Robots must be configured to execute their tasks based on defined rules and workflows. Initially, RPAs do not learn from their experience and cannot resolve the peculiarities they encounter when executing their tasks. The experts responsible for your programming must establish and modify the rules.

The Role Of RPA In The Digitization Of Companies

Robotic process automation plays a pivotal role in digital transformation, and companies are already reaping the benefits of its use. Without RPAs, it is not easy to digitally transform a traditional workplace. It is estimated that between 2020 and 2024, the number of RPAs in operation will triple, especially among large organizations that are acquiring additional capacity from their original provider or partners within the ecosystem.

At this time, RPAs make the most sense in this type of process:

• Mechanical and repetitive tasks.
• High-risk tasks due to human errors.
• Interaction with various systems.
• The large volume of processing.
• Importance and speed of monitoring.

Specifically, they are beneficial when we are faced with tasks that a human would require:

• Window navigation.
• Introduction of information.
• Repeated mouse actions.
• Swivel chair and copy/paste.
• Invocation of procedures.

The benefits are many and not only from the point of process optimization:

• Reduction of human errors.
• Reduction of processing time.
• Automation and digitization.
• Reduction of the cost of the task.
• Increased volume capacity.
• Dedication to higher-value tasks.

Although it has clear implications for future labor market transformations, process automation empowers workers by saving time to focus on higher-value tasks. However, a poorly designed RPA can end up displacing bottlenecks to other areas of the process. Automation solutions will be more effective if they can adapt to dynamic processes, optimizing workflows. RPA can reduce processing costs by up to 80%. Within a year, most companies realize a return on investment and the cumulative cost reduction can be as high as 20% over the long term.

Smart Automation

Work is already underway on the evolution of RPA that integrates artificial intelligence to make automation intelligent. Productivity could be multiplied by adding AI to the equation, improving large-scale data analysis and enabling decision-making. Thanks to machine learning, robots can learn to find patterns in unstructured data and implement precise processes.

RPA At Securitas Personnel Department

One of the most critical processes for our Personnel Department is the download, management and categorization of RCL and RNT documents and the associated bank receipts. These procedures are carried out on the last days of the month, coinciding in time with another essential procedure such as payroll payment.

In addition to being delicate and laborious, this process is susceptible to human errors that, through correctly parameterized automation, can be corrected without much difficulty. At the same time, the RPA is in charge of reading and classifying the different documents to make them available to the different geographical areas or internal departments.

To carry out this automation, we have relied on internal tools such as Sharepoint to centralize the documentation or Social Security applications to extract the necessary information. All this is orchestrated and processed by the RPA. In this way, we have redefined and optimized the original process to obtain additional insights that allow us to access the information in an agile, orderly and error-free manner.

Also Read: RPA Tool – 6 Strategic Sectors To Implement It

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In recent years, the market for collaborative robots, or cobots, has boomed in the industrial arena. These types of machines are designed to support human labor in industrial companies. That is to say, they do not imply the total automation of work but rather coexist with the workers to improve production methods.

Unlike traditional industrial robots, collaborative robotics is characterized by the ability to detect the presence of people and interact with them, too, among other things, to prevent accidents. These machines are more compact and lighter than conventional robots. They can be easily moved around the factory and are capable of performing different functions within the production chain itself.

With a lower cost, all these characteristics make collaborative robotics a more accessible option for SMEs than conventional robots. It will be possible to automate production in a more significant percentage of the industrial fabric thanks to it.

Collaborative Robotics And Smart Factories

The concept of industry 4.0 (or the fourth industrial revolution) would not be understood without the existence of Smart Factories. These intelligent factories are highly automated and digitized and use collaborative robotics to create a more efficient and fluid work environment.

Smart Factories put people at the center of their activity. Thanks to collaborative robotics, workers can focus on performing work that requires human capabilities. They are freed from routine, heavy or dangerous tasks, and optimized processes.

The most common collaborative robots are in charge of the “pick and place” function, that is, the task of picking up an object and placing it in another place. We can also find them in assembly lines or in the warehouses of intelligent factories, where they are capable of repeating the same movement with perfect precision.

