Convergence-driven innovation will further larger goals such as UN SDGs, which aim at addressing poverty and hunger, healthcare gaps, alleviation of income, education and gender gaps, improved accessibility to food and clean environment, and reduction of economic inequalities.
Some of our collective and common aspirations, say those set forth by the United Nations Sustainable Development Goals (UN SDGs), are based on our conviction in the human capacity to bring about a positive change.
We are convinced that this change can be enabled through the tools that sciences and the scientific method offer. The body of knowledge and enterprise we call the ’empirical sciences’ is undergoing a sweeping transformation today. Among the key drivers of this transformation are artificial intelligence (AI) and machine learning (ML). While AI includes reasoning, problem-solving, perception, natural language understanding, and decision-making, ML is about pattern training and pattern recognition across a variety of domains.
One may thus even say that ML is a sub-set of AI. For the researcher, AI immediately opens up a fund of opportunities as far as innovation and rapid discovery is concerned. Mankind has not had a better time than this to think of innovations that come from convergence across scientific and technical disciplines, catalyzed by AI. For instance, through AI, agricultural data and problems can be connected to governance decisions and resource planning like never before.
Such convergence-driven innovation will further larger goals such as UN SDGs, which aim at addressing poverty and hunger, healthcare gaps to facilitate the well-being for all, alleviation of income, education and gender gaps, improved accessibility to food, nutrition and clean environment, and reduction of economic inequalities that plague humanity.
UN SDGs further include partnerships to foster the achievement of these goals. These goals, along with the AI engines plugged into the sciences can offer governments, businesses, and universities remarkable opportunities to build a better future.
For instance, AI-driven innovation can potentially usher in an era of improved personalized health and well-being. The fund of opportunities AI and ML offer for that is indeed welcome! These technologies foster the potential to revolutionise diagnostics, personalize treatments, and extend healthcare access to remote areas which resonate deeply with the aspirations of the UN’s health-related SDGs.
Technology is part of the solution of course, but it cannot change society by itself. Citizenship and governance will be crucial to realising the potential of AI. Improved, transparent, and responsive governance, along with appropriate decision-making AI platforms could bring in an era of greater and hopeful citizenship voice; the very kernel of democracy.
The decision sciences that rely on AI and statistics will evidently play an important role, not just in driving investments in healthcare markets, but in daily decisions of governments and people. Such decision platforms may be plugged into the UN SDGs by design, so the governments and people stay on track and learn to course-correct continually, paving the way for humanity to head in the right direction collectively.
We may recall Margaret Chan, the former Director-General of the World Health Organisation (WHO), who said: “There is no single route to success in healthcare, but a sustainable future will need a combination of leadership, innovation, and collaboration.”
Indeed leadership, innovation and collaboration could synergize via AI platforms, in ever-new ways. In the realm of biotechnology, the aims of ensuring sustainable food production and advancing health align harmoniously. Drug discovery and targeted delivery and precision agriculture will impact both nourishment and health of the planet. Environmental sustainability, closely linked to UN SDGs, will find an enabler in AI/ML-driven technologies.
Unprecedented capacity to analyse vast datasets, predict climate patterns, optimise resource management, and develop eco-friendly solutions will be available to scientists, policymakers, and governments. Agriculture and nutrition of the future will be better informed, optimized and mapped to meet the direct and projected needs of the populations.
Meanwhile, materials science and engineering, rapidly facilitated by AI/ML will merge even more with the basic sciences and other technical disciplines. Syncretic disciplines like materials science and engineering will be catalyzed by AI-fostered inter-disciplinary alliances, and in turn will promote sustainable industrialization, which is a key to decent employment, sustainable communities adopting responsible consumption, and climate change mitigation at once.
An integration of several sustainability goals would drive materials and manufacturing sciences in strategic directions in an accelerated manner. This, if done well across the globe, would sustain nations – small and big. Novel materials and innovative processes crafted through research directed by a sustainability paradigm will offer fresh prospects for decent employment to address economic inequalities, while also being in harmony with environmental stewardship.
To summarize, AI-driven convergences will transcend diverse domains, from civil engineering to computer science. It will propel progress towards smarter infrastructure, sustainable cities, and even citizen-centered inclusive societies, if we get the governance-related nuts and bolts right. The pervasive influence of AI and ML across disciplines is accelerating scientific frontiers as we are speaking.
This is the time for engineers and scientists to talk across their disciplines, share data, strike meaningful conversations, open hitherto unsolvable problems, and use AI to strike collaborations that will usher in a new paradigm for sciences, hopefully oriented towards the UN SDGs.
Original News Link
https://www.iitm.ac.in/happenings/press-releases-and-coverages/lesson-iit-ai-and-ml-transformative-agents-sciences-their
