There are a ton of resources out there that claim to help you better understand AI. But not all of them are accurate, reliable, or particularly helpful.
We ran into this problem all the time when we started our learning journey.
We wasted days and weeks reading the same articles that had inaccurate or incomplete information. And it took us a long time to sort the signal from the noise.
That’s the bad news.
The good news is that you don’t have to make the same mistakes we did. This list of articles—updated regularly—contains a carefully curated list of material that we found most helpful to quickly understand AI the right way.
1. The Guide to Artificial Intelligence Terminology
(Mike Kaput, Marketing AI Institute)
Artificial intelligence is really an umbrella term that encompasses dozens of technologies—and you’ll hear a lot about them all as you learn more about AI. You don’t need to know everything about each bit of AI terminology, but it helps to have a passing familiarity with key words and phrases. Our guide to AI terminology should help you out.
2. Is This AI?
(Karen Hao, MIT Technology Review)
Now that you have a basic understanding of AI, you’ll likely start seeing it everywhere. But not everything people call AI is actually a true AI technology.
MIT Technology Review’s AI reporter, Karen Hao, has created a brilliant flowchart that walks you through the rules to determine whether or not something is actually AI.
The author, Karen Hao, spoke at MAICON 2019. You can watch her keynote, "What is AI?" here.
3. What Is Machine Learning?
(Karen Hao, MIT Technology Review)
OK, so you understand AI and what is/isn’t AI a bit better. There’s one more fundamental piece to quickly learning about before diving into AI for marketing:
Machine learning.
Machine learning is a fundamental technology that drives the ability of AI to make predictions and recommendations. It’s what gives AI its superpowers. Thankfully, Karen Hao also has a simple flowchart to understand this concept, too. Check it out below.
4. Top 25 Use Cases for Marketing in Artificial Intelligence
(Paul Roetzer, Marketing AI Institute)
How much will AI impact your job as a marketer in the next two years? Probably way more than you think. Find out how in this post with the top 25 use cases for AI in marketing today.
5. Become a Marketing AI Pioneer
(Paul Roetzer, Marketing AI Institute)
Whoa, you’ve only read a few articles—and now it’s time to become a pioneer in marketing AI? Well, actually, yes!
You see, the marketing industry’s adoption of AI is in its infancy. It’s still very early days.
If you’ve gone through the resources above, you’re already well on your way to understanding AI better than most other marketers.
The post below takes you one step further, into the realm of becoming an AI pioneer. (Hint: It’s not as hard as you think.)
The article will show you what’s possible in your company and career, as you work through the other in-depth and/or more unstructured resources in this guide.
6. Demis Hassabis on AI's Potential
(Demis Hassabis, The Economist)
Demis Hassabis, one of the top minds in AI today, describes the massive potential that AI has for businesses and the world, calling it "the most important technology ever invented." Learn why in this article.
7. 13 Mind-Blowing Things Artificial Intelligence Can Do Today
(Bernard Marr, Forbes)
Business expert Bernard Marr breaks down 13 incredible use cases that AI can already do today, showing you exactly what's possible with this technology.
8. The Next Word
(John Seabrook, The New Yorker)
Want to see just how crazy the future of business and marketing will get thanks to AI? This article deep dives into how machines are increasingly being used to predict what we'll write and say next.
9. Dealing With Bias in Artificial Intelligence
(Craig S. Smith, The New York Times)
Social bias can be reflected and amplified by artificial intelligence in dangerous ways, whether it be in deciding who gets a bank loan or who gets surveilled. The New York Times spoke with three prominent women in A.I. to hear how they approach bias in this powerful technology: Daphne Koller, co-founder of Coursera and the founder and chief executive of Insitro, Olga Russakovsky, assistant professor in the Department of Computer Science at Princeton University, and Timnit Gebru, research scientist and AI ethical team member at Google and co-founder of Black in AI.
10. A debate between AI experts shows a battle over the technology’s future
(Karen Hao, MIT Technology Review)
Two prominent figures in artificial intelligence, Gary Marcus, professor emeritus at NYU and the founder and CEO of Robust.AI, and Danny Lange, the vice president of AI and machine learning at Unity, debate how the field of AI might overcome issues such as continual bias, fragility in the face of attacks, and its inability to adapt to changing environments.
11. Accelerating AI impact by taming the data beast
(Anusha Dhasarathy, Ankur Ghia, Sian Griffiths, and Rob Wavra, McKinsey & Company)
Most government agencies around the world do not yet have all of the building blocks of successful AI programs, especially when it comes to data. How can governments get past pilots and proofs-of-concept to achieve broader results? A five-step, mission-based data strategy for AI programs can help sidestep these challenges.
12. The Panopticon Is Already Here
(Ross Andersen, The Atlantic)
Xi Jinping is using artificial intelligence to enhance his government’s totalitarian control—and he’s exporting this technology to regimes around the globe.
13. Coronavirus Will Finally Give Artificial Intelligence Its Moment
(Tae Kim, Bloomberg)
Changes in demand caused by the pandemic point to a bright future for AI. Advances in AI catalyzed by the coronavirus may be one of the silver linings we remember from 2020.
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