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Cybernetics will drive the future of robotics by empowering them with human intelligence

Robotics Industry is constantly rising in this automation world. According to reports, the Indian industrial robotics market is predicted to grow at a CAGR of 13.3% between 2019-2024. With its rising industry applications and productivity benefits, the study of cybernetics is likely to be a vital element in the advancement of robotics.

The craving for gadgets or machines that can keep up with the challenges of the present world and largely function in simpler and smarter ways is evident. Automation and autonomy have offered this by producing and delivering products and services that contain the least amount of human intervention, making certain jobs more convenient than ever before even when information is incomplete and uncertain. The appearance of new service robots and their wide evolution into new applications has further facilitated the world of automation. Due to the dynamic nature of robotics, numerous application sectors are now using robotics to perform predetermined tasks and enhance human efforts in both physical and analytic ways. Robotics has enhanced task efficiency, dependability, and quality, all of which were earlier, products of a laborious procedure. Being a critical component of automation, robotics is currently used in an ever-growing variety of fields, like manufacturing, transportation, healthcare & medical care, utilities, defence, facilities, operations, and more recently, information technology. Here Cybernetics enters as a primary element as robots need to be advanced.

Cybernetics is a study of science that focuses on developing technologies that act or think like humans by researching how electrical devices or machines and the human brain function to enhance the value of the job to be performed. Cybernetics is the best workaround physical embodiment of Artificial Intelligence (AI), Machine Learning (ML), and predictive analysis and control, investigating underlying systems/structures, possibilities, and limitations of complex mechanisms, including robotics, and generating an autonomous environment that uses minimal to no human interaction. AI and cybernetics are two dissimilar perspectives on intelligent systems or systems that may act to achieve an aim. Making computers imitate intelligent behavior using pre-stored world representations is the primary goal of AI. In general, cybernetics tells us how systems control themselves and can take actions autonomously based on environmental signals even when the information is minimal and subject to significant uncertainty or noise. These systems go beyond simple computation; they can also control biological (body temperature regulation), mechanical (engine speed regulation), social (managing a huge workforce), and economic (controlling a national economy) systems.

Every cybernetic systems aim is to be set up so that its operations are linked in a variety of input-output system configurations which are normally driven with reference control signs. This is achieved by processing feedback-based automatic closed-loop control systems that can decide which behaviors should be changed, which actions should be tracked, how to compare the actions to the reference, and how to adapt the application behaviors in the most effective way. In natural cybernetic systems, this regulatory mechanism generates or organizes by itself with the help of self-learning. On the other hand, artificial cybernetic systems behave or are influenced by human-implemented automatic control systems. Essential elements of cybernetic systems are sensors, the controller, actuators and the system to be controlled.

Cybernetics in robotics systems main objective is to use AI and machine learning in the sense-plan-act paradigm normally used to develop robots so they can operate productively in real-world scenarios. Developing a robot to understand and differentiate complex situations every day is highly demanding and getting the situation awareness correctly identified is crucial to ensuring the desired reference control signal can be identified for implementation. This can make sure an industrial robot recognizes and picks up the correct item for the next stage of the manufacturing process from a selection of parts to ensure the requests of the human to be served a variety of beverages will get the correct drink. Sensors and sensor systems that are perfectly calibrated are necessary for ensuring the situation awareness is achieved perfectly and in real-time using AI-based models which can be learned and applied in various situations such as driverless cars, medical robots, automated manufacturing, and home care robots.

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Cybernetics is the Only Way Robots Can Achieve Human Intelligence - Analytics Insight

If youve heard the term machine learning, but arent quite sure what it means, then youve come to the right place. Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being specifically programmed to do so. Basically, machine learning (MI) and artificial intelligence (AI) are helping businesses by improving customer service, reducing errors, managing automation and much more. Why do you need to know all of this? Well, for all of you out there looking to boost your income and career opportunities, you should consider this handy bundle that will give you the basics in machine learning.

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BELLEVUE, Wash. and WATKINSVILLE, Ga., Sept. 13, 2022 (GLOBE NEWSWIRE) -- AI Dynamics and PETTIGREW Medical announced today the formation of a joint venture, with the working name mAIcode, designed to accelerate the automation of medical record coding by applying advanced machine learning. AI Dynamics is an organization founded on the belief that everyone should have access to the power of artificial intelligence (AI) to change the world. PETTIGREW Medical is a pioneer in providing revenue cycle management services and has expanded into a diversified and accredited industry leader on a global scale.

