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Post: The Paradigm Shifts in Artificial Intelligence

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The Paradigm Shifts in Artificial Intelligence
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Artificial intelligence (AI) captured the world’s attention in 2023 with the emergence of pre-trained models such as GPT, on which the conversational AI system ChatGPT is based. For the first time, we can converse with an entity, however imperfectly, about anything, as we do with other humans. This new capability provided by pre-trained models has created a paradigm shift in AI, transforming it from an application to a general-purpose technology that is configurable to specific uses. Whereas historically an AI model was trained to do one thing well, it is now usable for a variety of tasks such as general conversations; assistance; decision making; and the generation of documents, code, and video—for which it was not explicitly trained. The scientific history of AI provides a backdrop for evaluating and discussing the capabilities and limitations of this new technology, and the challenges that lie ahead. Key Insights

Kuhn’s framework of scientific progress helps us understand how artificial intelligence has gone through several paradigm shifts during its scientific history, and how the representation and use of knowledge has widened in scope and scale.

The current general intelligence paradigm has breached a major barrier where machines can learn through self-supervision, transforming AI from an application to a general-purpose technology like electricity that will enable the rapid development of transformative applications across the economy.

Trust is the key barrier in the current AI paradigm, which must be overcome for the widespread adoption of AI.

Whereas historically an AI model was trained to do one thing well, it is now usable for a variety of tasks such as general conversations; assistance; decision making; and the generation of documents, code, and video. Economics Nobel Laureate Herbert Simon, one of the pioneers of AI who coined the term “artificial intelligence”, described it as a “science of the artificial.” 43 In contrast to the natural sciences, which describe the world as it exists, a science of the artificial is driven by a goal, of creating machine intelligence. According to Simon, this made AI a science of design and engineering. Just five decades later, pre-trained […]

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