Sunday, December 5, 2021

Microsoft Aims to Turn App Users Into Programmers With NLP AI

Microsoft recently held its annual Ignite conference, demonstrating why Microsoft is now the most valuable company globally

From the production quality of the event to how it showcased some impressive work on diversity and inclusion (D&I), this was one of the most powerful events so far this year. But the part I want to focus on is Microsoft’s effort to use natural language processing (NLP) to turn anyone into a coder and how this could evolve in the future to massively change the effectiveness of collaboration. 

This user capability reminded me of when I first started working in tech. PCs were new, but using software like Microsoft Office, we could build the tools we needed, mainly in Excel or Lotus Notes, that did what we needed to be done without involving management information services (MIS), which changed to IT. Working with MIS back then was a nightmare, as they rarely understood what you wanted. After months of work and thousands of dollars, they would kick back an only marginally better application than doing things manually. PCs were a godsend.   

They were also a huge problem, because these PC efforts often resulted in unsecure solutions with internal accuracy problems and applications that were tied to specific users. If the user got sick or left, you were out of luck, because we didn’t write manuals for our homegrown tools. But it was the only way we could get things done in a timely way, and some people still use Excel for much the same reason today.  

Using NLP AI for programing

Natural language processing may not sound like much, but it is arguably a more significant change to the man-machine interface than graphical user interfaces (GUI) were. 

Microsoft’s concept at the conference was a no-code system whereby a user could interface with an NLP AI and then the AI would automatically create the application the user needed. This effort could be done using a process that assured the result was secure and complied with company policies to avoid creating problems while solving them.  

At the heart of my problem years ago, MIS didn’t understand what I needed, and I didn’t understand the tools they used to create what I needed. This lack of initial leveling early on resulted in misunderstandings that caused projects like mine to fail and ensured that my department would pay more for something that didn’t work well initially.  

With NLP AI, the system can query the user to define what the user wants, create examples of what it hears, and iteratively evolve the result to create something as close to what the user wants as possible. Typically, we say “better, faster, cheaper,” pick any two: But we should create faster results and better meet the user’s needs with this approach. And since the effort is automated, it should cost a fraction of what it would have if human programmers had been used. 

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Next-generation collaboration with AI

This solution begs the question of what is coming next. Given that much of Microsoft Ignite’s focus was on embedding collaboration capability across Microsoft’s toolsets, programing and productivity, I think it will be AI moderation for collaboration activities and the increased use of AI as a collaboration partner.  

Suppose you could iterate an NLP AI system to arrive at a definition of an application that the AI can create. In that case, you should also be able to use a variant of that technology to monitor and moderate meetings, creating meaningful notes automatically and facilitating collaboration by active leveling — or getting parties on the same page and pointing out early areas of misunderstanding.  

Leveling is a process that is usually used in negotiations to reach common ground, assuring that the negotiators are on the same page and talking about the same things. This problem is challenging in the tech industry, because we reuse acronyms that don’t mean the same things. The same thing occurs when collaborating, because if one side doesn’t understand what the other side is saying, the project will flounder or go off track until that problem is corrected. A trained moderator can facilitate this process. Moderators and arbitrators, which are regularly used in negotiations, especially contract disagreements, could also facilitate collaboration efforts, making them far more effective and less time-consuming.  

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Working more with AIs 

We are beginning to see the next generation of AIs come into existence and make fundamental changes to the work process that could dramatically reduce costs, while just as dramatically increasing productivity.  

What Microsoft spoke about at Ignite was a massive improvement in collaboration resulting from integrating collaboration capabilities across its developer and productivity toolsets.  

But the announcements on zero-code programming, coupled with this move on collaboration, could suggest that we may soon be able to integrate AI into collaboration activities that should become far more efficient as a result. Over time, our collaboration efforts may gradually shift from collaborating with people to collaborating with AIs and then things will get interesting.  

This NLP AI effort is also consistent with having AIs enhance but not replace humans. This use of AIs would make users and collaborators more effective, without putting their jobs at risk of being replaced, at least not yet, by an AI.  

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