What it means to be "AI-first"

Artificial intelligence has become a buzzword in the tech industry. Companies are eager to present themselves as “AI-first” and use the terms “AI,” “machine learning,” and “deep learning” abundantly in their web and marketing copy.

What are the effects of the current hype surrounding AI? Is it just misleading consumers and end-users or is it also affecting investors and regulators? How is it shaping the mindset for creating products and services? How is the merging of scientific research and commercial product development feeding into the hype?

These are some of the questions that Richard Heimann, Chief AI Officer at Cybraics, answers in his new book Doing AI. Heimann’s main message is that when AI itself becomes our goal, we lose sight of all the important problems we must solve. And by extension, we draw the wrong conclusions and make the wrong decisions.

Machine learning, deep learning, and all other technologies that fit under the umbrella term “AI” should be considered only after you have well-defined goals and problems, Heimann argues. And this is why being AI-first means doing AI last.

Read the full article on TechTalks.

For more AI book reviews:

• The case for human-centered AI

• Why lifeless AI is not intelligent

• What AI researchers can learn from the self-assembling brain

• The challenges of applied machine learning