https://en.wikipedia.org/wiki/Probabilistic_logic_network :
> The basic goal of PLN is to provide reasonably accurate probabilistic inference in a way that is compatible with both term logic and predicate logic, and scales up to operate in real time on large dynamic knowledge bases.
> The goal underlying the theoretical development of PLN has been the creation of practical software systems carrying out complex, useful inferences based on uncertain knowledge and drawing uncertain conclusions. PLN has been designed to allow basic probabilistic inference to interact with other kinds of inference such as intensional inference, fuzzy inference, and higher-order inference using quantifiers, variables, and combinators, and be a more convenient approach than Bayesian networks (or other conventional approaches) for the purpose of interfacing basic probabilistic inference with these other sorts of inference. In addition, the inference rules are formulated in such a way as to avoid the paradoxes of Dempster–Shafer theory.
Has anybody already taught / reinforced an OpenCog [PLN, MOSES] AtomSpace hypergraph agent to do Linked Data prep and also convex optimization with AutoML and better than grid search so gradients?
Perhaps teaching users to bias analyses with e.g. Yellowbrick and the sklearn APIs would be a good curriculum traversal?
opening/baselines "Logging and vizualizing learning curves and other training metrics" https://github.com/openai/baselines#logging-and-vizualizing-...
https://en.wikipedia.org/wiki/AlphaZero
There's probably an awesome-automl by now? Again, the sklearn interfaces.
TIL that SymPy supports NumPy, PyTorch, and TensorFlow [Quantum; TFQ?]; and with a Computer Algebra System something for mutating the AST may not be necessary for symbolic expression trees without human-readable comments or symbol names? Lean mathlib: https://github.com/leanprover-community/mathlib , and then reasoning about concurrent / distributed systems (with side channels in actual physical component space) with e.g. TLA+.
There are new UUID formats that are timestamp-sortable; for when blockchain cryptographic hashes aren't enough entropy. "New UUID Formats – IETF Draft" https://news.ycombinator.com/item?id=28088213
... You can host online ML algos through SingularityNet, which also does PayPal now for the RL.