The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully automated data-driven model inference method. The method takes the input dynamical properties, given as a "snapshot" of the dynamics encoded by explicit transitions, and constructs a compatible model. The obtained model is guaranteed to be minimal, thus framing the approach as model compression (from a set of transitions into a set of rules). The compression is permissive to a lossy case, where the constructed model is allowed to exhibit behavior outside of the input transitions, thus suggesting a completion of the input dynamics. The task of graph transformation model inference is naturally highly challenging due to the combinatorics involved. We tackle the exponential explosion by proposing a heuristically minimal translation of the task into a well-established problem, set cover, for which highly optimized solutions exist. We further showcase how our results relate to Kolmogorov complexity expressed in terms of graph transformation.

Summary: This new study claims to be able to identify criminals based on their facial characteristics. Even if the data science is good has AI pushed too far into areas of societal taboos? This isn't the first time data science has been restricted in favor of social goals, but this study may be a trip wire that starts a long and difficult discussion about the role of AI. Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ.

Summary: This new study claims to be able to identify criminals based on their facial characteristics. Even if the data science is good has AI pushed too far into areas of societal taboos? This isn't the first time data science has been restricted in favor of social goals, but this study may be a trip wire that starts a long and difficult discussion about the role of AI. Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ.

Why does deep learning work so well? A recent paper by Henry W. Lin (Harvard) and Max Tegmark (MIT), titled "Why does deep and cheap learning work so well?" looks to examine from a different perspective what it is about deep learning that makes it work so well. It also introduces (at least, to me) the term "cheap learning." First off, to be clear, "cheap learning" does not refer to using a low end GPU; instead, the following explains its relationship to parameter reduction: This central idea of this paper is that neural network success owes as much to physics as it does to mathematics (perhaps more), and that simplistic physics functions owing to concepts such as symmetry, locality, compositionality, and polynomial log-probability can be viewed similarly to deep learning's relationship with the reality which it seeks to model. You may have heard something about this in September; this is the paper on which said news was based.

Stacking models for improved predictions - A case study for housing prices Building up a simple facial feature analysis platform - using Shiny and Microsoft Oxford API Massive Internet Attack Floods the World with Fake Data Analysis of 2 Million Hijacked Passwords (in Python) Difference Between Data Scientists, Data Engineers, and Software En...- According To LinkedIn Machine Learning: what it means and why it is the future of growth Has AI Gone Too Far? - Automated Inference of Criminality Using Fac... Your CRM data should reveal your future success (or demise) Salary history and career path of a data scientist - (Updated) Why so many Machine Learning Implementations Fail? Has AI Gone Too Far? - Automated Inference of Criminality Using Fac...

Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ. That's right, shades of The Minority Report (movie in which criminals are arrested before the crime occurs) and the 19th century studies of phrenology. These researchers claim 89.51% accuracy in making this classification on several sets of unlabeled validation images, each of about 1,500 facial images. I hope this has really taken your breath away.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)