The circular economy is a regenerative model aimed at minimizing waste and making the most of resources which is in contrast to our current economic model that emphasizes consumption and planned obsolescence. India, with its cultural inclination towards reuse and recycling, has the opportunity to incorporate principles of the circular economy in various sectors like construction, agriculture, and urban mobility, and businesses can do the same into their operations.

There is value in large datasets that reveal statistically relevant insights - such as trends in fast food visits or sleep patterns. This is relevant to India’s growing data economy given the potential to use aggregated anonymous data to make informed decisions. We need to implement privacy by design, and for that organizations need to de-identify personal information to prevent potential harm if the data is lost or stolen.

Biology’s evolution from empirical to engineered, driven by network computing and big data, is revolutionizing healthcare and drug discovery. Innovations like Patient Ping enhance coordinated care, while computational techniques streamline molecule identification, reducing trial and error. Advances in organ-on-a-chip and cell engineering enable more precise, individualized treatments, necessitating a reevaluation of regulatory and intellectual property frameworks.

The evolution of internet access in India, from a single-user bulletin board service to the vast, decentralized web, mirrors a broader trend towards centralization in digital services. Despite the internet’s expansive possibilities, most users gravitate towards a few familiar sites. This centralization tendency is also evident in Bitcoin’s blockchain technology, where mining pools’ dominance challenges the decentralized ideal, highlighting the need for potential regulatory intervention or system redesign.

In early 1800s America, credit was based on personal familiarity. Lewis Tappan revolutionized this by selling credit ratings, leading to the birth of credit reporting agencies. Today, these agencies hold extensive personal data, influencing major life decisions. India, formulating its first privacy legislation, faces a similar choice: regulate data collectors or empower individuals to control their data usage.

Urban mobility is on the brink of transformation with the convergence of on-demand transport, electric engines, and autonomous vehicles. This shift could lead to the end of car ownership, fossil fuel-powered vehicles, and traditional traffic management. It may also free up urban space, reduce transportation costs, and allow India, with its low automobile ownership, to lead this revolution with forward-looking policies and infrastructure development.

The emerging threat of autonomous drones equipped with facial recognition and AI technologies raises new concerns when it comes to the future of warfare. The moral and ethical concerns of fully autonomous weapons calls for an international agreement to ban such technology, akin to the ban on biological weapons.

The increasing reliance on social ratings and feedback loops in services like ride-sharing platforms is leading to a system where personal ratings may determine access to services. This trend, mirrored in China’s proposed national trust score, raises concerns about algorithmic discrimination and its societal impact.

DeepMind has developed the world’s first tabula rasa algorithm, AlphaGo Zero, which learns from scratch without relying on human expertise or existing data. Unlike previous AI models, it learns through self-play, achieving mastery in the game of Go and uncovering novel strategies. This approach could revolutionize areas like genomic research and law, reducing concerns about privacy and human bias in algorithmic decision-making, and possibly leading to true artificial general intelligence.

Algorithms in streaming services and e-book libraries curate content based on individual preferences, often with impressive accuracy. However, the downside to this is the eventual homogenization of content. By continually reinforcing original preferences, the algorithms can lead to a lack of diversity in the content presented. We need to seek out new and different content, recognizing that while algorithms are powerful tools, they have limitations. They are only as good as the data they’ve been trained on, and without regular updates to keep them fresh and relevant, they can become restrictive rather than expansive in their recommendations.