New applications envisioned for networks in 5G and beyond place emphasis on ultra-reliable and low-latency communications. To simultaneously support the seemingly contradictory requirements placed on the error-correcting systems in future networks, near-maximum likelihood decoding of short block-length codes has become a focus of recent research. In this talk we will present recent results on several different near-maximum likelihood decoding techniques for short block-length codes, including techniques for decoding short Reed-Muller and Polar Codes, AI-assisted decoding, and GRAND decoding. An emphasis will be placed on reducing the complexity of near-ML decoding algorithms towards practical hardware implementations.