Overview
Our upcoming meetup is all about human-AI collaboration. You'll learn about the concept of human-centered AI, practical ways to use crowdsourcing for data collection, and how humans can collaborate with ML models to achieve optimal quality and scalability for data labeling. Join the event to explore human-centered AI with us.
We welcome data scientists, researchers, ML engineers, and anyone who interacts with AI. Content is geared toward an audience with an intermediate level of AI experience. Snacks and drinks are provided.
Format: Hybrid (both online and offline)
Location: Northeastern University, West Village H, room 366
Agenda
* The time is indicated in Boston time zone (UTC-04)
Human-centered AI for the Future of Work
In recent years, there has been growing interest in human-centered artificial intelligence (AI) systems that can enhance the well-being of workers and improve their job performance. This talk will explore the concept of human-centered AI and how to apply it in the workplace to promote worker empowerment, creativity, and productivity.
We'll cover:
The potential benefits and challenges of implementing human-centered AI systems in different industries and work settings.
Examples from research and industry illustrating the importance of incorporating worker perspectives and needs in the design and development of AI technologies.
Ethical and social implications of human-centered AI, including issues related to privacy, bias, and transparency.
The ultimate goal of this talk is to foster a deeper understanding of the potential of human-centered AI to enhance the lives and work experiences of employees in a variety of industries and sectors.
Intro to Crowdsourcing with Toloka
As companies go through different stages of data maturity, their needs for data and ways of fulfilling them may change at every step. This introduction will demonstrate how to build crowdsourcing projects to guarantee high-quality results. We'll use the Toloka data labeling platform as an example with real projects. We'll also look at the advantages you can gain from crowdsourcing compared to other ways of collecting and labeling data.
Innovation capabilities of Generative AI for Materials Scientists
The talk explores the potential of generative AI in enhancing innovation capabilities for materials scientists. It discusses how AI can assist in the design and discovery of new materials, and how it can improve the efficiency of the materials discovery process. The talk also highlights some of the challenges and limitations associated with the use of generative AI in materials science.
