2021 Predictions: ML Automation, K-anonymity, Hyperautomation, Semantic Graphs 

 2021 Predictions: ML Automation, K-anonymity, Hyperautomation, Semantic Graphs 

[Ed. Note: We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.] 

Florian Douetteau, CEO and co-founder of Dataiku

From Florian Douetteau, CEO and co-founder of Dataiku: 

Inclusive engineering will begin to make its way into the mainstream to support diversity. In order to ensure diversity is baked into their AI plans, companies must also commit the time and resources to practice inclusive engineering. This includes, but certainly isn’t limited to, doing whatever it takes to collect and use diverse datasets. This will help companies to create an experience that welcomes more people to the field — looking at everything from education to hiring practices.  

There will be more of an organizational commitment to putting humans and diversity at the center of AI development. Companies will look to include people who are representative of those who will use the algorithms if they want to truly reduce bias and foster diversity. While most training datasets have been developed against a small percentage of the population, companies will now look to consider expanding their scope to design training datasets that are all-inclusive. The more inclusive the group building the AI and the datasets, the less the risk for bias. 

AI experimentation will become more strategic. Experimentation takes place throughout the entire model development process – usually every important decision or assumption comes with at least some experiment or previous research to justify those decisions. Experimentation can take many shapes, from building full-fledged predictive ML models to doing statistical tests or charting data. Trying all combinations of every possible hyperparameter, feature handling, etc., quickly becomes untraceable. Therefore, we’ll begin to see organizations define a time and/or computation budget for experiments as well as an acceptability threshold for usefulness of the model. 

 

Ryohei Fujimaki, Ph.D., Founder & CEO of dotData

From Ryohei Fujimaki, Ph.D., Founder & CEO of dotData:  

AI Automation will Accelerate Digital Transformation Initiatives:  “While the first wave of digital transformation focused on the digitization of products and services, the second wave – and what we will begin to see much more of in the coming year – will focus on using AI to optimize organizational efficiencies, generate deeper data-driven insights, and automate intelligent business decision-making. One of the key reasons that this is happening now is the availability of AI and ML automation platforms that make it possible for organizations to implement AI quickly and easily without investing in a data science team.” 

More AI in BI: “As organizations face increased pressure to optimize their

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Source - Continue Reading: https://www.aitrends.com/2021-ai-predictions/2021-predictions-ml-automation-k-anonymity-hyperautomation-semantic-graphs/

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