On May 20, 2021, at 3:53 PM, Stephen Bounds <km@...> wrote:



Interesting Tom,

I actually think it is still likely to be a failure from an enterprise point of view, because if you were to capture the majority of meetings you are likely to get lots of:

• irrelevant discussion
• tasking ("so we've agreed that X will do Y and come back by Z")
• acronyms and implied knowledge about past events
  

Additionally:

• meeting contexts will look pretty identical (either a non-descript meeting table or Zoom grid) and
  
• emotions tend to be pretty guarded

So I feel the pickings of the AI in this context will be slim. On the other hand, I'm sure the system will be a boon for the stock video and content creator market, since if you're looking for a quick way to find (context free) "cat runs into chair while chasing toy mouse" videos, it will have you covered 😁

What would be much more interesting to me is a system that fully and accurately transcribes videos, including assigning names to speakers and descriptions of video actions on screen. That's because the biggest drawback of videos is that you have to watch the damn things. If you could instead skim through text to find the bit at 42:55 where you show me exactly how to safely clear a jammed lathe, that would be a valuable knowledge tool.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
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On 21/05/2021 2:01 am, Curtis A. Conley wrote:

Thanks for sharing. Seems like a natural evolution of video-conferencing, given where it's at today. Many of the standard VC/streaming apps have built in auto-captioning and transcription capabilities, and if you bundle that in with all of the other asynchronous tools we're using to message and share knowledge, that could lead to a lot of interesting applications in the KM space.

On Thu, May 20, 2021 at 10:33 AM Tom Short <tshortconsulting@...> wrote:

Back in the early days of KM, big 4 consulting firms (I think there were six back then) saw the potential of KM and started experimenting with various tools and approaches. And it made sense for them to get onboard early: their assets were purely knowledge-based and went down the elevator every night. 

 

Back in 1989, Andersen Consulting hired AI guru Roger Schank and gave him $30million to play with and continue his research (he was at Stanford) hoping for some breakthroughs they could apply to their business. 

One KM-related project he worked on involved conducting and videoing knowledge elicitation interviews with experts. The thought was that if we could simply interview people and video it all, it would capture knowledge in a way that could be then inventoried, tagged and searched for future retrieval and re-use. I don’t know how much he spent on it, but word was it was in the millions. 

 

In any case, that didn’t work. When I learned about this effort I was at IBM, and I knew it wouldn’t work. We didn’t have the tools to cope with vast amounts of unstructured data, even when it was in text format, much less video format. 

 

But maybe now that is about to change. Some MIT alums are building a startup called Netra around an AI engine that is supposed to be able to parse video content and categorize it automagically. Might be a good one to watch. Who knows? Maybe Schank will be vindicated after all, and video knowledge elicitations will become a thing again. 

Now MIT alumnus-founded Netra is using artificial intelligence to improve video analysis at scale. The company’s system can identify activities, objects, emotions, locations, and more to organize and provide context to videos in new ways.

https://news.mit.edu/2021/netra-video-ai-0520

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-Tom
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