SEOUL, South Korea, Jan. 3, 2024 /PRNewswire/ — Hyundai Motor Company and Kia Corporation announced that they have signed an agreement on January 3 with Samsung Electronics for a Car-to-Home and Home-to-Car service partnership, aiming to enhance the connectivity between residential and mobility spaces.

Under this agreement, Hyundai and Kia customers will be able to remotely control digital appliances via touch and voice commands through their cars’ in-car infotainment systems. Conversely, they will have remote vehicle control via AI speakers, TVs and smartphone apps to control various vehicle functions.

This is made possible through the organic integration of Hyundai and Kia’s connected car services and Samsung’s Internet of Things (IoT) platform, ‘SmartThings’. Customers are expected to use it in various ways in their daily lives, enjoying uninterrupted connectivity experiences.

SOURCE Hyundai Motor Company; Kia Corporation

https://www.prnewswire.com/news-releases/hyundai-kia-and-samsung-electronics-to-collaborate-on-connecting-mobility-and-residential-spaces-302025800.html

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The Rise of Artificial Intelligence

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. Thanks to AI, machines can now perform tasks that traditionally required human intelligence. From self-driving cars to virtual assistants like Siri and Alexa, AI has become an integral part of our daily lives.

Transforming Industries with AI

The integration of AI has transformed various industries, including healthcare, finance, and manufacturing. In healthcare, AI algorithms can analyze complex medical data and assist in diagnosing diseases. AI-powered chatbots are revolutionizing customer service in the finance sector, providing personalized recommendations and assistance. In manufacturing, AI-powered robots can automate processes, resulting in improved efficiency and productivity.

The Internet of Things

The Internet of Things, on the other hand, refers to the network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity that allows them to connect and exchange data. This interconnectedness enables devices to communicate and share information, leading to smarter, more efficient systems.

AI and IoT Integration: A Powerful Combination

The integration of AI and IoT opens up a world of possibilities. By combining AI’s ability to process vast amounts of data and make intelligent decisions with IoT’s network of connected devices, we can create truly intelligent systems.

Enhanced Data Analysis

One of the most significant advantages of AI and IoT integration is the enhanced data analysis capabilities. IoT devices generate enormous amounts of data in real-time, and AI algorithms can analyze this data to extract valuable insights and make quick decisions. For example, in smart homes, AI algorithms can analyze data from various sensors to understand patterns and adjust energy usage accordingly.

Improved Automation and Efficiency

Integrating AI and IoT allows for improved automation and efficiency in various applications. AI algorithms can analyze data from IoT devices to optimize energy usage, streamline manufacturing processes, and predict system failures before they occur. This reduces costs, improves productivity, and enhances overall performance.

Smarter Decision-Making

The combination of AI and IoT enables smarter decision-making in real-time. For instance, in autonomous vehicles, AI algorithms process data from sensors to make split-second decisions, ensuring the safety of passengers and other road users. Additionally, in retail, AI algorithms can analyze data from IoT devices such as beacons and shelf sensors to optimize inventory management and create personalized shopping experiences.

Challenges and Considerations

While the integration of AI and IoT brings numerous benefits, it also presents several challenges that need to be addressed.

Data Privacy and Security

With the increased connectivity of IoT devices, data privacy and security become critical concerns. Collected data can be vulnerable to breaches, and AI algorithms need to ensure that personal and sensitive information is protected. Stricter regulations and robust cybersecurity measures must be in place to address these challenges.

Interoperability

Another challenge in the realm of IoT (Internet of Things) is achieving interoperability among different IoT devices, a challenge where AI avatar can play a pivotal role. As the number of connected devices continues to proliferate, it becomes increasingly essential to ensure seamless communication and data exchange between them. Common standards and protocols need to be established to enable interoperability and prevent fragmentation. AI avatars, with their advanced artificial intelligence capabilities, can serve as intelligent intermediaries, capable of understanding and adapting to various device protocols, thereby facilitating efficient communication and interoperability among the diverse ecosystem of IoT devices. This symbiotic relationship between AI avatars and IoT devices holds the promise of a more harmonious and interconnected future.

