Free will, Cosmological argument, Machine learning, Pattern recognition, Learning, Algorithm
The explanation game: a formal framework for interpretable machine learning
RT @ds_wats0n: New article with @Floridi, just published in the @SpringerPhil journal Synthese. We reframe algorithmic explanation as a collaborative game where agents jointly optimise a three-part objective. If you like a good Pareto frontier then you are in luck! https://t.co/cXKpHKoU5l
OpenWe propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation(s) for a given algorithmic ...
RT @ds_wats0n: New article with @Floridi, just published in the @SpringerPhil journal Synthese. We reframe algorithmic explanation as a collaborative game where agents jointly optimise a three-part objective. If you like a good Pareto frontier then you are in luck! https://t.co/cXKpHKoU5l
OpenThe explanation game: a formal framework for interpretable machine learning
We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised ...
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The Seven Tools of Causal Inference, with Reflections on Machine Learning
The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.
Causal model
[3] Definition[edit] Causal models are mathematical models representing causal relationships within an individual system or population. [5]:253 Refinements to the technique[clarification ...
The Variational Autoencoder as a Two-Player Game — Part I
Alice and Bob at the Autoencoding Olympics
Click here to read the article
[Interventional Do-see test] Consider a causal graph D on the set of observable variables V = {Vi}i2[n] and latent variables L = {Li}i2[m] with edge set E. If (Vi, Vj) 2 E, then Pr(Vj |Vi = ...
ArchitectsOfIntelligence.pdf
We were constantly looking for ways of making associative memories, dealing with perception, object recognition, the encoding of visual scenes; all the tasks that we knew are important for ...
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The goal of this workshop is to investigate how much progress is possible by framing these problems beyond learning correlations, that is, by uncovering and leveraging causal relations: ...
Book: Machine Learning: a Probabilistic Perspective
Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automa…
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital ...
CS7792 - Counterfactual Machine Learning
Fall 2018 Prof. Thorsten Joachims Cornell University, Department of Computer Science & Department of Information Science Time and Place First meeting: August 24, 2018 Time: Fridays, 10:10am ...
Materials discovery and design using machine learning
The screening of novel materials with good performance and the modelling of quantitative structure-activity relationships (QSARs), among other issues,…
Machine Learning Tutorial for Beginners – Learn Machine Learning
Learn Machine Learning Tutorial For Beginners-What is Machine Learninf, types of machine learning, Data Mining vs Machine Learning, Decision Making tree,SVM