AI Talks: Use Cases in Machine Learning + Networking

Step into the world of machine learning with General Assembly and Boston New Technology. During an evening of lightning talks, see how modern businesses are harnessing the power of data and machine learning to drive innovation.

Showcasing real-world use cases, you'll:

• Learn what machine learning experts do and the types of problems they solve

• Walk through the typical workflow and see how the pros solve business problems with powerful business predictions

• Explore key tools and processes to use to analyze, visualize, and model data

Pizza, salad and soda will be provided, courtesy of UiPath!

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About the Talks & Presenters:

Robert Love, Technical Consultant & Northeast Team Lead for Presales, UiPath

"Robotic Process Automation in an AI/ML World"

About Robert Love:

Robert is a Technical Consultant & Northeast Team Lead for Presales at UiPath - the leading provider of Robotic Process Automation (RPA) software. He specializes in educating, informing, and demonstrating the power of software automation and digital transformation to customers and audiences across many different industries and occupations. Mr. Love previously worked as a management consultant. Over a decade, he led teams of consultants to bridge the gap between technology and business through the intelligent application of data, analytics, and technology. He received his B.S in Electrical Engineering and Computer Science from Tennessee State University, and an M.S. in Technology & Policy from the Engineering Systems Division at the Massachusetts Institute of Technology.

Anuj Dutt, Deep Learning Researcher at Bose Corporation

"Machine Learning Inference on Mobile Devices"

A lot of the cool new features that we love on our phones like face recognition in photos, Face-ID for authentication, smart reply in Gmail etc. rely on Machine Learning, most of which takes place on device. But, it can be really challenging to deploy these Machine Learning models on edge devices like Mobile Phones, embedded processors etc. In this talk, we will discuss that why we should look at doing Machine Learning inference locally on the mobile devices and also explore that what kind of an expertise is required for a developer to deploy a Machine Learning model on a Mobile Device. We will also develop and train some Machine Learning models for different use cases, deploy the trained models in an Android/iOS App and see that how we can leverage the power of the CPU/GPU/AI Chips on our Mobile Phones/embedded devices etc. for doing inference locally.

About Anuj Dutt:

Anuj is a Machine Learning Researcher at Bose Corporation. His work involves exploring, innovating and developing new machine learning models to create solutions that will amaze the customers. Apart from that, his work also involves optimizing and deploying machine learning models on mobile devices [iOS/Android], embedded devices like ARM Cortex-A/Cortex M processors as well as on Blockchains for quick proof-of-concept development. Anuj is also a contributor to the OpenMined project, a project to develop tools for secure, privacy-preserving, value-aligned Artificial Intelligence.

Joining for the panel discussion:

Alex Casparino, Data Analyst at Fidelity Investments

Alex Casparino is a Data Analyst on the Advanced Data Analytics team at Fidelity Management & Research Company (FMR Co.). In this role, she is responsible for identifying alternative data sets and using AI to derive investment insights for Fidelity’s equity division. Prior to her current role, Alex worked in operations, supporting Fidelity’s quantitative equity research team. Before Fidelity, Alex worked on the data science team at a start-up incubator in Cambridge, and in a Bioinformatics lab at the Broad Institute.