Leader of team project Google Brain
research.google/people/jeff
facebook.com/jeff.dean.507464
twitter.com/JeffDean
linkedin.com/in/jeff-dean-8b212555
Jeff Dean on Wikipedia
Projects:
TensorFlow, open source software machine learning library
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Leader of team project Google Brain
research.google/people/jeff
facebook.com/jeff.dean.507464
twitter.com/JeffDean
linkedin.com/in/jeff-dean-8b212555
Jeff Dean on Wikipedia
Projects:
TensorFlow, open source software machine learning library
https://youtu.be/1-3Ahy7Fxsc
Jeff Dean: "Achieving Rapid Response Times in Large Online Services" Keynote - Velocity 2014
Published on Jun 25, 2014
Quote:
Jeff Dean's keynote from the 2014 O'Reilly Velocity conference in Santa Clara, CA.
Today's large-scale web services provide rapid responses to interactive requests by applying large amounts of computational resources to massive datasets. They typically operate in warehouse-sized datacenters and run on clusters of machines that are shared across many kinds of interactive and batch jobs. As these systems distribute work to ever larger numbers of machines and sub-systems in order to provide interactive response times, it becomes increasingly difficult to tightly control latency variability across these machines, and often the 95%ile and 99%ile response times suffer in an effort to improve average response times. As systems scale up, simply stamping out all sources of variability does not work. Just as fault-tolerant techniques needed to be developed when guaranteeing fault-free operation by design became unfeasible, techniques that deliver predictably low service-level latency in the presence of highly-variable individual components are increasingly important at larger scales.
In this talk, I'll describe a collection of techniques and practices lowering response times in large distributed systems whose components run on shared clusters of machines, where pieces of these systems are subject to interference by other tasks, and where unpredictable latency hiccups are the norm, not the exception. Some of the techniques adapt to trends observed over periods of a few minutes, making them effective at dealing with longer-lived interference or resource contention. Others react to latency anomalies within a few milliseconds, making them suitable for mitigating variability within the context of a single interactive request. I'll discuss examples of how these techniques are used in various pieces of Google's systems infrastructure and in various higher-level online services.
In this talk, I'll describe a collection of techniques and practices lowering response times in large distributed systems whose components run on shared clusters of machines, where pieces of these systems are subject to interference by other tasks, and where unpredictable latency hiccups are the norm, not the exception. Some of the techniques adapt to trends observed over periods of a few minutes, making them effective at dealing with longer-lived interference or resource contention. Others react to latency anomalies within a few milliseconds, making them suitable for mitigating variability within the context of a single interactive request. I'll discuss examples of how these techniques are used in various pieces of Google's systems infrastructure and in various higher-level online services.
This talk presents joint work with Luiz Barroso and a number of other colleagues at Google.
About Jeff Dean (Google):
Jeff joined Google in 1999 and is currently a Google Senior Fellow in Google's Knowledge Group. He has co-designed/implemented five generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He is currently working on large-scale distributed systems for training deep neural models for speech, vision, and text understanding. He is a Fellow of the ACM and the AAAS, a member of the U.S. National Academy of Engineering, and a recipient of the ACM-Infosys Foundation Award in the Computing Sciences.
https://youtu.be/4hqb3tdk01k
Large-Scale Deep Learning for Building Intelligent Computer Systems
Published on Mar 16, 2015
Quote:
Over the past few years, we have built large-scale computer systems for training neural networks and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas and our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. In this talk, Google Senior Fellow Jeff Dean highlights some of the distributed systems and algorithms that Google uses in order to train large models quickly. He also discusses ways Google has applied this work to a variety of problems in its products, usually in close collaboration with other teams.
Jeff Dean, senior fellow, Google Knowledge Group
https://youtu.be/g_k1wzH-gsY
Google Senior Fellow Jeff Dean on machine learning
Published on Nov 9, 2016
Quote:
Guillaume Laforge in conversation with Google's Senior Fellow Jeff Dean for Devoxx BE 2016.
Article "Voices in AI – Episode 4: A Conversation with Jeff Dean"
by Byron Reese
October 2, 2017
https://youtu.be/J-D7K1d5Alc
Jeff Dean - Artificial Intelligence at Google
Published on Dec 14, 2017
Quote:
Jeff Dean is an American computer scientist and software engineer. He is currently a Google Senior Fellow in the Google Brain team.
