Large-scale language models like ChatGPT have taken the world by storm, dazzling users with their ability to pen convincing marketing copy, suggest recipes, and converse a lot like humans. But these models, for all their strengths, have some hefty (and concerning) limitations. And what other kinds of AI could be on the horizon? In this complimentary webinar, Kellogg faculty David Ferrucci, the AI researcher who started and led the IBM Watson team from its inception through its landmark Jeopardy success in 2011, and Brian Uzzi, a professor of management and organizations, will walk us through the inner workings and social ramifications of today's AI--and tomorrow's.

Activity Description Artificial intelligence (AI) and machine learning (ML) are increasingly being used in healthcare settings with applications in decision support, patient care, and disease management. If the underlying data on which AI depends is inherently biased or lacks a diverse representation of populations, the algorithms cannot produce accurate outputs and will further widen the gap of equitable care. This activity will discuss how AI and ML can be used to address social determinants of health and create more equitable healthcare solutions and improve health outcomes. Learning Objectives 1. Identify approaches to create inclusive data sets that produce positive health outcomes for all patients.

MobiSpaces delivers an end-to-end mobility-aware and mobility optimised data governance platform, that concerns the offerings of data acquisition, in-situ processing and all the security- and privacy-related operations. MobiSpaces envisions a set of toolboxes, suites, and tools that implement the MobiSpaces concept. MobiSpaces identifies the AI-based Data Operations Toolbox, including an additional list of tools, namely the Declarative querying, Decentralized Data Management, and Online Data Aggregator. In addition MobiSpaces utilises the Edge Analytics Suite, including a further list of tools, namely the XAI Prediction Modelling, Edge-driven Federated Learning, and Visual Analytics. MobiSpaces currently has five use cases utilising these tools including; iRoute, SmartSense, MarineTrafficTracker, Vessel Edge, and CrowdSeaMapping. Join us in our first introductory webinar " Meet the MobiSpaces Use Cases: Innovations for Urban and Maritime Domains " on 31 January 2023, 11:00-12:15 CEST where you can hear directly from the project.

Since the 1990s, organisations have attempted to improve safety culture. They have tried top-down approaches, with senior management making statements about how important safety is. However, many safety culture programmes stall after some initial improvements. Senior managers have invested in the programme. Workers on the frontline want to be safer and healthier. For a busy manager there just isn’t time to get ahead with improvement programmes while they are doing the day-to-day supervision and management tasks. AI can provide some solutions to this stalling pattern, providing the extra set of hands – and eyes – that will help managers and supervisors to get on top of the workload and spend more time developing a proactive safety culture. Our AI Reducing Accidents & Promoting A Positive Safety Culture Webinar will tackle the following: • Relationship between safety culture and AI • AI promoting a proactive and learning safety culture by • Reporting • Mutual trust • Active participation of workers M&S use case • How their EHS team reduced incidents by 80% at their Castle Donington site • How AI contributed to raising awareness of safety amongst floor workers

Hear about the whole patient care pathway and how AI enhances the patient journey This webinar will share the experience of Steps2Wellbeing (Dorset Healthcare) of using AI to reduce administration times, maximize clinical time, and improve access without increasing risk. Mayden’s Fiona Dawson will discuss the essential role that data flow and information sharing have in ensuring that - as we move toward a more digital future - we have an integrated and regulated ecosystem. The webinar will cover: - Improving access with new digital tools - How to support patients 24/7 whilst they wait for treatment - Is it safe to use AI in a clinical setting? - Case study: Dorset's new chatty front door!

Decentralize the data & crack the code to make a difference in the finance industry! Does financial forecasting excite you? And do you want to put your machine learning skills to make some predictions for the finance industry? #CryptoPrediction challenge, by Rocket Capital Investment (RCI), in association with MachineHack, gives you a chance to participate in the longest blockchain tournament on financial markets. As a result, you can now make the best machine learning applications for the finance industry. Join this AMA session to learn how you can join this twelve-week hackathon to source and incentivize the best in machine learning applications for finance. The tournament comprises a series of challenges, where the participants stand a chance to win over INR 4 Lakh cash. Who can participate - Data scientists - ML practitioners - Blockchain enthusiasts - Developers & Programmers - AI & Data science aspirants - Engineering students

Speaker: Dr. Eng Lim Goh, senior VP and CTO of AI, HPE Abstract: Join us for a free, 60-minute session where you can connect with experts who offer valuable insights into today’s most popular technologies. This month, hear from HPE’s own Dr. Eng Lim Goh, senior VP and CTO of AI, on the importance of sharing data to gain insights and how to do so responsibly. In his talk, Dr. Goh will illustrate how AI can advance the human condition, exploring a variety of industry use cases and lessons we’ve learned.

Do you want to begin a career in Artificial Intelligence? If yes, then this webinar is for you! The AI-900 certification takes you through Azure AI Fundamentals and therefore is a great first step in your AI journey. In this Free Webinar, we will introduce you to the basic concepts of Azure AI and help you understand the benefits of taking up this certification, exam preparation methods, recommended resources, and much more. We will also have our own AI experts facilitate this session to give you the best insights possible! Who Can Attend? Absolutely Anybody! What’s In It For You? 1. Learn from Microsoft Certified in-house experts who curate our training modules for industry professionals to address their unique requirements. 2. Get access to CloudThat’s Exam Ready Assessment Platform, where you can practice exam questions and assess your preparedness before taking the AZ-500 certification exam.

The 2022 AI Index report just published by the Stanford Institute for Human-Centered Artificial Intelligence says that “AI systems are starting to be deployed widely into the economy, but at the same time they are being deployed, the ethical issues associated with AI are becoming magnified.” While ethical principles in AI use have been a focus for years, it is an open question whether we are making progress toward actualizing those principles or toward establishing safety in the use of intelligent systems. In this panel, we’ll discuss the ethics within and of safe AI, and how to make progress toward realizing them. As safety is an inherently value-centered notion, how do we identify the values that matter for safety and who articulates them? How do we implement, measure, and test those values in the context of safety? The panel will explore where progress is being made and how to take the next steps toward an operationalized ethical practice of AI that structures the design, development and deployment of intelligent systems to be intentionally safe for those they impact.

This conference is presented as part of the Montreal Speaker Series in the Ethics of AI. SPEAKER Cynthia Rudin Professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretability of machine learning models is critical in high stakes decisions. In this talk, I will focus on one of the most fundamental and important problems in the field of interpretable machine learning: optimal scoring systems. Scoring systems are sparse linear models with integer coefficients. Such models first started to be used ~100 years ago. Generally, such models are created without data, or are constructed by manual feature selection and rounding logistic regression coefficients, but these manual techniques sacrifice performance; humans are not naturally adept at high-dimensional optimization. I will present the first practical algorithm for building optimal scoring systems from data. This method has been used for several important applications to healthcare and criminal justice. More information: https://sites.google.com/view/dmartin/ai-ethics/speakers?#h.nihlg6vib2nz