AUSTIN, Texas, March 23, 2020 /PRNewswire-PRWeb/ --Novi Labs ("Novi") today announced the release of Novi Prediction Engine version 2.0. This provides critical economic data to E&P workflows such as well planning or acquisition & divestitures. Novi customers can now run a wide range of large-scale scenarios in minutes and get immediate feedback on the economic feasibility of each plan. As price headwinds face the industry, having the ability to quickly and easily evaluate hundreds of scenarios allows operators to efficiently allocate capital.

In addition to the economic outputs, Novi Prediction Engine 2.0 also includes new features targeting enhanced usability and increased efficiency. Novi is now publishing confidence intervals as a standard output for every prediction. This allows customers to understand how confident the model is of each prediction it makes, which is critical decision-making criterion. A video demonstration of Novi Prediction Engine version 2.0 is available at https://novilabs.com/prediction-engine-v2/.

"With the integration of economic outputs and confidence intervals into Novi Prediction Engine, customers have increased leverage, transparency and certainty in what the Novi models are providing in support of their business decisions. This form of rapid scenario driven testing that is unlocked by the Novi platform is vital in today's uncertain market," said Scott Sherwood, Novi's CEO.

About Novi Labs Novi Labs, Inc. ("Novi") is the leading developer of artificial intelligence driven business applications that help the oil & gas industry optimize the economic value of drilling programs and acquisition & divestiture decisions. Leveraging cutting-edge data science, Novi delivers intuitive analytics that simplify complex decisions with actionable data and insights needed optimize capital allocation. Novi was founded in 2014 and is headquartered in Austin, TX. For more information, please visit http://www.novilabs.com.

SOURCE Novi Labs

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Novi Releases v2.0 of Prediction Engine, Adding Critical Economics to Its Machine Learning Outputs - Benzinga

Fraud mitigation is one of the most sought-after artificial intelligence (AI) services because it can provide an immediate return on investment. Already, many companies are experiencing lucrative profits thanks to AI and machine learning (ML) systems that detect and prevent fraud in real-time.

According to a new report, Highmark Inc.s Financial Investigations and Provider Review (FIPR) department generated $260 million in savings that would have otherwise been lost to fraud, waste, and abuse in 2019. In the last five years, the company saved $850 million.

We know the overwhelming majority of providers do the right thing. But we also know year after year millions of health care dollars are lost to fraud, waste and abuse, said Melissa Anderson, executive vice president and chief audit and compliance officer, Highmark Health. By using technology and working with other Blue Plans and law enforcement, we have continually evolved our processes and are proud to be among the best nationally.

FIPR detects fraud across its clients services with the help of an internal team made up of investigators, accountants, and programmers, as well as seasoned professionals with an eye for unusual activity such as registered nurses and former law enforcement agents. Human audits performed to detect unusual claims and assess the appropriateness of provider payments are used as training data for AI systems, which can adapt and react more rapidly to suspicious changing consumer behavior.

As fraudulent actors have become increasingly aggressive and cunning with their tactics, organizations are looking to AI to mitigate rising threats.

We know it is much easier to stop these bad actors before the money goes out the door then pay and have to chase them, said Kurt Spear, vice president of financial investigations at Highmark Inc.

Elsewhere, Teradata, an AI firm specialized in selling fraud detection solutions to banks, claims in a case study that it helped Danske Bank reduce its false positives by 60% and increased real fraud detection by 50%.

Other service operators are looking to AI fraud detection with a keen eye, especially in the health care sector. A recent survey performed by Optum found that 43% of health industry leaders said they strongly agree that AI will become an integral part of detecting telehealth fraud, waste, or abuse in reimbursement.

In fact, AI spending is growing tremendously with total operating spending set to reach $15 billion by 2024, the most sought-after solutions being network optimization and fraud mitigation. According to theAssociation of Certified Fraud Examiners (ACFE)inauguralAnti-Fraud Technology Benchmarking Report,the amount organizations are expected to spend on AI and machine learning to reduce online fraud is expected to triple by 2021.

Mitigating fraud in healthcare would be a boon for an industry that is plagued with many structural inefficiencies.

The United States spends about $3.5 trillion on healthcare-related services every year. This staggering sum corresponds to about 18% of the countrys GDP and is more than twice the average among developed countries. However, despite this tremendous spending, healthcare service quality is lacking. According to a now-famous 2017 study, the U.S. has fewer hospital beds and doctors per capita than any other developed country.

A 2019 study found that the countrys healthcare system is incredibly inefficient, burning through roughly 25% of all its finances which basically go to waste thats $760 billion annually in the best case scenario and up to $935 billion annually.

