We present a Markov chain Monte Carlo scheme based on merges and splits of groups that is capable of efficiently sampling from the posterior distribution of network partitions, defined according to the stochastic block model (SBM). We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the Markov chain by several orders of magnitude in typical cases. We also show how the scheme can be straightforwardly extended to nested versions of the SBM, yielding asymptotically exact samples of hierarchical network partitions.

In an new column, "Tech-Enabled A2J", we will take a look at how legal start-ups and legal technology innovations are impacting the push toward better Access to Justice for more citizens. Whereas LSOs have found past success in reaching clients through basic tools like texting, they are now moving to more advanced platforms like document automation to better streamline internal processes. Some are even going one step further by embarking on artificial intelligence (AI) and machine learning (ML) projects to determine how they can help address the 86% of civil legal problems reported by low-income Americans that aren't fully resolved. Access to justice starts with literal access: figuring out how clients best receive, digest, and act on legal information. On the lower-tech end, text messaging has proven to be a successful tool for reaching those in need.

In the wake of the coronavirus crisis, lockdowns and social distancing are accelerating the adoption of e-commerce, online learning, remote work, and online entertainment. In the wake of the coronavirus crisis, lockdowns and social distancing are accelerating the adoption of e-commerce, online learning, remote work, and online entertainment. And since online platforms and marketplaces generate so much data and rely so heavily on artificial intelligence (AI), their growth will only expand the AI gold rush. Already, global e-commerce sales have grown more than 20% per year each of the past three years. The number of students taking at least one course online has grown by almost two percentage points per year.

Bashing Silicon Valley has become one of the few things both political parties agree on this election cycle. And they have good reason--the artificial intelligence (AI) and automation technology developed by the Valley's best and brightest minds is projected to displace the jobs of between one-quarter and one-third of American workers by 2030. The Brookings Institute estimates that 36 million Americans could have 70% of their work tasks replaced by automation. These alarming figures are attracting the attention of policymakers and politicians alike. Some are calling for a tax on robots. Others believe massive open online courses (MOOCs) are the answer to retraining millions of displaced workers.

This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

Recent work on few-shot learning [1,2,3] shows that simply fine-tuning a well-trained feature extractor outperforms prior meta-learning methods. I think this phenomenon is not limited to the few-shot classification/detection but is a general problem of machine learning: that most (if not all) problems can be formulated as the two-step (input- feature, feature- output) approach. I feel that a pretty large amount of the "method" papers are stuck in the local minima or revisiting the known observation to the slightly different settings. In my understanding, the machine learning problems essentially break down into the 1) representation learning (input- feature) and 2) prediction (feature- output) problems, hence any proposed "method" paper should outperform the combination of the SOTA feature extractor and the SOTA prediction network (e.g., MoCo/SimCLR cosine classifier for few-shot classification). However, frustratingly few papers provide such a good ablation study.

Machine learning is changing the landscape of business. It's allowing us to make better decisions, understand our target market, and offer comprehensively better experiences for our prospects, customers, and employees. The problem is that it's a mystery to many of our shareholders and corporate managers. Directives come down from meetings to incorporate machine learning, but there are a lot of choices and a lot of directions. To build your solution or to buy it.

After attending some recent conferences and participating in a few online discussion communities in the ISTE networks, I've noticed more of an interest in finding strategies, specific tools or other resources for getting students started with coding. A lot of the questions are focused on how to get started with coding in the early years, the elementary and middle school levels, and where to find the best tools. There are definitely a lot of tools out there for educators to choose from. Some are web-based and offer free coding applications and activities for students. There are also many paid options that are more complex, perhaps involving robots, other necessary equipment, materials or starter kits.

Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through crowdsourcing services like Amazon Mechanical Turk. With the advent of the Internet, data has become abundant and a large number of machine learning based systems started being trained with user-generated data, using categorical data as true labels. However, little work has been done in the area of supervised learning with user-defined labels where users are not necessarily experts and might be motivated to provide incorrect labels in order to improve their own utility from the system. In this article, we propose two types of classes in user-defined labels: subjective class and objective class - showing that the objective classes are as reliable as if they were provided by domain experts, whereas the subjective classes are subject to bias and manipulation by the user. We define this as a subjective class issue and provide a framework for detecting subjective labels in a dataset without querying oracle. Using this framework, data mining practitioners can detect a subjective class at an early stage of their projects, and avoid wasting their precious time and resources by dealing with subjective class problem with traditional machine learning techniques.