Sana Benefits, an Austin company that wants to disrupt the healthcare insurance industry with more efficient software, said it has raised $6.3 million in seed funding. The company joins a raft of upstarts disrupting a notoriously inefficient healthcare system. These startups include players like Oscar Health, Clover, and Bright Health -- all of which have raised hundreds of millions to billions of dollars over the last few years. Most of them have been focused on individuals and not as much on the employer plans that cover half of Americans. Sana distinguishes itself in that it's one of the few new providers focused on providers, and specifically the market for small and medium sized businesses (a thousand or fewer employees).

Model interpretation, or explanation of a machine learning classifier, aims to extract generalizable knowledge from a trained classifier into a human-understandable format, for various purposes such as model assessment, debugging and trust. From a computaional viewpoint, it is formulated as approximating the target classifier using a simpler interpretable model, such as rule models like a decision set/list/tree. Often, this approximation is handled as standard supervised learning and the only difference is that the labels are provided by the target classifier instead of ground truth. This paradigm is particularly popular because there exists a variety of well-studied supervised algorithms for learning an interpretable classifier. However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels. We call this the active-query property, suggesting that we should consider model interpretation from an active learning perspective. Following this insight, we argue that the active-query property should be employed when designing a model interpretation algorithm, and that the generation of synthetic instances should be integrated seamlessly with the algorithm that learns the model interpretation. In this paper, we demonstrate that by doing so, it is possible to achieve more faithful interpretation with simpler model complexity. As a technical contribution, we present an active algorithm Active Decision Set Induction (ADS) to learn a decision set, a set of if-else rules, for model interpretation. ADS performs a local search over the space of all decision sets. In every iteration, ADS computes confidence intervals for the value of the objective function of all local actions and utilizes active-query to determine the best one.

Identity fraud, in which a slice of your identity ranging from new credit cards to entire bank accounts is taken over by criminals, rose by 49 per cent in 2015 on the previous year. That totalled almost 170,000 cases, according to data collected by Cifas, the financial industry's non-profit fraud advisory service. The reason for the rise is that more and more we use the internet for financial transactions, but have very few ways to verify our identity without cumbersome systems involving human interaction, which are also vulnerable to fraud. Cifas' 2015 Fraudscape report shows that 86 per cent of identity fraud happened online, with bank accounts and credit or debit cards most targeted, closely followed by loans and communications, typically mobile phone accounts. Businesses looking to tackle fraud are turning to artificial intelligence and deploying neural networks because the systems learn in a manner like the brain's own neurons to try to bust fraud Traditionally, companies dealing with such problems have acted after the fact, trying to unravel complex or opportunistic frauds by working back through audit trails.

NLP is an interdisciplinary field concerned with the interactions between computers and human natural languages (e.g: English) -- speech or text. Okay, now we get it, NLP plays a major role in our daily computer interactions, let's see more business-related NLP use-cases: NLP is divided into two fields: Linguistics and Computer Science. The Linguistics side is concerned with language, it's formation, syntax, meaning, different kind of phrases (noun or verb) and whatnot. The Computer Science side is concerned with applying linguistic knowledge, by transforming it into computer programs with the help of sub-fields such as Artificial Intelligence (Machine Learning & Deep Learning). Scientific advancements in NLP can be divided into 3 categories (Rule-based systems, Classical Machine Learning models and Deep Learning models).

In recent years, machine learning has become prevalent in almost every industry. Machine learning has the ability to learn automatically and bring about changes and improvements from experiences. However, most of the businesses today are facing a lot of hurdles while implementing machine learning projects. For those companies in this space, several organizations are offering support for effective machine learning implementation. Quickpath is one such company that is proving itself quite realistic by offering AI and machine learning solutions where businesses can take benefits.

This variation of production quality often depends on the heterogenous nature of the raw materials, even in the same batch, and in variables that cannot be controlled by the operators, such as the exterior temperature, the humidity, etc. Still, can't we do anything when rule-based systems and classic engineering cannot compensate in real time for these conditions? "To obtain a constant high quality, companies can either invent new predefined recipes, or must dynamically change the existing recipe", analyzes Jean-Philippe Hugo, CEO of Wizata. "Nowadays, recipes are highly complex and traditional methods aren't enough to stay ahead of the competition: your engineers and operators need to be assisted by a new set of eyes, powered by an artificial intelligence that takes advantage of the predictive power in the data to know in advance the final product quality, and how to select the optimal parameters to achieve a better result."

BLOOMINGTON, Ind., Oct. 22, 2019 (GLOBE NEWSWIRE) -- Megaputer Intelligence, Inc. will share an innovative new tool for building training datasets for use in machine learning during a presentation at the Text Analytics Forum '19 held in Washington, DC on November 7. Dr. Sergei Ananyan, CEO of Megaputer Intelligence, Inc., will present a cutting-edge topic entitled, "NLP & Rule-Based Approach for Fact Extraction: Launchpad for Machine Learning Techniques" on Thursday, November 7 at 11:15 AM EST. The Text Analytics Forum will host the presentation at the JW Marriott in Washington, DC as part of its comprehensive programming, running from Nov 4-7. The content of the presentation is designed for people interested in discovering how to achieve higher accuracy from machine learning, relieve the burden of needing experts to manually create a gold standard training dataset, and illuminate the black box surrounding machine learning as much as possible with insight into today's latest technological advances. Professionals such as text analysts, data scientists, DBAs, information knowledge architects, knowledge organizers, taxonomists, ontologists, CIOs, CKOs, research scientists, and data quality managers will benefit greatly from this technique to overcome well-known challenges of machine learning. One fundamental obstacle for using machine learning (ML) to accurately extract facts from free-text documents is that it requires huge quantities of pre-categorized data for training a model.

Rule-based Anomaly Detection: This is already framed by specific set of rules that describe an anomaly and assigns the thresholds and limits. We typically rely on the experience of industry experts whose findings are ideal to detect known anomalies. These anomalies are familiar anomalies to us and we can easily recognize, whether it is normal or abnormal. One of the major flaws of rule-based systems is that they don't detect anomalies automatically as patterns change. To learn new patterns, a new model would have to be built each time.

Google has discontinued selling its artificial intelligence-powered camera device called'Clips'. The device, which was launched in 2017 at a price of $249, uses machine learning to learn and recognise faces and automatically records short motion images of things it finds "interesting". Google said it has begun integrating'Clips' technology into the'Photobooth' feature starting with its Pixel 3.

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