Collaborative robotics reduces the health risks derived from tasks that require the use of force, forced postures, or the handling of dangerous substances. Furthermore, cobots are designed to ensure the safety of workers when interacting with them. Some of the most common uses of robots in factories are painting, welding or packaging, and handling pallets.

Also Read: Robotics – How Robots Will Help In Education

Safety Is A Priority For Collaborative Robots

Collaborative robots have several features that improve safety in Smart Factories. For example, they have low-inertia servo motors or elastic actuators, which offer a very reliable response and are made of light materials and rounded contours. The defining aspect of collaborative robotics is sensors that ensure safe interaction with people. According to the international standard ISO 10218, we find four types of robots according to their safety functions:

1. Safety Guarded Stop

These cobots typically work independently, performing a primary task, while the worker performs a secondary role. However, sometimes employees need to interact with the machine. When a human approaches a certain distance, this robot completely stops its activity. The safety monitored stop function is implemented in conventional robots utilizing special sensors.

2. Speed ​​And Separation Monitoring

In this case, the robot monitors its surroundings and can detect the proximity of workers. Unlike the first type, it progressively reduces its speed depending on the workers’ distance. If it gets too close, it can completely stop its activity. This feature of collaborative robotics is used in jobs that require the frequent presence of people.

3. Hand Guide

This type of robot can learn trajectories with the help of a person. This is a valuable function for quickly programming a new guideline, for example, for a pick and place machine. They are conventional robots equipped with a sensor like the two previous types.

4. Limitation Of Force And Energy

It is the most common and safest type of collaborative robot. At the same time, the former use standard industrial robots with additional sensors, these types of machines are specifically designed for interaction with people.

These cobots have all kinds of sensors that allow them to detect abnormal forces in their activity. This allows them to respond immediately when they contact a human. In this way, they can stop to avoid injury or dissipate some of the energy of the impact. In addition to being very light, these robots sometimes have an outer shell that absorbs shock.

Advantages Of Using Robots In Smart Factories

We have seen how safety is the main feature distinguishing collaborative robotics from traditional industrial robots, and its application can reduce and avoid risks and injuries characteristic of factories. In conclusion, collaborative robotics is a key tool for security understood as ” safety, “that is, the protection of employees.

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Its growth potential is high, and this can be stated unequivocally. However, market participants producing equipment and software for industrial automation must be mobile and aggressive enough to take advantage of all the new strategic and technological directions that will be important in the next ten years. 

Small and startups will drive innovation in promising segments, generate high profits, and change the size of the robotics industry. The global robotics market by 2030 will grow from the current $ 25 billion to $ 160-260 billion, experts predict in The Boston Consulting Group (BCG) document. At the same time, the market share of robots providing professional services can reach $ 170 billion. Sales of industrial and logistics robots will amount to about $ 80 billion.

Today, the robotics industry is represented by more than 500 companies that produce products in four main categories: conventional industrial robots and collaborative robots or cobots, robots for stationary professional services, for example with medical and agricultural applications, robots for professional mobile services, professional cleaning, construction, underwater activities, and self-guided vehicles for the transport of large and small loads in logistics or assembly lines. According to BCG experts, seven major trends will define and shape the robotics market in the next decade.

Mastery Of Professional Service Robots

Sales of robots for professional services ($ 90 billion to $ 170 billion) will more than double the sales of conventional and logistics robots.

Change Consumer Preferences And Social Trends

This trend will increase the demand for advanced robotic solutions. On the one hand, increased consumer demand for faster deliveries of customized products will increase the ability of robots to customize production and logistics. On the other hand, an aging population will increase the demand for mobile service robots to help with personal hygiene, exercise, food delivery, and more. The growing public interest in recycling will require robots to perform complex dismantling and sorting tasks.

Robots Will Take Low-Wage Jobs

The replacement of people by robots will lead to a shortage of manual workers and higher wages in countries with traditionally low wages.

Advanced Technologies Will Improve Human-Robot Interaction

Disruptive technologies in intelligence, communications, and machine control will expand the capabilities of robots and simplify their interaction with humans. For example, AI will allow robots to face unexpected situations. Imaging systems will improve offline checks. 5G communication technology will increase the working range of the robot.

