The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel machine learning tools to identify providers that overbill Medicare, the US federal health insurance program for elderly adults and the disabled. Using large-scale Medicare claims data, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for Medicare fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and several case studies validate our approach and findings both quantitatively and qualitatively.

Predictive maintenance: Most industrial equipment is built to last and operate with minimal downtime. As a result, there is often limited historical data with which to work. Because unsupervised ML can detect anomalous behavior even in limited data sets, it can potentially identify developing defects in these situations. Here too, it can be used for fleet management, providing early warning of defects while minimizing the amount of data that needs to be reviewed. Quality assurance/inspection: A machine that's operating improperly can produce substandard product.

In this paper, we propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We argue that the majority of available data in the nature for Artificial Intelligence (AI), specifically Machine Learning (ML) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. However, prior work in the literature of MDSE has considered supervised ML approaches, which only work with labeled training data. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain. Moreover, we validate the proposed approach using a portion of the open data of the REFIT reference dataset for the smart energy systems domain. Our model-to-code transformations (code generators) provide the full source code of the desired IoT services out of the model instances in an automated manner. Currently, we generate the source code in Java and Python. The Python code is responsible for the ML functionalities and uses the APIs of several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow. For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are deployed. In addition to the pure MDSE approach, where certain ML methods, e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian Mixture Model, Self-Training, Label Propagation and Label Spreading are supported, a more flexible, hybrid approach is also enabled to support the practitioner in deploying a pre-trained ML model with any arbitrary architecture and learning algorithm.

Facebook, one of the most popular social media application with 2.6 billion users worldwide, is also a leading developer and an active user of technology. Facebook uses its own AI based translations that helps users from different worldwide regions, to convert news feed, and facebook stories in their own languages. Taking its technology one step further, facebook has trained an AI model that will not require transcribed data. Facebook will use this AI model for building the speech recognition system. Facebook's unsupervised speech recognition model Wav-2vcu will be fed with unknown data with no previously defined datasets. The system will teach itself to classify data.

This primer on all things artificial intelligence, written by Grooper Product Manager Chris Dearner, PhD., exposes the truth about AI. In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving". As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.

In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods, or even small variations of the same method, can produce significantly different outcomes. While intuitively somewhat surprising, this can naturally occur when applying unsupervised ML to highly dimensional data, where there can be many reasonable yet different answers to the same question. In such a case the outcome of any single unsupervised ML method should be considered a sample from a conceivably wide range of possibilities. We therefore suggest an approach that eschews finding an optimal outcome, instead facilitating the production and examination of many valid ones.

Many FIs and merchants that have fallen victim to fraud traditionally respond by assessing the damage, pinpointing how the attack succeeded and implementing new measures to prevent similar schemes from happening again. Some businesses are looking for solutions that will help them stop fraud from happening in the first place as criminals become increasingly creative and aggressive in their efforts to steal data and funds. The push for more intelligent anti-fraud solutions comes as the costs of such attacks are reaching new heights. Fraud losses hit $14.7 billion last year, according to the latest DataVisor Fraud Index Report. Account takeover (ATO) fraud proved to be particularly effective, causing $4 billion in losses.

Moore's Law, advocated by Gordon Moore of Intel fame, says that the computational capabilities will double every 18 to 24 months. And we've seen that really unfolding over the last 30 years (see chart). It's really stoked people's imagination, so much so that many believe that the promise of artificial intelligence (AI) could become reality, and computers could actually learn to think like humans. I believe it's still a number of years away, but it is fueling a lot of hype regarding AI. What it's truly capable of, where it can be effective, and what it takes to implement it, all of which have become somewhat inflated in the market today.

There has been a lot of hype around AI to the point where some people are simply tuning it out. I think this is a mistake. While there are limits to what AI can do, there also are sophisticated attacks that we'd miss without it. The need for AI is driven by three fundamental yet significant changes in the enterprise computing environment. Taking all of these factors together leads me to believe that AI is not only a viable solution, but it may be the only solution.

Machine learning (ML) based data analytics is rewriting the rules for how enterprises handle data. Research into machine learning and analytics is already yielding success in turning vast amounts of data--shaped with the help of data scientists--into analytical rules that can spot things that would escape human analysis in the past--whether it be in pursuit of pushing forward genome research or predicting problems with complex machinery. Now machine learning is beginning to move into the business world. But most organizations haven't truly grasped how machine learning will change the way they do business--or how it will change the shape of their organizations in the process. Companies are looking to ML to automate processes or to augment humans by assisting them in data-driven tasks.