Processing the Difference Between AI and Machine Learning

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It’s one of the bugbears of vendors who might have a genuinely artificially intelligent product that so many of their rivals claim the same for inferior technology. Likewise, the use of AI or Machine Learning is employed interchangeably without due diligence for its veracity. Putting AI/ML on a press release is often lazy marketing.

https://amplify.nabshow.com/articles/processing-the-differences-between-ai-and-machine-learning/

Emily Yale, Principal Data Scientist at F5 Shape Security, is frustrated too.

“This vagueness is the fundamental problem with simply describing your product as ‘using AI.’ ” she argues in a column for Information Week. “You haven’t told anything about what the product is actually doing, why it qualifies as AI, and how they should evaluate it.”

She attempts to clarify. AI is a broad field that aims to bring human intelligence to machines, while ML is a subset of AI that focuses on learning from data without explicit programming. Use of ML does qualify as use of AI, but use of AI does not imply use of ML.

Yale also supplies some questions for prospective buyers — and urges them to ask them: “What are the components of the AI system? Why do they warrant classification as AI? How are they established, tested, and updated? If these kinds of details can’t be provided or seem thin and vague, be wary of snake oil and signatures repackaged as ‘AI.’”

She calls out vendors not being accurate with the truth.

“If your AI system is AI only because it is using ML, stop diluting its description by calling it an AI system and call it an ML system instead.”

Likewise, there are specific questions to ask about systems described as ML: “How is the data populated, labeled (if at all), and updated? What type of models are being used and how are they trained? What output do they produce, and how can that be tailored to specific performance goals and risk tolerances?”

Using the right language is a critical step forward in navigating the buzzword hype around AI and ML. If AI is the right term to go in a product description, then use it, Yale urges, but be prepared to justify why it is warranted and accurate. If a product description is better served with ML instead, then ditch AI and be precise.

Her broader point applies to any product description caught up in meaningless marketing speak which does no good for the seller or the buyer.

“Product descriptions should cue buyers on what they need to ask in order to understand if a purchase is the right fit for them, and they should enable sellers to easily articulate the product’s strengths and use of technology.”