Designing Human-Centered AI Products - Interaction20
Curious about designing human-centered AI products? Let’s walk through a case study of how a Google product team has made design decisions that are people-first. You’ll learn frameworks and tips for building your own AI products.
AI is not only for engineers. “AI for Everyone”, a non-technical course, will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today’s AI can – and cannot – do. Finally, you will understand how AI is impacting society and how to navigate through this technological change.
Lecturer in Creative Computing (BSc CC) - University of the Arts, London
Vacancy Id: 6949. Title: Lecturer in Creative Computing (BSc CC). College/Service: Academic Development and Services. Salary: £38,694 to £46,423 per annum. Closing Date: 20/06/2021, 23:55
MIT xPRO Designing and Building AI Products and Services | Online Program
This program is designed for professionals to devise AI-based solutions, and drive successful implementation of AI and ML technologies in organizations.
TRK "technically responsible knowledge" is a platform for data labelling and AI/ML training. Unlike other services, crowd-sourced workers and workers can set fair prices on this platform towards a living wage. TRK is open source, and offered under the MIT license.
Learn IBM’s AI Essentials Framework from experts, see an example in practice, and gather the resources you need to get started on your own project today.
Creating people-centered AI experiences, with Google's People + AI Guidebook
Join Gabe Clapper, Staff Designer at Google’s People + AI Research team to review the new edition of the People + AI Guidebook - a set of methods, best practices,...
The AI Readiness Canvas and Workshop + Voltage Control
My AI Readiness Canvas and Workshop was inspired by the many conversations I’ve had with CEOs who are excited about the potential of AI. Sometimes, I leave these moments feeling like I’ve just…
Machine Learning Scientist with Python Track | DataCamp
Master the essential skills to land a job as a machine learning scientist! You'll augment your Python programming skill set with the toolbox to perform supervised, unsupervised, and deep learning. You'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. In the process, you'll get an introduction to natural language processing, image processing, and popular libraries such as Spark and Keras.
Discover the awesome power of creative AI, and learn what machine learning is and how it works with this online course from UAL Creative Computing Institute.
Transforming the User Experience through Artificial Intelligence | Stanford Online
How do we design AI systems that augment and empower people? This course connects human-computer interaction (HCI), the multidisciplinary field that focuses on designing interactions between humans and technology, to the transformative effects of AI so that you can better serve your customers and drive your company forward.
You’ll learn to make informed decisions on how and when our company should be designing smart and AI-based products to change how you work, learn and communicate.
MOOC: AI in Practice - Applying AI | TU Delft Online
Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner.
Introduction to Creative AI - Online Course - FutureLearn
Discover how AI is transforming the creative industries with this online creative computing course from UAL offering practical examples and skills guides.
Cassie Kozyrkov: The AI safety mindset: 12 rules for a safer AI future
As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov guides teams in data-driven decision process and AI strategy. She is the innovator behind bringing the practice of Decision Intelligence to Google, personally training over 15,000 Googlers. In this video, Cassie provide tips for staying in the “AI safety mindset”. Kozyrkov cautions, “the objective is always subjective”: exactly what you want the AI to learn will depend on exactly what you’re using it for.
From Daniel Leufer and AI Myths team (Mozilla Fellowshop Project) | With every genuine advance in the field of ‘artificial intelligence,’ we see a parallel increase in hype, myths, misconceptions and inaccuracies. These misunderstandings contribute to the opacity of AI systems, rendering them magical, inscrutable and inaccessible in the eyes of the public.