5 myths about industrial AI

Many rumors about AI are swirling around the enterprise. Here are the five most common myths, and the truth behind them.

How to use intelligent technology to maximize business value At the 2019 SAP SAPPHIRE NOW conference, Darwin Deano spoke with TechRepublic about how organizations can prepare and make the most of intelligent technologies.

Enterprise adoption of artificial intelligence (AI) has grown by more than 270% over the past four years, according to a Gartner report. Since AI has the capability to speed up business process and generate greater returns on investment (ROI), some 37% of organizations have now fully embraced the technology, the report found.

SEE: Special report: Managing AI and ML in the enterprise (free PDF) (TechRepublic)

As AI use cases have increased, many misconceptions have surfaced around the new technology. To help shed light on the conflicting viewpoints of AI, Teradata practice director Cheryl Wiebe shared the five most popular myths about industrial AI, and the truth behind each.

Here's what she found:

1. In goes data, out comes intelligence

Many organizations assume that tools like data analytics are able to immediately give organizations the exact answers they are looking for, according to Wiebe. Cognitive AI tools are able to produce answers from data, but only of the system is given the correct amount of context.

For example, most AI isn't able to provide predictive maintenance on systems it isn't given some form of background information to work from, Wiebe said. If an organization wants to use AI to find out which equipment will fail next, the AI would need information such as equipment components, previous uses, and other relevant information, Wiebe noted.

2. AI allows organizations to cut corners

This is a common misconception, said Wiebe, but AI actually often requires organizations to take more steps. Executing data processes with AI requires even more engineering and computational power. AI demands its own manual processes, often through the process of labelling.

The more frequently AI is used, the more manual labelling must occur, as the data must be labeled before it is fed to the AI, Wiebe said. AI expedites many manual processes, but also creates some new ones.

3. AI will replace workers

People have long feared AI eliminating their jobs, but this is a large misconception. AI will only replace small, simple tasks—not entire job titles, said Wiebe. Freeing people from these small tasks will actually give them more time to work on valuable projects.

4. AI can tell you why something has occurred

Some of the most current AI systems are able to provide a path to its reasoning, but the technology isn't able to create direct causal links to results, Wiebe said. For example, AI can show you that a machine is broken, as well as ways to fix it, but not necessarily why the machine broke in the first place.

5. AI is psychic

While AI is revolutionary, it is not able to predict the future, Wiebe said. At the moment, AI is only able to confirm facts, not make predictions. If an organization wants the AI system to make educated guesses on situations, then the company must provide a load of data points. Even so, the system can still only guess, not predict.

For more, check out TechRepublic's article on How to implement AI and machine learning.

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Image: iStockphoto/gorodenkoff

By Macy Bayern

Macy Bayern is an Associate Staff Writer for TechRepublic. A recent graduate from the University of Texas at Austin's Liberal Arts Honors Program, Macy covers tech news and trends.