As these technologies are introduced, many customers will choose modular systems consisting of a core, peripheral controllers, machine vision software, and artificial intelligence for autonomous, intelligent actions rather than buying basic robotic systems. They will be easy to implement and will be able to solve a specific range of tasks independently.

The convergence of IT, which manages data, and OT, which contains operating machines, will accelerate the development of intelligent robotic systems and simplify their connection to complex manufacturing plants. Already today, dozens of startups are focused on working in specific niches in these cutting-edge areas.

Robots Will Be Able To Learn

Today’s simulation tools allow robots to be trained to solve current problems. However, these methods do not provide the ability to introduce them to respond to unexpected events flexibly and intelligently. The study conducted by the non-profit artificial intelligence analysis center OpenAI made it possible to create the first application that includes a humanoid robotic arm. You can solve Rubik’s Cube problems entirely without human intervention. The automated capabilities that will emerge from these technologies will be precious in single-batch custom manufacturing processes.

Also Read: Big Data, AI, Robotics And Cybersecurity, The Technologies That Contribute The Most To Curbing Covid-19

Semi-Autonomous Mobile Machines In Pre-mapped Environments

These vehicles drive themselves on relatively good roads and in clear weather conditions, transferring control to the driver when they encounter a difficult situation. Level 4 auto navigation is autonomous and has a backup system that can shut down the machine in rare unexpected problems but does not require human intervention.

Asian Robotics Companies To Take Competitive Market Share

Korean, Chinese, and Japanese robotics companies that emerged in the last decade and now occupy a small market share will be able to grow significantly. Major Asian retailers are beginning to modernize their warehouses to meet growing demand, and the logistics will require a large volume of robotic equipment.

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What Will The Classes With Robots Be Like

The current educational framework is changing considerably. And much of the blame is on the digital revolution of the Internet or mobile devices. Gone is the time when you had to memorize each name and date. Especially since many of the new concepts soon become obsolete.

The future education will be based on creativity, personal values, critical thinking, and debate. The teaching will be personalized for each student, who will have to solve their challenges to overcome. How do teacher robots fit into this novel concept of education?

Robots can make teaching a lot more fun at all levels. But of course, especially boys and girls. This is essential but that the students remain motivated and interested. Classes with robots will be more interactive, giving free rein to students’ concerns. The dynamics in which students spend most of the time sitting and listening will end forever.

These artificial teachers will become real eminences in the field of science and engineering. Your data will be stored in the cloud and, therefore, shared with the rest of the robots, analyzing the results and improving their methods is a real revolution in teaching.

Also Read: The Advantages Of Collaborative Robots In The Industry

What Will Be The Roles Of The Robot Teachers Of The Future

Robots can be excellent teachers’ assistants. Your analytical skills, data processing speed, and unerring memory will be invaluable as classroom support. Their appearance will not be intimidating, and on the contrary, they will develop excellent social skills.

They will be great teachers. Today, they provide optimal results in various subjects such as languages, engineering, mathematics, and other similar branches. And they interact with children to teach them to sing or dance. However, it is in the humanities field where its performance is yet to be determined.

Will we be able to discuss philosophy with a teacher robot in the future? Of course, they will have the necessary academic knowledge, learn from their mistakes, and evolve. But perhaps this is not enough to replace a flesh and blood teacher in advanced courses. However, at the primary and medium levels of education, robots will perform practically any function.

For this, machines must emulate human behavior. And even more important that they know how to read the body language of their students. The goal is for your responses to be emotional and as human-like as possible. We know that teacher robots can be infallible in many things, but their capacity for empathy has yet to be developed.

And we also have doubts about its programming. For example, if the robot’s mission is to help students learn more, it may suggest challenging problems for them. Conversely, a robot aiming to pass students could ask too simple questions.