Ethical Considerations

The integration of AI and IoT also raises ethical considerations. AI algorithms should be designed to prioritize ethical decision-making. For example, in autonomous vehicles, AI algorithms need to address ethical dilemmas and make decisions that prioritize passenger safety and the greater good.

Summary

In conclusion, the integration of Artificial Intelligence and the Internet of Things brings forth a new era of possibilities. The enhanced data analysis, improved automation and efficiency, and smarter decision-making capabilities make this integration a powerful force in various industries. However, challenges such as data privacy, interoperability, and ethical concerns need to be addressed. With the right measures in place, the integration of AI and IoT has the potential to revolutionize the way we live and work.

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Embracing the Raspberry Pi Revolution

Imagine a wallet-sized computer with the potential to function as a fully-fledged computing powerhouse, all while fitting in the palm of your hand. That’s precisely what the Raspberry Pi (RPi) offers. This series of single-board computers is gaining increasing prominence as the go-to choice for connecting IoT devices. With the Raspberry Pi, you can effortlessly plug into a computer monitor, delve into computing, and master programming languages like Scratch and Python. It’s not just about learning; the Raspberry Pi can handle a multitude of tasks, blurring the line between a traditional computer and an IoT device. From interfacing with the outside world to fueling digital maker projects, its capabilities are boundless – from music machines to parent detectors, weather stations, and much more.

Why Raspberry Pi?

Raspberry Pi has etched its name in the annals of tech enthusiasm. Its affordability, robust processing power within a compact board, an array of interfaces, a wealth of readily available examples with strong community support, and a host of other features have democratized access to IoT development. Over the years, several RPi models have graced the market, each featuring a Broadcom system on a chip (SoC) housing an integrated ARM-compatible central processing unit (CPU) and an on-chip graphics processing unit (GPU). On the board, you’ll find an HDMI port, a 3.5 mm analogue audio/video jack, four USB 2.0 ports, Ethernet, Camera Serial Interface, and Display Serial Interface. For Raspberry Pi enthusiasts, the Raspbian Operating System is a popular choice. Based on Debian OS and optimized for RPi hardware, it brings a seamless experience to users. However, despite its exciting capabilities, Raspberry Pi might not be the ideal choice for professional-grade projects. According to a recent AAC survey, nearly 20 percent of respondents admitted to using “maker” boards in their end products, showcasing the Raspberry Pi’s versatility and applicability in various settings, including professional development.

Bridging the Gap: Raspberry Pi and IoT Services

One of Raspberry Pi’s standout features is its ability to serve as an internet gateway. With a quad-core ARM Cortex A7 CPU running at 900 MHz and 1 GB LPDDR2 SDRAM, it’s well-equipped to function as the Internet Gateway Device, facilitating seamless data transfer between IoT devices and the cloud. By integrating Raspberry Pi with off-the-shelf sensors, IoT projects become more accessible and efficient. As IoT necessitates microcontrollers for data processing, Wi-Fi integration for cloud communication, and actuators for operational control, Raspberry Pi emerges as the preferred choice for innovators worldwide looking to embark on IoT ventures.

In Conclusion

The Internet of Things is a transformative force reshaping industries and redefining what’s possible in the digital realm. Amid this revolution, the Raspberry Pi stands tall as a beacon of innovation and accessibility. Its affordability, computational prowess, and versatility make it a key player in the IoT landscape, enabling both beginners and experts to explore, create, and innovate.

Whether you’re a hobbyist delving into maker projects or a professional developer seeking to bridge the gap between IoT and the real world, Raspberry Pi has proven its worth. Its remarkable capabilities in conjunction with IoT services make it a powerful tool in the hands of those looking to shape the future of technology.

So, are you ready to harness the full potential of the Raspberry Pi in your IoT endeavors? With the Raspberry Pi by your side, the possibilities are virtually limitless. Start your IoT journey today!

