Dots
60 LEADERS ON AI-COVER-FINAL2.png
60L-FINAL-COVER.jpg

'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.

ABOUT 60 LEADERS

'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders. 

DOWNLOAD (PDF 256 pages)

'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.

DOWNLOAD (PDF 286 pages)

What is the academic curriculum for a successful career in AI?

Cynthia Rudin

Q.

I generally recommend that before students take a machine learning course, they should know how to program, and they should have taken somewhat advanced courses in probability and linear algebra. Many professors post their course notes and lecture videos online (as I do), so I would recommend trying to watch those to gain more knowledge.

The best way (in my view) to learn Machine Learning after taking a course is to work on a real problem. There is a lot to learn because real data science is messy: it takes effort to specify the goals correctly (based on what the domain experts say) and to collect and clean the data. It is common to find that the problem has been formulated in a problematic way and needs to be fixed. Important pieces of advice are:

1. Keep in mind that Neural Networks are excellent for computer vision, but they don't tend to give an advantage on many other kinds of data. Thus, it is worthwhile to learn other techniques besides Neural Networks, as they can be difficult to train, and are often black boxes.

2. Interpretable Machine Learning is critical for fairness and trust, but it is a topic that is rarely taught in courses.

3. Ethics are important. Data scientists should evaluate the ethics of the project themselves, not just do what their manager tells them! Ethics is another topic rarely taught in data science courses.

Cynthia Rudin is a professor at Duke University. Her goal is to design predictive models that are understandable to humans. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (the "Nobel Prize of AI").

LI-In-Bug.png

Cynthia Rudin

"Interpretable Machine Learning is critical for fairness and trust."

Professor of Computer Science

MEET THE LEADERS #

ARTIFICIAL INTELLIGENCE

linkedin.jpg

Created by many, offered to all. Help us reach more people!

facebook.jpg
twitter.jpg