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Digital mental health company Cerebral is using a machine learning algorithm to help pinpoint patients in crisis. And the recently unveiled initiative is just the beginning of Cerebrals use of machine learning-enabled solutions, according to the company.

Machine learning (ML) and artificial intelligence (AI) are critical tools in the advancement of mental health care, but these benefits are only possible at scale, a team of Cerebral researchers explained in a company post. Both technologies require many data points to test and validate hypotheses in order to prove that the systems are working effectively.

Dubbed the Crisis Message Detector 1 (CMD-1), the newly touted tool is designed to identify messages from patients experiencing a mental health crisis, then refer those patients to a crisis response specialist.

Specifically, the tool was trained to spot signs of suicidal ideation, homocidal ideation, non-suicidal self-injury or domestic violence, according to the company post. If a patients messages to Cerebral have been flagged, a specialist will reach out directly and assess the patients risk level.

This mental health professional will then be able to call emergency contacts or local responders, if needed.

Cerebral is pitching this as an alternative to relying on patients to call 911 if there is an emergency. The company claims the tool is accurate in properly identifying individuals in crisis.

During a week-long pilot, CMD-1 screened over 60,000 EMR messages and flagged more than 500 potential crises, the Cerebral researchers wrote. The model successfully detected over 99% of all crisis messages and, as a result, crisis specialists were able to respond to patients in less than 9 minutes on average.

Cerebral receives several thousand patient messages each day via its online chat system or mobile app. The company says that somebody from the patients care team reviews and addresses those messages, but that human-led process isnt always as fast as it needs to be.

The company plans to expand its machine learning initiatives in the upcoming months and focus on issues such as response times for medication concerns, scheduling issues and general support requests.

Because of Cerebrals experience serving a quarter-million people (and counting), we are uniquely suited to develop and implement cutting edge ML/AI tools to supplement the expertise of our clinicians and help improve clinical outcomes, the companys post added.

CMD-1 is being rolled out nationally and will be available 24/7.

In addition to demonstrating the companys interest in ML and AI, the patient-identification tool is also reflective of its previously discussed commitment to quality control moving forward.

Earlier this year, Cerebral came under fire for its prescribing practices of controlled substances. In June, news surfaced that the Department of Justice (DOJ) launched an investigation into the company based on a potential violation of the Controlled Substance Act.

In turn, the digital health company changed up its leadership team. Founder Kyle Robertson stepped down as CEO, and Chief Medical Officer Dr. David Mou took on the role.

I will say that we have made mistakes, Mou said at the American Telemedicine Association conference in May. And Ill also admit that we will continue to make mistakes and learn.

Cerebral also announced layoffs for July 1. In June, Mou told BHB that the layoffs were reflective of the companys priorities to keep behavioral health front and center while moving into value-based care.

As for the future, the company is looking to treat serious mental illness (SMI) and developing its value-based care proposition, Mou previously told BHB.

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Cerebral Uses Machine Learning to Identify Patients in Mental Health Crisis, Vows 'Just the Beginning' for AI Investments - Behavioral Health Business

Artificial intelligence and ML are making considerable inroads in finance. They are the critical aspect of variousfinancial applications, including evaluating risks, managing assets, calculating credit scores, and approving loans.

Businesses use AI and ML:

Taking the above points into account, its no wonder that companies like Forbes and Venture beat are usingAI to predict the cash flow and detect fraud.

In this article, we present the financial domain areas in which AI and ML have a more significant impact. Well also discuss why financial companies should care about and implement these technologies.

Machine learning is a branch of artificial intelligence that allows learning and improvement without any programming. Simply put, data scientists train the MI model with existing data sets and automatically adjust its parameters to improve the outcome.

According to Statista, digital payments are expected to show an annual growth rate of 12.77% and grow to 20% by 2026. This vast number of global revenues, done online requires an intelligent fraud system.

Source: Mordor Intelligence

Traditionally, to check the authenticity of users, fraud-detection systems analyze websites through factors like location, merchant ID, the amount spent, etc. However, while this method is appropriate for a few transactions, it would not cope with the increased transactional amount.

And, analyzing the surge of digital payments, businesses cant rely on traditional fraud-detection methods to process payments. This gives rise to AI-based systems with advanced features.

An AI and ML-powered payment gateway will look at various factors to evaluate the risk score. These technologies consider a large volume of data (location of the merchant, time zone, IP address, etc.) to detect unexpected anomalies, and verify the authenticity of the customer.

Additionally, the finance industry, through AI, can process transactions in real-time, allowing the payment industry to process large transactions with high accuracy and low error rates.

The financial sector, including the banks, trading, and other fintech firms, are using AI to reduce operational costs, improve productivity, enhance users experience, and improve security.

The benefits of AI and ML revolve around their ability to work with various datasets. So lets have a quick look at some other ways AI and ML are making roads into this industry:

Considering how people invest their money in automation, AI significantly impacts the payment landscape. It improves efficiency and helps businesses to rethink and reconstruct their process. For example, businesses can use AI to decrease the credit card processing (gettrx dot com card processing guide for merchants) time, increase automation and seamlessly improve cash flow.

