Written by Jeremiah Peter, Solution specialist-Advanced Services Group, Powerupcloud technologies
A Not So Distant Future
As we usher into an era dominated by technological innovation, Artificial Intelligence continues to draw heated debates for its unparalleled ability to automate tasks and eliminate human dependency. The growing cynicism on automation has captured our imagination in leading cinematic marvels such as ‘2001 A Space Odyssey’ and ‘Terminator’. Painting a deeply poignant future, these movies induce fears of a machine-led holocaust sparked by AI’s transcendence into Singularity- a point where AI supersedes human intelligence. Veering away from the dystopian narrative and objectively analyzing the realm of AI, it seems apparent that we can leverage AI for social good without descending into chaos. The call for beneficence in intelligent design is best captured by American computer scientist, Alan Kay’s words- “The best way to predict the future is to invent it”.
The blog presents a new frame of analysis for the responsible use of Artificial Intelligence technologies to augment human and social development. Additionally, the blog also delineates key Machine Learning and Computer Vision (Object Detection) concepts to solve a real-world problem, outlining a discourse for pragmatic solutions with broad social impact.
The Next Frontier
Under the dense canopy of commercial AI clutter, there are several AI initiatives that continue to garner both awe and adulation in the social sciences and humanities spectrum. Cancer detection algorithms, disaster forecast systems and voice-enabled navigation for the visually impaired are a few notable mentions. Although socially-relevant applications have achieved a fair degree of implementational success, they fail to attain outreach at a global level due to a lack of data accessibility and the dearth of AI talent.
Alternatively, innovative technologies that supplement large scale human-assistance programs in enhancing efficacy could be considered a worthwhile undertaking. Infusing computer vision technology in human-centered programs can dramatically improve last-mile coverage, enhance transparency, mitigate risks and measure the overall impact of assistance programs. In the next section, we delve into some core issues that afflict large-scale human assistance programs and underscore the need for technological intervention.
The State of Human-assistance Programs
According to The State of Food Security and Nutrition in the World Report (2019), around 820 million people in the world are hungry and over 66 million of them are under the age of 5. With numbers increasing steeply in most parts of Africa, South America, and Asia, the fate of Sustainable Development Goal of Zero Hunger by 2030 hangs by a thread. Perturbed by the growing scourge, some nations responded by releasing a slew of measures to take corrective action.
One such initiative called The Midday Meal Scheme (MDMS), launched by the government of India in 1995, serves around 120 million children across government and government-aided schools. Recognized as one of the largest food assistance programs in the world, MDMS was laid out with a bold vision to enhance enrolment, retention, and attendance with the overarching aim of improving nutritional status among children in India. However, not including the logistical and infrastructural shortcomings, the initiative loses significant funds to pilferage each year (Source: CAG Report2015). Shackled by a lack of resources, the program struggles to counter aberrant practices with institutional measures and seeks remediation through innovative solutions.
Given the unprecedented magnitude, large-scale human assistance schemes such as MDMS require well-crafted solutions that can instill governance and accountability into their dispensation process. In our constant endeavor to design Intelligent Apps with profound social impact, Powerup experts examined a few libraries and models to carve out a computer vision model that could reign in governance under such programs. The following section explores the pre-requisites for formulating a desirable solution.
Empowering Social Initiatives with Object Detection
Evidently, the success of an AI application is hugely predicated on a well-conceived algorithm that can handle varying degrees of complexity. However, developing a nuanced program from scratch is often a cumbersome and time-intensive process. To accelerate application development, programmers usually rely on pre-compiled libraries, which are frequently accessed code routines used iteratively in the program. After gleaning several open-source image processing libraries (VXL, AForge.Net, LTI-Lib), Powerup team narrowed down on OpenCV for its unique image processing functions and algorithms.
Besides a good library, the solution also requires a robust image classification engine to parse objects and scenes within images. However, despite several key advances in vision technology, most classification systems fail to interact with the complexities of the physical world in that these systems can only identify a limited set of objects under a controlled environment. To develop advanced object detection capabilities, the application needs to be powered by an intelligent model that can elicit a more refined outcome- to make sense of what it sees.
In order to develop a broad understanding of the real-world, computer vision systems require comprehensive datasets that consist of a vast array of labeled images to facilitate object detection within acceptable bounds of accuracy. Apart from identifying the position of the object/s in the image, the engine should also be able to discern the relationship between objects and stitch a coherent story. Imagenet is a diverse open-source dataset that has over a billion labeled images and, perhaps, serves as a foundation for similar computer vision explorations.
Moreover, computer vision systems also hinge on neural networks for developing self-learning capabilities. Though popular deep-learning based models such as R-CNN, R-FCN, and SSD offer ground-breaking features, YOLO (You Only Look Once) stands out for its capabilities in super real-time object detection, clocking an impressive 45 FPS on GPU. The high processing power enables the application to not only interact with images but also process videos in real-time. Apart from an impressive processing capacity, the YOLO9000 is trained on both the ImageNet classification dataset and the COCO detection dataset that enables the model to interact with a diverse set of object classes. We labeled and annotated local food image sets containing items such as rice, eggs, beans, etc. to sensitize the model toward domain specific data.
As demonstrated in the image above, the model employs bounding boxes to identify individuals and food items in the picture. Acting as a robust deterrent against pilferage, the application can help induce more accountability, better scalability, and improved governance.
A New Reckoning
While a seventh of the population goes hungry every day, a third of the food in the world is wasted. The figure serves as a cause for deep contemplation of the growing disparities in a world spawned by industrialization and capitalism. As we stand on the cusp of modern society, phenomena such as unbridled population growth, disease control, climate change and unequal distribution of resources continue to present grave new challenges. Seeking innovative and sustainable solutions, therefore, becomes not just a moral obligation, but also the 21st century imperative.
Aside from the broad benefits, the domain of AI also presents a few substantive concerns that need general oversight. To that effect, the evolving technological landscape presents two inherent risks: wilful misuse (Eg- Cambridge Analytica Case) and unintended consequences (COMPAS- a biased parole granting application).
While concerns such as wilful misuse raise moral questions pertaining to data governance and preservation of user self-determination, risks such as algorithm bias and inexplicability of decision-making expose design loopholes. However, these apprehensions can be largely mitigated through a commonly accepted framework that is vetted by civil society organizations, academe, tech & business community, and policymakers around the globe. Owing to this pressing need, the European Parliament launched AI4People in February 2018 to design a unified charter for intelligent AI-based design. Upholding values such as protection of human self-determination, privacy, and transparency, the initiative is aimed at proposing recommendations, subsequently leading to policies, for ethical and socially preferable development of AI.
Governed by ethical tenets, innovative solutions such as Object Detection can operate within the purview of the proposed framework to alleviate new-age challenges. With reasonable caution and radical solution-seeking, AI promises to be an indispensable vector of change that can transform society by amplifying human agency (what we can do).
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