Most money is being wasted due to unnecessary administrative complexity, including billing and coding waste this alone is responsible for $265.6 billion annually. Drug pricing is another major source of waste, account for around $240 billion. Finally, over-treatment and failure of care delivery incurred another $300 billion in wasted costs.

And even these astronomical costs may be underestimated. According to management firm Numerof and Associates, the 25% waste estimate might be conservative. Instead, the firm believes that as much as 40% of the countrys healthcare spending is wasted, mostly due to administrative complexity. The firm adds that fraud and abuse account for roughly 8% of waste in healthcare.

Most cases of fraud in the healthcare sector are committed by organized crime groups and a fraction of some healthcare providers that are dishonest.

According to the National Healthcare Anti-Fraud Association, the most common types of healthcare frauds in the United States are:

Traditionally, the most prevalent method for fraud management has been human-generated rule sets. To this day, this is the most common practice but thanks to a quantum leap in computing and Big Data, AI-based solutions based on machine learning algorithms are becoming increasingly appealing and most importantly practical.

But what is machine learning anyway? Machine learning refers to algorithms that are designed learn like humans do and continuously tweak this learning process over time without human supervision. The algorithms output accuracy can be improved continuously by feeding them data and information in the form of observations and real-world interactions.

In other words, machine learning is the science of getting computers to act without being explicitly programmed.

There are all sorts of various machine learning algorithms, depending on the requirements of each situation and industry. Hundreds of new machine learning algorithms are published on a daily basis. Theyre typically grouped by:

In a healthcare fraud analytics context, machine learning eliminates the use of preprogrammed rule sets even those of phenomenal complexity.

Machine learning enables companies to efficiently determine what transactions or set of behaviors are most likely to be fraudulent, while reducing false positives.

In an industry where there can be billions of different transactions on a daily basis, AI-based analytics can be an amazing fit thanks to their ability to automatically discover patterns across large volumes of data.

The process itself can be complex since the algorithms have to interpret patterns in the data and apply data science in real-time in order to distinguish between normal behavior and abnormal behavior.

This can be a problem since an improper understanding of how AI works and fraud-specific data science techniques can lead you to develop algorithms that essentially learn to do the wrong things. Just like people can learn bad habits, so too can a poorly designed machine learning model.

In order for online fraud detection based on AI technology to succeed, these platforms need to check three very important boxes.

First, supervised machine learning algorithms have to be trained and fine-tuned based on decades worth of transaction data to keep false positives to a minimum and improve reaction time. This is harder said than done because the data needs to be structured and properly labeled depending on the size of the project, this could take staff even years to solve.

Secondly, unsupervised machine learning needs to keep up with increasingly sophisticated forms of online fraud. After all, AI is used by both auditors and fraudsters. And, finally, for AI fraud detection platforms to scale, they require a large-scale, universal data network of activity (i.e. transactions, filed documents, etc) to scale the ML algorithms and improve the accuracy of fraud detection scores.

According to a new market research report released earlier this year, the healthcare fraud analytics market is projected to reach $4.6 billion by 2025 from $1.2 billion in 2020.

This growth is attributed to more numerous and complex fraudulent activity in the healthcare sector.

In order to tackle rising healthcare fraud, companies offer various analytics solutions that flag fraudulent activity some are rule-based models, but AI-based technologies are expected to form the backbone of all types of analytics used in the future. These include descriptive, predictive, and prescriptive analytics.

Some of the most important companies operating today in the healthcare fraud analytics market include IBM Corporation (US), Optum (US), SAS Institute (US), Change Healthcare (US), EXL Service Holdings (US), Cotiviti (US), Wipro Limited (Wipro) (India), Conduent (US), HCL (India), Canadian Global Information Technology Group (Canada), DXC Technology Company (US), Northrop Grumman Corporation (US), LexisNexis Group (US), and Pondera Solutions (US).

That being said, there is a wide range of options in place today to prevent fraud. However, the evolving landscape of e-commerce and hacking pose new challenges all the time. To keep up, these challenges require innovation that can respond and react rapidly to fraud. The common denominator, from payment fraud to abuse, seems to be machine learning, which can easily scale to meet the demands of big data with far more flexibility than traditional methods.

See more here:
Artificial intelligence for fraud detection is bound to save billions - ZME Science

New software has been created that can predict a wide range of reaction outcomes but is also more flexible than other programs when it comes to dealing with completely different chemical problems.The machine-learning platform, which uses structure-based molecular representations instead of big reaction-based datasets, could find diverse applications in organic chemistry.

