Definition of Robotic Process Automation (RPA)

Essentially, RPA is a method of automating processes in which software robots perform repetitive and manual digital tasks. These are known as RPA bots or digital assistants. The nature of an RPA bot is to mimic human behavior in software interfaces. In general, a distinction is made between the two types of Automation.

“Attended” RPA

This is supervised Automation. This is mainly used when the entire process can only be automated partially with human interaction. This means that in some cases, the Automation has to be triggered by a human, for example, by pressing a button. The term Robotic Desktop Automation (RDA) or “digital assistant” is often used here since “attended” bots are usually aimed at front-house activities, and the activities on the desktop can be traced.

• Communication with employees is a prerequisite.
• Employees coordinate interactions between people and bots within the organization.
• Bots run both on private servers and in the cloud.

“Unattended” RPA

With this form of Automation, no human action is required as certain events automatically trigger it. “Unattended” bots mostly perform back-office tasks without the user’s noticing. These can analyze large amounts of data for the employee and make them available in a structured way. The term “digital worker” is used here.

• Automates extensive back-office processes.
• Bots follow set procedures without human guidance.
• Specific commands in the process chain trigger bots.
• Bots run both on private servers and in the cloud.

Also Read: RPA – Threat or Opportunity For The Business World?

Pros & Cons of RPA

RPA technology offers significant benefits to many organizations. Simply put, the greatest benefit is increased business performance. This is due to several factors:

• more efficient workflows
• Lower personnel costs due to reduced working hours and job cuts
• Minimizing human errors in data processing
• better and consistent data quality
• more time for innovative tasks through relief in the back office area
• no failures/illness/fatigue
• Increase in employee satisfaction
• increased attractiveness for qualified specialists
• increased data security, data protection and compliance with legal regulations

Since the process of RPA implementation is difficult for many due to insufficient knowledge about Automation, there are also some disadvantages. This should be clear in advance to avoid complications as early as possible:

• Damage to reputation in job cuts
• Risk potential in the implementation of well-established workflows
• The configuration of a bot is not always flexible

Which Processes Are Suitable For RPA?

Opening emails and attachments
Logins to applications
Moving files and folders
copy and paste
filling out forms
Reading data from the intranet
Communication with existing API
Data matching and calculation
extracting data
Gathering Statistics

How To Successfully Introduce It In Your Company?

To enable the process of Automation and to make it efficient in the long term, it is necessary to go through the following 6 phases.

Understand

The first step is understanding what RPA is and its benefits. It is also essential to explore the market to find a suitable provider. Each provider has different priorities; these should match their own long-term goals.

Identify

The process landscape of a company is made up of many mixed processes. These are usually incompletely documented, or there needs to be documentation. However, RPA requires a predefined approach. It is, therefore, important to correctly identify the processes and determine which are suitable for RPA.

Selection

Once the processes eligible for RPA have been identified, they should be ranked according to their specific priority. It is good to initially focus on processes with the least complexity and impact on the company.

Pilot

In the fourth phase, one of the previously defined processes is automated together with the company. At this point, the company should already be able to automate the process with little support from an IT professional and thus generate new knowledge internally. In the beginning, important key figures are identified, which can be used to assess the pilot’s success.

Evaluate

After a four-week testing period, the results are either positive or negative. This evaluation serves as a basis for deciding whether to use the procedure regularly or abolish it. All data must be properly documented.

Scale

Once the company has successfully implemented the first pilot and gained sufficient experience, developing a long-term automation strategy that aligns with the company’s digital transformation is important. At the same time, a Center of Excellence (CoE) should be created. Management support is necessary in this phase since the transition to an automated company should be accompanied by change management.

Also Read: Identity Theft On The Internet – The Keys To Respond

Why Do You Need A Center of Excellence (CoE)?

An RPA Center of Excellence is a major group that oversees all RPA initiatives’ success. This category includes people from the process manager to IT specialists to various company representatives. At best, the group will have visibility into all RPA initiatives, allowing them to establish accurate RPA standards and practices that ensure the organization’s RPA investment is worthwhile.

