| | MARCH 20258CIOReviewIN MY OPINIONINTELLIGENT...ARTIFICIAL INTELLIGENCE?Since the introduction of ChatGPT, Generative AI and Large Language Models (or "LLMs," with "large" referring to the model number of parameters), more specifically, have generated enormous excitement and interest with their ability to compose stories and poems, answer questions, and engage in conversations. But how intelligent are these AIs? By intelligence, I mean the ability to respond successfully to novel challenges. And does this even matter? From a practical standpoint, their potential is undeniable: even if they are not intelligent per se, users can boost productivity with LLMs, both in content consumption and creation.Yet, the answer to this question matters. To effectively address the potential risks, we must understand the capabilities and limitations of LLMs, both crucial to mitigating the risks of either excessive reliance on AI-generated information or unwarranted fears of automation replacing humans, both of which, though distinct, can have adverse consequences. In what follows, I offer some considerations for readers to ponder as they contemplate this and related topics.Why the "perfect" model is not the best.When we effectively fit a model to data, we are trying to find a data compression mechanism. For example, say we fit a line to 1000 points; assuming the fit is good, this would mean that we managed to store most of the information in the data in only two parameters: the slope and the intercept of the line. So, if we wanted to communicate the information the 1000 data points carry, we can now do this with only two values rather than 1000. Good models yield data compressions that are efficient and with small information loss. Efficient means that the model captures the data informational content with only a few model parameters ­ much smaller than the data size. And small information loss means it produces values close to the real data. That is why we find the best model parameters by minimizing metrics that represent the loss - how far the fit predicted values are from the real data (think least squares). The perfect minimal information loss scenario would be when the model values are equal to the real data. This can happen if the model has the same number of parameters as the number of data points: each parameter will "store" the information of exactly one data point. Yet this "perfect" fit achieves nothing compression-wise: it uses as many parameters to capture the data information as the number of data points... By Argyro (Iro) Tasitsiomi, PhD., Head of AI, Investments Data Science & Research, T. Rowe PriceArgyro (Iro) Tasitsiomi
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