This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic block model (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects of the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.

A principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e. the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal structures at multiple scales; 2. A very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges, but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

Many network systems lack a natural low-dimensional embedding from which we can readily extract their most prominent large-scale features. Instead, we have to infer this information from data, typically by decomposing the observed network into modules [1]. A principled approach to perform this task is to formulate generative models that allow this modular decomposition to be found via statistical inference [2]. The most fundamental model used for this purpose is the stochastic block model (SBM) [3], which groups nodes according to their probabilities of connection to the rest of the network. However, a central limitation of most SBM implementations is that they are defined strictly for simple or multigraphs. This means that they do not incorporate extra information on the edges, which are typically present in a variety of systems, and are required for an accurate representation of their structure. For example, to the existence of a route between two airports is associated a distance, to the biomass flow between two species in a food web is associated a flow magnitude, etc. In this work, we develop variations of the SBM that allow for this type of information on the edges to be incorporated into the network model and guide the partition of the nodes into groups in a statistically meaningful way. We follow the same basic idea put forth by Aicher et al. [4], who adapted the SBM to weighted networks by including edge values as additional covariates.

Artificial intelligence is starting to play a transformational role in the healthcare industry, even if opportunities for using it are just beginning to be explored. The Department of Health and Human Services and the Robert Wood Johnson Foundation commissioned the report; the names of the scientists who developed the report are not being released. Computers can match human competence in image recognition and, in some studies, can make diagnostic decisions on medical images that match or exceed the ability of clinicians. Technology is also getting better at speech recognition and natural language processing. However, while the healthcare industry has a huge amount of data, the quality and accessibility to pertinent data at an affordable cost remains a challenge, as does the protection and sharing of data.

"Developing a custom model often requires rare expertise and extensive resources," said Fei-Fei Li, Google Cloud AI's chief scientist. So her team developed a drag-and-drop interface. Users can just upload labeled photos of the items they want to be recognize. Jia Li, Cloud AI's head of R&D, says that a model can be created in a day with just a few examples of each item it needs to recognize. "The smallest quantity we've tried so far is like tens of images," she says.

It has been dubbed the first'sports tank', boasting supercar performance on tank tracks. Originally developed to help to military avoid IEDs, the Ripsaw'sports tank' has become a hit with car enthusiasts. Now, a new version has received a huge overhaul - and has been dubbed'the most obnoxious vehicle ever built'. The newest incarnation, the EV3-F1, is'the most extreme and most terrain dominant Ripsaw ever developed,' the firm behind it says. The Ripsaw, developed by twin brothers Mike and Geoff Howe, both 40, from Maine-based company Howe and Howe technologies, has been around for several years in various forms.

Artificial Intelligence (AI) is of great interest to the research world today, potentially driving innovative problem-solving. Both the federal and provincial governments have imagined this potential. The Ontario government has invested in the Vector Institute for Artificial Intelligence, a flagship of its development in science, technology, engineering and mathematics (STEM) to make Ontario a source of high-quality professionals and to attract an industrial base of the information technology (IT) and the AI sectors. The Ministry of Research, Innovation & Science is also commissioning a report to develop a provincial strategy. On the federal side, Ottawa has invested $120 million of direct support and $36 million has been allocated for Vector AI chairs for existing and newly recruited individuals.

Congressman John K. Delaney is the founder of the House AI Caucus and represents Maryland's Sixth District. One of the more unexpected sights within the United States Capitol lies just outside the Old Supreme Court Chamber. There you can find a plaque marking the first ever long-distance communication by electronic telegraph, which took place inside the Capitol in 1844, when Samuel Morse sent and received messages from Baltimore. Morse's first experimental line was made possible thanks to a grant from the federal government. The telegraph would go on to transform American life, allowing instant communication across vast distances.

Artificial intelligence (AI) will in future be used to help reduce human error in physical safety in workplaces such as laboratories, operating theatres or building sites, pending the results of a UK government-funded trial of AI and video networking technology. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Summary: IBM's Watson now to do your taxes at H&R Block? This is a good opportunity to explore the differences between Question Answering Machines (Watson) and Expert Systems. If you were paying attention during the Super Bowl you saw something unprecedented, an advertisement aimed at data scientists. It was the H&R Block announcement that it was rolling out IBM's Watson to all 80,000 of its tax preparers. So far we've seen Watson deployed primarily on more complex and obscure data like chemical reactions, cancer diagnoses, and environmental engineering.