The RPA-CoE may have responsibilities including:

• RPA service and support
• training of employees
• Setting RPA standards
• Establishment of a company-wide RPA strategy
• Monitoring the success of all RPA processes

Consider the CoE a critical component of your RPA project and start implementing it as early as possible. A well-organized and managed CoE ensures that your RPA investment pays off in the long term and that you are always one step ahead of your competitors.

The Largest Technology Partners on The Market

The demand for providers of robotic process automation has increased significantly in recent years. Researching RPA vendors alone can lead to over 15 different vendors. Many of these companies have long been involved in test automation and have since expanded into process automation. Today’s providers have other focuses or target groups. It isn’t easy to keep track of things here. Above all, choosing the wrong provider can be expensive, and the intended success does not materialize. Gartner publishes the Magic Quadrant annually to better understand the market to provide an up-to-date overview.

Vendor UI Path

UI-Path is probably the most well-known RPA software provider out there. Founded in 2005, the company focuses primarily on automation scripts and has gradually focused on process automation. UI-Path was specially developed for Windows and required the installation of extensive software. However, there is also a stripped-down community version that’s free for businesses. The paid version of UI-Path has numerous licenses that must be individually tailored to the customer’s requirements. There are ready-made packages, but some of them require additional licenses.

Provider Automation Anywhere

Automation Anywhere is arguably the most popular RPA software provider. A unique selling point of the provider is its platform independence. This means that the application is accessed directly from the Internet browser and does not require extensive installation on the computer. However, a small piece of software must be installed on the computer to automate a process. This installation does not require any additional permissions. Because of this simplicity, Automation Anywhere is an easy-to-use tool suitable for companies of all sizes.

Conclusion

Organizations using intelligent Automation can leverage RPA to automate processes that are efficient, accurate, and aligned with business goals. Rather than wasting people’s time on pointless and unproductive activities, such as B. using software that uses few solutions, intelligent Automation handles increasingly complex tasks with infinite flexibility, better connectivity and greater accuracy.

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Mobile Robotics: Opportunity And Challenge

Do you deal with the topic of mobile robotics? You are not alone there. More and more production managers are currently dedicating themselves to automating their intralogistics. Companies recognize the advantages that this step offers them:

• Increased productivity and flexibility
• Error prevention
• Material traceability
• More effective use of employees as their most valuable resource

No wonder, then, that mobile robotics is very popular.

Consider The Degree of Flexibility Needed.

The first question regarding mobile robotics is: What solution do I need? The counter-question is: How flexible can it be? The individualization of products and, consequently, the flexibilization of production systems is constantly increasing. Nevertheless, the degree of flexibility that a mobile robotic solution must bring is different in each application. Many mobile robots are on the market – from track-bound models (AGV = Automated Guided Vehicle) to autonomously driving solutions (AMR = Autonomous Mobile Robot).

Autonomous mobile robots generally offer more flexibility than their track-bound relatives. Nevertheless, there are still enough use cases for all models today. When choosing your mobile robotics solution, we recommend an as-is analysis to identify how much flexibility is needed in your factory today and in the future.

Put Your Current Intralogistics Concept To The Test.

In most cases, the first reflex when automating intralogistics is to convert the existing concepts and systems to an automated variant. A mobile robot, therefore, carries out the activities that were previously carried out manually. This can be the right way, but it doesn’t have to be.
The manual execution of intralogistics activities and the use of mobile robotics are two different approaches. An ideal intralogistics concept looks correspondingly different. When discussing the optimal use of mobile robotics, it makes sense not to regard them as simple employee replacements. Mobile robotics is a complementary technology that offers new perspectives – through individual structures, combination with other technologies (e.g., cobots), or interaction in the fleet network.
Therefore, we recommend: Let us show you your options and decide, independently of existing structures, what makes the most sense for your factory.

Perform A Technology-Adapted ROI Calculation.

The ROI can be calculated relatively easily with classic automation – for example, an assembly or test process. The automation saves cycle time X, scrap Y, or complaint costs Z. This, in turn, can be easily converted to the respective component.

