In this course, you will explore the impact of generative artificial intelligence (AI) on society, the workforce, organizations, and the environment.
This course is suitable for anyone interested in learning about the ethical, economic, and social implications of generative AI and how generative AI can be used responsibly. It will benefit professionals, executives, policymakers, and students.
In this course, you will learn about the ethical concerns of generative AI, including data privacy, biases, copyright infringement, and hallucination. You will identify the misuses related to generative AI, including deepfakes.
Further, in the course, you will examine the considerations for the responsible use of generative AI. You will explore the broader implications of generative AI on transparency, accountability, privacy, and safety. Finally, you will learn about the socioeconomic impacts of generative AI.
The examples and cases included in the course help to realize the considerations for generative AI in real-life scenarios. You will hear from practitioners about the realities, limitations, and ethical considerations of generative AI.
This module delves into the various limitations, concerns, and ethical issues associated with generative AI. You will gain insights into the limitations related to training data and the lack of accuracy, explainability, and interpretability. You will learn about various ethical issues and concerns…
In this module, you will examine the importance and considerations for responsible development and use of generative AI. You will discover the perspective of different key players, including IBM, about the ethical use of AI. You will also understand how corporations can use generative AI beyond the…
The module includes a final project based on the concepts covered in the course. The module also includes a glossary to enhance comprehension of generative AI-related terms. This module includes a graded quiz to test and reinforce your understanding of concepts covered in the course. Finally, the m…
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IBM is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (84/100).
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
Instructor: Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This course is an introduction to mathematical modeling of computational problems, as…
This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow;…
View the complete course: http://ocw.mit.edu/6-046JS15 Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch 6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms. License: Creative Commons BY-NC-SA More information at http://ocw.mit.…
View the complete course: http://ocw.mit.edu/6-851S12 Instructor: Erik Demaine Videos of 6.851 Lectures Spring 2012 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
