AI Week November, 2019 Tel Aviv University Info I Events Diamond Sponsor In cooperation with Blavatnik Interdisciplinary Cyber Research Center WEEKLY AGENDA Tracks Roundtable Workshop Hackathon SUNDAY, NOV 17 – WORKSHOPS DAY SUNDAY, NOV 17 – WORKSHOPS DAY Naftali Bldg. Naftali Bldg. Naftali Bldg. Naftali Bldg. Naftali Bldg. Administration Room 001 Room 004 Room 104 Room 208 Room 527 Bldg. Room 308 08:00 08:00-09:00 Gathering & Registration 08:00 08:00-09:00 Gathering & Registration 09:00-12:00 09:00-12:00 09:00-12:00 09:00-12:00 09:00-12:00 09:00-12:00 09:00 09:00 Transformers Reinforcement Herding Hello Neuron: Going Prototyping a "Deep Learning at - building Learning for cats: Product A hands-on unconventional: the models Recommendation management Julia, HPC and the Edge" usecase 10:00 10:00 intro to deep using the Intel powering BERT Systems in the machine machine learning Movidius Neural learning era learning Compute Stick 11:00 11:00 12:00 12:00 13:00-16:00 13:00-16:00 13:00-16:00 13:00-16:00 13:00-16:00 13:00 13:00 Best Practices in Building a Data Science Serving Deep Ray - Deep Learning Reinforcement from Research Learning Models Distributed and the Art Learning based to Production from a Data Platform by UC 14:00 14:00 Science and of Research solution with with Jupyter, Berkeley Engineering Maintenance RL Coach Kubeflow & Nuclio Perspective 15:00 15:00 16:00 16:00 17:00 17:00 18:00 18:00 Tracks Roundtable Workshop Hackathon Tracks Roundtable Workshop Hackathon MONDAY, NOV 18 MONDAY, NOV 18 Smolarz Nature Naftali Bldg. Recanati Bldg. Jaglom Administration Auditorium Museum Room 003 Leon Auditorium Auditorium Bldg. Room 308 08:00-18:00 08:00 08:00 Main Plenary & Research Symposium 09:00 09:00 + Poster Exhibition 10:40-17:10 10:40-17:10 10:40-17:00 10:40-15:30 10:00 10:00 NLP Track AI in Hardware for Automotive & Healthcare AI Track Autonomous Track Track 11:00 11:00 12:00 12:00 13:00 13:00 14:00 14:00 15:00 15:00 16:30-18:30 16:00-18:00 16:00 16:00 New Frontiers France-Israel in Training AI Round Table: Experts for Together 17:00 17:00 Industry Achieving AI for Humanity 18:00 18:00 Tracks Roundtable Workshop Hackathon Tracks Roundtable Workshop Hackathon TUESDAY, NOV 19 TUESDAY, NOV 19 Beit Hatfutsot, Smolarz Nature Recanati Bldg. Jaglom Bnei Zion Auditorium Museum Leon Auditorium Auditorium Auditorium 08:00-18:00 08:00 08:00 Main Plenary & Innovation Track 09:00 09:00 + Startup Exhibition 10:00 10:00 11:00-17:20 11:00-17:20 11:00-17:20 11:00-17:20 11:00 11:00 Algorithms Systems for AI Computer AI in Corporate Track Track Vision Track Track 12:00 12:00 13:00 13:00 14:00 14:00 15:00 15:00 16:00 16:00 17:00 17:00 18:00 18:00 Tracks Roundtable Workshop Hackathon Tracks Roundtable Workshop Hackathon WEDNESDAY, NOV 20 THURSDAY, NOV 21 Intel Petach ICRC Meeting Intel Petach Tikva Room Tikva 12:30-15:30 07:00-21:00 07:00 12:00 The Italy- AI Hackathon Israel Bilateral – AI for Social Workshop 08:00 Good 13:00 on AI 09:00 14:00 10:00 15:00 11:00 16:00 12:00 17:30-24:00 17:00 AI Hackathon 13:00 – AI for Social Good 18:00 14:00 15:00 19:00 16:00 20:00 17:00 21:00 18:00 22:00 19:00 23:00 20:00 Workshops Day: Sunday, November 17th 09:00-12:00 Naftali Bldg. Room 004 The workshops are for registered participants only Reinforcement Learning for 08:00-09:00 Gathering & Registration Recommendation Systems Run by - Sergey Ermolin: Principle Solutions Architect, 09:00-12:00 Naftali Bldg. Room 001 AI/ML, Amazon Web Services Transformers – Building the Models Summary: Powering BERT • In this tutorial, there will be a step-by-step overview on how to implement, train, and deploy an RL-based recommender Run by - Yuval Peleg: NLP Research Engineer, SparkBeyond system with realtime multivariate optimization. AWS Summary: SageMaker RL will be used as a platform. • The concept of BERT (Bidirectional Encoder Representations from Transformers) was published by researchers at Google AI Language last year, and since then it has been used to yield top results in many NLP benchmark tasks. This workshop will dive deep into the workings 13:00-16:00 Naftali Bldg. Room 004 of BERT. Participants will build a transformer model in Pytorch. Prerequisites: Building a Reinforcement Learning Based • Knowledge of Python, experience with PyTorch is recommended, experience with deep neural networks. Solution with RL Coach Run by - Dan Elbaz: Research Engineer, Intel 13:00-16:00 Naftali Bldg. Room 001 Summary: • Coach is a python reinforcement learning framework Best Practices in Deep Learning and the Art containing implementation of many state-of-the-art of Research Maintenance algorithms. It exposes a set of easy-to-use APIs for (Intro to TRAINS open source experiment manager and version control) experimenting with new RL algorithms, and allows Run by - Dan Malowany: Head of AI, Allegro.ai simple integration of new environments to solve. Basic Summary: RL components (algorithms, environments, neural network • The workshop will cover deep learning best practices - Hyperparameter