Three questions to separate AI from the hype

Vendors around the world claim that their tools include some form of artificial intelligence or machine learning. Here are three questions to ask to separate the technology from the marketing hype.

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One of the many challenges of being a modern technology leader is separating marketing hype from reality when it comes to acquiring new hardware or software. Product marketing often tends towards hyperbole and focuses on the positive rather than the negative. With tech products, there’s the added wrinkle of complex technical elements that require specialist understanding.

Mix the historic hyperventilation of most marketing products with trending technology, and you’re forced to wallow through a dense wall of promises, buzzwords, and assertions to determine if a product will work for your organization. . This is especially true in the age of artificial intelligence, where it seems like everything from supply chain software to office furniture claims to have an AI element built in. One could almost imagine a late-night infomercial host shouting that the product he shills: “Now includes 30% more machine learning!”

SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)

The problem with evaluating products that include AI is that definitions of what constitutes AI can vary widely. If your definition assumes learning algorithms that intelligently categorize new data, and your vendor considers AI to include little more than a bit of sophisticated computation, you’ll be disappointed. In order to determine what your vendor means when they tell you that AI elements are included in their product, here are three simple questions that can help separate the hype from the reality.

How does the AI ​​model learn?

A fundamental element of most true AI technologies is that they improve based on the data they receive or include technologies that test potential future outcomes and strengthen their calculations based on those results. Gaming AIs are a classic example of this technology, where the AI ​​can simulate playing thousands of iterations of a game and improves its performance based on the outcome of each game.

Ask your vendor for details on how the AI ​​learns and improves. What data does it use? Does it simulate potential scenarios and use them to learn? How many simulations can it run? By asking these types of questions, you may quickly discover that the vendor’s touted “AI-based learning” is actually running some basic calculations on your existing dataset rather than actually adjusting its algorithms based on a learning ability.

How is AI monitored and adjusted?

Real-world AI systems adapt their predictions based on a combination of the inputs they receive and their ability to run different simulations to test potential outcomes. As such, the AI ​​will need to be monitored and possibly retrained or have additional input data.

Asking your vendor how the AI ​​is monitored and tuned will tell if their product actually contains some degree of intelligence compared to some fancy standard algorithms. Suppose your supplier claims that no monitoring or adjustment will ever be necessary. In this case, you can safely assume that AI is marketing hyperbole rather than a technology embedded and beneficial in the product under consideration.

Do you share customer data to train AI?

Another essential question to ask your suppliers actually covers two essential topics. First, it helps to know if your data is mixed with data from other customers to train AI in a product. This may or may not be beneficial. For example, if you’re considering a supply chain management solution, allowing your data to inform AI in exchange for the benefit of other companies’ data could be an interesting exchange, because a data set more extensive should make the product more effective. Conversely, if you are working with unique and very specific data, the fact that the AI ​​is influenced by other data can be a handicap.

This question should also lead your vendor to explain how customer data informs and enhances AI. Suppose they don’t have an answer to this question, or mention that no customer data actually impacts the AI’s ability to make predictions. In this case, it is a likely indicator that the product in question does not in fact include any suitable AI technology.

It’s easy to tout “AI Inside” as an advantage for a tech tool. However, the imprecise definition of artificial intelligence makes the job of a technology leader difficult. Using these questions to determine how well AI powers your technology can be a strong differentiator when selecting a technology.

About Marilyn Perkins

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