However, mobile robotics does not affect a single process but the entire production. As already described, mobile robots do not replace a specific employee in a specific activity but complement the production system as a holistically integrated technology. To determine the benefit, you should therefore think across processes. For example, ask yourself the following questions:

• On which systems can mobile robotics start as a downstream process?
• How many transport routes can be saved by mobile robotics every day?
• How much time is freed up that employees can use to add value?
• To what extent are the employees’ working conditions (ergonomics) improving?

Some effects can be determined ad hoc (transport routes, transport times, etc.), and others can be seen over the long term (sick days, fluctuation rate, etc.). However, the more relevant factors you include in your calculations, the more precisely you can determine and argue the benefits of mobile robotics for yourself. As you can see, it pays to look at the overall picture.

Think Big But Start Small.

Mobile robotics works across processes. It creates value in your entire production. The long-term goal should be to exploit this value. “Think Big” is a good approach, especially in the conception phase of your project: you think outside the box. They develop alternative intralogistics concepts. Looking at the big picture allows such a strategy development.
In a concrete implementation, however, a reverse approach is recommended: “Start Small.” A hasty start with many robots, models, and providers is extremely difficult to realize – both technically and organizationally, as well as culturally. One must not forget that mobile robotics is a process that means cultural change. Start with 1 mobile robot first, and counteract it. You can learn, pick up your people, and scale based on your insights.
You must rely on a scalable concept right from the start. Even if you start small, a mobile robot rarely comes alone. Functioning fleet management is essential in the medium term.

Also Read: The Technologies Behind The Rise Of Robots

Get Relevant Stakeholders On Board Early And Transparently.

A mobile robotics project will not only find supporters in your company. For your project’s success, taking people with you is important. It’s about transparent communication right from the start. In this way, concerns and misunderstandings can be eliminated. You will also receive valuable input to help you as the project progresses. But who are the relevant stakeholders?

• Management (C-Level)
• works for council
• employees in production

Ultimately, anyone whose work is affected by mobile robotics is interested in your project. Your decisive success factor will be finding and promoting so-called missionaries as technology drivers. At the same time, you should stay in close contact with those stakeholders who are not yet convinced. In this way, you ensure that your entry into mobile robotics is not only a technological success but a cultural one.

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Cobots can work alongside humans, doing the same activities and helping them complete their tasks. This article will look at the positives and negatives of the interaction between cobots and humans.

Interaction Between Humans And Robots: How It Works

Technology is advancing faster, leading to more significant interaction between humans and machines. The so-called collaborative robots or cobots are more and more widespread in our lives at home and professional levels.

In the home, cobots can be used for different activities, such as cleaning the house or preparing meals. In the professional field, on the other hand, cobots are used to carry out repetitive and sometimes even dangerous tasks, such as handling heavy materials or industrial machinery.

The Interaction Between Cobots And Humans Can Have Both Positive And Negative Sides.

Let’s see what the disadvantages of using collaborative robots are:

• From the point of view of the human worker in collaboration with the cobot, psychological risks must be addressed.

• The cost of cobots is relatively high (not accessible to any company). Their price can be a deterrent for many companies, tiny and medium-sized ones.

• Cobots can be dangerous if not used correctly. If placed in the wrong positions or handled incorrectly, they can almost prove to be a weapon for company employees.

Cobots also need maintenance to be carried out meticulously and on schedule to prevent accidents at work.

The negativity of the aspects can be quickly neutralized by the many positive aspects regarding using these very high-efficiency machines.

The positives mainly concern:

• Increase the efficiency of the production process.

• Reduce production times and increase the quality of the final product.

• By being used in dangerous or difficult areas for humans, the risk of injury is minimized.

• Cobots can be programmed to perform different tasks, which allows workers to focus on other tasks.

From whatever perspective you look at it, integrating manufacturing force with collaborative robots is a boon to business productivity. Knowing more details is easy; just contact Homberger Robotics and explain your needs.

These third-generation robots are on the rise. We believe that in a short time, many Italian companies (many more than expected) can choose to buy cobots, giving life to a real turning point in the industrial market and beyond.

The greater the market demand, the more extensive the offer will be; consequently, significant changes will be integrated, improving safety and versatility of use.

