“Clean water and health care and school and food and tin roofs and cement floors, all of these things should constitute a set of basics that people must have as birthrights.”1
– Paul Farmer, American Doctor, Anthropologist, Co-Founder,
Partners In Health
Obtaining clean water is a critical problem for much of the world’s population. Testing and confirming a clean water source typically requires expensive test equipment and manual analysis of the results. For regions in the world in which access to clean water is a continuing problem, simpler test methods could dramatically help prevent disease and save lives.
To apply artificial intelligence (AI) techniques to evaluating the purity of water sources, Peter Ma, an Intel® Software Innovator, developed an effective system for identifying bacteria using pattern recognition and machine learning. This offline analysis is accomplished with a digital microscope connected to a laptop computer running the Ubuntu* operating system and the Intel® Movidius™ Neural Compute Stick. After analysis, contamination sites are marked on a map in real time.
Background and History
Peter Ma, a prolific contributor in the Intel® AI Academy program, regularly participates in hackathons and has won awards in a number of them. “I think everything started as a kid; I've always been intrigued by new technologies,” Peter said.
Winning the Move Your App! Developer Challenge in 2010, a contest hosted by TEDprize, led to a speaking appearance at TEDGlobal and reinforced Peter’s desire to use technology to improve human lives. The contest was based on a challenge by a celebrity chef and restaurateur, to tackle child obesity.
Over several years, Peter has established an active consulting business around his design and development skills. “I build prototypes for different clients,” Peter said, “ranging from Fortune 500 to small startups. Outside of my consulting gigs, I attend a lot of hackathons and build out my own ideas. I built the Clean Water AI specifically for World Virtual GovHack, targeting the Water Safety and Food Security challenge.”
Based in Dubai, United Arab Emirates, the GovTechPrize offers awards annually in several different categories to acknowledge technology solutions that target pressing global challenges. The World Virtual GovHack was added to the awards roster, framed as a global virtual hackathon, to encourage students and startups to tackle difficult challenges through experimentation with advanced technologies.
Develop the future of ∀I for All
Figure 1. Peter Ma demonstrates the clean water test system.
“We originally started to work on this December of 2017,” Peter said, “specifically for World Virtual GovHack. I won first place and was presented USD 200,000 by His Highness Mansoor of Dubai at the awards ceremony in February 2018. This makes it possible to take the project much further. We are currently in the prototyping stage and working on the next iteration of the prototype so it can be in one single IoT device. I think in the world of innovation, there is never completion—only improvements from your last iteration.”
Peter’s success rate at hackathons is impressive, inspiring other projects, including Doctor Hazel, Vehicle Rear Vision, and Anti-Snoozer. “I think I do well in most hackathons,” he said, “because I focus mostly on how technologies can better people's lives—rather than just what technologies can do.”
Notable Project Milestones
- Started development work in December 2017.
- Submitted the project in February 2018 and won first prize in the World Virtual GovHack, receiving USD 200,000 that will help fund the next phase of the Clean Water AI project.
- Began work on a prototype for a new version of the test system that can be embedded in a self-contained Internet of Things device.
- Garnered a first place finish in the SAP Spatial Hackathon by mapping out a water system in San Diego to demonstrate how water contamination can be stopped once it has started.
- Slated to present a demo at the O’Reilly AI Conference in New York in April 2018.
Figure 2. Peter Ma receives the top GovTechPrize in the Water Safety and Food Security category.
Every minute a newborn dies from infection caused by lack of safe water and an unclean environment.2
– World Health Organization, 2017
The Clean Water AI project benefited from access to the Intel® AI DevCloud, a free hosting platform made available to Intel AI Academy members. Powered by Intel® Xeon® Scalable processors, the platform is optimized for deep learning training and inference compute needs. Peter took advantage of Intel AI DevCloud to train the AI model and Intel Movidius Neural Compute Stick to perform water testing in real time. The Neural Compute Stick supports both the Caffe* and TensorFlow* frameworks, popular with deep learning developers.
The Intel® Movidius™ Software Development Kit also figured heavily in the development, providing a streamlined mechanism to profile, tune, and deploy the convolutional neural network capabilities on the Neural Compute Stick. Because the Clean Water AI test system must be able to perform real- time analysis and identify contaminants without access to the cloud, the self-contained, AI-optimized features of the Neural Compute Stick are essential to the operation of the test system. The Neural Compute Stick is a compact, fanless device, the size of a typical thumb drive, with fast USB 3.0 throughput, making it an effective way to deploy efficient deep learning capabilities at the edge of an Internet of Things network.
“Intel provides both hardware and software needs in artificial intelligence—from training through deployment. For startups, it is relatively inexpensive to build the prototype. The AI is being trained through Intel AI DevCloud for free; anyone can sign up. The Intel Movidius Neural Compute Stick costs about USD 79, and it allows the AI to run in real time.”