Benefits Of Robots In Education

• Digital resources are increasingly important in teaching, and these machines are authentic databases of maps, videos, graphics, and other indispensable tools for study.
• The robots are being implanted in the first courses so that the youngest of the house are soon familiar with their presence. In this way, they will see technology as something natural. And those who want to dedicate themselves to science in the future will have the best possible motivation in front of them.
• Robots prioritize participatory education. Group dynamics are essential for students to learn as soon as possible to work as a team.
• It also allows the emergence of critical thinking and leadership, stimulating young people to think “out of the box” to approach the proposed challenges creatively.
• Another of the great benefits of robots is that they will reach the most remote areas of our geography, covering essential education services at a low cost. And they will also fill the jobs for which there are too many vacancies. For example, in Japan, it is difficult to find native English teachers. For this reason, robots have been successfully implemented that are meeting this demand.
• Learn while having fun. Improve the teaching process. End frustration in the classroom and boost self-esteem. These are just some of the advantages of robots providing our children in a much nearer future than it seems.

Also Read: Big Data, AI, Robotics And Cybersecurity, The Technologies That Contribute The Most To Curbing Covid-19

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And it is that Artificial Intelligence and machine learning-based systems make us optimistic. They will be able, very soon, to replace us for repetitive tasks that involve interaction with humans, and that is precisely one of the best news for sectors such as e-commerce and, especially, their customer service. Let’s see why.

Automating Repetitive Tasks in Customer Service

The qualified human staff that attends the queries and requests in the customer service centers has to perform very repetitive tasks throughout the day that, in reality, consume time that could be devoted to more complex and high-level tasks. That is, they could invest their time in more appropriate tasks for a human brain.

Every day, several requests are met that are virtually the same: they require the same answers, the same actions and do not need a singular effort. For example, refunds, cancellations, updates … Such tasks could be automated by a machine-based system and, in turn, allow people to focus on other aspects.

Because, deep down, we have to keep in mind that the reality that “conversations” (real and real conversations with customers by email, chatbots, phone calls, etc.) is barely accounted for is only 30%. of the average customer service experience. The other 70% consists of actions and processes that customer service agents must perform manually each time a customer requests a refund or the status of an order, to give two examples.

That is why machine-based systems focus on tasks that, a priori, seem so simple to us.

A Customer Service Center is Ideal For Training Smart Systems

Contact centers or customer service centers are ideal for training systems based on machine learning. They have extensive data records and past queries that form a perfect training data set for the predictive capabilities that machine learning provides.

Upon completion of this training, machine learning models are able to understand the questions that are asked, even if their construction is different from what they already know. These models determine the client’s objective based on the conversation, among other things, and are able to pass the information to the professional, who scores it, and if it is correct, said response is sent to the client. In this way, the attention times are cut and excellent customer experience is offered.

The future is already present, and today we have customer services that have agents based on Machine learning that, in a transparent way for the client, carry out the first phases of the process. After that, they pass the information to the professionals in charge of the back-end processes that will solve the “tickets” and close the case.

Customers come to the customer service center because they need to solve very specific problems in the shortest possible time. Therefore, machine learning models are transforming customer service, since they either immediately resolve the most obvious issues, or accelerate the process for the customer, which will be more satisfied.

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“Although knowing the main cybersecurity predictions can help you be prepared, don’t forget that cybersecurity, as an industry, is constantly changing,”

Security Predictions for 2020

More than 50% of business traffic will go to the cloud

For this next year 2020, the cloud-based SaaS and IaaS applications will continue to be essential for companies, as users will increasingly use mobile devices to access their data, which implies an adaptation of the cloud in security.

Legacy security will remain blind to cloud traffic

The security industry evidences the increase in cloud traffic, which is why this year professionals should look for a way to improve visibility, optimization and security posture in the cloud.

Cloud phishing will increase as the main tactic for APTs

Cybercriminals will be able to take advantage of any factor to access the cloud of mobile devices and thus attract the opening of any malware content. In addition, the confidence that consumers have over cloud apps will negatively boost phishing attacks through emails.

