring data scientists and ML practitioners. He believes that adaptability and interdisciplinary knowledge are essential in this rapidly changing field.
Tools, Knowledge, and Skills: Programming, machine learning algorithms, and computer architecture are essential for understanding computation at a high level of abstraction. Knowledge of cache and branch predictors, as well as the strengths and weaknesses of different accelerators, is beneficial. Interdisciplinary skills, such as healthcare, neuroscience, or quantum chemistry, can be valuable when applying machine learning to various problems.
Impact of AutoML Tools:
Dean recognizes the transformative potential of AutoML tools in automating repetitive tasks, allowing experts to focus on strategic decision-making and innovation. He foresees these tools expanding into new domains and potentially tackling global challenges.
Dean believes that AutoML tools have the potential to revolutionize the field of AI by automating repetitive tasks and allowing experts to focus on more strategic decision-making and innovation. He anticipates that these tools will continue to expand into new domains and potentially help solve global challenges.
AutoML Tools and the Human Role:
– AutoML techniques can automate the iterative process of running experiments, analyzing results, and designing new experiments in machine learning.
– AutoML tools can enhance human capabilities by automating repetitive tasks and enabling higher-level insights.
– Humans should focus on guiding AutoML tools, interpreting results, and making strategic decisions rather than performing manual, repetitive tasks.
Limitations of AutoML Tools:
– Current AutoML tools are limited in their ability to generate completely new techniques or explore vast search spaces.
– AutoML tools excel in searching within defined architectural spaces, but struggle with excessively large search spaces.
Multitask Learning and Sparse Activation:
His vision for the future of AI includes the development of models capable of multitask learning with sparse activation. This approach, focusing on efficiency and adaptability, is a testament to his forward-thinking philosophy.
Dean’s vision for the future of AI includes the development of models capable of multitask learning with sparse activation. He believes that this approach, which focuses on efficiency and adaptability, will be essential for solving complex real-world problems.
A Single Model for All Machine Learning Problems:
– Jeff Dean envisions training a single model with large capacity and sparse activation to solve all machine learning problems.
– Different parts of the model would be specialized for different tasks, and the routing would be learned to activate the relevant parts for a given task.
– This approach would allow for sharing of knowledge across related tasks and examples.
Promoting Representation and Inclusiveness:
A significant aspect of Dean’s ethos is the promotion of inclusivity and representation in the field of machine learning. He underscores the importance of inspiring a diverse range of individuals to explore computing and AI, advocating for a culture of respect and support in workplaces.
Dean is a strong advocate for diversity and inclusion in the field of machine learning. He believes that it is essential to inspire a diverse range of individuals to explore computing and AI and to create a culture of respect and support in workplaces.
Fostering Inclusivity and Diversity in Machine Learning:
– Dean expresses his concern about the lack of gender and racial diversity in the machine learning field, particularly among his daughter’s peers.
– He emphasizes the need to inspire and expose all individuals, regardless of background, to the possibilities and career paths available in computing.
– Dean stresses the importance of creating inclusive work environments that promote respect and inclusion for everyone, recognizing the value of diverse perspectives in shaping machine learning products and their impact on society.
Addressing Socioeconomic Disparities:
– Dean acknowledges the inherent advantages enjoyed by individuals with access to technology and resources at home, which can create disadvantages for those who lack such privileges.
– He advocates for initiatives and efforts aimed at reducing these disparities and ensuring equal opportunities for all individuals interested in pursuing a career in machine learning.
Jeff Dean’s story is not just a tale of personal achievement; it’s a blueprint for future generations in AI and ML. His journey underlines the importance of lifelong learning, the willingness to embrace new challenges, and the need for diversity and inclusivity in technology. Dean’s contributions extend beyond his technical accomplishments, influencing the way we approach, teach, and think about AI and ML. As the field continues to evolve, his insights and experiences provide valuable lessons for those looking to make their mark in this dynamic and impactful domain.
The Future of Machine Learning and the Importance of Inclusion and Diversity in the Field:
– Jeff Dean believes there is great potential for machine learning to solve complex tasks and make a positive impact in areas such as healthcare and education.
– He sees opportunities for advancements in machine learning systems that can address real-world problems and drive meaningful outcomes.
– The conversation concludes with a note of gratitude to the audience and a preview of the final day of CareerCon 2019, focusing on “Getting the Job” with a lineup of engaging sessions and activities.
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