When I was a consultant, my world was Excel, PowerPoint, and Notepad. And in large environments, what you always have is many, many work streams of activity. For instance, there's a my commitments feature assembly where it listens. So because it's present on all your meetings, it's listening in on when you're committing to something. This my commitments feed automatically generates this list for me without any prompting. It's just listening Without clicking or commanding the item that finds an item that should be working on its own. You can also send an AI agent to attend the meeting instead of you. The ability to multiply effectiveness throughout all this busy activity really helps with productivity.
Artem Koren, co-founder and Chief Product Officer at Sembly AI, started the company in January 2019 to bring the power of AI to online meetings.
Artem and his team developed an app that listens in on virtual meetings and does all the note-taking for you including recommending action items and suggesting the most important topics. These are hard AI problems to solve and Sembly’s success is an indication they’re off to a great start.
Before Sembly, Artem was an executive and co-founder at companies including Neusana and Visual Trading Systems and he spent time as a manager in big company land at Ernst & Young.
Listen and learn...
- Why Artem and his co-founder decided to fix the problem of broken meetings
- Why the evolution of online meetings… is like the evolution of airplanes
- Why we’ll soon send AI agents to attend meetings on our behalf
- When meetings are required… and how to make them more efficient
- How neural nets are solving traditional voice transcription problems related to accents and background noise
- How to solve the problem of automatically determining who said what in a conversation
- How Sembly uses generative AI to summarize meetings
- What are the risks of having AI decide what tasks to assign to meeting participants
- How to prevent sensitive information from being passed to large language models as training data
References in this episode...