Although machine-learning methods have been widely used to predict the molecular properties and biological activities of target molecules, their application in predicting reaction outcomes has been limited because current models usually cant be transferred to different problems. Instead, complex parameterisation is required for each individual case to achieve good results. Researchers in Germany are now reporting a general approach that overcomes this limitation.

Previous models for accurately predicting reaction results have been highly complex and problem-specific, says Frank Glorius of the University of Mnster, Germany, who led the study. They are mostly based on a previously gained understanding of the underlying processes and cannot be transferred to other problems. In our approach, we use a universal representation of the involved compounds, which is solely based on their molecular structures. This allows for a general applicability of our program to diverse problem sets.

The new tool is based on the assumption that reactivity can be directly derived from a molecules structure and uses an input based on multiple fingerprint features as an all-round molecular representation. Frederik Sandfort, who also participated in the research, explains that organic compounds can be represented as graphs on which simple structural (yes/no) queries can be carried out. Fingerprints are number sequences based on the combination of many such successive queries, he says. They have originally been developed for structural similarity searches and were proven to be well-suited for application in computational models. We use a large number of different fingerprints to represent the molecular structure of each compound as accurately as possible.

Glorius points out that their platform is very versatile. While our model can be used to predict molecular properties, its most important application is the accurate prediction of reaction results, he says. We could predict enantioselectivities and yields with comparable accuracy to previous problem-specific models. Furthermore, the model was applied to predicting relative conversion based on a high-throughput data set which was never tackled using machine learning before.

The program is also easy to use, the researchers say. It only requires the input data in a very simple form and some problem-specific settings, explains Sandfort. He adds that the tool is already online and will be updated further with the teams most recent developments.

Robert Paton at Colorado State University and the Center for Computer Assisted Synthesis, US, who was not involved in the study, notes that machine-learning methods are being increasingly used to identify patterns in data that can help to predict the outcome of experiments. Chemists have managed to harness these techniques by converting molecular structures into vectors of numbers that can then be passed to learning algorithms, he says. Representations using information only from a molecules atoms and their connectivity are agnostic to the particular reaction and as a result may be used across multiple reaction types for different types of predictions. Future developments in interpreting these predictions a challenge shared by all machine learning approaches will be valuable.

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Structure-based AI tool can predict wide range of very different reactions - Chemistry World

Adelaide Tuesday March 24, 2020

Australian Institute of Machine Learning creates an AI keyboard that creates its own lyrics.

Dr Jamie Sherrah, a researcher based at the University of Adelaide institute in South Australia, created the singing keyboard as a way to demonstrate machine learnings capacities outside the usual sectors.

Named Voog the voice version of an old synthesizer brand called Moog the keyboard uses machine learning for singing synthesis from text as a melody is played.

Dr Sherrah said he planned to eventually commercialise a prototype that creates meaningful lyrics.

He also hoped to attract musicians already experimenting with voice in their music to begin using Voog.

Its a human driven performance, you still choose the pitch and the timing of the notes by playing, usually you would play those notes and then sing but with this, you are playing and its singing, Dr Sherrah said.

For awhile Yamaha has had a software product to do that offline but I havent seen a live keyboard like this before.

Dr Sherrah said while there was extensive AI work happening at the Australian Institute of Machine Learning within the health, defence and education sectors, the institute was also exploring an alternative arts stream.

This is a way of trying to connect with people and to try and show them the breadth of applications for machine learning and the kinds of things were working on, Dr Sherrah said.

Dr Sherrah, who plays guitar, based his PhD on using genetic programming to automatically learn features for pattern recognition and is now also working with several startup companies on other machine learning projects.

Based from Adelaide, Dr Sherrah is also chief scientist with Canadian startup FTSY that has created an app where a mobile phone can be used to 3D reconstruct a users feet so they can then find the right shoe size and shape when buying online.

Another of his Australian Institute of Machine Learning projects is a self-help guru Froyd AI designed as a Twitter bot.

Froyd AI has been busy delivering a thought-provoking online message based on data from hundreds of millions of web pages.

The bot was developed through the institutes arts space program, which recently hosted avant-garde New York artist Laurie Anderson as its first artist-in-residence for a arts meets AI hackathon.

Each day the contemplative bot uses machine learning to deliver one pithy observation crafted with a grain of truth.

One of the big developments in machine learning in the last two years or so has been on this model trained on lots of English natural language text that is able to generate very realistic looking text, Dr Sherrah said.

Among the pearls of artificial intelligence are I am a mind trapped in a computer and there is no way around it and The meaning of life as we know it is a great one. It is a place where all things are created and consumed and must be continually transformed.

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Making music with Voog the singing keyboard - The Lead South Australia