March 2016
Article "Google veteran Jeff Dean takes over as company’s AI chief"
John Giannandrea is stepping down from his role as head of search and AI
by James Vincent
April 3, 2018
https://youtu.be/Kxpdlr6WJSY
Jeff Dean discusses the future of machine learning at TF World ‘19 (TensorFlow Meets)
Nov 12, 2019
Quote:
AI Advocate Laurence Moroney sits down with Google Senior Fellow, Jeff Dean following his keynote presentation at TensorFlow World. They discuss how advances in computer vision and language understanding are expanding what’s possible with machine learning, as well as Jeff’s ideas about the future of ML.
https://youtu.be/Tw2ntkjCo7E
Jeff Dean with Devi Parikh on Humans of AI: Stories, Not Stats
Dec 15, 2020
Quote:
Jeff Dean is Google Senior Fellow and SVP of Google Research and Google Health.
Humans of AI: Stories, Not Stats is an interview series with AI researchers to get to know them better as people. We don't talk about AI or their work or the stats of their life like what college they went to. They share what they think about, what they are insecure about, what they get excited about. They share the stories of their day-to-day life.
00:00 Introduction
00:44 What were you doing just before this call?
01:10 What is your daily routine like?
01:56 What is your favourite part of your day?
02:23 What is your least favourite part of your day?
02:56 What one chore do you dislike the most and why is that?
03:33 Do you struggle with procrastination?
03:49 Do you struggle with time management?
04:30 Do you set an alarm in the morning? Do you hit snooze often?
04:56 If I asked your friends, “What is Jeff like?”, what are 3 adjectives they’d use?
05:38 How much of that is true? Is anything exaggerated or missing?
05:49 Are you happy with the number of close friends you have?
06:18 What is one thing you’re worse at than people around you?
06:42 What is your single biggest strength?
07:03 What is a recurring moral conflict that you struggle with?
07:35 Is there a specific instance where you distinctly recall feeling privileged?
08:45 What are you insecure about?
09:12 Do you feel like an impostor?
09:38 What is something you’re trying out these days, and how is that going?
10:21 What is your favourite tool/trick/hack that makes your life more efficient or fun?
11:09 What do you tend to think about most when you’re not intentionally trying to think about something?
11:48 What is something surprising about you that the rest of us might not guess?
12:45 What is something about the world that surprises you?
13:17 What is the most recent unexpected thing that happened?
13:45 What is one way in which you wish your life was different?
14:33 What are you looking forward to, tomorrow or next week?
15:35 Do you think you’re average, above average, or below average happy relative to people around you?
15:50 When was the last time you danced?
16:36 What was your most recent dream that you remember?
17:26 Are you more optimistic or pessimistic than people around you?
18:05 Do you think there is a point to life or our existence?
18:22 What do you struggle with, in life?
18:52 How do you decide what to work on?
19:15 What are a couple of common traits in some of the best collaborators/colleagues you’ve worked with?
20:26 Have you found ways to spot these traits early?
21:07 Oceans or hills?
21:19 What is something that you love doing that you’re terrible at?
21:47 What is something you did recently that surprised people who believe they know you well?
22:08 When was the last time you felt like a kid in a candy store?
22:25 What is something you did not like at the time, but you are glad happened?
22:54 What do you easily get nostalgic about?
23:25 Is there something that made you smile today?
23:51 What is some of the best advice you’ve gotten or given?
24:47 Being as honest as possible, why did you agree to do this interview with me?
25:06 Is there anything you’d like to talk about in terms of who you are and what your life is like, that we haven’t covered?
https://youtu.be/J-FzHIQ7SOs
Jeff Dean: AI isn't as smart as you think -- but it could be | TED
Jan 12, 2022
Quote:
What is AI, really? Jeff Dean, the head of Google's AI efforts, explains the underlying technology that enables artificial intelligence to do all sorts of things, from understanding language to diagnosing disease -- and presents a roadmap for building better, more responsible systems that have a deeper understanding of the world. (Followed by a Q&A with head of TED Chris Anderson)