Increase in data breaches and security flaws 

Data breaches caused by accidental exposure and poor configuration of cloud applications will increase this year, and 95% of cloud security failures will be caused by the user. That is why security teams will be responsible for dealing with increasing data exposure, fueled by increased cloud adoption. There are many types of attacks and threats that can arise when we connect to the network

Personal instances of cloud applications published in white lists  

Organizations should look for security solutions that go beyond the URL itself, in order to apply the correct protection to avoid possible external and internal attacks. Therefore, cloud applications will force security teams to integrate, together with corporate, personal instances in a white list of URLs. 

The use of remote devices to access corporate data is growing 

Security teams with the mobile workforce currently in place should rethink the way they protect their data.

The Evolution of the SASE Market

The growing consolidation of equipment on-premise security and cloud networks and the increased pressures to reduce costs will lead companies towards an architecture based on Secure Access Service Edge.

The data protection market will be completely reversed

Through the most intelligent and scalable native cloud technologies such as Artificial Intelligence and Machine Learning, companies will carry out a transformation of their data protection policy.

Blindness against traffic that does not come from the browser 

In 2020, access to the most popular cloud applications, such as OneDrive, Box, Microsoft Teams or Slack will continue to occur both from a browser and from an application installed locally on a device. 

The change from VPN Legacy to Zero Trust Network Access will accelerate

The term zero trusts will evolve with the challenge of providing secure access to private applications and data, regardless of where users, applications or data are located, thereby altering the VPN market.

Reduce the volume of consoles, priority for security teams

This year, and with the objective of making the most of the resources, the CISOs will reduce the number of consoles to be managed, since the consolidation of tools and the cloud security stack is imminent.   

Also Read: Top 5 Servers For The Business Market

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The huge amount of information currently available on the Internet is raising some technologies that until recently were almost exclusively part of Universities, Research Centers and Large Companies. I refer to technologies related to Big Data, as well as those related to Machine Learning.

What is Machine Learning?

Machine Learning is a part of computer science, closely linked to Artificial Intelligence (AI), which from the application of algorithms can learn, establish patterns on the data, and make predictions on them.

What Are Its Practical Applications?

This somewhat futuristic definition and that a priori seems so far from our daily routine, has applications as current as:

• Spam detection in emails.
• Detection of fraud with credit cards.
• Voice recognition.
• Face detection to identify people.
• Product recommendations on online sales websites.
• Medical diagnoses to identify diseases based on their symptoms.
• Customer segmentation to determine if a potential customer that is in a certain phase in the sales process will buy our product or service.
• Prediction of home sales.
• In cybersecurity, to detect attacks and mitigate their possible effects

The big companies are already using it to improve and enhance their services:

• Google is developing a service called “ Smart Reply ” that will be integrated into Gmail and that will allow us to automatically respond to incoming emails. But there are already applications integrated into our devices, such as Google Now¸ that are already implementing these technologies for a long time :
• Microsoft will integrate the services of “Azure Machine Learning” in its CRM Dynamics 2016 to help companies obtain a faster and more effective experience for customers, providing a knowledge management system that allows companies to register and access information in problem-solving. The system will learn continuously as the client-employee interaction grows.
• Facebook through its AI team called FAIR (“Facebook AI Research”) is working on algorithms that allow users to display information much more selectively, and this, of course, includes filtering images and photos.

A Brief Review of The Machine Learning Algorithms

The first classification of the algorithms addresses the way in which data is presented to the learning system, and we can have:

• Supervised learning :The perfectly defined and labeled input data is presented and the outputs we want to obtain are known. For example, given a set of images of animals labeled according to their breed, we want to predict to which animal breed a new image supplied to the system belongs.
• Unsupervised learning :The input data is undefined, and the algorithm is left to find the structure and behavior patterns in them. Following the previous example, we can provide a set of images of animals without labeling the system, with the aim of establishing groupings based on similarity patterns between them.

