This talk is a preview of a tentatively scheduled reading paper for JRSS-B titled ``The EM Algorithm --- An Old Folk Song Sung To A Fast New Tune" (Meng and van Dyk, 1996), which has the following abstract: It has been (almost) 20 years since the publication of Dempster, Laird, and Rubin (1977), the paper that popularized the EM algorithm. To celebrate this event, after a brief historical account, we discuss strategies that aim to make EM converge much faster while maintaining its simplicity and stability (e.g., monotone convergence in likelihood. First, we introduce the idea of constructing efficient data-augmentation schemes and thus, fast EM implementations, by minimizing the augmented-data Fisher information as a function of a working parameter that indexes a class of data-augmentation schemes. Second, we establish a theoretical framework for a general EM-type algorithm that couples flexible data augmentation and model reduction to achieve more efficient computations, in terms of computer and human time. This general algorithm, which we term the Alternating Expectation/Conditional Maximization (AECM) algorithm, uses conditional maximization to simplify the M-step of EM and, at the same time, allows the data-augmentation scheme to vary within and between iterations which can substantially improve the rate of convergence. We illustrate these methods using multivariate t-models with known or unknown degrees of freedom and Poisson models for image reconstruction. We show, through both empirical and theoretical evidence, the potential for a dramatic reduction in computational time with little increase in human effort. The dramatic reduction (e.g., by a factor of 100) in computational time was also found with random-effects models, an application we present in an accompanying paper due to space limitations. We also discuss the intrinsic connection between EM-type algorithms and the Gibbs sampler, and how our techniques may provide useful insight for speeding up the latter. The main conclusion of the paper is that it is possible to construct algorithms that are simple, stable, and fast, a triplet that is appreciated by every user. The talk will focus on t-models and random-effects models and demonstrate that it is possible to have a ``free lunch" -- algorithms that are simple, stable, and fast.