Artificial Intelligence: Need and Challenges

Context

Recently, all the nations members of the UN Educational, Scientific and Cultural Organization (UNESCO) adopted a historical text that defines the common values and principles needed to ensure the healthy development of AI.

What is Artificial Intelligence?

• Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
• AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data.
• Most AI examples rely heavily on deep learning and natural language processing.
• Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
• AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
  • Machine Learning
    Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
  • Neural Networks
    A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
  • Deep Learning                                                                                                                                                                Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Key Recommendations in the UNESCO’s Historical Text

• The text provides a guide to ensure that digital transformations promote human rights and contribute to the achievement of the Sustainable Development Goals, addressing issues around transparency, accountability and privacy, with action-oriented policy chapters on data governance, education, culture, labour, healthcare and the economy.
• The text aims to highlight the advantages of AI while reducing the risks it also entails.
• One of its main calls is to protect data going beyond what tech firms and governments are doing to guarantee individuals more protection by ensuring transparency, agency and control over their personal data.
• The Recommendation also explicitly bans the use of AI systems for social scoring and mass surveillance.
• The text also emphasises that AI actors should favour data, energy and resource-efficient methods that will help ensure that AI becomes a more prominent tool in the fight against climate change and in tackling environmental issues.

Some of the sectors that have adopted AI

Health Care
AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding us to take our pills, exercise or eat healthier.

Retail
AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.

Manufacturing
AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.

Banking
Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.

Challenges of AI

While there are a few good AI success stories in India, overall, less than 25% of Indian enterprises have deployed AI solutions thus far. Some of the barriers to increases adoption in India include:

• Limited understanding of AI: Many Indian companies may have not yet understood the full benefits of leveraging AI in their companies.
• Low investments and less evolved startup ecosystem: AI requires an initial investment/incubation period (example, for POCs, discerning real use-cases). The startup/investment funding ecosystem in India is yet to scale up in terms of AI startups and service providers.
• Limited availability of AI trained talent: There is limited infrastructure to ‘democratise’ and scale-up important AI skills such as deep learning and neural networks.

What should India do to adopt AI?

• Countries like China, USA and Israel currently lead the way in terms of AI adoption.
• India can consider a few learnings from these countries to further scale up its AI ecosystem while keeping in mind the overall social development and inclusiveness agenda.
• This requires a focus on three key areas:
  • Clear central strategy and policy framework: The National Strategy for Artificial Intelligence (NITI Aayog, June 2018) which is focused on inclusive AI (AI for all), and the New Education Policy (NEP, 2020) which addresses the need to inculcate AI in the curriculum are the right strategic steps in this direction to encourage core and applied research. However, AI adoption in India can be accelerated through the formulation of more focused policies related to innovation, for example, patent control and security.
  • Collaboration among government, corporates and academia: These three critical stakeholders need to come together and work synergistically to undertake actions like nurturing entrepreneurship, promoting re-skilling, encouraging research and development, and driving the policies on the ground. While this is happening in pockets, there is a need to drive this in a structured and consistent manner with clear outcomes.
  • Leveraging MNCs and their GICs: MNCs and their GICs are leading the way in terms of AI adoption in India, their experience (for example approach, business solutions) can be leveraged effectively to help other companies learn about AI applicability in their industry to further accelerate AI adoption.

Conclusion:

More than ever, rapid and scalable innovation has become critically important for companies and countries to survive and thrive in this rapidly evolving complex economic and social environment. As Abraham Lincoln famously said ‘The best way to predict the future is to create it. AI will play a big role in creating this future, and India, due to its inherent strengths, has the potential to lead the way if it makes the right choices now.

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