The other classification addresses the function of the algorithm and what we expect from it. In this sense we can mainly have the following:

• Regression: Try to model the relationships between variables through multiple iterations that are refined based on an error measure.For example, it could be applied to predict the price of a home taking into account multiple factors, such as the area, number of rooms, city, neighborhood, etc.
• Classification: It is used to estimate discrete values ​​(0/1, True / False, Yes / No) based on a set of independent variables. It is also known as “Logistic Regression.An example of the application would be the classification of an e-mail as spam, depending on the text, subject, issuer, etc.
• Clustering: Try to find patterns in the structure of the data to organize them in a way that allows clustering by the greatest possible similarities.It could be applied, for example, to classify any type of article by subject according to its content: sports, science, literature, etc.
• Recommendation: It seeks to predict the degree of preference that a user provides to any element. This is the case of the recommendation of products for online sales, based on previous purchases, preferences of previously visited items, history of other customers’ purchases, and even personal characteristics, such as sex, age, etc.
• Deep Learning: They build larger and more complex neural networks to solve cases in which we have large volumes of data that may not be labeled, or partially. It is used for example in topics related to computer vision, for questions such as the classification of an image according to certain facets that can identify it.

Conclusion, The Future Is Here

We can take a look at the “State of the Art” in Machine Learning, with the sectors and companies already using these technologies. As mentioned, we are already using although these algorithms go unnoticed in many of the facets of our daily lives, The applications fully integrated with our devices, but their use will go much further, and researchers continue to work on algorithms every time more perfect. With the increasingly easy access to these technologies by anyone, the competitive advantages are so important that no technology company can neglect its use.

Also Read: Best Antivirus & Security Applications For Android

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Existence Of Artificial Intelligence(AI) So Far

Artificial intelligence(AI) is one of the fastest-growing areas of digital technology. It is transforming our lives and also our investment portfolios. The latest devices from Amazon, Microsoft and Google allow you to turn on lights, check the weather forecast, check newspapers, listen to music, boil the kettle or book a taxi – all with voice commands.

Soon, Artificial Intelligence(AI) – computer replication of actions that normally require human intelligence – will be able to achieve much more and investment in companies with machine learning systems will be an important source of growth. In fact, robots are taking the world, not with an army of cyborgs, but in a soft and subtle infiltration of intelligent technology.

Now, through learning algorithms, machines can use data to find patterns and predict. The next step is the learning of errors by computers, with the refinement of predictions with each new piece of information. They are increasingly skilled at recognizing images and processing natural language like human beings, as well as responding based on context. Artificial intelligence is already present in health, retail, and finance.

Also Read: How to become an AI Engineer

Artificial Intelligence(AI): Advantages and Disadvantages of its Adoption in Companies

The use of technologies related to Artificial Intelligence is increasingly common in companies. From basic telephone service systems, through chatbots for customer service and reaching more complex production control systems, or even to the search for talent, Artificial intelligence is gaining ground in process management, with its advantages and its disadvantages
The Artificial intelligence systems they are, today, complex systems dedicated in most cases to simple or uncritical tasks. This is true for the vast majority of companies and applications, especially outside the big global technology. There is some concern that artificial intelligence systems end up eliminating jobs and, therefore, unemployment increases and opportunities for many people diminish.
On the other hand, Artificial Intelligence allows to streamline processes, automate increasingly complex tasks and increase safety in certain sectors (such as the chemical or pharmaceutical sector). These consequences of the use of artificial intelligence in companies are positive because, for example, they free the human worker from the most tedious tasks and leave time available to perform more creative and productive tasks.

Also Read: Internet of Things(IoT): Revolution For The Future That Begins Today

The Advantages Of AI In The Company

• The probability of error in their operation is almost nil, they are reliable, accurate systems and have a very low probability of error in their tasks. Naturally, it depends on the design and implementation, but there are business solutions with an excellent level of efficiency.
• They offer the possibility of creating bots to interact with consumers: one of the great areas of application of Artificial intelligence is the development of chatbots capable of redirecting simple queries to the appropriate departments for their management.
• Suitable for repetitive tasks: this allows free human capital to devote itself to higher-level tasks or creative tasks.
• They are Perfect systems to perform dangerous or high-precision tasks. For example, for the preparation of chemical compounds such as medicines or dangerous products. Machines are capable of measuring proportions and quantities with accuracy beyond human capacity and, therefore, are safer when performing tasks.
• Artificial intelligence never gets tired: this allows you to perform tasks without rest, 24 hours, seven days a week, 365 days a year, increasing productivity tremendously.