– Peter Ma, Software Innovator, Intel AI Academy
The neural compute stick acts as an inference accelerator with the added advantage that it does not require an Internet link to operate. All of the data needed by the neural network is stored locally, which makes the rapid, real-time operation possible. Any test system dependent on accessing data from a remote server is going to be burdened by availability of connections (particularly in rural areas where the testing is very important), as well as potential service disruption and lag time in performing analyses. For developers that need more inference performance for an intensive application, up to four compute sticks can be combined at once for a given solution.
Clean Water AI Test System
The Clean Water AI test system is composed of simple, inexpensive, off-the-shelf components:
- A digital microscope, available at many sources for USD 100 or less
- A modestly equipped computer running the Ubuntu operating system
- An Intel Movidius Neural Compute Stick running machine learning and AI in real time
The entire test system can be constructed for well under USD 500, making it within reach of organizations that cannot usually afford expensive traditional test systems.
Figure 3 shows the basic test setup.
Figure 3. The basic test system—microscope, laptop, and compute stick—can be assembled for less than USD 500.
The convolutional neural network at the heart of the test system determines the shape, color, density, and edges of the bacteria. Identification at this point is limited to Escherichia coli (E. coli) and the bacterium that causes cholera, but because different types of bacteria have distinctive shapes and physical characteristics, the range of identification can be extended to many different types. Project goals on the near horizon include distinguishing between good microbes and harmful bacteria, detection of substances such as minerals, and satisfying the certification requirements necessary in different geographies.
To refine the approach and sharpen the precision of identification, Peter has continuing training. Currently, the confidence level for testing is above 95 percent, as high as 99 percent, assessing clean water compared with contaminated water, but this is likely to improve further as additional images are added to the system and more training is performed.
In a video demonstration of the Clean Water AI test system, Peter uses the microscope to first capture an image of clean water and then compares that with a sample showing contaminated water. The AI immediately detects the harmful bacteria and can flag the contamination on a map. All of these activities are carried out in real time.
E. coli bacteria, shown in the rendering in Figure 4, is typically present in contaminated water and can be accurately identified by the AI according to shape and size.
For more information visit Peter Ma's Clean Water AI project.
Figure 4. A rendering of E. coli bacteria, one of the most common and dangerous water contaminants.
AI is Changing the Landscape of Business and Science
Through the design and development of specialized chips, sponsored research, educational outreach, and industry partnerships, Intel is firmly committed to advancing the state of artificial intelligence (AI) to solve difficult challenges in medicine, manufacturing, agriculture, scientific research, and other industry sectors. Intel works closely with government organizations and corporations to uncover and advance solutions that solve major challenges.
For example, an engagement with NASA focused on sifting through many images of the moon and identifying different features, such as craters. By using AI and compute techniques, NASA was able to achieve its results from this project in two weeks rather than a couple of years.
The Intel AI portfolio includes:
Intel Xeon Scalable processors: Tackle AI challenges with a compute architecture optimized for a broad range of AI workloads, including deep learning.
Framework Optimization: Achieve faster training of deep neural networks on a robust scalable infrastructure.
Intel® Movidius™ Myriad™ Vision Processing Unit (VPU): Create and deploy on-device neural networks and computer vision applications.
For more information, visit this portfolio page.
“At Intel we have a pretty pure motivation: we want to change the face of computing and increase the capabilities of humanity and change every industry out there. AI today is really a set of tools. It allows us to sift through data in much more scalable ways, scaling our intelligence up. We want our machines to personalize and change and adapt to the way we shop and the way we interact with others.
There are already vast changes taking place, which are happening under the hood. Intel has a broad portfolio of products for AI. We start with the Intel Xeon Scalable processor, which is a general-purpose computing platform that also has very efficient inference for deep learning.”
– Naveen Rao, Intel VP and GM, Artificial Intelligence Products Group
The Promise of AI
The possibilities of AI are only beginning to be recognized and exploited. Intel AI Academy works collaboratively with leaders in this field and talented software developers and system architects exploring new solutions that promise to reshape life in today’s world. We invite interested, passionate innovators to join us in this effort and become part of an exciting community to make contributions and take advanced technology in new directions for the benefit of the global community.
“I see AI playing a major part in helping governments and non government organizations in the future,” Peter said, “especially in terms of monitoring resources, such as ensuring water safety. AI can reduce costs and provide more accurate continuous monitoring than current systems. An AI device for water safety typically requires very little maintenance, because it will be based on optical readings, rather than chemical based.”
“We have the ability to provide clean water for every man, woman and child on the Earth. What has been lacking is the collective will to accomplish this. What are we waiting for? This is the commitment we need to make to the world, now.”3
– Jean-Michel Cousteau
Clean Water AI
Clean Water Project in Intel DevMesh
Build an Image Classifier on the Intel Movidius Neural Compute Stick
Getting the Most Out of IA with Caffe* Deep Learning Framework
Rapid Waterborne Pathogen Detection with Mobile Electronics
Intel Movidius Neural Compute Stick
Artificial Intelligence: Teaching Machines to Learn Like Humans
Intel Processors for Deep Learning Training