Data everywhere - How can AI help governments !

The advent of digital technologies, social media and Internet of Things is resulting in huge amounts of data being created. It is a famous saying, Data Never Sleeps. Some estimates and market reports suggest that by 2020, 5200 GB data would be created each year for every person on this planet. And, this data exists in various forms, shapes and sizes. For e.g. every minute, over 2 million photographs are shared, 4 million YouTube videos are viewed, over 12 million texts and 150 million emails are sent. Phew!

The irony, however, is that despite this information overload many CIO's still take decisions based on insufficient data. You might ask why?

While there’s no shortage of data out there, the ability to access and act on it whenever, wherever, and however is limited.

This is where AI can help!

With AI, we can:

• Understand: any form of data, which could either in the form of video, sound, blogs, image and any other.
• Reason: provide context to the data
• Learn: With machine learning capabilities can continuously learn and update.  
• Interact: Interact with humans using natural language processing. This can break the barrier between human and machines. Infact, it is estimated that by 2021, at least 20% citizen conversations would be done using AI chatbots.

Not surprisingly, AI is gaining popularity, as evident from worldwide spending on Cognitive and Artificial Intelligence Systems which is forecast to reach $77.6 billion by 2022, nearly 3 times its 2018 forecast.

Taking a closer look at AI efforts, Governments are focusing on sectors that are envisioned to benefit the most from AI in solving societal needs. Some of these are:

• Healthcare: increased access and affordability of quality healthcare,
• Agriculture: enhanced farmers’ income, increased farm productivity and reduction of wastage.
• Education: improved access and quality of education,
• Smart Cities and Infrastructure: efficient and connectivity for the burgeoning urban population, and
• Smart Mobility and Transportation: smarter and safer modes of transportation and better traffic and congestion problems.

Let’s look at some of the use cases across some of these sectors:

Agriculture: We have worked to build precision agriculture application for various districts of India. With the help of crop data base, satellite images and weather information we can able to predict and update: Ground water, any kind of crop diseases, natural calamities well in advance to take preventive actions.

Health: AI is being used in Life Sciences where it has potential to reduce the time of new drug to the market. A recent study found a correlation of burn injuries with cancer; with this we should able to reason and find the root cause analysis to save many lives.

Education: As students globally struggle to complete their courses on time, AI is being used to make learning more personalized for them. Additionally, as teachers struggle to balance busy work and course schedules with the demands of advanced learning, AI-based tools can help teachers save time planning effective and aligned lessons

Challenges and Concerns

However, there are some challenges to AI adoption which are dictating the pace and extent of adoption of AI. As per a McKinsey study, still only 21% say their organizations have embedded AI in several parts of the business, and only 3% of large firms have integrated AI across their full enterprise workflows:

• Shortage of Skills: Globally skills necessary to tackle serious artificial intelligence research is not available easily and requires Investing in AI-relevant human capital and infrastructure to broaden the talent base capable of creating and executing AI solutions to keep pace with global AI leaders.
• Bias: AI systems are only as good as the data we put into them, and bad data used to train AI can contain implicit racial, gender, or ideological biases. As a result, biases find their way into the AI systems. More than 180 human biases have been defined and classified, and any one of which can affect how we make decisions. Bias in AI systems could erode trust between humans and machines that learn. AI systems that will tackle bias will be the most successful.
• Services available via API: Technologist and researchers have been building innovative application and services to deploy on premise or in the cloud exposing either SOAP or REST services. Often, the justifiable enthusiasm around a cloud strategy tempered with concerns about how cloud workloads will talk to legacy apps.
• Policy: Despite the obvious opportunities for efficiency and effectiveness, the role of AI government policy and service delivery remains contentious. For example, when is it acceptable to use deep-learning models, where the logic used for decisions cannot possibly be explained or understood very easily. Citizens generally feel positive about government use of AI, but the level of support varies widely by use case, and many remain hesitant.

AI is an area where skepticism may be high, its adaptation methodology and investments might be argued, but smartly targeted AI and machine learning tools, with well-deployed algorithms fueled by huge data sets, can drive lasting improvements across various social delivery applications of governments. As a survey done by McKinsey suggests, AI adoption could raise global GDP by as much as $13 trillion – which is 1.2% additional GDP growth per year – by 2030.