Most organizations would love to integrate AI into their systems to acquire competitive advantage. AI has revolutionized output generation and performs tasks in a blink of an eye. As these models have been trained on mammoth datasets events including ever expanding internet (some 157 trillion gigabytes), these models are excellent, and are able to distinguish signal from noise while navigating the information.
APIs democratise access to generative AI. They serve as user-friendly tool-kits, and enable development of context-specific and organisation-specific applications on the foundation model. These APIs allow access to even small organisations.
Generative AI becomes standard for data analysis and reporting. The results are homogenous. There is no distinctiveness. The landscape tends to be uniform. It is necessary to mobilize context-specific datasets. Proprietary datasets can provide a strategic edge. Pharma companies can use anonymized patient data from various hospitals. It will enhance drug development process.
One can elicit creative results from the model. We have to craft skillful prompts and insightful queries. Prompt engineering skills must be acquired.
There should be adequate compute power. AI needs advanced Nvidia processors. More computer power means faster iteration cycles, larger training models with more parameters.
There should be AI research and development. We are still away from AI that equals human capabilities. Over time, this gap will narrow.
The excitement for AI today resembles the exacitement of the early days of IT option. Later, IT becomes standardised. The issue then was, ‘Does IT matter?’ IT had become commoditised. It could no longer give a competitive advantage. Later the issue was reframed — IT does matter. Here adoption of AI is not enough. To make AI competitive, we will have to consider the above-discussed issues.
In a fast-changing world of technology, the technology advances faster than the regulatory response. In fact, the first driving licenses were issued two decades later than the cars navigating the American streets became a common sight. There is a difference between the pace of innovation and the regulatory response. The gap is ever widening.
Some unscrupulous businessmen develop the whole business models around ‘regulatory arbitrage’. Profitable areas of business are chosen. Nobody had turned any attention to these areas. The businesses become a fait accompli. Regulators find it hard to wish them away.
In case of generative AI, there is an unprecedented challenge. No one knows why they generate a particular answer. AI as an innovation can dwarf any other innovation ever since the transistor was invented. Politicians are already concerned about AI-generated fake news. It has degraded even the trust in real things.
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