Once upon a time, the fictional idea of robots and humanoids propelling global crises would have been a far reaching fascination among science fiction aficionados.
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However, there is a such thing as too much data—which is why fine-tuning task-specific models is a common approach to transfer learning. As data growth has become exponential over time, foundation models are further trained on target-specific data. Thus, the model becomes “fine-tuned” to perform specific tasks.
“Foundation models keep getting larger and more complex, that rather than building new models from scratch, many businesses are instead customizing retrained foundation models to turbocharge their AI journeys”, according toTo Mr. Gensler’s point, as deep learning becomes more broadly adopted in financial and supply chain ecosystems, systemic risk can promulgate along the data pathway.
Mr. Gensler believes that deep learning models present a challenge of limited robustness since its latent features are unobservable, thus emphasizing systemic risks stemming from adversarial or cyberattacks. The smallest perturbations to a model’s unobservable latent features could result in flawed determinations, outcomes, or predictions that can be easily transferred between models.
As deep learning becomes more broadly adopted in the financial and supply chain ecosystems, AI developers must be cognizant of representational and societal biases as well as performance disparities. By working to drive greater financial inclusion when deploying models, developers can help mitigate financial fragility and systemic risk that could in turn lead to an AI-induced financial crisis.
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