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It’s the application of artificial intelligence, machine learning, deep learning, and big data to manage, automate and improve IT operations.

The latest Gartner report on AIOps reveals that AIOps adoption is increasing across organizations. Organizations are adopting AIOps with different maturity levels across these domains and are increasingly being adopted across application operations, infrastructure operations, Cloud Management, DevOps, Security.

A quick view of the AIOps Maturity and Adoption model below will give you a view of the use-cases being adopted across multiple maturity levels in different organizations.

Organizations have adopted a pragmatic approach and gained initial success in adopting intelligent Alerting for full-stack monitoring, co-relation, and Automated ticket resolution for service management. As referred to in the above maturity model many organizations are already reaping benefits by early adoption of use-cases up to Level 3 of the AIOPS maturity model.

How do organizations move to the next level of AIOPS Adoption and what are the adoption challenges?

Data Siloes don’t help.

Just IT-centric data is not enough… it is critical to bring data from business as well, we need to bring data from DevOps, cloud management, Security as well. for eg: for telecom customers, data from the BSS/OSS systems is critical. For a retailer, data from the supply chain, POS, and warehouse systems are critical. It is important to break these data siloes. Organizations need to enable distributed data and data mesh architectures and enable leveraging existing Data domains for the same. AIOps can be enabled based on co-relating existing data domains and generate new Insights.

Organizations are yet to unleash the power of Data and AI.

It is important to leverage all data small or big to come with Rich AI-based Insights. Mostly the power of algorithms becomes richer with more and more data being leveraged for decision making. Heuristic-based approaches enable us to get the system working quickly, however, the power of data for decision making should be a structured and ongoing effort. Also, AI engineering is not fully operational for many of these systems and hence the power of Data and AI is not fully realized. It is important to put proper AI engineering frameworks and practices in place for Data Operations, Model Operations to enable accurate algorithmic-based decision making.

User Experiences

The single pane of Glass as the silver bullet for all problems AIOps intends to solve has certainly allowed initial adoption. The truth for the users we have not yet allowed the users to be free from the drudgery of data and dashboards. Unless we provide them personalized insights and enable decision-making, users will find it difficult to bite the Silver Bullet. Also, users are increasingly working remotely and with hybrid work becoming the norm, user experience is very critical to higher adoption of AIOps.

IT systems don’t work in isolation. They are part of a complex business landscape driving the digital experiences for customers, employees, and partners. While the power of data can drive Insights, it is the understanding of the business process and application landscape, its impact, and the historical context that helps navigate AIOps enabling complex decisions for businesses for eg: Change Impact analysis or a causal analysis and recommendations. This is also called Observability. Existing tools or frameworks allow us to build this partially because of the operational siloes… Building this context enables us to enable the right decision-making for AIOps thus enabling higher business benefits.

The Holy Grail of AIOps is not right here and right now, but it involves a structured approach to solve these challenges through a coherent enterprise-wide AIOps approach.

How would your enterprise approach this? Would like to know your ideas and suggestions…​

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In the world of data mining, regression analysis is one of the most fundamental and widely used techniques. Regression algorithms are statistical models that are used to estimate the relationship between a dependent variable and one or more independent variables. These algorithms can be used for various purposes, including prediction, forecasting, and estimating causal relationships. In this article, we will discuss the top 10 regression algorithms used in data mining and their applications in industry.

Now that we have covered the basics of regression algorithms, it’s time to delve deeper and explore the top 10 regression algorithms used in data mining and their applications in industry.

1. Linear Regression

Linear regression is one of the simplest and most commonly used regression algorithms. It is used to establish the relationship between a dependent variable and one or more independent variables. In other words, it helps in predicting the outcome of a dependent variable based on one or more independent variables. Linear regression has a wide range of applications, such as in predicting stock prices, sales forecasts, and real estate prices.

2. Logistic Regression

Logistic regression is used to predict the probability of an event occurring. It is widely used in machine learning for binary classification problems where the dependent variable is either 0 or 1. For example, it is used in credit scoring to predict the likelihood of a person defaulting on a loan.

