interpretation


Thomas Bayes - Wikipedia

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Thomas Bayes (/beɪz/; c. 1701 – 7 April 1761)[2][3][note 1] was an English statistician, philosopher and Presbyterian minister who is known for formulating a specific case of the theorem that bears his name: Bayes' theorem. Bayes never published what would become his most famous accomplishment; his notes were edited and published after his death by Richard Price.[4] Thomas Bayes was the son of London Presbyterian minister Joshua Bayes,[5] and was possibly born in Hertfordshire.[6] He came from a prominent nonconformist family from Sheffield. In 1719, he enrolled at the University of Edinburgh to study logic and theology. On his return around 1722, he assisted his father at the latter's chapel in London before moving to Tunbridge Wells, Kent, around 1734.


The Byzantine Generals Problem and AI Autonomous Cars - AI Trends

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Let's examine the topic of things that work only intermittently, which as you'll soon see is a crucial topic for intelligently designing and building AI systems, especially for self-driving autonomous cars. First, a story to illuminate the matter. My flashlight was only working intermittently, so I shook it to get the bulb to shine, hoping to cast some steady light. One moment the flashlight had a nice strong beam and the next moment it was faded and not of much use. At times, the light emanating from the flashlight would go on-and-off or it would dip so close to being off that I would shake it vigorously and generally the light would momentarily revive. We were hiking in the mountains as part of our Boy Scout troop's wilderness-survival preparations and I was an adult Scoutmaster helping to make sure that none of the Scouts got hurt during the exercise. At this juncture, it was nearly midnight and the moon was providing just enough natural light that the Scouts could somewhat see the trail we were on. We had been instructed to not use flashlights since the purpose of this effort was to gauge readiness for surviving in the forests without having much in-hand other than the clothes on your back. There were some parts of the trail that meandered rather close to a sheer cliff and I figured that adding some artificial light to the situation would be beneficial. Yes, I was tending to violate the instructions about not using a flashlight, but I was also trying to abide by the even more important principle to make sure that none of the Scouts got injured or perished during this exercise. Turns out that I had taken along an older flashlight that was at the bottom of my backpack and mainly there for emergency situations. At camp, I had plenty of newer flashlights and had brought tons of batteries as part of my preparation for this trip.


Measuring War: Cognitive Effects in the Age of AI - War on the Rocks

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Editor's Note: This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the first question (part a.) on the character of war, and the third question (part d.) on the types of data that would be most useful for developing applications. Billy Beane, general manager of the struggling Oakland Athletics baseball team, faced a problem in the early 2000s. He needed to field a competitive team with one of the league's smallest budgets. Beane turned to what became known as a "moneyball" approach -- a new analytical method that valued a player's ability to get on base over traditional statistics like batting average and home runs.


AI Can Read A Cardiac MRI In 4 Seconds: Do We Still Need Human Input?

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You are, and welcome to the present and the future of automated machine learning programs that have the ability to significantly increase the speed of analysis of specialized MRI scans. Don't worry though--it's not ready for prime time just yet! Now, new research sheds light on just far we have come in terms of development of such machine learning programs. According to a new study published in the journal, Circulation: Cardiovascular Imaging, analysis of cardiac MRI scans using automated machine learning can be performed significantly faster and with comparable accuracy to human interpretation by trained cardiologists. It generally takes about 13 minutes for a trained physician (cardiologist) to interpret a cardiac MRI.


AI Can Read A Cardiac MRI In 4 Seconds: Do We Still Need Human Input?

#artificialintelligence

You are, and welcome to the present and the future of automated machine learning programs that have the ability to significantly increase the speed of analysis of specialized MRI scans. Don't worry though--it's not ready for prime time just yet! Now, new research sheds light on just far we have come in terms of development of such machine learning programs. According to a new study published in the journal, Circulation: Cardiovascular Imaging, analysis of cardiac MRI scans using automated machine learning can be performed significantly faster and with comparable accuracy to human interpretation by trained cardiologists. It generally takes about 13 minutes for a trained physician (cardiologist) to interpret a cardiac MRI.


Switched linear projections and inactive state sensitivity for deep neural network interpretability

arXiv.org Machine Learning

We introduce switched linear projections for expressing the activity of a neuron in a ReLU-based deep neural network in terms of a single linear projection in the input space. The method works by isolating the active subnetwork, a series of linear transformations, that completely determine the entire computation of the deep network for a given input instance. We also propose that for interpretability it is more instructive and meaningful to focus on the patterns that deactive the neurons in the network, which are ignored by the exisiting methods that implicitly track only the active aspect of the network's computation. We introduce a novel interpretability method for the inactive state sensitivity (Insens). Comparison against existing methods shows that Insens is more robust (in the presence of noise), more complete (in terms of patterns that affect the computation) and a very effective interpretability method for deep neural networks.


How to Research a Machine Learning Algorithm

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Algorithms are a big part of the field of machine learning. You need to understand what algorithms are out there, and how to use them effectively. An easy way to shortcut this knowledge is to review what is already known about an algorithm, to research it. In this post you will discover the importance of researching machine learning algorithms and the 5 different sources that you can use to accelerate your understanding of machine learning algorithms. Discover how machine learning algorithms work including kNN, decision trees, naive bayes, SVM, ensembles and much more in my new book, with 22 tutorials and examples in excel.



On Model Stability as a Function of Random Seed

arXiv.org Machine Learning

In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-based and surrogate model based (LIME) interpretations. Our analysis suggests that random seeds can adversely affect the consistency of models resulting in counterfactual interpretations. We propose a technique called Aggressive Stochastic W eight Averaging (ASWA) and an extension called Norm-filtered Aggressive Stochastic W eight Averaging (NASWA) which improves the stability of models over random seeds. With our ASW A and NASW A based optimization, we are able to improve the robustness of the original model, on average reducing the standard deviation of the model's performance by 72% . 1 Introduction There has been a tremendous growth in deep neural network based models that achieve state-of- the-art performance. In fact, most recent end-to-end deep learning models have surpassed the performance of careful human feature-engineering based models in a variety of NLP tasks. However, deep neural network based models are often brittle to various sources of randomness in the training of the models. This could be attributed to several sources including, but not limited to, random parameter initialization, random sampling of examples during training and random dropping of neurons. It has been observed that these models have, more often, a set of random seeds that yield better results than others.


Model Evaluation in the Land of Deep Learning

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Applications for machine learning and deep learning have become increasingly accessible. For example, Keras provides APIs with TensorFlow backend that enable users to build neural networks without being fluent with TensorFlow. Despite the ease of building and testing models, deep learning has suffered from a lack of interpretability; deep learning models are considered black boxes to many users. In a talk at ODSC West in 2018, Pramit Choudhary explained the importance of model evaluation and interpretability in deep learning and some cutting edge techniques for addressing it. Predictive accuracy is not the only concern regarding a model's performance.