Furthermore, the interaction between humans and cobots is increasingly innovative and insightful. Collaborative robots are equipped with artificial intelligence capable of automating the production process in the workplace.

Thanks to the uninterrupted exchange of information, cobots are increasingly capable of overcoming obstacles and choosing alternative routes to routine ones.

The ultimate goal will be to make the cobots completely autonomous. In the not-too-distant future, the development of more robust processors and a broader vision will allow cobots to roam factories without necessarily having to interface with humans.

Also Read: Robots – History, Types & Application

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When the word future is used from a technological point of view, the name Elon Musk is usually missing. Today, there is perhaps no one who does not know this, not quite American, visionary. He managed to get rich selling Zip2 and PayPal projects at a young age. However, countless articles have been written about his past.

So let’s move on to the dream world for a moment and imagine what it would mean for humanity if its other dreams succeeded. Instead of the history of the companies in question, we focus on their efforts and goals. Unlike other global companies, many are not built on pockets and bank accounts but primarily on progress. After reading the following lines, you may say that this is an apparent utopia, and you will not be so far from the truth. However, if only one of them managed to make it to the end, it could change the world forever.

SOLARCITY – CLEAN ENERGY AVAILABLE

Renewable energy sources have several problems. Buying and installing solar panels is very expensive, and the return on investment can climb up to several years. In addition, they are constantly evolving, and the new generation brings better efficiency. So why should the average consumer buy them when they have to pay for electricity?

SolarCity was aware of this problem and tried to solve it. So the current idea of ​​the company is to rent solar panels and pay for energy as you would with a regular provider. Of course, this solution is limited to a few US states, and it is of fundamental importance, for example, in sunny California. The biggest problem is storing energy at night when the panels are not exposed to the sun’s rays. This could be solved by the upcoming high-capacity PowerWall batteries intended for households. Each customer would have a separate panel system along with the battery. If there were a problem or system failure, a SolarCity employee would come and solve the problem.

TESLA – A REVOLUTION AT THE EDGE OF THE ABYSS

Many people may not realize this, but Tesla is not just electric cars. Tesla is first and foremost an idea that had, and still can, mean a revolution in transportation. The original idea was that customers would get free and unlimited access to the Supercharger network by buying a vehicle. These were initially powered by solar energy, and their operation was essentially free. However, Musk realized that the solution would be financially unsustainable, so the company changed its policy several times.

The lack of stations discourages customers from buying, and Tesla does not build another due to insufficient interest. But the situation is different in the USA. A fast-charging network covers almost the entire country. If you need to go on a more extended trip, enter the destination in the navigation. The car will automatically select Superchargers as stops if you exceed the maximum range. One of the software updates has introduced a feature where the battery temperature starts to rise automatically when you approach the charger. This allows you to charge at full speed right from the start.

Other elements of the carmaker are also built on software updates. In the beginning, Tesla was famously famous for his autopilot. So far, it is officially limited to driving on the highway, although as tests have shown, it can handle ordinary roads with almost no problems. The driver must be careful and touch the steering wheel regularly due to government regulations. However, all currently sold Teslas already contain the necessary hardware for autonomous level 5 control. When legally possible, an update will come, and cars will be able to drive independently, stop at stopwatches and traffic lights or give way. Did you see what the cockpit of the Model 3 looks like? All dashboard controls are centered on a single display in the center—no separate section for a driver with a tachometer or tell-tales. The Tesla Model 3 looks as if it was designed that the driver will not even drive it.

It’s a nightmare of conservatives and driving fans, but the autopilot will always be safer because it eliminates the human factor from a safety standpoint. The aviation industry has been running a computer system for years if you think about it. Of course, it is incomparable with regular traffic. However, the safety and fluidity of road traffic would increase unimaginably, drunk driving, tired drivers, and racers would disappear from the roads.

From a functional point of view, of course, not everything is ready yet. Tesla has been on the verge of collapse several times during its existence, and in a way, it still is. However, if the Supercharger network could be expanded to every city and autonomous management brought to perfection, the future would come.

