Our AI writing assistant, WriteUp, can assist you in easily writing any text. Click here to experience its capabilities.

The AI Hype Cycle Is Distracting Companies

View Original View Raw

Summary

This article discusses the AI hype cycle and how it can be distracting for companies looking to use machine learning (ML) for practical applications. It argues that ML should be referred to as ML rather than AI, as the term "AI" oversells and misleads, contributing to a high failure rate for ML business deployments. All ML initiatives do not qualify as "AI" as it alludes to human-level capabilities which most ML projects lack. The article discusses the difficulty of defining "AI" and how it has become a research challenge unto itself. In order to overcome this definition dilemma, the article suggests that "AI" should be defined as artificial general intelligence (AGI) which is software capable of any intellectual task humans can do. Finally, the article argues that calling ML tools "AI" oversells what most ML business deployments actually do and encourages companies to focus on the precise way ML will improve business operations.

Q&As

What is the purpose of most practical ML projects?
The purpose of most practical ML projects is to improve the efficiencies of existing business operations.

What is the AI Effect?
The AI Effect is the paradox that, once a computer can do something, it is no longer seen as intelligent.

How can ML projects achieve success?
ML projects can achieve success by keeping their concrete operational objective front and center.

What is artificial general intelligence?
Artificial general intelligence (AGI) is software capable of any intellectual task humans can do.

Why is it problematic to call ML projects “AI”?
It is problematic to call ML projects “AI” because it oversells what most ML business deployments actually do and inflates expectations, contributing to a high failure rate for ML business deployments.

AI Comments

👍 This article provides a clear-cut and informative explanation of the difference between AI and ML and how the hype around them can be misleading.

👎 The article is quite long and may be difficult for readers to digest in one go.

AI Discussion

Me: It's about how the AI hype cycle is distracting companies from understanding how they can use machine learning to improve their business operations. The article argues that calling ML tools "AI" oversells their capabilities and can lead to a high failure rate for ML business deployments.

Friend: That's interesting. What are the implications of this article?

Me: Well, the article suggests that companies should focus on the concrete operational objectives of ML projects rather than getting caught up in the hype of AI. It also suggests that companies should be more aware of the “AI” buzzword and how it can be deceptive in selling ML tools. Additionally, the article reminds us that AI is an elusive concept to define and that it often leads to unrealistic expectations for ML projects.

Action items

Educate yourself on the differences between ML and AI and be sure to use the correct terminology when discussing them.
When discussing ML projects, focus on the concrete operational objectives and how ML will render business processes more effective.
When evaluating ML projects, be sure to set realistic expectations and avoid overselling the capabilities of the technology.

Technical terms

AI (Artificial Intelligence): Artificial intelligence (AI) is a broad term used to describe a computer system that can learn and adapt to its environment, and perform tasks that normally require human intelligence.
ML (Machine Learning): Machine learning is a type of artificial intelligence that enables computers to learn from data and make predictions without being explicitly programmed.
AGI (Artificial General Intelligence): Artificial general intelligence (AGI) is a term used to describe a computer system that is capable of performing any intellectual task that a human can do.
NLP (Natural Language Processing): Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages.
Computer Vision: Computer vision is a field of computer science that focuses on enabling computers to see, interpret, and understand the visual world.
Turing Test: The Turing Test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.