You can predict credit, lending, security, trading, baking, and process optimization with AI and machine learning.

Human error has always been a huge problem; however, with machine learning models, you can reduce human errors compared to humans doing repetitive tasks.

Incorporating security and ease of use is a challenge that AI can help the payment industry overcome. Merchants and clients want a payment system that is easy to use and authentic.

Until now, the customers have to perform various actions to authenticate themselves to complete a transaction. However, with AI, the payment providers can smooth transactions, and customers have low risk.

AI can efficiently perform high volume; labor-intensive tasks like quickly scrapping data and formatting things. Also, AI-based businesses are focused and efficient; they have minimum operational cost and can be used in the areas like:

Creating more Value:

AI and machine learning models can generate more value for their customers. For instance:

Improved customer experience: Using bots, financial sectors like banks can eliminate the need to stand in long queues. Payment gateways can automatically reach new customers by gathering their historical data and predicting user behavior. Besides, Ai used in credit scoring helps detect fraud activity.

There are various ways in which machine learning and artificial intelligence are being employed in the finance industry. Some of them are:

Process Automation:

Process automation is one of the most common applications as the technology helps automate manual and repetitive work, thereby increasing productivity.

Moreover, AI and ML can easily access data, follow and recognize patterns and interpret the behavior of customers. This could be used for the customer support system.

Minimizing Debit and Credit Card Frauds:

Machine learning algorithms help detect transactional funds by analyzing various data points that mostly get unnoticed by humans. ML also reduces the number of false rejections and improves the real-time approvals by gauging the clients behavior on the Internet.

Apart from spotting fraudulent activity, AI-powered technology is used to identify suspicious account behavior and fraudulent activity in real-time. Today, banks already have a monitoring system trained to catch the historical payment data.

Reducing False Card Declines:

Payment transactions declined at checkout can be frustrating for customers, putting huge repercussions on banks and their reputations. Card transactions are declined when the transaction is flagged as fraud, or the payment amount crosses the limit. AI-based systems are used to identify transaction issues.

The influx of AI in the financial sector has raised new concerns about its transparency and data security. Companies must be aware of these challenges and follow safeguards measures:

One of the main challenges of AI in finance is the amount of data gathered in confidential and sensitive forms. The correct data partner will give various security options and standards and protect data with the certification and regulations.

Creating AI models in finance that provide accurate predictions is only successful if they are explained to and understood by the clients. In addition, since customers information is used to develop such models, they want to ensure that their personal information is collected, stored, and handled securely.

So, it is essential to maintain transparency and trust in the finance industry to make customers feel safe with their transactions.

Apart from simply implementing AI in the online finance industry, the industry leaders must be able to adapt to the new working models with new operations.

Financial institutions often work with substantial unorganized data sets in vertical silos. Also, connecting dozens of data pipeline components and tons of APIS on top of security to leverage a silo is not easy. So, financial institutions need to ensure that their gathered data is appropriately structured.

AI and ML are undoubtedly the future of the financial sector; the vast volume of processes, transactions, data, and interactions involved with the transaction make them ideal for various applications. By incorporating AI, the finance sector will get vast data-processing capabilities at the best prices, while the clients will enjoy the enhanced customer experience and improved security.

Of course, the power of AI can be realized within transaction banking, which sits on the organizations usage. Today, AI is very much in progress, but we can remove its challenges by using the technology. Lastly, AI will be the future of finance you must be ready to embrace its revolution.

Featured Image Credit: Photo by Anna Nekrashevich; Pexels; Thank you!

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AI and Machine Learning in Finance: How Bots are Helping the Industry - ReadWrite

It is hard to classify documents. At least manually.

Imagine this: you head into a standard bookstore where pieces are supposed to be classified as genres like thriller, romance, science fiction, and more. You want to pick Andy Weirs Hail Mary a novel with thriller/mystery and science fiction elements.

While the book choice seems on point, the question is: which genre should you head towards? The book can be on the science fiction shelf or on the thriller counter. It can be anywhere. And that is when the manual document classification becomes troublesome.

Sweating already? Fret not, as machine learning is here to help. Not to throw shade at the manual document classification, but they can be tedious if you plan on looking at a world outside books including inventories and databases.

Yet, document classification with machine learning can be a game changer, courtesy of the relevant and available technologies like NLP, Robots, Sentiment Analysis, OCR, and more.

Lets take a deeper dive into all of these.

Simply put, document classification is the automation process where relevant/classifying documents are stacked into relevant classes or even categories.

Often regarded as one of the sub-domain of text classification, an oversimplified version of document classification means tagging the docs and setting them right into predefined categories for the purpose of easy maintenance and efficient discovery.

In hindsight, the process is simple. Its all about extracting and retrieving information. Yet, due to the sheer size of data sets, companies often need to rely on deep learning and machine learning technologies to get ahead of document classification, albeit with a focus on speed, accuracy, scalability, and cost-effectiveness.

And just to mention, document classification can be considered a sub-domain of IDP or intelligent document processing. But more on that later.

As for the approach, document classification takes the text and visual classification techniques into consideration primarily for analyzing the document-specific phrases and also the visual structure.

