This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.
- 5 stars72.09%
- 4 stars21.72%
- 3 stars4.44%
- 2 stars0.74%
- 1 star0.98%
BIG DATA, ARTIFICIAL INTELLIGENCE, AND ETHICS からの人気レビュー
Volume and speed of recordings are not as good. Needed to follow text most of the time, except for Dr Hilbert.
It's very good course!. it is equipped with basic information about big data and AI that for common person (never learnt about AI before) like me is easily to understand.
Excellent course on How the Big Data, AI and ML technology plays a big role in developing the world. Thanks to all the professors who made it even easier to understand the subject with clarity.
I was expecting more about the ethics side of AI, from the critical point of view. Besides, some quiz questions were not clear enough. But anyway, I learnt a lot of things.
What do students say after completion?
Since this Specialization is a collective effort from all UC campuses, who teaches it?