Entrepreneurs show enthusiasm about transformative potential of generative AI

(MENAFN) Numerous experts and entrepreneurs have expressed enthusiasm about the transformative potential of generative AI. While these sentiments may be characteristic of the optimism often found in successful entrepreneurs, investors are advised to approach the subject with a sense of rationality and composure. Expectations surrounding generative AI, however, often surpass the inherent constraints imposed by reality.

As investments in generative artificial intelligence continue to surge, the pressure to develop new use cases and applications intensifies. According to IDC Data Group, expenditure on generative artificial intelligence is projected to reach USD143 billion by 2027, a substantial increase from the USD16 billion estimated for the current year. Despite the notable impact of artificial intelligence hype on the Nasdaq Composite Index, which has seen a 36 percent rise this year, it's crucial to acknowledge the potential overestimation of AI capabilities.

OpenAI itself is actively seeking additional funding to pursue its ambitious goal of achieving human-like artificial intelligence. Investors should bear this in mind when evaluating the company's strategy centered around the concept of "super intelligence," implying cognitive capabilities surpassing those of humans.

While AI models excel in prediction, the critical distinction lies in their inability to comprehend. This limitation raises skepticism about the feasibility of AI attaining a general level of intelligence comparable to humans. The text generation produced by large linguistic models relies on training data, and while they excel in repetitive concepts, they struggle with novel scenarios beyond their training scope.

This was evident in the case of Google DeepMind's AI weather forecasting model, which outperformed other models in routine weather patterns but struggled with identifying unusual and severe cases. Large linguistic models face challenges in recognizing errors, and requesting corrections doesn't necessarily lead to more accurate responses. Originality AI's study of large language models, including OpenAI's GPT-4 Chat, revealed inaccuracies in about a third of responses.

Investors in AI must maintain a realistic perspective, understanding that while these technologies offer valuable applications, they are not infallible. Chief financial officers, for instance, are incorporating AI tools for practical yet less glamorous tasks, such as analyzing employee performance reviews and optimizing waste collection schedules.



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