Data Tech

UiPath Robots are learning new skills with increasingly sophisticated Pragmatic AI capabilities to enable automation of progressively complex, cognitive tasks. AI and RPA, when paired together, can: read, write, listen and make decisions to work effectively. UiPath is making major strides to provide a delivery mechanism for AI skills by building its own embedded AI offerings, while providing the ability to host custom skills for partners and customers, allowing last mile delivery of AI capabilities. RPA and AI fix major challenges in being able to apply AI to solve real-world problems.

In this presentation, we will discuss how Pure1 Meta, the Pure AI engine, will help further advance the vision of self-driving storage. We will recap our innovation efforts, including building a global sensor network that collects over 1 trillion data points per day. 

AmeriPride's journey to transform data into a trusted partner. 

We present a non-factoid QA system that provides legally correct, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain. This commercially available system is entirely based on NLP and IR, and does not rely on a structured knowledge base. Our system aims to provide concise one sentence answers for basic questions about the law. It is not restricted in scope to particular topics or jurisdictions. The corpus of potential answers contains approximately 20M documents classified to over 120K legal topics.

Schools use targeted interventions to help students at risk of school dropout or late graduation. Identifying these students takes significant investment in people, process, and technology, and warning signs are often context-specific and scattered across data sources. Extremely high counselor caseloads compound the problem, and are worse in schools serving children with other structural disadvantages. We describe how machine learning technology can improve existing educational systems like the Minnesota Early Indicator and Response System, and provide details on this new technology's statewide implementation in Kentucky. We discuss key technical challenges for early warning data systems and best practices for overcoming them. These challenges specifically include foregrounding fairness, accountability, and transparency, and we offer a discussion of how computational public policy systems can mitigate or worsen equitable access for all students.

Dockets for federal court cases contain a wealth of information for lawyers to develop a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. We used Natural Language Processing (NLP) techniques and deep learning methods in order to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations. 

In the rapidly growing field of data analytics and AI it is becoming increasingly difficult to attract and retain qualified workers. Historically, finding talent through immigration provided adequate solutions but with changes to policies and increased global competition to train and attract skilled workers that option is no longer resolving the challenge. Hear how Canada is trying to address this same challenge now and into the future through a skills development model at the corporate and academic levels coupled with open immigration policies.

Montreal International will discuss how it is collaborating with key players of the Montreal AI ecosystem including Scale.AI, Canada’s lead in the AI Super Cluster, to feed an effective workforce.

Deep learning certainly has roots in the autonomous vehicle space. However, most trucking companies have a substantial investment in existing class 8 semi-trailer trucks that are not going to be replaced overnight. Trimble Transportation is using deep learning technologies, in conjunction with other advanced analytic techniques and state of the art DevOps approaches to help ensure the safe operation of trucking fleets. While it may be premature for many trucking fleets to embrace autonomous vehicles, Trimble Transportation has made it possible for those same companies to leverage deep learning as a way to reduce costs and improve safety.