The proposed approach is to formalise the probabilistic puzzle in equational FOL. Two formalisations are needed: one theory for all models of the given puzzle, and a second theory for the favorable models. Then Mace4 - that computes all the interpretation models of a FOL theory - is called twice. First, it is asked to compute all the possible models M p .Second, the additional constraint is added, and Mace4 computes only favourabile models M f. Finally, the definition of probability is applied: the number of favorable models is divided by the number of possible models. The proposed approach equips students from the logic tribe to find the correct solution for puzzles from the probabilitistic tribe, by using their favourite instruments: modelling and formalisation. I have exemplified here five probabilistic puzzles and how they can be solved by translating the min FOL and then find the corresponding interpretation models. Mace4 was the tool of choice here. Ongoing work is investigating the limits of this method on various collections of probabilistic puzzles

Human listeners have the extraordinary ability to hear and recognize speech even when more than one person is talking. Their machine counterparts have historically been unable to compete with this ability, until now. We present a modelbased system that performs on par with humans in the task of separating speech of two talkers from a single-channel recording. The models of speech use temporal dynamics to help infer the source speech signals, given mixed speech signals. The estimated source signals are then recognized using a conventional speech recognition system.

This developer code pattern demonstrates how you can create your own music based on your arm movements in front of a webcam. It uses the Model Asset eXchange (MAX) Human Pose Estimator model and TensorFlow.js. This code pattern is based on Veremin, but modified to use the Human Pose Estimator model from the Model Asset eXchange, which is hosted on the Machine Learning eXchange. The Human Pose Estimator model is converted to the TensorFlow.js It is a deep learning model that is trained to detect humans and their poses in a given image.

Human listeners have the extraordinary ability to hear and recognize speech even when more than one person is talking. Their machine counterparts have historically been unable to compete with this ability, until now. We present a modelbased system that performs on par with humans in the task of separating speech of two talkers from a single-channel recording.

Their machine counterparts have historically been unable to compete with this ability, until now.