SpaceX – MARS IS COMING SOON AND TRANSPORTED ACROSS THE PLANET

Perhaps the most ambitious Musk company is SpaceX. The company’s long-term plan is to colonize the planet Mars. When this information first came to the public, it aroused ridiculous reactions, and however, they were soon reassured by considerable success.

SpaceX is the first space company in the world to have a rocket whose engines return to Earth after landing in orbit and land on Earth. Thanks to Falcon 9, the cost of one flight has been reduced so significantly that Today, SpaceX is regularly paid for by private companies that need to get their satellites into orbit. The Falcon Heavy was also a huge success. Musk used the first flight to promote Tesla and decided that the cost would be the first car – the Roadster. The Falcon Heavy can carry a heavier load by engaging three boosters simultaneously. Two of them then land back on the launch pad, and one lands safely on an autonomous drone platform at sea. You can watch all stars live online.

However, the most exciting project is the forthcoming rocket, The Big Falcon Rocket, introduced in September 2017. Its primary task is to travel to Mars. The estimated date of the cargo flight was set for 2022, and two years later, the human crew should fly. However, as shown in the past, there are always several problems on the way to the result, so the time horizon must be taken with a grain of salt.

However, there is another option for reusable rockets, namely, travel within our planet in addition to space travel. Given the technical possibilities, it is not surprising that the Earth to Earth project is a matter of the distant future. Musk would like to build a network of launchers across the Earth, and rockets would leave them as commercial flights. Movement in a vacuum in orbit is not limited by atmospheric resistance, which significantly increases the maximum speeds. This would make it possible to transport within one hour across our planet.

FULFILLED DREAMS

Musk also has other plans with The Boring Company and Hyperloop. Some of the mentioned projects may only remain on paper and as animations on YouTube. But it is at least admirable that someone is trying to carry out plans that others would only dream of.

Also Read: Software Robots With Artificial Intelligence: The Future Is Already Present

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How it expedited various operations

Automatic palletizers have, time and again, proved and outwitted the conventional ways to increase operations in the field of manufacturing and others. This invention boosted up the production process and is also demanded for being able to adapt to variable needs and products.

Not only has this invention amped up efficiency, but it also lifted a huge burden off of human labour’ chest of loading and unloading(which is done by robotic depalletizer systems pallets. This ultra-modern invention has made workplaces a lot safer than they used to be. 

Inarguably, traditional automated palletizing systems have been a great help to push the targets. However, these systems demanded quite a lot of space within the facility. Additionally, these systems lacked the capability of surmounting and carrying complex tasks and ideas through. 

To be precise, traditional automated palletizing systems proved to be below par when they were faced with the job of palletizing mixed loads or to switch between products in less time. When the investors were faced with the cost and whether it was a great investment, the system somewhere lacked in coming up to scratch.

As time ticked by, the situation demanded palletizing systems that allow for relatively more flexibility, efficiency and readiness to get the tasks to a desirable end. And over here comes into the picture, the robotic palletizing system. Some of the benefits of such robotic palletizing machines are outlined below:

Simple case palletizing

First and the foremost advantage of hugging robotic palletizing machines is that these require limited hardware, thereby making it ideal for robotic palletizing. These do require some kind of intervention like replacing pallets or removing the ones having full loads so the task of human labour isn’t completely on the chopping block. You may need to be constantly on the lookout for infeed conveyors, robots and safety standards. 

Multiple lines can be dealt at the same time

Such robotic palletizers oils the wheels of multi-line palletizing tasks by assuaging the need for intricate upstream accumulation conveyor systems. 

Additionally, graphics-driven software smooths the path for end users to make changes in the pallets after they are content with new SKUs or other trailblazing changes that may have been introduced.

Bag palletizing

The next advantage of such buttressed palletizer systems would be that these can handle and palletize bags upto 20 cycles per minute and they are nearly impervious to limited spaces or unsatisfactory environment (in terms of cleanliness). 

This improved efficiency wasn’t found in the case of traditional palletizing systems. Needless to say, clamshell tooling allows the arms of such robots to lift up the bags without tampering with what is inside of them,thereby banging out straight and square loads.

