AIIM survey: leveraging deep learning and machine learning capabilities

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This survey indicates some of the immediate impacts of AI and ML on the way we do business and where organizations currently stand in regards to these initiatives.

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During the course of this eBook, we have quoted excerpts from the Artificial Intelligence module in AIIM’s Emerging Technologies in Information Management course, created by Alan Pelz-Sharpe, and noted in this eBook as “AIIM Emerging Tech.”

We strongly recommend that those who are new to Machine Learning and Deep Learning concepts take this course; it provides a solid grounding in core concepts.

Artificial Intelligence (AI) — and its sidekicks “Deep Learning” and “Machine Learning” — are obviously all the rage. As a result, just about every technology product in the world now seems to have the artificial intelligence “label” attached to it.

Which is ironic, because AI has actually been with us for decades, not months. People have been thinking about the relationship between people and machines going all the way back to ancient times, and process automation goes back to the early 20th century. Frank Chen, a partner at Andreessen Horowitz, does a great job discussing the origins of modern AI in AI, Machine Learning, and Deep Learning: A Primer. He notes that in the Summer of 1956, a group of computer scientists came together as the Dartmouth Summer Research Project on Artificial Intelligence to program computers to behave like humans.

“Expert systems” — a form of AI — emerged in the late 70s and really took off in the 80s. Expert systems allowed a person to create encoded rulesets to automate specific tasks. This core focus of AI — understanding exactly how something is done, turning these steps and actions into rules, and computerising them — governed the discipline until recently. So what’s so different now about AI, and how does it need to be incorporated into your thinking about process automation?

All along, a different variation of Artificial Intelligence — based on emulating the human brain and how it learns, or Machine Learning — co-existed with this rule-based school of AI. But optimising machine learning struggled for many years from a lack of computing power, a lack of data, and a lack of resources. Obviously, all of that has now changed.

In this eBook, we look at four key questions related to Machine Learning and Deep Learning:

  1. Where do organisations currently stand with regards to their Machine Learning and Deep Learning initiatives? Is the interest real or hype?
  2. What kinds of processes will be the initial target for Machine Learning capabilities?
  3. What do organisations see as the primary drivers for a Machine Learning initiative? What do they see as the primary obstacles?
  4. What spending plans do organisations have for some of the key IIM technologies supporting Machine Learning: 1) Multi-channel intelligent capture; 2) Content analytics and semantics; 3) Data recognition, extraction & standardisation; 4) PII identification and protection; and 5) Robotic Process Automation (RPA)?

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Managing your documents in a digital focused era can be tough. According to a new AIIM research report, for 81% of organisations, deep learning and machine learning are key to their future technology and business planning. Leveraging machine learning to find patterns in your data will allow you to automate & streamline your current business operations.

In a joint Survey with AIIM, the non-profit association dedicated to nurturing, growing and supporting the information management community, we uncovered where organisations currently stand in regards to their Machine Learning and Deep Learning initiatives.