8 Most Controversial Terms in AI, Explained

8 Most Controversial Terms in AI, Explained

This course is part of Harnessing AI for Business Impact Specialization

Instructor: Philip - AI Explained

Access provided by Coursera Learning Team

8 modules

Gain insight into a topic and learn the fundamentals.

4.8

(29 reviews)

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

4.8

(29 reviews)

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Harnessing AI for Business Impact Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 8 modules in this course

This is aimed at absolutely anyone interested in AI, from c-suite and software engineers to curious parents and engaged amateurs. No technical background is necessary, as no matter your background you will leave much better informed about key (and controversial) terms in AI.

You will learn about 8 of the key terms used in AI - which are bandied around as if everyone should know them. Each term comes with complex and storied histories and implications, relevant to both practitioners and interested onlookers. What does 'stochastic parrot' actually mean, and are LLMs merely parrots? Do language models actually 'reason' and has the idea of 'emergent behaviors' been debunked already? Why is the term 'open source' being replaced with 'open weights' and what on earth is meant by 'artificial general intelligence'? I challenge anyone to leave this short 3-5 hour course without a much more nuanced understanding of the key debates in AI.

Learn about a controversial label for LLMs, whether it holds up, and what on earth parrots have to do with it.

What's included

1 video1 assignment1 plugin

Deciding if you think models display emergent behaviors could shape your perspective on AI, so take the time to learn why this term is so controversial.

What's included

2 videos3 assignments

In some quarters, the most controversial term of them all. Here, we mainly focus on the difference between open source and open weights - a key distinction!

What's included

1 video1 assignment1 plugin

You won’t (I hope) be bitter after learning about the bitter lesson - a seminal insight onto what works best in AI, and why.

What's included

1 video2 assignments

Now we’re getting deep. Reflecting on whether models actually ‘reason’ - and what that word actually means - or just pattern-match, is crucial for anyone interested in using AI.

What's included

1 video1 assignment1 plugin

It’s easy to ‘know’ that models hallucinate and are biased, but actually exploring demonstrations on how they are, and how to minimise that, is a whole other matter - a matter we discuss in this deep dive!

What's included

1 video2 assignments

‘Takeoff' and 'takeoff speeds' are terms worth learning about, even if they are deeply theoretical, and even if you disagree with the entire notion of a takeoff. The fact that these terms have led to debate even at CEO-level is enough to make it worth grasping their meaning, whichever 'side' you are on.

What's included

1 video1 assignment1 plugin

Perhaps the most controversial term of them all. Whatever your stance is, knowing the origins, nuances and key positions on this term will serve you well.