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RapidMiner 2021 Predictions: Top 6 predictions for artificial intelligence in 2021

vmblog 2021 prediction series 

Industry executives and experts share their predictions for 2021.  Read them in this 13th annual VMblog.com series exclusive.

Top 6 predictions for artificial intelligence in 2021

By Ingo Mierswa, PhD., Founder & CTO, RapidMiner

One of the best parts of working at RapidMiner is that we get to interact with people from all over the world, in all kinds of enterprises, from manufacturing to academia, and learn what they're doing with machine learning and artificial intelligence. This puts me in a unique-and very fun!-position to think about the direction that the field as a whole is going in the future.

To find out what people are thinking about as 2020 comes to a close, I surveyed a number of RapidMiner employees who work on the front lines of model training and deployment to put together what they think are the top six trends that will take shape in 2021.

ONE. Edge computing and new AI chips will drive change

AI (predictive algorithms paired with automation) will continue to empower technology-oriented startups to reinvent old industries and adapt them for a vastly changed and quickly changing world in the wake of COVID-19. These reinventions will be driven by developments in edge computing-where algorithms operate close to the front lines, rather than in the cloud-as well as a boom of AI-enabled chips. With these developments, we'll see further adoptions of AI based around natural language processing (NLP) as well as image mining use-cases in industries where such data is easily collected (such as plant floors and driverless cars, for example).

TWO. Resiliency will beat out accuracy

The COVID-19 pandemic as underscored just how sensitive our models are to large scale changes in the world (read: human behavior). It's become clear that rather evaluating our models solely based on data science measures like accuracy, we need to evaluate their resilience-how well they can still make good predictions even if their input data changes.

THREE. Models will become more explainable and understandable

Easy-to-understand model predictions and summaries (for example, automatically generated text explanations of what a model recommends) will further empower non-data scientists to harness the power of machine learning and artificial intelligence. This will create a flywheel of model development and adoption-as models are better understood, they'll be more likely to be implemented. This will demonstrate their value, in turn leading for a demand for more models.

FOUR. Bias will continue to be in focus-but maybe not how you think

Bias in machine learning has been extensively talked about over the last few years, and that will certainly continue in 2021. But we see two possible paths here, depending on the extent to which organizations adopt explainable and understandable models. If model understandability and explicability are adopted, as we think it will be, there's a real opportunity to better understand when models are biased, why it's happening, and be able to fix it. This is a big win for everyone involved.

However, if model explainability is not widely adopted, we see the growth of a potential disconnect between machine learning experts, who understand the decisions that models are making, and thought leaders and businesspeople on the other hand who don't. Unable to understand how the models are working, these leaders will bow to public opinion and refuse to implement any model that could be biased. This would have a huge negative impact on the ability of AI to support business initiatives.

FIVE. More models will be developed and deployed automatically

As machine learning technology improves, as does the desire to implement it into more areas of business, organizations are going to continue to come across problems related to having a lack of data scientists. Because of this, new technologies that allow anyone with basic computer skills to load in data, let a computer automatically develop a model with very little oversight, and then deploy it, are likely to become more and more popular.

SIX. Simplicity over complexity

Machine learning and artificial intelligence are fantastic tools to solve business problems. But sometimes, the desire to use the latest and greatest model or algorithm can be a detriment. In the coming year, we think you'll see companies doubling down on the least complex viable solution (LCVS) to solve their business problems.

Wrapping up

There you have it-what I think will be the biggest trends taking place in the machine learning and artificial intelligence world in the year that's coming. But if 2021 is anything like 2020-well, we'll have to see how things shape up!

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About the Author

Ingo Mierswa 

Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Mierswa, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. He is responsible for strategic innovation and deals with all big picture questions around RapidMiner's technologies. Under his leadership RapidMiner has grown up to 300% per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the US as well as the UK and Hungary. After two rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world's best team to RapidMiner.

Published Friday, December 18, 2020 7:29 AM by David Marshall
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