Media Summary: Coding Partial Derivatives in Python is a good way to understand what Bayesian logic is already helping to improve We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains ...
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Coding Partial Derivatives in Python is a good way to understand what Bayesian logic is already helping to improve We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains ... There's a lot of talk of image and text AI with large language models and image generators generating media (in both senses of ... The algorithm for differentiation relies on some pretty obscure mathematics, but it works! Mark Williams demonstrates Forward ... Peforming operations in parallel on big data. Rebecca Tickle explains MapReduce.
After seemingly insurmountable issues with Artificial General Intelligence, Rob Miles takes a look at a promising solution: ... Non deterministic finite state automata described and then shown in Python by Professor Thorsten Altenkirch Here is the code ... Putting search algorithms into practice. Dr Mike Pound reveals he likes nothing more in his spare time, than sitting in front of the ... How about a Neural Net where the neurons are actual atoms? Professor Phil Moriarty shows a paper demonstrating the principle ... Dicussing implementation with Professor Brailsford. Professor Brailsford emailed me after we recorded this to say that of course ... With the explosion of AI image generators, AI images are everywhere, but how do they 'know' how to turn text strings into ...
One of the cleanest ways to cut down a search space when working out point proximity! Mike Pound explains K-Dimension Trees. They're called 'Finite State Automata" and occupy the centre of Chomsky's Hierarchy - Professor Brailsford explains the ultimate ... Laziness is a virtue - well, in programming anyway! Professor Thorsten Altenkirch on how you can use the 'yield' to compute ...