3. Ridge Regression

Ridge regression is a linear regression algorithm that is used when the data suffers from multicollinearity. Multicollinearity is when two or more independent variables are highly correlated with each other. Ridge regression adds a penalty term to the cost function, which shrinks the coefficients of highly correlated variables.

4. Lasso Regression

Lasso regression is another linear regression algorithm that is used for variable selection. It works by adding a penalty term to the cost function, which forces some of the coefficients to be exactly zero. This means that some of the independent variables are not considered in the model. Lasso regression is commonly used in feature selection and image processing.

5. Polynomial Regression

Polynomial regression is a type of regression algorithm that models the relationship between the dependent variable and independent variables as an nth degree polynomial. It is used when the relationship between the variables is not linear. For example, in physics, polynomial regression is used to model the relationship between force and acceleration.

6. Decision Tree Regression

Decision tree regression is a non-parametric regression algorithm that is used for both classification and regression problems. It works by dividing the data into smaller subsets based on certain conditions. It then builds a decision tree to predict the value of the dependent variable. Decision tree regression is widely used in finance, marketing, and healthcare.

7. Random Forest Regression

Random forest regression is an ensemble learning algorithm that combines multiple decision trees to create a more accurate prediction. It works by building multiple decision trees on different random subsets of the data and averaging the predictions of all the trees. Random forest regression is commonly used in the field of finance for predicting stock prices.

8. Support Vector Regression

Support vector regression is a type of regression algorithm that is used to predict continuous values. It works by finding the hyperplane that maximizes the margin between the predicted values and the actual values. Support vector regression is commonly used in the field of finance for predicting stock prices.

9. Gradient Boosting Regression

Gradient boosting regression is an ensemble learning algorithm that combines multiple weak models to create a more accurate prediction. It works by building a decision tree on a subset of the data and then adding subsequent trees that correct the errors of the previous trees. Gradient boosting regression is commonly used in the field of marketing for predicting customer behavior.

10. Neural Network Regression

Neural network regression is a type of regression algorithm that is based on the structure of the human brain. It works by using multiple layers of neurons to model the relationship between the dependent variable and independent variables. Neural network regression is commonly used in the field of image and speech recognition.

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1. Amper Music

Amper Music is an AI-powered music composition platform that allows you to create original music in minutes. You simply select the mood, genre, and instruments you want to use, and Amper Music will generate a complete song for you. You can also customize individual parts of the song, such as the melody, chords, and percussion, to create a unique composition.

2. AIVA

AIVA is an AI-powered composer that creates original compositions in various styles and genres, from classical to electronic. You simply choose the style and mood you want to create, and AIVA will generate a complete composition that you can edit and customize. AIVA also offers a music assistant feature that can help you improve your composition skills by providing real-time feedback and suggestions.

3. Google Magenta

Google Magenta is an open-source project that uses AI and machine learning to create music and art. It offers a range of tools and models for music generation, including a neural network-based model called NSynth that can create new sounds by combining existing ones. Google Magenta also has a web-based interface called the Magenta Studio that allows you to experiment with music generation and learn more about the technology behind it.

4. Jukedeck

Jukedeck is an AI-powered music creation platform that allows you to create custom music tracks for videos, games, and other media. You simply choose the genre, mood, and length of the track you want to create, and Jukedeck will generate a complete composition for you. Jukedeck also offers a range of customization options, including the ability to adjust the tempo, key, and instrumentation of the track.

5. Melodrive

Melodrive is an AI-powered music generation platform that allows you to create original music in real-time for games, virtual reality, and other interactive experiences. Melodrive uses AI to analyze the user’s emotional state and adjust the music accordingly, creating a personalized soundtrack for each individual. You can also customize the music by adjusting the mood, tempo, and instrumentation in real-time.

