Global Law Assembly Technical Report Series an Indo-centric standpoint is to be maintained, the cost of manufacture, research, development, supply as well as labor is bound to increase by some amount which may or may not be significant. The factor that is perhaps the most important is the need for the R&D of resilient infrastructure much faster, in comparison to the other sectors to inhibit & keep at the bay the influence of China as envisaged by the India, U.S., Australia & Japan. The race in perfectly replicating technologies while keeping the costs minimal is something that would cause the cost to go up significantly. Other than these assumptions, no other determinations can be accurately made.
Role of AI Hype in the Economic Ecosystem Yet another factor to be borne in mind is the Hype with respect to disruptive technologies inclusive of Artificial Intelligence in general. The hype can potentially have an effect on the economy of production, R&D & Supply. Andrew Ng – pioneer in AI & ML applications, founder of Google Brain & Coursera, in a session hosted by DeepLearning.AI & Stanford HAI, said that “Those of us in machine learning are really good at doing well on a test set, but unfortunately deploying a system takes more than doing well on a test set.” Ng brough up the case in which Stanford researchers were able to develop an algorithm to diagnose pneumonia from chest X-rays, which when tested, in fact – performed better than human radiologists (Perry, 2021). It is to be understood that there are challenges in making a research paper into something useful in a clinical setting. He notably remarked that “All of AI, not just healthcare, has a proof-ofconcept-to-production-gap, the full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back [into the model], showing safety – doing all things that need to be done [for a model] to be deployed. [That goes] beyond doing well on the test set,
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