Elijah and Gabriel Videos and Press Home
are YouTube videos of demos, along with brief context and explanation
of significance. Each demo illustrates a different Gabriel
application or cloudlet-based concept. Also included
are mentions of Gabriel and Elijah in the press over time.
Everything is listed chronologically, most recent to oldest.
Last updated by Satya (May 29, 2017)
RibLoc System for Surgical Repair of Ribs: Wearable Cognitive Assistant (January 2017)
Unsolicited by us, this video was made by a startup company
VIZR Tech (http://vizrtech.com) to illustrate the potential of wearable
cognitive assistance in medical training. The video provides
background to explain relevant concepts to the company's target
audience. The company already uses Google Glass (with the camera
blocked) in medical training, to show videos of complex medical
procedures to trainees. We created this new Gabriel application
to give a tutorial on the RibLoc system for surgical repair of ribs,
which is made by AcuteInnovations, Inc.
(http://acuteinnovations.com). Today, this training is
given to a doctor by an AcuteInnovations technician traveling to the
doctor's site. The Gabriel application illustrates
how this training could be delivered more efficiently. In
addition, the application is available to the doctor to refresh
training at any time. The principals of VizrTech appear in
this video and share their thoughts about why this is a game-changing
innovation. From a technical point of view, the computer vision
in this application is particularly difficult because the parts are
small, differ in subtle ways (e.g. color of screw), and easily confused
under different lighting conditions. The object detectors
are all implemented using deep neural networks.
- IKEA Table Lamp Kit: Wearable Cognitive Assistant (January 2017)
In our talks on Gabriel, we have often mentioned assembly of
IKEA kits as an example of how step by step guidence and prompt
detection of errors could be valuable. This video shows a
Gabriel application to assemble a genuine IKEA kit (a table lamp)
purchased off the shelf at IKEA. An interesting first is
the use of short video segments (rather than still images) in the
Google Glass display to guide the user. The use of videos in this way, combined with the active,
context-sensitive real-time guidance from the Gabriel application, is
- Making a Sandwich: Google Glass and Microsoft Hololens Versions of a Wearable Cognitive Assistant (January 2017)
This demo shows two things. First, it shows how
Gabriel can use much more sophisticated computer vision (based on
convolutional neural nets) than the much simpler computer vision
algorithms used in demos such as Lego and Ping-Pong.
Second, it shows how different kinds of wearable devices (Google Glass
and Microsoft Hololens) can be used for the same application using the
same Gabriel back-end.
- RTFace: Denaturing Live Video on Cloudlets (November 2016)
This demo shows how cloudlets can improve the scalability of
video analytics and how they can be used to enforce privacy policies
based on face recognition. The demo also illustrates use of
the OpenFace face reognition system that we have created.
RTFace combines OpenFace with face tracking across frames to achieve
the necessary frame rate for live video.
- Gabriel on CBS 60 Minutes (October 9, 2016)
Wearable Cognitive Assistance can be viewed as
"Augmented Reality Meets Artificial Intelligence". This
90-second excerpt from the October 9, 2016 CBS 60 Minutes special
edition on Artificial Intelligence highlights the table-tennis wearable
cognitive assistant on Google Glass.
- 7 course projects (many based on cloudlets and Gabriel) (December 6, 2016)
The Fall 2016 offering of 15-821/18-843 "Mobile and
Pervasive Computing" course included many 3-person student projects
based on cloudlets and wearable cognitive assistance. Examples include
wearable cognitive assistance for use of an AED device,
cloudlet-based privacy mediator for audio data, etc. This
web page contains brief descriptions of the projects, and videos of the
student projects captured on the final day of class. The PDFs of
the posters used by the students to explain their projects are also
- FaceSwap: Cloud versus Cloudlet Comparison of User Experience (June 2016)
This demo shows the difference between using a cloud and a
cloudlet for an application where the impact of latency is easily
perceivable by users. We have created an Android application
called "FaceSwap" that is available in the Google Play
Store. A back end VM image for an Amazon cloud site is also
available. The VM image can also be run on a cloudlet.
TPOD System for Creating Deep Neural Net Object Detectors for Cloudlets (May 2016)
Creating object detectors for wearable cognitive assistance
is difficult. TPOD is a web-based system that we have created to
simplify the creation of training data sets for object detectors based
on deen convolutional neural networks. This demo shows an
early version of TPOD.
- National Public Radio (WESA) segment on Gabriel (February 9, 2016)
This short (4-minute) NPR radio piece and associated web page on wearable cognitive assistance was broadcast in Spring 2016.
- Drawing Assistant with Google Glass (December 2015)
Can a legacy application for training be modified to use
Gabriel? This demo shows how a Drawing Assistant created by
researchers at INRIA in France has been modified to use a wearable
device (Google Glass). In its original form, a user would receive
instruction to improve his drawing skills on a desktop display, and
provide input using a pen-based tablet. This demo shows how the
system has been modified to retain the application logic for
instruction, but use any writable surface (e.g., paper, whiteboard,
etc.) for input. Computer vision on the video stream from Google
Glass is used to generate input and display streams that are
indistinguishable from the original.
- PingPong Assistant with Google Glass (December 2015)
This conceptually simple demo has proved to be especially
popular because it brings out the importance of low
latency. A person wearing Google Glass plays ping-pong with
a human opponent. The video stream from the Glass
device is streamed to a cloudlet and analyzed on each frame to detect
the ball and the opponent, compare their positions from the previous
frame, and then to infer their trajectories. Based on this,
the application guides the user to hit to the left or right in order to
increase the chances of beating the opponent. To avoid annoying
the user, the application tries not to give advice too frequently and
only when it is confident of its advice.
- "New AI Platform 'Gabriel' Will Whisper Instructions Into Your Ear" (December 3, 2015)
Article in Tech Times.
- 10 course projects (many based on cloudlets and Gabriel) (December 1, 2015)
2015 offering of 15-821/18-843 "Mobile and Pervasive
Computing" course included many 2-person student projects based on
cloudlets and wearable cognitive assistance. Examples include wearable
cognitive assistance for gym exercises, using cloudlets for Google
Street View hyper-lapse viewing, real-time cloudlet-based
super-resolution imaging, etc. This web page contains brief
descriptions of the projects, and videos of the student projects
captured on demo day. The PDFs of the posters used by
the students to explain their projects are also included.
- (December 1, 2015)
Article in HNGN
- "‘Gabriel’ Is A New Artificial Intelligence Named After The Messenger Angel" (December 1, 2015)
Article in Popular Science
- "New AI 'Gabriel' wants to whisper instructions in your ear" (December 1, 2015)
Article in Engadget.
- Task Assistance Demo with Lego Assembly on Google Glass (September 2015)
This is the world's very first wearable cognitive assistance
application! We chose a deliberately simplified task
(assembling 2D lego) since it was our first attempt. The demo
seems easy, but the code to implement it reliably was challenging
(especially with flexible user actions and under different lighting
- Impact of high offload latency on mobile user experience (June 2012)
YouTube videos show the effect of end-to-end latency on an Android front-end application with a
compute-intensive back-end that is offloaded to an Amazon EC2 cloud or a