There is a growing desire to bring autonomous algorithms and systems into all aspects our daily lives to make our jobs, chores, and downtime easier and more enjoyable. At the same time, there are many individuals in our society who are impoverished, in danger, gravely ill, or otherwise generally requiring assistance. AI can help these individuals who require assistance by detecting bacteria in water quickly and accurately, identifying children who are at risk of being a victim of sexual trafficking or exploitation, helping a doctor identify cancer and diseases more quickly, and developing drugs in a more cost-efficient manner.
AI is not limited to helping people in need. We can use AI to study and protect wildlife, help restore historical landmarks, and monitor our planet. AI is being used to increase crop production with a reduced amount of resources, to help feed a planet of more than 7 billion. It can also be used by first responders to study our cities and our surrounding environments to plan for and respond to disasters, saving countless lives in the process. This presentation will provide technical highlights of the solutions Intel has created to address these issues.
The use cases of AI are limitless, the trick is determining how to use or modify existing algorithms and systems in such a way that they truly aid the end users. Even more so than typical AI systems, it is important to ensure that users want and trust an algorithm’s aid. This presentation will provide an overview of the steps necessary for creating AI for social good projects and various ways to become involved in a growing community around this type of work.
An example of AI for social good is the TrailGuard camera, a smart camera device that utilizes deep learning based workloads to detect poachers in wildlife reserves such as the African Grumeti. Improving dramatically upon classical motion sensors thanks to modern object detectors, the TrailGuard camera is able to avoid common false positives such as animals moving through the camera frame, but correctly flag humans in off-limits areas of the reserve.
One major challenge of these “camera traps” is the risk during installation and maintenance. Conservation staff are at risk any time they are in the field setting up or maintaining these devices, in addition to the risk that the location of these camouflaged devices being exposed during this work. To minimize risk due to frequent maintenance, Resolve decided to implement their deep neural networks on the ultra-low power Intel Movidius Myriad 2 vision processing unit with the hopes of taking battery life from an approximate 2 months, to a targeted 12 months for the new design. In order to achieve this ultra-efficient design, Resolve and Intel engineering had to design a low power hibernation mode in addition to an efficient sleep/wake capability in the device. The presentation will detail some of the power saving design decision made to enable the device to operate on a battery while deployed in the wild for months at a time.
Miniaturizing the device was also crucial to successful camouflaging. Reduction of PCB design to just 4.5 inches long by 0.5 inches wide means the cameras can be convincingly embedded into tree bark and rocks without detection. The presentation will contrast early designs against the deployed design to show how new silicon solutions with dedicated inference capabilities are enabling much smaller form factors, with fewer components, and ultimately lower cost – crucial factors for non-profits relying on the funding of other organizations.
Lastly, the TrailGuard program is growing in number of deployments around the world, and future versions are being developed to go beyond poacher detection, but actually flag when rare species of animal make an appearance. Learn about the data considerations of the black rhino for example, where image datasets of the creature are tightly held by governmental bodies trying to preserve this endangered species.
Jack joined Intel as part of the company’s acquisition of Movidius in 2016, and continues to work in the Movidius division of Intel’s Internet of Things Group. Prior to his role at Movidius, Jack worked in the field of augmented reality and commercial finance. Jack holds a Bachelor’s of Science, where he studied the complexities of the human vision system and cognitive processes. Jack also holds a degree in finance, and later obtained a Master’s in Global Business from the University of Victoria.
Anna is a deep learning data scientist at Intel within the Artificial Intelligence Products Group where she is the Head of AI for Social Good. This includes researching and designing fair, transparent, and accessible AI systems. It also includes establishing partnerships with social good organizations, enabling their missions with Intel’s technologies and AI expertise. She is an active member of the Intel AI Lab, developing deep learning NLP algorithms as part of the NLP Architect open source repository. Anna received an M.S and B.S. in Aerospace Engineering from MIT in 2009 and 2007 respectively and previously worked as a geospatial data scientist at MIT Lincoln Labs and Argonne National Labs, and a senior data scientist at Lab41.
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