Pail Palletizing

It wouldn’t be fair to talk about palletizers without addressing this important task which is undertaken by robotic palletizers. These robots possess the efficiency of pail nesting inside the lids of the layer beneath it.  These robotics prove to be quite precise at this specific job.

Mixed, partial or display loads

These complex loads require fourway labels out orientation, several SKUs in one layer or load, quite a few sheets within one load/layer(as mentioned), specific type of dunnage and other detailed specialisation. Though conventional palletizing machines are capable of carrying these loads forth, the robotic palletizing machine does the task without letting too much money slip off your bank account. Such robotic palletizers are cost-effective in this manner.

Final Words

The aforementioned article talks about robotic palletizer systems at length and they have also been contrasted with conventional methods of palletizing loads and unloading. In the later paragraphs, the article tried to put forth different advantages of robotic palletizer and why you must opt for them.

The readers can go through the article if they are conflicted with what they must go for and which one would be most viable and economical without scrimping on the end quality or efficiency.

Also Read: The Technologies Behind The Rise Of Robots

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This article aims to give you an overview of machine learning paradigms and the types of problems they are commonly used for.

Machine Learning Paradigms

As a general rule (there are exceptions), machine learning algorithms build a model representing the knowledge they have been able to extract from the data provided as input. Depending on the additional information supplied to the algorithm, we can differentiate between different paradigms to guide the learning process. Below I briefly describe the best known:

• Supervised learning. It consists of indicating to the algorithm, as it learns if the output it has generated for a particular case (the prediction) is correct or not. The most common action is for the algorithm to adjust the model it generates each time it is told that it has made a mistake to improve its predictions.
• Unsupervised learning. The only information that is delivered to the algorithm is the data samples without further details. From these samples, it is possible to analyze the distribution of the values, the similarity or distance between the models, the degree of concurrence of some variables with others, etc. The applications are multiple, as we will see later.
• Semi-supervised learning. It is a case halfway between the previous two. From the available data set, the correct output is known only for some samples. The algorithm uses them to build an initial model that, later, provides a forecast of the output value for the rest of the pieces. In this way, the model is expanded and adjusted, taking advantage of the available information.
• Reinforcement learning. The algorithm to which the data is provided is not supplied with the accurate outputs to adjust its model, as is the case in the supervised point. Still, it is awarded a more or less significant prize depending on how well the sequence of actions is carried out. In this way, the behaviour is reinforced towards the objective pursued.

These paradigms allow specific types of problems to be solved and implemented using different tools: the models that represent knowledge. Depending on the chosen model: a tree, a neural network, a set of rules, etc., a specific algorithm will be used to generate and fit it.

Types of Problems in Machine Learning

Machine learning is used to solve a wide range of real-life problems. These problems, or tasks as they are also known, can be categorized into a few types. Although it is not a strict rule, each situation is usually addressed through a specific learning paradigm. For this reason, the most common types of tasks are outlined below according to the paradigm with which it is traditionally approached.

Supervised learning Tasks

There are two fundamental types of problems that are solved by supervised learning, described below. The actual outputs, known in advance for the data, will allow the algorithm to improve its model parameters. Once the teaching or training of the model is completed, it will be able to process new samples and generate the appropriate output without any help.

• Classification. Each data sample has associated one or more nominal outputs, called class labels, labels, or simply class. To automatically classify, a predictive model is created, to which, by delivering the input variables, it generates the corresponding class labels as output. A classifier can be used to process credit or risky loan applications, differentiate incoming email messages as spam or essential, find out whether or not a person’s face appears in a photograph, etc.
• Regression. As in the previous case, each sample also has an associated output value, but in this case, it is of an objective type (continuous, not discrete, that is, with possible results within a continuum), so the techniques used to generate the model are usually different from those used for classification. However, the procedure for fitting or training the model is similar: known accurate outputs are used to correct its parameters and improve prediction. With a regression model, it is possible to determine the height of a person based on their sex, age and nationality, or to predict the distance that will be able to travel a transport taking as input variables the weight of the load, the volume of fuel available and the ambient temperature.