6. Amadeus Code

Amadeus Code is an AI-powered songwriting assistant that can help you generate melodies and chord progressions for your compositions. You simply input a few parameters, such as the key, tempo, and style you want to create, and Amadeus Code will generate a range of melodies and chord progressions for you to choose from. You can also customize individual parts of the composition and export the final result to your favorite music software.

7. Humtap

Humtap is an AI-powered music composition app that allows you to create original music by humming or singing a melody. Humtap uses AI to analyze your voice and generate a complete song based on your melody. You can also customize individual parts of the song, such as the instrumentation, tempo, and genre, to create a unique composition.

8. WaveAI

WaveAI is an AI-powered app that allows you to create original songs using just your voice. You simply record yourself singing or humming a melody, and WaveAI will generate a complete song based on your input. You can also customize the song by adjusting the style, tempo, and mood, and export the final result to your favorite music software.

9. Odesli

Odesli is an AI-powered music promotion tool that can help you generate smart links for your music. These smart links allow you to share your music across multiple platforms and track how your audience is engaging with it. Odesli uses AI to analyze your music and recommend the best platform for each track, optimizing your exposure and reach.

10. JAMM

JAMM is an AI-powered music collaboration platform that allows you to collaborate with other musicians around the world. JAMM uses AI to match you with other musicians based on your musical style and interests, and provides a range of tools for online collaboration, including live streaming, file sharing, and real-time editing. With JAMM, you can create original music with musicians from around the world, without ever leaving your home.

In conclusion, these ten online AI tools offer a range of options for music creation, from composition to editing and even songwriting. Whether you are a professional musician or a complete beginner, these tools allow you to explore your creativity and generate your own music without any prior experience.

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Rehinged, Inc., an AI startup that transforms external market data into actionable intelligence, today announced the launch of Carenet AI, their market intelligence platform specifically built for the healthcare industry.

“The healthcare market is the perfect entry point for the commercialization of the Rehinged AI platform,” said Jim Sagar, founder and CEO of Rehinged. “It’s a 4.1 trillion-dollar market and 19.7% of the U.S. GDP. There’s a tremendous volume of data, a tremendous market need and tremendous value when providers are matched to the facilities with the greatest need.”

Carenet AI is a new brand that speaks directly to the needs of commercial teams at companies selling into United States healthcare facilities. The platform is a complex cloud-based series of applications that turn real-time data into actionable intelligence for end-users.

“While there are some data options available in the healthcare space, such as Definitive Healthcare, the challenge for commercial teams is twofold: good, reliable data is expensive, and it’s very difficult to make that data actionable for a commercial team,” said Robert Crousore, Rehinged investor and its healthcare commercialization strategist. “Carenet AI solves these problems. Real-time, actionable data is the holy grail for teams forced to sell from their home office, instead of meeting directly with doctors and care providers.”

Commercial healthcare and med tech teams selling into nursing homes, doctor’s offices, long-term care facilities, hospitals and other clinical care facilities can get a dynamic feed of their ideal customer targets based on digital signals emitted daily, weekly, monthly and quarterly.

For each customer, Carenet AI identifies the relevant combination of data containing their ideal customer signals then combines and scores it to automate prospecting. This eliminates the need to invest in data science and data engineering infrastructure to make their commercial data actionable.

Teams access the platform through a SaaS subscription. Learn more at Carenet.ai.

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Computer Vision for Agriculture

Computer vision may seem simple enough to understand, but below the surface, there are complex and interdisciplinary disciplines related to a variety of technologies, old and new.

According to the requirements of the image, computer vision can be used in a variety of cameras as the “eyes” of the machine. A common method of fruit detection is to use a color camera to identify the fruit from the tree and an additional stereo camera to detect the relative position of the fruit for automatic harvesting. However, it is far from the only computer vision model. You can use infrared, multispectral, thermal, and even 3D cameras. Computers can use identification based on points or curves, or they can detect objects rather than shapes or textures. For agricultural applications, detecting a given object is not enough. Products are rarely uniform and can vary in size and color and require additional processing to accurately identify the fruit. In short, the “seeing” aspect of computer vision is just the start. The next step is to identify or understand the image. “Understanding” this image is quite a complex matter, and the process is generally segmented into machine learning or deep learning. Deep learning is essentially a subset of machine learning that uses artificial neural networks to collect information, identify patterns, and learn while doing. Deep learning is more complex because it mimics the way the human brain learns and can be applied to complex problems such as natural language processing and image recognition.

