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Weekly AI Digest: Debunking AI Myths & Cutting Through the Hype

As I look through the news each week, it really amazes me how often I see the term Artificial Intelligence misused or applied in ways that aren’t really realistic based on our current understanding. Some misappropriation is understandable, given the term itself is quite broad and we’re only just starting to understand how the technology can be used. However, overhyping AI is a very common theme in a large portion of the news I read.

Last week Gartner posted a great article that is worth taking the 3 minutes to read. It identifies and debunks the top 5 AI myths, some which may surprise you. The first myth that Gartner lists I found particularly relevant:

 

Myth 1: Buy an AI to solve your problems

Reality: There is no such thing as “an AI.” Enterprises don’t need an “AI.” They need business results in which AI technologies may play a role.

 

“AI is a collection of technologies that can be used in applications, systems and solutions to add specific functional capabilities. Organizations should select best-fit, best-of-breed AI technologies to meet targeted business needs,” says Alexander Linden, research vice president at Gartner.

 

This is a myth that we often have to debunk here at Yseop. There are many products available today that use AI to help with data analysis, collection, customer relationship management, and so on. You have to be careful when it comes to talking about AI. Don’t let the hype confuse you when it comes to identifying how AI can be applied and what it takes to embed and implement the technology to fulfill a particular goal or project. Take for example chatbots. There are many forms of AI technology you might need in order to make a chatbot successful:

  1. Voice or text recognition (Natural Language Understanding)
  2. Sentiment analysis
  3. Machine learning
  4. Text generation (Natural Language Generation)

So, when you read an article talking about chatbots, you need to keep in mind there are many pieces of AI technology that have to come together to make it all work. Basically, it’s not as simple as one might be lead to believe which is a good skepticism to have when debunking AI myths. It’s especially important to understand these nuances when you’re looking to purchase a new AI technology. Make sure to ask questions about integration, security, and UI’s to make sure you understand the full picture.