Also Read: What Are Artificial Intelligence (AI), Machine Learning (ML), And Deep Learning (DL)?

Unsupervised Learning Tasks

As indicated above, the types of problems faced with this learning paradigm are characterized because the data samples only have the input variables. There is no way out to predict that can guide algorithms. Therefore, the models generated, if they exist, are not predictive but descriptive. The most common tasks are:

• Grouping. Analyzing the similarity/dissimilarity of the data samples, for example, calculating the distance they are from each other in the space generated by the values ​​of their variables. Several disjoint groups are created. This technique, also known as clustering, facilitates visual data exploration and can be used as a primary classification method when the required class labels are not available to generate a classifier.
• Association. The search for associations between specific values ​​of the variables that make up the samples is carried out by looking for the concurrence between them, that is, by counting the times they appear simultaneously. As a result, this type of problem can generate a set of association rules, a technique widely used in all kinds of electronic and physical businesses to arrange their products or recommend them.
• Variable reduction. By analyzing the distribution of the values ​​of the variables in the set of samples, it is possible to determine which of them provide more information, which is correlated with others and therefore are redundant, or whether it is possible to find an underlying statistical distribution that generates these data, which would simplify its original representation. There are many possible techniques in this type of task, from the selection and extraction of variables to manifold learning, consisting of finding the aforementioned underlying distribution.

Other Types of Learning Tasks

A vast majority of the problems addressed through machine learning fall into the categories listed in the previous two sections. However, there are other types of tasks that require different approaches. An example would be optimization problems in general, of which perhaps the best-known exponent is the travelling salesman. This task consists of finding the shortest itinerary to visit in cities. When n is enormous, the problem becomes unapproachable to the exhaustive search: evaluating all the possible alternatives to determine the best one.

There are many other cases within this category, and the difficulty is usually always the same: the optimal point is not known, so it cannot be known whether a potential solution is more or less good, and the number of possible solutions, or steps to reach them, it is enormous. There are two categories of techniques that are commonly applied to deal with these problems:

• Bio-inspired algorithms. This group includes genetic algorithms, evolutionary strategies, optimization based on particle systems, etc. All of them start from the same concept: reproduce mechanisms existing in nature such as evolutionary selection in living beings, the behaviour of flocks of birds, colonies of ants, etc. Thanks to them, it is possible to find an acceptable solution to the optimization problem in a reasonable period.
• Reinforcement learning. This paradigm, described at the beginning of the section, can also be applied to optimization problems, although in recent times, it has gained notoriety for its success in learning to play and win certain games.

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When people talk about modern work today, one often hears the terms digitization and artificial intelligence. Still, almost as often, they talk about robots that will change the world of work. But what is a robot? Why is it called that, and when did it become built as a robot for the first time? And above all: What types of robots are there and what can they be used for? If you are toying with ​​using a robot, you should know what kind of machine you are dealing with.

What Are Robots?

The answer to this question is not that easy because there is no uniform definition. We have come across the term robot quite often recently: lawn mower robots, vacuum cleaner robots, toy robots, social robots for pensioners, or a robot dog. The short form ‘bot’ can be found in so-called chatbots or the Google bots that scour websites in the computer world. The camera drone or autonomous driving is also sorted here again and again. And then, of course, there are the industrial robots, which are mainly known from the automotive industry. What they all have in common is that they do something automatically.

So is every automatic machine a robot? No. A device is first and foremost a powered (motorized) tool that humans control. A drill as well as an automobile or a coffee machine.

An automat is a machine that can perform a specific task automatically, but only this one. So the coffee machine can brew coffee by itself, but nothing else. And a CNC milling machine can be programmed, but in the end, it can only mill. If this particular process is no longer required, an automat is useless.

On the other hand, a robot can be programmed and converted and can therefore be used for a large number of tasks. The specialty of a robot is its flexibility. Where a machine can only repeat the predefined work process, a robot can be reprogrammed so that it grips or assembles, advertisement.

The History of Human-Like Robots

What Was The First Robot, and Who Invented it?

