As we look ahead, the interoperable power of ‘bits’, ‘neurons’, and ‘qubits’ has the potential to solve some of the toughest problems that challenge the limitations of classical computing – for instance, untangling complex logistics problems at an exponential level, discovering optimal delivery routes, or even enhancing forecasts for climate science. This convergence is taking place by:
Problem-solving through interdisciplinary context analysis, problem finding, framing, and idea generation, along with in-field experimentation, validation, and adaptation; Use of advanced science to underpin the solution; Engineering for tech and commercial feasibility While classical computing will continue to be good at data mining and large-scale analysis, AI can learn to reason in order to solve real-world problems. However, to succeed, all these capabilities will have to be tied together to explore what can be achieved with an AI algorithm that has the reach of the entire cloud behind it, as well as the processing ability that quantum computing could unlock. If that happens, then we can address large scale chemistry challenges like those in drug development and material design challenges; Industry 4.0 solutions; energy and climate challenges.
Current limitations faced by such deep tech
Denne historien er fra November 11, 2022-utgaven av The Times of India Hyderabad.
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Denne historien er fra November 11, 2022-utgaven av The Times of India Hyderabad.
Start din 7-dagers gratis prøveperiode på Magzter GOLD for å få tilgang til tusenvis av utvalgte premiumhistorier og 9000+ magasiner og aviser.
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