Microsoft recently held its annual Ignite conference, demonstrating why Microsoft is now the most valuable company in the world.
From the quality of the event’s production to the way it showcased some impressive diversity and inclusion (D&I) work, 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) turning anyone into a coder and how that could evolve in the future to massively change the effectiveness of collaboration.
This user capability reminded me of when I started working in technology. The PCs were new, but using software like Microsoft Office we could create the tools we needed, mostly in Excel or Lotus Notes, that did what we needed to do without involving Management Information Services (MIS) , who switched 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 return an app only marginally better than doing things manually. The PCs were a godsend.
They were also a huge problem, as these efforts on computers often resulted in insecure solutions with internal accuracy issues and applications tied to specific users. If the user got sick or left, you were out of luck, because we didn’t write manuals for our in-house tools. But it was the only way to get things done in a timely manner, and some people still use Excel for the same reason today.
Using AI NLP for Programming
natural language processing That might not sound like a lot, but it’s arguably a bigger change to human-machine interface than graphical user interfaces (GUIs) were.
Microsoft’s concept at the conference was a no-code system where a user could interface with an NLP AI, and then the AI would automatically build the application the user needed. This effort could be done using a process that ensured the outcome was secure and compliant with company policies to avoid creating issues while resolving them.
At the heart of my problem years ago, MIS didn’t understand what I needed, and I didn’t understand the tools they were using to create what I needed. This initial lack of leveling led to misunderstandings early on that caused projects like mine to fail and my department to pay more for something that didn’t work well to begin with.
With NLP AI, the system can query the user to define what they want, create examples of what they hear, and iteratively evolve the result to create something as close as possible to what the user wants. user wants. Generally, we say “better, faster, cheaper”, choose two: but we should create faster results and better meet user needs with this approach. And since the effort is automated, it should cost a fraction of what it would have cost if human programmers had been used.
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Next-gen collaboration with AI
This solution raises the question of what’s next. Given that much of the focus of Microsoft Ignite was on building collaboration capability into Microsoft’s toolsets, programming, and productivity, I think it will be be AI moderation for collaboration activities and increased use of AI as a collaboration partner.
Suppose you can iterate an NLP AI system to arrive at a definition of an application that AI can create. In that case, you should also be able to use some variation of this technology to monitor and moderate meetings, automatically creating meaningful notes and facilitating collaboration through active leveling – or getting the parties on the same page and pointing out early areas of misunderstanding.
Leveling is a process typically used in negotiations to reach common ground, ensuring that negotiators are on the same page and talking about the same things. This problem is difficult in the tech industry because we reuse acronyms that don’t mean the same thing. The same thing happens when collaborating, because if one party doesn’t understand what the other party is saying, the project will flounder or go off the rails until that problem is fixed. A trained moderator can facilitate this process. Moderators and arbitrators, which are regularly used in negotiations, especially contractual disagreements, could also facilitate collaborative efforts, making them much more efficient and less time-consuming.
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Work more with AI
We are beginning to see the next generation of AI emerge and make fundamental changes to the work process that could dramatically reduce costs, while also dramatically increasing productivity.
What Microsoft talked about at Ignite was a massive improvement in collaboration resulting from the integration of collaboration capabilities into its development and productivity toolsets.
But announcements about no-code programming, coupled with this move towards collaboration, might suggest that we may soon be integrating AI into collaborative activities that should become much more efficient as a result. Over time, our collaboration efforts can gradually shift from collaborating with people to collaborating with AIs, and then things will get interesting.
This AI NLP effort is also consistent with the fact that AIs enhance but do not replace humans. This use of AI would make users and collaborators more efficient, without running the risk of their work being replaced, at least not yet, by AI.
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