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Making AI Ubiquitous to Bridge Digital Inequality – Looking at the Possibilities
Making AI Ubiquitous to Bridge Digital Inequality – Looking at the Possibilities

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A few years back, Kursat Ceylan, a person with vision impairment, was trying to locate his hotel using his smartphone and cane while pulling heavy baggage by himself. It was then that he accidentally walked into a pole and got himself injured. This incident inspired him to create Wewalk – an AI-enabled smart walking cane that can detect objects above chest level. The cane can seamlessly pair-up with apps such as Google Maps and help visually impaired people navigate the world without having to use a smartphone.

This AI innovation was called one of the best inventions in 2019 by Forbes and the company later went on to join Microsoft’s AI for Accessibility program to enhance the product’s capability. This is not a one-off case. Over the past few years, the evolution of deep learning for AI has fueled numerous such innovations across all fields of technology.

When AI as a field emerged at the start of the 21st century, many envisioned a distant future of self-operating cognitive machines and computers that would simplify human life and, in the process, accentuate it significantly. Today, generalized AI – underpinned by data, algorithms, and computing advances – is already paving the path for machines to achieve human-level intelligence, and perhaps beyond. Looking at the future, we can expect AI to mature further and yield endless possibilities for the greater good of society.

The Next Frontier – Democratizing AI

Not so long ago, the use of AI was largely limited, primarily due to its lack of accessibility for millions of developers. Without a strong data science background, it was virtually impossible for a developer to leverage advanced AI tools. Thankfully, that is now changing.

The proliferation of IoT, data lakes, and recent advancements in computational infrastructure and machine learning (ML) has made AI accessible like never before. Moreover, global tech giants like Google and Microsoft are aggressively embedding smart AI tools to their cloud service portfolios to simplify and democratize the deployment of AI for businesses across industries. These tools are, in turn, allowing companies to break the barriers of AI adoption and helping them create their own chatbots and predictive ML models.

The resultant democratization of AI applications is expected to propel the AI market to reach a whopping USD 309.6 billion by 2025. It’s no surprise that Gartner identified this easy accessibility of AI as a megatrend dominating the AI landscape in 2020 and beyond. The accessibility of AI as a technology will only increase in the coming years and this is expected to create a huge impact on AI engineers, researchers, and developers.

The Prospects

The easy availability of AI will also open several interesting avenues of tech-driven innovation in sectors like healthcare and agriculture. And the good news is that the movement has already begun. In the healthcare industry for instance, AI is already making its presence felt by fostering innovation in treatment procedures and care delivery. Recently, an AI application that has the capability of identifying and prescribing sepsis treatment entered clinical trials in London. This application essentially leverages advanced ML models to mine medical data to suggest more effective ways of diagnosing and treating serious medical conditions like Sepsis – a disease that kills around 6 million people globally every year.

 

If AI manages to deliver on its promise, it can boost healthcare access for communities at lower costs – a boon for countries like the USA where the average annual healthcare cost of a person was USD 10966 in 2019. AI-led telemedicine systems can free up the bandwidth of overworked doctors and other medical staff while reducing the risk of glaring medical errors that may kill thousands of patients every year. Additionally, AI can also spur innovations in the field of precision medicine, personalizing treatments for every patient based on their genetic, behavioral, and environmental context.

Much like healthcare, AI has the potential to transform the global agriculture sector as well. According to research, the AI in agriculture market is poised to reach a valuation of USD 4 billion by 2026, growing at a CAGR of 25.5% from 2020 till 2026. This momentum can be attributed to factors such as the growing usage of AI in plant image recognition, ground data visualization, creation of soil moisture maps, and increasing demand for crop health monitoring and analyzing tools.

In developing countries like India, however, AI in agriculture is still in its nascent stage. The technology has the capability to radically transform conventional agricultural practices by supporting farmers and infusing transparency and objectivity within the post-harvest farming value chains.

Sensing the opportunity, many tech-driven startups have started burgeoning in the space. Recently for instance, CropIn, a leading agri-tech startup, raised 20 million in a Series C. This company leverages AI, ML, and remote sensing to create a network of agricultural stakeholders at different levels. It allows farmers to analyze and visualize data to gather actionable insights on standing crop.

Conclusion

The current application of AI across sectors is just a tiny representation of what is possible when the full potential of the technology is realized. Instead of underestimating or overestimating the possibilities, public and private sector industry stakeholders need to prioritize collaboration to make widespread AI adoption a reality.

As the global population increases and chronic diseases become more widespread, food and affordable healthcare for all will continue to remain a critical objective. And when we look at the tools that this technology grants us in fighting these imminent adversities, the one thing that is clear is that the time to harness AI is now.

 

This article is authored by:

Navin Sabharwal

 

 

 

 

 

Navin Sabharwal

Fellow & Chief Architect, HCL Technologies


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