Visual and text classification can help companies classify every kind of document (stills, pictures, large data modules, and more) with ease.

Short story: intelligent models scan through structured, unstructured, and even semi-structured documents to match them with the corresponding categories.

Long story: The following machine learning techniques are put to use for classifying documents according to categories:

Regardless of the approach, businesses need to find a good way to classify documents as going manual can be time-consuming, erroneous, and obviously hard.

However, if you are looking for broader shades in regards to the process, here are the steps associated with an automated and efficient document classification process:

Theoretical discourse is all cool, but what about the use-cases for document classification. We have it all sorted for you.

Opinion Classification: Businesses use this feature to segregate positive reviews from negative ones.

Spam Detection: Have you ever thought about how your email provider separates standard emails from spam emails? Well, document classification is the answer.

Customer support classification: A random day in the life of a customer support executive can be stressful. Document classification helps them understand the tickets better, especially when the request volume far exceeds their patience.

In addition to the mentioned use cases, document classification can also be used for social listening, document scanning, and even object recognition.

Every organization is information-dependent. Yet, every kind of information isnt meant for everyone. This is the reason why document classification becomes all the more important helping organizations collect, store, and eventually classify details as per requirements. And if you are still a manual evangelist, remember one thing: automation is the key to the future.

About the author: Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives. Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/

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What is document classification, and how can machine learning help? - Robotics and Automation News

Rokt has partnered with Uber Technologies (NYSE:UBER) and AMC Theatres (NYSE:AMC) to help both companies make more money on their websites and mobile apps.

Rokt is an ecommerce tech company using machine learning to help tailor transactions to each shopper. The idea behind the technology is to give companies the chance to get additional revenue, find customers at scale and give extra options to existing customers by using machine learning to present offers to each shopper as theyre entering into the final stages of a transaction. The analog here would be the impulse buying section prior to a checkout line, except specifically tailored due to each consumer due to collected data.

Uber and AMC Theatres are two of the most recognized brands in the world and were extremely pleased to partner with both of them as we accelerate our growth globally. Our global partnership with Uber will support the Uber Eats internal ad network and unlock additional profitability for the company. Our partnership with AMC has already begun generating outstanding results for the company. We look forward to expanding our relationships with both of these companies in the future, said Elizabeth Buchanan, chief commercial officer of Rokt.

Rokts deal is ecommerce technology that helps customers find the full potential of every transaction to grow revenue. Existing customers include Live Nation, Groupon, Staples, Lands End, Fanatics, GoDaddy, Vistaprint and HelloFresh, but also extend out to include 2,500 other global businesses and advertisers. The company is originally from Australia, but its moved its headquarters to New York City in the United States, and has expanded out to include 19 countries across three continents.

Rokts partnership with Uber will initially launch with Uber Eats in the US, Canada, Australia and Japan, with Rokts machine learning technology driving additional revenue for Uber during the checkout experience. AMC has partnered with Rokt to drive revenue and customer lifetime value across the companys online and mobile channels.

As millions of moviegoers come to AMC each week to enjoy the unmatched entertainment of the big screen, its important that we are offering a guest experience thats personally relevant across the entire moviegoing journey. Our partnership with Rokt enables us to better personally engage our consumers and drive higher value per transaction by optimizing each online touchpoint without adding additional cost to the moviegoer, said Mark Pearson, chief strategy officer for AMC Theatres.

Rokt uses intelligence taken from five billion transactions across hundreds of ecommerce businesses to allow brands to create a tailored customer experience wherein they can control the types of offers on display to their customers. Businesses that partner with Rokt can unlock profit upwards to $0.30 per transaction through high performance techniques relevant to each individual from the moment where the customer puts the item in their digital cart to the time their payment goes through.

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Uber and AMC bring machine learning company Rokt onboard to drive revenue - Mugglehead

Abstract:Machine learning has by now become a widely used tool within materials science, spawning entirely new fields such as materials informatics that seek to accelerate the discovery and optimization of material systems through both experiments and computational studies. Similarly, the increasing use of robotic systems has led to the emergence of autonomous systems ranging from chemical synthesis to personal vehicles, which has spurred the scientific community to investigate these directions for their own tasks. This begs the question, when will mainstay scientific synthesis and characterization tools, such as electron and scanning probe microscopes, start to perform experiment autonomously?

In this talk, I will discuss the history of how machine learning, automation and availability of compute has led to nascent autonomous microscopy platforms at the Center for Nanophase Materials Sciences. I will illustrate the challenges to making autonomous experiments happen, as well as the necessity for data, computation, and abstractions to fully realize the potential these systems can offer for scientific discovery. I will then focus on our work on reinforcement learning as a tool that can be leveraged to facilitate autonomous decision making to optimize material characterization (and material properties) on the fly, on a scanning probe microscope. Finally, some workflow and data infrastructure issues will also be discussed. This research was conducted at and supported by the Center for Nanophase Materials Sciences, a US DOE Office of Science User Facility.

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Autonomous Experiments in the Age of Computing, Machine Learning and Automation: Progress and Challenges - Argonne National Laboratory