Headroom uses A.I. to make work meetings suck less

SAN FRANCISCO, CA, USA, October 8, 2020 /EINPresswire.com/ -- Work meetings can be brutal. To make them suck less, two former Google and Magic Leap executives are teaming up on a video conferencing product called Headroom that will use machine learning and computer vision technology to do things like take notes, summarize key moments, generate real-time interactive transcripts, recognize non-verbal cues like hand raising, a thumbs-up, or group confusion or excitement, and more. The new company was announced today in a Medium post by co-founder Julian Green.

Headroom is piloting the software with select participants, and is welcoming additional pilot users through its website www.goheadroom.com. The company has raised $5 million in seed funding from a number of well-respected investors, including Anna Patterson, Gradient Ventures; Evan Nisselson, LDV Capital; Jerry Yang, AME Cloud Ventures; Ash Patel, Morado Ventures; Anthony Goldbloom, Co-Founder and CEO of Kaggle.com; and Serge Belongie, Cornell Tech Professor of Computer Vision and Machine Learning.

Said co-founder Andrew Rabinovich, “Think about your last meeting: Was it actually necessary? Productive? Were you really engaged? Or too busy taking notes? Were people excited to be there? Or just showing their faces? Does anybody actually know?”

“Current video-conferencing solutions may efficiently send pixels, but they deliver fatigue, frustration, and failed communication. We can do better,” said co-founder Julian Green.

Headroom is a virtual meeting interface that empowers people to do what they do best -- form relationships, think creatively, solve problems -- while letting AI take care of the rest. Key features include:

Real-Time, Interactive Transcripts -- The technology powers accurate, real-time transcripts that identify who is speaking. Participants can interact with this transcript, flagging important moments or points with a click. At the end of the meeting, the entire meeting transcript is available to participants, along with a summary of the important moments flagged during the meeting.

More Inclusive Meetings -- Computer Vision can identify when participants look confused or excited, raise their hand to ask a question, or signal their agreement or disagreement with a thumbs up or down. Speakers can also be prompted when their share of speaking time grows unbalanced, or if someone hasn’t had a chance to collaborate.

Action Items -- A new feature in development will automatically identify phrases like “Action Items” and generate a list of those to-dos at the end of a meeting.

Smart Recordings -- Interactive video summaries of flagged moments allow participants to watch only highlights at a desired speed, rather than being stuck with original meeting time.

Meeting Understanding -- Participants can search for specific keywords and topics in the transcript and watch the associated video replays.

More Secure Meetings -- Business meetings are highly confidential, and securing that information is Headroom’s top priority. Users can control exactly who can see information generated from the platform. By default, all meetings are private and confidential, with only participants able to access transcripts, summaries, and recordings. Users have the option to prevent sharing, directly control permission access to individuals or groups, and to not record a meeting.

Headroom is co-founded by Julian Green and Andrew Rabinovich, both of whom have extensive experience in machine learning, computer vision tech, and creating products that are used by billions of people.

A successful entrepreneur, Julian Green has founded a number of startups, including Jetpac, which leveraged A.I. and deep learning to create travel recommendations based on an analysis of publicly available photos. Jetpac was acquired by Google in 2014. Following the acquisition, Green held a number of senior positions at Google running Computer Vision Products, including Google Cloud Vision API, which allows people to develop insights from their images in the cloud; Mobile Vision, the on-phone Computer Vision API; Google Lens for Augmented Reality Visual Search; and a number of deep tech projects at Alphabet’s Moonshot Factory X.

Andrew Rabinovich most recently led AI efforts at Magic Leap, a spatial computing company that bridges the gap between the physical and digital world with wearable technology. Before joining Magic Leap, Rabinovich spent seven years at Google where he worked on on a number of initiatives, including object recognition and scene understanding projects for photo-sharing app Picasa; helping to launch the first AR app for Android, Google Goggles; visual search in Google Photos; the Inception neural network architecture; and developing a recognition engine for estimating calories from images of food. In 2013 and 2014, together with his colleagues, Rabinovich won ImageNet challenges for fine grained object classification and general object classification and detection, respectively.

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