Computer Vision in Robotics for Path Detection

In agriculture, well-trained robots can be used to perform various tasks (planting, weeding, harvesting, etc.). Recently, autonomous agricultural robots have been widely adopted to increase crop productivity and work efficiency. Navigation systems are an important part of these autonomous robots. However, computer vision-based systems are more common due to their low cost, ease of use, and widespread use of vision-based sensors. One of the major problems with agricultural robot computer vision navigation is the precise detection of rows of crops to guide robots. Path detection is an important issue in mobile robot and autonomous car applications. Nowadays, most methods get reliable results only in certain structured environments. This blog offers a new vision-based approach to finding rows of crops in different regions.

To train the computer vision-based AI model, annotated data in image or picture format are used to make the subject or object of interest recognizable by machines using algorithms. machine learning for similar predictions. To solve the navigation problem and detect the path using Computer Vision methods where generally the image segmentation is the best choice to allocate and classify each pixel into Crop rows class or Background class (you can add any particular classes like persons or vehicles, etc. to add them). In image segmentation tasks, there are many types of models that can handle this complexity, but limited annotated data can be a major problem in generalizing the trained model to different fields in the crop rows. Although CNN has made great strides in image segmentation, it generally requires a large number of high-density annotated images for training and is difficult to generalize to new objects of the same category. Therefore, a Few shot segmentation has been developed to learn how to perform image segmentation from a few annotated examples. From the various models of few shot segmentation, the PANet model, a novel Prototype Alignment Network is used to learn classific prototype representations from a few support images with an embedding space before performing segmentation over the query images with matching each pixel to learned prototypes. To train the PANet model, we annotate 170 images and save them into Pascal VOC format, and split them into :

Table 1: Table of the split data for each type, Training, and Validation 

Before training the PANet, we got to set the number of ways to one way, the number of shots between 5 and 7 shots and the number of steps to 30000. After training the model, we achieved 72% mIOU score in the validation dataset. Here are some examples from testing the model on new images:

Image 1: some results from the test

For the next step, the complexity will be in detecting and finding the center line of the crop rows and comparing it to the middle axe of the image (the camera was placed in the centre of the robot).

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With diverse mobile fleets comes an extended need for communication between the different types and brands of robots. Interoperability refers to the ability of computer systems or programs to exchange information. At the moment, there is a clear lack of industry-wide adoption of interoperability practices — especially when it comes to mobile robot fleets. 

The need for interoperability is driven by two main factors: the growing demand for mobile robots and the diversification of robotic fleets. In this article, we will discuss how a universal fleet management system can address these interoperability challenges.

Growing Demand for Mobile Robots

With the tremendous growth of the robotics industry, the global robotics market is now estimated to reach USD 209.38B by 2025, growing at a 26% Compound Annual Growth Rate (CAGR). While such market growth offers many opportunities, it also comes with quite some challenges — especially with the ongoing adoption of Autonomous Mobile Robots (AMRs). 

With this increasing demand for mobile robots, it is no surprise that many warehouses around the world are automating their facilities. In fact, the warehouse automation market is projected to reach a value of USD 30B in 2026 at a 10.41% CAGR during the forecast period — up from USD 15B back in 2019.

Robotics Diversification Trend

With technological advances, mobile robots are seeing new levels of specialization as they are now capable of serving more specific use cases. The growing demand and diversification of mobile robots will result in manufacturers not being able to keep up with the demand. This means that companies will need to buy robots from multiple manufacturers to fulfill their operational needs. 