Possible Disadvantages Of Using AI-Based Systems

• They only act for what they are programmed, since, for now, AI-based systems are quite simple and only handle specific tasks.
• They do not have emotions like human beings, this invalidates them for tasks that require empathy, as we said when we talked about jobs that are not at risk before the arrival of robots. It should be clarified that this is a disadvantage of AI-based systems, but it is an advantage for us humans as workers.
• They are systems that lack creativity, and for the same reasons as in the previous section, they are systems that cannot replace us in creative works, at least for the moment.
• In the wrong hands, they could involve certain dangers. As with almost any technology, misuse of Artificial Intelligence can have negative consequences. We speak from the use of malware, through phishing or scam attempts, and reaching more dangerous situations such as the manipulation of critical software for the control of an autonomous car, for example.
• If they began to replace a human being in all fields we would live a time of mass unemployment. The reality is that the adoption of artificial intelligence solutions is highly likely to cause profound changes in the labor market. There will be expendable jobs because machines can perform at a lower cost, other jobs will be transformed, and new occupations will emerge. We cannot predict the future, but yes, there will be a great labor transformation.

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Internet of Things which is called IoT is the interconnection of any product with any other around it, that is, connecting any object and element through the Internet. That the different things, objects, and devices that we have at home communicate with each other and offer us data and details about their operation.

If they were able to apply everything they have raised, it could mean not only a technological transformation but also an economic and social one. If technology makes computers transmit information with the elements of real life, the significance can be even greater than the digital revolution.

One of the technologies whose impact will directly affect our daily life will be the IoT (Internet of things), which will enjoy significant growth thanks to the infinite possibilities offered in the business field.

History of the Internet of Things(IoT)

Internet of Things was born with the idea of ​​simplifying people’s lives from connectivity through the Internet of objects and devices. For example, we currently have technological systems that help us control the irrigation of our crops, but with IoT we can go a little further, being able to know the status of each of the plantations, their ideal time depending on the type of harvest, its evolution over time, the remaining time to reach its optimum point, etc.

The term was launched by Kevin Ashton in 1999, director of Procter & Gamble, at the MIT Auto-ID Center, where the research was carried out on network radio frequency identification and sensor technologies.

IoT goes beyond being a concept that transcends the technological, referred to millions of connected objects. It is a concept that makes us reflect on how it will affect our lives.

In recent years, connected objects are growing exponentially, and forecasts are for even greater growth. The most recent estimates already place the IoT market above 20,000 million connected objects in the coming years.

Changes That IoT (Internet of Things) Will Bring

IoT is an enabler of digital transformation whose mission is to connect people with the things that matter to them. In the industry, it allows companies to be more agile, competitive and efficient in every way, which implies a reduction in costs and capacity to adapt more dynamically to changes in the sector. In fact, the 2019 report confirms that the industrial use of IoT has matured while this sector remains the engine that drives the implementation of Internet of Things solutions to a greater extent. Spanish industries have passed the IoT test phase to enter a new one in which they increasingly demand developments and concrete applications of IoT projects and solutions.

Connection Between Some Machines and Others

This will facilitate the exchange of information, as well as its use to make them more productive and efficient. For example, in an assembly line.

Obtaining More Data With Better Quality

Thanks to the connection of objects to the Internet we obtain better conclusions based on Big Data.

New Business Models

All technology that emerges goes hand in hand with business opportunities, and IoT is no exception. The existence of this type of communication generates, by the companies, the need to make the most of Big Data. That is, an insurance company could, for example, adapt the rates of its clients to its accident history, or control directly the status of each of the policies.

Greater Security For Employees

Among other things, from its use it is possible, for example, to identify areas of the building that are being damaged and that may present themselves as a danger to the safety of employees.

Cheaper & Efficient Technology

Thanks to IoT we can regulate, for example, the temperature or lighting of buildings intelligently, making them, on the one hand, more sustainable, and on the other, less expensive.

Also Read– 5G: The Transformation That 5G Brings Towards The Digital World

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