The joint venture with AI Dynamics will enable us to create an automated coding solution that provides an order of magnitude improvement in productivity, efficiency and accuracy, while also reducing costs, said David Young, president and chief financial officer, PETTIGREW Medical. Once the joint venture is fully operational, we look forward to serving a larger percentage of the $18 billion annual medical coding market, which is growing at a compounded annual growth rate (CAGR) of eight percent.

Today, the medical coding market is highly complex, with more than 68,000 diagnostic codes and over 10,000 Current Procedural Terminology (CPT) codes. Current coding approaches are expensive, with the median salary of a medical coder in the U.S being more than $50,000. The typical coder can code at most a few hundred medical records a day, with each record costing between $2 to $20 to code. The volume of content to code is enormous and growing, meaning costs will grow as well.

Unlike other AI coding companies that are focused primarily on the cloud, we have developed mAIcode to be equally efficient for customers that want to manage their data on premise or in a more secure environment. We are also orienting the solution to audit-level accuracy, backed by the NeoPulse Platform, said Rajeev Dutt, founder and CEO of AI Dynamics. Our solution relies on multiple deep learning models built on the NeoPulse Platform, that provides the joint venture with the unique ability to continuously improve its own medical coding capabilities based on experience the AI solution is learning continuously.

The solution is built on a SaaS model that can be run in the cloud or at the customers location. At the core of the solution is AI Dynamics NeoPulse Framework, which will enable customers to manage their entire AI workflow and infrastructure from one place. NeoPulse enables lower cost and faster design and deployment of AI solutions. Customers can adopt the solution to their own medical chart formats. It also features clear explainability, which is necessary for audits, increasing confidence in decisions, and providing peace of mind to clients and auditors. It incorporates federated learning data privacy technology; data never leaves the data owners firewall but the solution enables data users to generate insights from the data, ensuring all parties remain in compliance with HIPAA, U.S state data privacy regulations and data residency requirements. As mAIcode learns from customer use, it will quickly outperform manual solutions.

About AI Dynamics:AI Dynamics aims to make artificial intelligence (AI) accessible to organizations of all sizes. The company's NeoPulse Framework is an intuitive development and management platform for AI, which enables companies to develop and implement deep neural networks and other machine learning models that can improve key performance metrics. The company's team brings decades of experience in the fields of machine learning and artificial intelligence from leading companies and research organizations. For more information, please visit aidynamics.com.

About Pettigrew Medical:PETTIGREW Medical specializes in billing, coding, accounts receivable management and contact center solutions for healthcare billing companies, hospitals, private practices and insurers with large central business office operations. Since 1989, PETTIGREW has provided superior, aggressive, and compliant services to our clients. PETTIGREW is continuously seeking ways to make the experience of running a facility or group easier on owners and medical directors, and of making their practice's information easily accessible. For more information, please visit pettigrewmedical.com.

Media Contact:Madi Oliv / Valeria CarrilloUPRAISE Marketing + PR for AI Dynamics and PETTIGREW Medicalaidynamics@upraisepr.com

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AI Dynamics and PETTIGREW Medical Announce Joint Venture that Applies Advanced Machine Learning to Accelerate Automation of Medical Record Coding -...

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Everything Youve Ever Wanted to Know About Machine Learning - KDnuggets

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. The American insurance industry is one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology like machine learning, insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen.

Insurance data is vast and complex, composed of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different from automobile insurance, health insurance is different from property insurance, and so forth. While some of the processes are similar, the data can vary greatly.

As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.

ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit.

In this post, we lay out how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines. Read on to see how ML can fit within your insurance organization.

ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex requests and analyses.

There are several use cases for ML within an insurance organization regardless of insurance type. Below are some top areas for ML application in the insurance industry:

For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer's previous actions and history, which enables salespeople to have more effective conversations with buyers.

For a majority of customers, insurance can seem daunting, complex, and unclear. It's important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be very helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email, or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer.

ML utilizes data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns.

Unfortunately, fraud is rampant in the insurance industry. Property and casualty insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P&C losses. ML can mitigate this issue by identifying potential claim situations early in the process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim.

Claims processing is notoriously arduous and time-consuming. ML technology is a tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.

Fraud is one of the biggest problems for the insurance industry, so let's return to the fraud detection stage in the insurance lifecycle and detail the benefits of ML for this common issue. Considering the insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums each year, there are huge opportunities and incentives for insurance fraud to occur.

Insurance fraud is an issue that has worsened since the COVID-19 pandemic began. Some industry professionals believe that the number of claims with some element of fraud has almost doubled since the pandemic.

Below are the various stages in which insurance fraud can occur during the insurance lifecycle:

Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection are the following:

ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.

Implementing digital technologies, like ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud.

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5 use cases for machine learning in the insurance industry - Digital Insurance