Episode 48: I Don’t Think I Could Code My Way out of a Paper Bag - a podcast by Phil Nash & Jon Kalb

This week we chat with Frances Buontempo and Andy Balaam about Machine Learning, Artificial Intelligence and Genetic Algorithms.

We learn how ML is mostly just "multiplying and adding up" with a bit of "randomly trying stuff out" but that you might need a kill switch - except when you don't.

We also revive the "C++ Lamentations" debate and try to make an iota of difference.

Special Guests: Andy Balaam and Frances Buontempo.

Links:

• Frances' book, "Genetic Algorithms and Machine Learning for Programmers" — Build artificial life and grasp the essence of machine learning. Fire cannon balls, swarm bees, diffuse particles, and lead ants out of a paper bag.
• Amazon link for Frances' book
• Andy's postcast — Movie and tech podcast with "Clueless" Andy Balaam and "Expert" Andy Cockerill
• Frances' ACCU 2017 keynote — It has been said, to err is human, to really foul things up requires a computer [citation needed]. Given the long tradition of AI, which sometimes attempts to make a sentient being from hardware, or body parts (think Frankenstein’s monster), are humans unique, or is this dream possible? Or desirable?
• "Modern" C++ Lamentations — The post that kicked off the "modern C++ is un-debuggable" debate
• Ben Deane's response to "Modern C++ Lamentations" — TL;DR:
  
  The C++ committee isn’t following some sort of agenda to ignore the needs of game programmers, and “modern” C++ isn’t going to become undebuggable.
• Sean Parent's response to "Modern C++ Lamentations" — This post is a response for a number of people who have asked me to give my 2¢ to a large Twitter thread, and post by Aras Pranckevičius, that is rooted in a post by Eric Niebler regarding C++20 standard ranges.
• Genetic Algorithms (wikipedia) — In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
• Your Code as a Crime Scene — Use Forensic Techniques to Arrest Defects, Bottlenecks, and Bad Design in Your Programs
• NorDevCon — Tech conference in Norwich, UK
• ACCU Conference — Tech (with strong C++ focus) conference in Bristol, UK
• C++ on Sea — Standard ticket pricing ending soon!

Further episodes of cpp.chat

Further podcasts by Phil Nash & Jon Kalb

Website of Phil Nash & Jon Kalb