Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimensional

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As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.

Technology

Anima Anandkumar
MODERN ML:
DEEP, DISTRIBUTED,
MULTI-DIMENSIONAL

3
MOORE’S LAW: A SUPERCHARGED LAW
 More than a billion
operations per image.
 NVIDIA GPUs enable
parallel operations.
 Enables Large-Scale AI.
COMPUTE INFRASTRUCTURE FOR AI: GPU

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DISTRIBUTED TRAINING INVOLVES COMPUTATION & COMMUNICATION
Parameter
server
GPU 1 GPU 2
With 1/2 data With 1/2 data

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DISTRIBUTED TRAINING INVOLVES COMPUTATION & COMMUNICATION
Parameter
server
GPU 1 GPU 2
With 1/2 data With 1/2 data
Compress?
Compress?
Compress?

6
DISTRIBUTED TRAINING BY MAJORITY VOTE
Parameter
server
GPU 1
GPU 2
GPU 3
sign(g)
sign(g)
sign(g)
Parameter
server
GPU 1
GPU 2
GPU 3
sign [sum(sign(g))]
Jeremy Bernstein, Jiawei Zhao, Kamyar Azzizadenesheli, Yu-Xiang Wang, A

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SIGNSGD PROVIDES “FREE LUNCH"
Throughput gain with almost same accuracy
P3.2x machines on AWS, Resnet50 on imagenet

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SIGNSGD ACROSS DOMAINS AND ARCHITECTURES
Huge throughput gain!

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SIGNSGD IS BYZANTINE FAULT TOLERANT
SignSGD is robust

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TAKE-AWAYS FOR SIGN-SGD
• Convergence even under biased gradients and noise.
• Faster convergence than SGD in theory and in practice.
• For distributed training, similar variance reduction as SGD.
• In practice, similar accuracy but with far less communication.
https://github.com/PermiJW/signSGD-with-Majority-Vote
Pytorch code at

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TENSORS:
MULTI-DIMENSIONAL PROCESSING
Image: 3 dimensions
Width * Height * Channels
Video: 4 dimensions
Width * Height * Channels * Time

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OPERATIONS ON TENSORS: TENSOR CONTRACTION

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DEEP NEURAL NETS: TRANSFORMING TENSORS

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DEEP TENSORIZED NETWORKS
Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, A
Pytorch notebook: https://github.com/JeanKossaifi/tensorly-notebooks

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SPACE SAVING IN DEEP TENSORIZED NETWORKS

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T E N S O R L Y : H I G H - L E V E L A P I F O R T E N S O R
A L G E B R A
• Python programming
• User-friendly API
• Multiple backends:
flexible + scalable
• Example notebooks in
repository

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TENSORS:
TOPIC DETECTION IN TEXT
Co-occurrence
of word triplets Topic 1 Topic 2
STORM
WORLD SERIES
AUSTRALIA
STOCK MARKET
WASHINGTON
HEALTH
CRISIS
MACHINE
LEARNING
LIBRARY OF
NEWS ARTICLES
Amazon
Comprehend
LIST OF TOPICS

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UNSUPERVISED LEARNING OF TOPIC MODELS THROUGH TENSOR METHODS
Justice
Educatio
n
Sports
Topics

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TENSOR-BASED LDA TRAINING IS FASTER
• Mallet is an open-source framework for topic modeling
• Benchmarks on AWS SageMaker Platform
• Bulit into AWS Comprehend NLP service.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
5 10 15 20 25 30 50 75 100
Timeinminutes
Number of Topics
Training time for NYTimes
Spectral Time(minutes) Mallet Time (minutes)
0.00
50.00
100.00
150.00
200.00
250.00
5 10 15 20 25 50 100
Timeinminutes
Number of Topics
Training time for PubMed
Spectral Time (minutes) Mallet Time (minutes)
8 million documents
22x faster on average 12x faster on average
300000 documents

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24NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
RESEARCH LEADERS AT NVIDIA
Robotics
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Learning &
Perception
Jan KautzBill Dally Dave Luebke Alex Keller Aaron Lefohn
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Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI

AI Frontiers

Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 1: Heuristi...

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Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A*

Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...

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In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-lecture 2: Incremen...

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Training at AI Frontiers 2018 - Udacity: Enhancing NLP with Deep Neural Networks

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Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 3: Any-Angl...

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Percy Liang at AI Frontiers : Pushing the Limits of Machine Learning

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In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.

Ilya Sutskever at AI Frontiers : Progress towards the OpenAI mission

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I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.

Mark Moore at AI Frontiers : Uber Elevate

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Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in ...

AI Frontiers

The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.

Arnaud Thiercelin at AI Frontiers : AI in the Sky

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Wei Xu at AI Frontiers : Language Learning in an Interactive and Embodied Set...

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Sumit Gupta at AI Frontiers : AI for Enterprise

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The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.

Alex Ermolaev at AI Frontiers : Major Applications of AI in Healthcare

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The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.

Long Lin at AI Frontiers : AI in Gaming

AI Frontiers

Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.

Melissa Goldman at AI Frontiers : AI & Finance

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Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial...

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Ashok Srivastava at AI Frontiers : Using AI to Solve Complex Economic Problems

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Nearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more. Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.

Rohit Tripathi at AI Frontiers : Using intelligent connectivity and AI to tra...

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