I've got the February issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence lying on my coffee table. Let's evesdrop on what the professionals are up to

• Offline loop investigation for handwriting analysis

• Robust Face Recognition via Sparse Representation

• Natural Image Statistics and Low-Complexity Feature Selection

• An analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

• Geometric Mean for Subspace Selection

• Semisupervised Learning of Hidden Markov Models via a Homotopy Method

• Outlier Detection with the Kernelized Spatial Depth Function

• Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching

• Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Vido: Data, Metrics, and Protocol

• Information Geometry for Landmark Shape Analysis: Unifying Shape Representation and Deformation

• Principal Angles separate Subject Illumination spaces in YDB and CMU-PIE

• High-precision Boundary Length Estimation by Utilizing Gray-Level Information

• Statistical Instance-Based Pruning in Ensembles of Independent Classifiers

• Camera Displacement via Constrained Minimization of the Algebraic Error

• High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration

These researchers are writing footnotes to Duda and Hart. They are occupying the triple point between numerical methods, applied mathematics, and statistics. It is occassionally lucrative. It paid my wages when I was applying these techniques to look down capability for pulse doppler radar.

The basic architecture of all this research is that the researchers have a monopoly on thinking, mathematics, and writing code and the computers crunch the numbers, both during research and later in a free standing but closed application. There is nothing foomy here.

As William has pointed out, AI running amok is already a standard trope.

As steven0461 has pointed out, this may well make it less likely to be taken seriously.

The people that the world needs to take FAI seriously are the people working on AI.

Knowing about FAI might lead people concerned with existential risk, or more generally futurism or doing the maximally good thing, to become newly interested in AI. (It worked for me.)