Swansenreport

• Frequentist: Parameters (e.g., model weights) are fixed but unknown values. Inference relies solely on observed data, often through maximization (e.g., maximum likelihood estimation, or MLE). Probability is interpreted as long-run frequencies in repeated experiments. No prior beliefs are incorporated; uncertainty is handled via tools like confidence intervals or p-values, which have objective, repeatable properties.
• Bayesian: Parameters are random variables with probability distributions. Inference starts with a prior distribution(encoding beliefs before seeing data), then updates it to a posterior distribution using Bayes’ theorem and the likelihood of the data. Probability is subjective (degrees of belief), and it naturally quantifies uncertainty via full posterior distributions or credible intervals.

Why Most AI (Especially Deep Learning) Has a Frequentist BiasStandard training of neural networks and many ML models uses techniques rooted in frequentism:

• Optimization via empirical risk minimization (e.g., minimizing loss on training data) → closely resembles MLE, a frequentist method.
• Regularization (e.g., L2 weight decay) → acts as a penalty to prevent overfitting, without explicit priors.
• Point estimates → Models produce a single set of “best” parameters after training, not a distribution over possible parameters.

This frequentist flavor dominates because:

• It is computationally scalable for massive datasets and high-dimensional models (e.g., billions of parameters in large language models).
• It avoids the need to specify priors (which can be subjective and controversial) or compute intractable posteriors.
• Techniques like stochastic gradient descent are efficient approximations to frequentist objectives.

Deep learning’s success in tasks like image recognition, natural language processing, and generative modeling stems from this practical, data-driven approach, which prioritizes fitting the observed data extremely well.Bayesian Elements and Advantages in AIPure Bayesian methods (e.g., full posterior inference via MCMC or variational inference) are less common in mainstream AI due to computational cost, but they appear in specialized areas:

• Bayesian neural networks → treat weights as distributions, enabling better epistemic uncertainty estimation.
• Gaussian processes or probabilistic models → provide full posteriors.
• Hyperparameter tuning (e.g., Bayesian optimization).

Bayesians argue their framework is more principled for AI because:

• It naturally separates aleatoric uncertainty (irreducible noise in data) from epistemic uncertainty (reducible ignorance due to limited data or model misspecification)—crucial for reliable AI in safety-critical applications (e.g., medical diagnosis or autonomous driving).
• Frequentist models often suffer from overconfidence on out-of-distribution data, while Bayesian posteriors can express “I don’t know.”

However, even Bayesian approximations in deep learning (e.g., variational autoencoders) often borrow frequentist tools for efficiency.Why the Frequentist Dominance Persists

• Scalability → Bayesian inference scales poorly with model size and data volume.
• Historical and practical success → Frequentist-inspired methods (e.g., backpropagation + regularization) powered breakthroughs like AlphaGo, GPT models, and modern AI.
• Hybrid realities → Many “Bayesian” tools in AI are approximations that achieve frequentist-like guarantees (e.g., good coverage in repeated deployments).

In summary, AI has a strong frequentist bias because it prioritizes empirical performance on data over probabilistic beliefs about parameters. This has driven tremendous progress, but as AI moves toward more reliable, uncertainty-aware systems, Bayesian ideas are gaining traction—suggesting the field may shift toward hybrids rather than pure adherence to one paradigm. The “bias” isn’t absolute; it’s a reflection of trade-offs between philosophical rigor, computational feasibility, and real-world results.