As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

This post is the first one of the series. The goal is to share our experience about how to leverage open-source resources to enable deep learning for objection detection on RPi3. As for the first one of the series, the post will talk about why running object detection on RPi3 is difficult. First, deep learning (or to be more specific, CNN) on Raspberry Pi is nothing new. Pete Warden had released DeepBelief SDK for image recognition in 2014 [1], and SqueezeNet [2] was another alternative released in 2015 which aimed to bring lighter solution for embedded systems.

New deep learning models predict the interactions between atoms in organic molecules. These models, which were generated using supercomputers at the San Diego Supercomputer Center and the Los Alamos National Laboratory, help computational biologists and drug development researchers better understand and treat disease. According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics.

I have a huge corpus of documents that are filled with acronyms. It is mostly government stuff. Currently we use regex to translates, but the regex performs poorly and requires a lot of manual fixing. I haven't been able to google this question (it just brings up lists of machine learning/deep learning acronyms).

Building, monitoring, and improving machine learning systems is no walk in the park, no matter the circumstances. Data scientists and engineers have to monitor fine-grained quality and diagnose errors in sophisticated apps, not to mention field contradictory or incomplete corpora. To ease the development burden somewhat, Apple developed Overton, a framework intended to automate AI system lifecycles by providing a set of novel high-level abstractions. Given the query "How tall is the president of the United States," for example, Overton generates a model capable of supplying an answer. Apple researchers say that Overton has been used in production to support "multiple applications" in both near-real-time and back-of-house processing, and in that time, Overton-based apps have answered "billions" of queries in multiple languages and processed "trillions" of records.

You might have noticed several artifacts in the Outputs sub-tab. That's because we save a checkpoint at the end of each epoch!

Hail can be among the most damaging of severe weather phenomena, but predicting whether a passing thunderstorm might start spitting pea-sized (or golf ball–sized) hailstones is notoriously difficult. A new approach using machine learning techniques related to facial recognition technology is giving meteorologists a new tool for mapping how various components of a storm might add up to dangerous hail conditions. Some types of thunderstorms, such as supercells, are more likely to produce hail than others. But the sheer scale of thunderstorms, which can stretch for kilometers and contain multitudes of intrastorm interactions, makes it difficult for computers to accurately model and predict storm behavior, said David John Gagne, a machine learning scientist at the National Center for Atmospheric Research (NCAR) in Boulder, Colo., and lead author of the new study, published in Monthly Weather Review. Drawing upon machine learning technology sometimes used to identify features of individual faces, Gagne and colleagues at NCAR trained a deep learning model called a convolutional neural network to recognize various storm features known to produce hail.

These word vectors are analogous to the meaning of the word. A limitation of word embeddings are that, they are learned by the Natural Language Model (word2vec, GloVe and the like) and therefore words must have been seen in the training data before, in order to have an embedding. This articles provides an approach that can be used to handle out-of-vocabulary(OOV) words in natural language processing. Given an OOV word and the sentence it is in, language modelling is used to sequence words in the sentence and predict the meaning of the word by comparison with similar sentences. This is an elegant way of learning word meanings on the fly.

In this Nature article backed by Google, the investigators develop then externally validate a deep learning model for predicting lung cancer using CT scans. All confidence intervals were computed based on the percentiles of 1,000 random resamplings (bootstraps) of the data. Confidence intervals for differences were derived by computing the metric of interest and then computing a reader–model difference on each bootstrap. When I read up how to obtain confidence intervals using the bootstrap method, what I understand is that the model must be retrained for every single bootstrap, and that the statistic is calculated for each retrained model (and the model is applied to the original pre-bootstrapped data). This implies that Google retrained their deep learning model on a bootstrap of the training sample 1000 times to obtain these intervals.

Many application domains increasingly require AD, when anomalies carry critical and actionable information. These include: (1) Cyber-security and intrusion detection in Cloud and IT systems, also in government, defense and security agencies; (2) Fraud detection in financial institutions; (3) Manufacturing, IoT, industry and resource exploration; (4) Healthcare; etc. We shall address the problem of detecting and predicting general anomalies in high-dimension KPI performance metrics, i.e., high dimension and dynamic range multivariate non-stationary time series collected from large Cloud / IT environments. Using Keras / TF etc., we will build an ML-based AD framework for transfer, attention and meta-learning that must remain robust also with reduced/missing and noisy training data. Besides feature engineering – e.g., selection, reduction, compression techniques – explainability will also be necessary for the model prototype.