architectures, exploration policies, ...) are well decoupled, search, data biasness, diminishing returns and productive use of an so that extending and reusing existing components is experiment management platform. There will be an Introduction to fairly painless. Trains - Open source experiment manager for deep learning projects. The workshop will: Participants will take part in the following: 1. Introduce reinforcement learning basic concepts » Working with Trains I - Effective hyperparameter search with Trains 2. Introduce Intel's Coach reinforcement learning framework » Working with Trains II - Data auditing with Trains » Installing Trains Backend - Installing and working with local 3. Go through a hands on step by step tutorial for building trains-server a Reinforcement Learning based solution from scratch Prerequisites: with RL Coach. • Basic knowledge of Python, Jupyter notebook and deep learning 12 | Sunday, November 17th Sunday, November 17th | 13 09:00-12:00 Naftali Bldg. Room 104 09:00-12:00 Naftali Bldg. Room 208 Herding Cats: Product Management in the Hello Neuron: A Hands-on Intro to Deep Machine Learning Era Learning Run by – Ira Cohen: Chief Data Scientist, Anodot Run by - Dr. Eyal Gruss: Machine learning researcher Summary: and new-media artist • This tutorial will go through the cycle of developing machine learning Summary: based capabilities (or entire products) and the role of the (product) manager in each step of the cycle. • The workshop will introduce deep neutral networks, and • While the role of the manager does not require deep knowledge of go through hands-on building, training and evaluation of machine learning algorithms - it does require understanding of how convolutional neutral networks for image recognition. A ML based products should be developed and bridge the gap between laptop and google account are required to participate. product/business requirements and the inherent uncertainty that is at the basis of any machine learning based solution. This uncertainty Prerequisites: follows an ML based solution at all phases: there is rarely certainty • Knowledge of Python, Scikit-learn, Logistic regression, that any ML based solution can solve a given business problem, the development cycle involves research iterations to continuously try Binary classification, Cross entropy, Multi class classification, improving the results - a cycle that is not deterministic. Multi label classification, Overfitting, TPR, FPR, ROC, AUC 13:00-16:00 Naftali Bldg. Room 104 13:00-16:00 Naftali Bldg. Room 208 Data Science from Research to Production Serving Deep Learning Models from a Data with Jupyter, Kubeflow & Nuclio Science and Engineering Perspective Run by - Or Zilberman: Chief Data Scientist, Iguazo Run by - Jenia Gorokhovsky: Algorithms Team Lead, Taboola Summary: Summary: • Deploying machine learning models from training to • A serving system for Deep Learning models is a tricky production requires companies to deal with the complexity design problem. It’s part of the Data Scientist’s core loop of moving workloads through different pipelines and re- - so it should be very flexible, and running an experiment writing code from scratch. The workshop will demonstrate on live traffic should be easy. It's often also part of the how simple it is to automatically transfer a full machine core production flow - so we want it to scale well, adapt learning pipeline from Jupyter notebook to scale-out to changing traffic patterns, and have low latency. In this serverless functions for event-driven and real-time workshop participants will discuss key design considerations applications. It will also address versioning challenges, and build a POC for such a system locally using the showing how serverless functions can enable developers technologies we use at Taboola for serving TensorFlow to update machine learning models and code together as models. Participants will then build a (hopefully realistic) a single versioned entity. The session will include a deep example of each component in the system - training, walkthrough and interactive demos. deployment, serving, configuration, and metrics Prerequisites: Prerequisites: • Knowledge of Python, Basic ML, Basic docker • Working knowledge of modelling in TensorFlow. Ability to understanding is helpful although not necessary build and run Docker containers. 14 | Sunday, November 17th Sunday, November 17th | 15 09:00-12:00 Naftali Bldg. Room 527 09:00-12:00 Administration Bldg. Room 308 Going Unconventional: Julia, HPC and Prototyping a “Deep Learning at the Edge” Machine Learning usecase using the Intel Movidius Neural Compute Stick Run by - Gleb Ivashkevich: Y-DATA instructor, founder at datarythmics Run by - Vishnu Madhu: AI Technical Solutions Engineer, Intel Summary: Summary: • The goal of the workshop is to introduce Julia both as • The session will highlight the use of Intel Movidius NCS for a language and as an actual tool for solving specific ML offloading the compute in a power constrained environment.