Schedule: Ethics, Security, and Privacy sessions

Nicholas Cifuentes-Goodbody leads you through a nontechnical overview of AI and data science. You’ll learn how to apply common techniques in organization and common pitfalls. You’ll pick up the language and develop a framework to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making. Read more.

AI product managers (PMs) are rising. With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. Mayukh Bhaowal explores new ways to manage AI products and outlines how AI executive roles are different and what toolbox you'll need to succeed in the era of artificial intelligence. Read more.

Robot technologies are becoming more capable and affordable. Yet even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it's often difficult to quantify human social behavior with algorithms. Dylan Glas and Phoebe Liu highlight some of the ongoing research to enable human-robot interaction. Read more.

Tzvika Barenholz and Induprakas Keri detail Intuit’s efforts to deploy fully homomorphic encryption (FHE) in production, which allows models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. You'll take a sneak peak at some of the optimizations and tricks that make FHE practical. Read more.

The use of AI is growing rapidly and expanding into applications that impact people’s lives. Raj Minhas explores how, while researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications. Read more.

Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.

As machine learning (ML) models get deployed to high-stakes tasks like medical diagnosis, credit scoring, and fraud detection, an overarching question that arises is why the model made its prediction. Ankur Taly explores techniques for answering this question and applications of the techniques in interpreting, debugging, and evaluating machine learning models. Read more.

Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also details some of his experiences and the lessons he learned applying ML, DL, and AI to the development of SaaS-based enterprise solutions. Read more.

While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice. Read more.