This will result in even more diverse mobile robot fleets. We can already see now that many warehouses, hospitals, logistic centers, and factories are deploying different types of robots from different manufacturers. They could have a specific type of robot for lifting heavy items, one from a different brand for lifting small items, another one for security, one for cleaning, etc. 

As for now, this can cause real problems for warehouse operations. Each manufacturer supplies its robot with its own operating system and when one of their robots is introduced to a fleet with robots from other manufacturers, these robots would not be able to communicate with each other. It is of great importance that as robots become more autonomous, they need to begin to communicate effectively to avoid collisions, serious accidents on the work floor, delays in operations, and so forth. 

Interoperability Pain Points

In their recent report “Robotics Interoperability: A solution to the communication issues of diverse mobile robot fleets”, Meili Robots have included a case study that explores the pain points of interoperability.

The project was carried out at a Danish hospital that has deployed different types of mobile robots from different brands. The project aimed to test, adjust, and customize a fleet management solution. It was no surprise that the outcome showed that the robots’ own independent control systems were unable to integrate with the hospital’s logistics system or the operating systems of other robots within the fleet. 

This proved that there is a need for a universal fleet manager that offers a full overview of the entire fleet — meaning all types and brands of robots — with detailed information on the individual robots as well as data analytics features. It is crucial that, in order to avoid collisions or other accidents, third-party robots’ routes, speed, locations, etc. can be controlled in a leveled way. This will also optimize operational efficiency. 

If you would like a more in-depth analysis of the importance of interoperability in the robotics industry, you can download the full report for free here. 

A Universal Fleet Management System for Mobile Robots and Its Benefits

What Is a Universal Fleet Management System?

A universal fleet management system is a system that can perform the complete and centralized management of a fleet of mixed vehicles. These vehicles can be of different types, brands, or sizes — in this case, a fleet of mixed mobile robots. 

These types of systems can include multiple important features such as task management, fleet monitoring (the location of each robot, statuses, battery levels, missions in progress, etc.), route planning, traffic control, and/or data analytics. Some advanced systems also incorporate artificial intelligence and machine learning to bring their functionalities to another level of automation, making their systems smarter and more effective while at the same time saving operational costs and energy. 

3 Major Benefits of a Universal Fleet Management System

In addition to the benefits mentioned above, there are three significant advantages of a universal fleet management system that are worth taking into consideration. Let’s take a look at them.

Increase Operational Efficiency

With a universal fleet management system like Meili FMS, you can easily detect disruptions or other issues as they occur and notify the prospective robots in real-time. Via the comprehensive overview that comes with a universal fleet manager, you can not only respond to problems more quickly and remotely, but you can also reduce the downtime of your robots significantly while simultaneously increasing the efficiency of your fleet.

Optimize Operational Safety

Traffic control is one of the most essential features of a fleet management system as it enables your entire fleet to detect other robots, forklifts, human workers, and blocked areas — making it easier for them to detect and predict bottlenecks. For example, rather than driving right into a predicted bottleneck and causing collisions, the robot can now automatically create a new route, divert, and reach its destination in a safer and more efficient way.

Accelerate Sustainable Operations

It happens too often that robots are driving around a facility without a payload, wasting lots of energy. Through its smart tasking algorithms, a universal fleet manager like Meili FMS provides you with an automated task allocation feature. This allows you to assign tasks to the right robots at the right time and eliminate unnecessary fleet idle time — thereby, saving lots of energy and improving your sustainability records.

Final Thoughts

Evidently, the robotics industry is facing a major challenge: the demand for robots keeps growing while robots are also becoming more and more specialized — meaning that due to their unique, individual operating systems, they cannot communicate amongst each other. 

In order to enable this communication and prevent operational delays, accidents, and collisions, a universal fleet management system is needed. Not only will this optimize operational processes and increase profits, but it will also help scale businesses to the next level.

The post Universal Fleet Management System: The Answer to Robotics Interoperability Issues first appeared on AITechTrend.

