• ML at Work #2: Data bazaars, AI for creatives, & DL-powered shopping carts

    ***PLEASE RSVP THROUGH EVENTBRITE LINK*** https://mlatwork2.eventbrite.com Machine Learning at Work meets to discuss topics in artificial intelligence and machine learning. Our goal is to bring together those actively working, studying, or interested in working in the AI / ML space. Attendees will share ideas, projects, algorithms and results with their peers in NYC. We're restarting our meetup series as we've moved into a larger office space in Brooklyn! This month we will hear about AI-assisted shopping carts, data bazaars, and how creatives can take advantage of machine learning. Event timeline: 6:15pm Doors open 6:30pm - 7:00pm Networking/ Refreshments 7:00pm - 8:30pm Speakers Speakers Lineup: • Lindon Gao - Caper.ai Lindon Gao is the Co-founder and CEO of Caper, a startup that is creating a frictionless consumer shopping experience by building smart shopping carts that are powered by deep learning and computer vision to enable cashierless checkout. Essentially, Caper is the cart-based point-of-sale system that aims to rival AmazonGo. Shoppers can simply place items into their Caper cart (which have built-in sensors/cameras that identify the items and tally a virtual basket) and once finished, they can conveniently pay on the cart and leave the store. The technology is currently deployed in a number of NY supermarkets and is piloting with 3 of the largest grocery companies in the world. To date, Caper has raised $3M in seed funding led by First Round Capital. • Brendan O' Brien - Qri.io Qri is a free and open-source dataset version control system operating at the intersection of open data and file versioning. Qri is led by a small team in Brooklyn, NY. Today's balkanized "data cathedrals" force us to work with data we depend on without controlling it. We're incapable of building on each other's work because others' data is hard to trust, find, clean, adjust, move, and build on. We must replace this "cathedral approach" with a data bazaar, allowing each of us to freely compose, share, and build upon each other's data the way we do with software today. The Qri team will explore how content addressing and the distributed web provide the tools necessary to construct a data bazaar. They will demonstrate how this data bazaar integrates with existing tools (like Qri) and invite users to contribute to growth of a true data commons. • Cristóbal Valenzuela - Runway ML - runwayapp.ai Artificial Intelligence for Augmented Creativity. Runway is a toolkit that allows creators of all kinds to use artificial intelligence in an intuitive way. Artificial Intelligence (AI) empowers new ways of creating, collaborating and interacting with computers. AI models are the building blocks of those capabilities and with Runway, you can interact with AI models in familiar terms. Runway is built for creatives with or without machine learning experience. A simple interface allows you to add creative intelligence capabilities to a variety of applications, softwares, and design platforms.

  • ML at Work #1: Adversarial Autoencoders, Insight Data Science, and more!

    ***PLEASE RSVP THROUGH EVENTBRITE LINK*** https://adversarialautoencoders.eventbrite.com Machine Learning at Work meets to discuss topics in artificial intelligence and machine learning. Our goal is to bring together those actively working, studying, or interested in working in the AI / ML space. Attendees will share ideas, projects, algorithms and results with their peers in NYC. Practical considerations for AI implementations are the focus of this meetup, and members are encouraged to bring a laptop and be prepared to write / modify some code. Speakers Lineup: • Felipe Ducau - NYU - Masters in Data Science / Machine Learning Adversarial Autoencoders: Unsupervised approach to learn disentangled representations with adversarial learning as a key element in a Variational Autoencoder-like architecture. We will look at the model in Makhzani et al., discuss the intuition behind it and look at some sample code and implementation considerations. • Ross Fadely - Artificial Intelligence Lead at Insight Data Science The landscape of data careers is constantly evolving. Most recently there has been a growing demand across many industries for people with talent in AI. At Insight, we run a free 7 week Fellowship program to help people make the jump to AI careers. During the program, Fellows build AI focused projects over the span of 4 weeks. I will briefly discuss the current landscape of AI roles, followed by a deep dive into 2 projects recently completed by Insight AI Fellows. Along the way I will highlight practical tips and hurdles tackled during the projects, as well as resources for people to kick-start similar AI projects. • Laura Graesser, Wah Loon Keng - OpenAI Lab (https://github.com/kengz/openai_lab) Deep Q Learning: Brief introduction to reinforcement learning with the Deep Q-Learning (DQN) algorithm using the OpenAI Lab. The Lab is an experimentation system for Reinforcement Learning using the OpenAI Gym, Tensorflow, and Keras. During this session we will learn how to solve the classic cart-pole problem, reviewing the main components of the DQN algorithm and why they matter, as well as introducing the Lab. Optimization Challenge We will provide sample code for a fast-to-converge deep learning task. The goal of this challenge is to tune the code at will to get the lowest value of the cost at the end of 30 minutes. The playground to run the code will be provided through Paperspace machines.

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