Machine learning for front end users and AI

Write about... “What are your tech predictions for 2016? #tech2016”

I think things will take a shift in automation, machine learning and enhancements to AI (Artificial Intelligence).  Tech companies are not (if they aren't already) investing in computer science engineers, HCI PhDs, and other big data scientists to figure out the following:

1. Making search more intuitive based on patterns or habits--stuff that can already be "logged" with virtual assistants like Siri or Cortana.  Based on where you work and past requests, they will in their next builds be able to suggest more. 

2. More AI will be built into DAMs.  Since I work in digital archives and have worked with a lot of DAMs (Digital Asset Management) tools, I predict that engineers will start building more sophisticated search functions addressing the need for more "relevant" search results. Suggested metadata also will be built into DAMs more and more making it easy for asset managers to do the final selection of key metadata.

3. "Machine learning" built into DAMs. While most machine learning is now done with computer language, there may be DAMs evoloving to create "front end" machine learning tools--which help these sophisticated information retrieval systems--without the need to write a script. User end machine learning functions will develop.  Why? Because not a lot of users know how to code. So, it's better for software engineers to build front end machine learning tools for an average user.  With more average users "teaching" machines/applications how to think--for instance, the ability to correct an AI's suggested tags so that it learns a different set--the system will become stronger. AI needs human input.  Sadly, this human input is coming from specialists.  However, if simple add-ons and tools can be built without having to code, your typical user will actually have "fun" correcting an AI system. Overall, it will enhance its knowledge.