Processing all this data, however, has long been one of the main challenges that insurance companies must face. Verifying, categorizing, and analyzing information can be extremely labor-intensive. On the one hand, this is expensive and time-consuming. On the other, humans are prone to errors and bias, which can lead to losses for the company and needlessly high costs for the customer. 

Machine learning and AI provide better alternatives to manual information handling, and a majority of the world’s insurance companies have already taken note. With things like predictive algorithms and telematics being applied to companies’ big data analytics strategies, the future of insurance may very well be upon us. Underwriting, sales, claims processing, and fraud prevention are just four of the most powerful use cases for these technologies.

Underwriting

Underwriting is the process by which insurance companies and their partners decide whether or not to provide coverage to a potential client. In the legacy system, this is a lengthy and labor-intensive procedure, during which the insurer must evaluate risks, determine the likelihood of a loss, and calculate the premium. Using AI and machine learning, most of this work can be automated, while losses can be radically reduced. 

Insurance companies have already begun harnessing machine learning algorithms to analyze data and make better decisions about their customers. The technology is applied both before the issuance of a policy and afterward, and it is frequently combined with telematics. 

In fact, several car insurance companies have already released mobile applications which can be downloaded by customers to collect data about their driving behavior. If collected data indicates safe driving on the part of the driver, a more profitable policy for the company, with a lower premium for the customer, can be issued.

Moreover, this technology is frequently leveraged in the sharing economy. Ridesharing and carsharing companies are able to reduce liability and insurance costs by proving good driver behavior to insurers through advanced telematics and data analytics.  

Insurance Sales

Machine learning technology has, over the last few years, managed to radically disrupt the insurance sales funnel. Customers who reach an insurer’s website via social media and affiliate links no longer need to communicate with a human agent in order to receive personalized service and solutions. 

Insurance companies today regularly interface with clients using chatbots deployed on messaging apps. While this occurs at the front end, underwriters are able to apply predictive analytics to review customer profiles. All it takes is a quick survey in a text chat for the company to provide a potential client with a list of tailor-made products or more general insurance advice.  

As with all things related to machine learning, this technology will only get more sophisticated as more data is collected. With near-universal adoption of messaging apps like WhatsApp, and with about two-thirds of people feeling comfortable sharing data from wearable devices with insurers, greater adoption of this use case in the insurance industry is virtually assured. 

Claims Processing 

One of the most tedious processes in the insurance industry is claims processing. From the point when claims are registered until they are settled, a great number of documents must be verified. Decisions need to be made regarding payouts and future premium increases. To make these things run smoothly, insurers are increasingly applying machine learning. 

For instance, AI can be used to process handwritten documents submitted by clients and assessors. What had previously been a labor-intensive procedure, largely carried out offline, can now be executed in seconds. Then, the processed documents can be uploaded to the cloud and converted into a format to which predictive algorithms can be applied, reducing fraud (see below) and automating adjustments to client premiums. 

Fraud Prevention

According to a study carried out by the FBI insurance companies in the United States lose more than $40 billion per year from fraudulent insurance claims and other schemes.  On average, this costs the consumer between $400 and $700 annually. For this reason, companies have begun to be able to deploy predictive analytics that identify potential fraud before it is ever committed. 

Machine learning algorithms are far more efficient than traditional predictive models when used to review unstructured, semi-structured, and structured data to find fraud. During the claims process, surveyors must spend a large amount of time gathering information, including photos, client interviews, and police reports. This information may be stored in multiple databases, or have been sent by the client in multiple e-mails and messaging apps, opening up room for mistakes during the data validation process. 

AI is able to eliminate human error and instantly compile client data, applying evaluative algorithms to determine the validity of claims all along the way. Ultimately, this not only reduces losses but also decreases labor costs. 

Conclusion

Just like other industries that rely heavily on data, including finance, e-commerce, and transportation, insurance (and insurtech) can get a lot of benefit from big data analytics, and from machine learning more broadly. With the addition of related technologies, like telematics and the IoT, this is absolutely a space to watch. If you’re interested in integrating one of these solutions into your business, make sure to reach out to the Daiger team here. 

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