People have always invented things that make their work easier. And the desire, on the other hand, for a mechanical helper who tirelessly takes on complex and tedious tasks is probably as old as humanity itself. Even Aristotle thought of automatically working machines with the ancient Greeks. But it would be a good 2,000 years before the world was ready. From a purely mechanical point of view, inventors were prepared to build sophisticated automatons as early as the late Middle Ages. But it was only the transistor technology invented in the 1940s that laid the foundation for it to program it.

When we think of the articulated arm robots that are typical today, we cannot ignore George Devol and Joseph Engelberger. With Unimate, these two developed and built the first robot that electronics could program. As early as 1954, inventor George Devol applied for a patent for a programmable arm. However, he initially lacked funding to implement the ambitious project. In 1956 he met the investor, entrepreneur, physicist, and engineer Joseph Engelberger at a cocktail party. He was a big fan of Isaac Asimov’s robot stories and was immediately ready to join the project. Engelberger took part so much enthusiasm for his work that today he is considered the father of modern robotics.

What Are So-Called cobots?

Robots are usually made of metal and, thanks to the electric motors used, are also powerful. They can move very quickly under certain circumstances. Therefore, there is, of course, a considerable risk of injury if a human gets in the way of a robot during operation, which is why most classic robots operated behind a protective fence. But there is another way.

With these devices, a new type of robot was founded: the cobots. The name is made up of the English terms Collaborative Robot, i.e., robots for human-machine collaboration. To achieve the necessary security, the manufacturers of cobots use different methods, sometimes in combination. They measure the forces that their motors generate and compare them with the planned path. Some have force-torque sensors in some or all of the joints. Some use an air-filled sensor skin, others use capacitive sensors (like a touch display). They all have in common that as soon as they encounter an obstacle or a person, they stop.

To further reduce the risk of injury, the edges of the cobots are rounded, and they travel at reduced speeds and forces. As a result, they sometimes seem a bit sluggish, which is why many models can work much faster in the absence of people (ensured by a fence or sensor monitoring).

Lightweight robots and cobots are certainly the most sensible solution to be used in small businesses, laboratories, or in trade. In addition to the low price, it is above all the ease of use and programming that makes cobots so attractive.

Also Read: The Artificial Intelligence In Three phases To Autonomous IT

What Robots Can & Cannot do, The Areas of Application

Of course, some jobs would not be possible without intelligent mobile robots. Especially in extreme environments such as deep-sea research or space travel, researchers rely on the support of robots. Technically demanding tasks are also not an issue for autonomous systems.

Then there are the uses in industrial mass production. Here, the six-axis, Scara, or Delta systems are primarily responsible for efficiency; they are supposed to weld, paint, load machines, and process workpieces. Fast and around the clock.
But what about small businesses and craftsmen? Artisans, in particular, are often proud to do their work themselves, with their own hands. It shows their ingenuity, their craftsmanship. That is a good thing, yet there is one or the other task that justifies using an industrial robot or cobot. Of course, even systems equipped with AI and machine learning cannot replace the experience and creativity of a master craftsman or skilled worker. Artificial intelligence is far from being that good. But jobs that are stupidly repetitive, physically difficult, or dirty and dusty, robots can do very well. And let’s be honest: there is work like this everywhere. If you’ve never thought about a steel or aluminum colleague before, maybe you should. It could change your work life. Positive.

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In this way, RPA can reproduce human actions and integrate them into digital systems. This technology can be implemented in different departments within a company, drastically changing its operation, with the sole objective of eliminating the time invested in repetitive tasks and reducing errors.

The RPA is made up of an automation system that reproduces the activities of a professional. Thus, the company has the opportunity to eliminate work overload and overstaffing.

How does RPA work?

RPAs use bots, that is, software solutions controlled by computer code capable of executing programmed actions and learning new functions. These bots are capable of imitating almost all human actions, such as:

• Login to mobile applications;

• Move files and folders;

• Copy and paste data;

• Complete forms;

• Multi-system connection;

• Extract data from documents;

• Read and write to databases;

• Automatic replies to emails;

• Perform calculations;

• Collection of data from the web;

• Compilation of social media statistics.

RPA bots use the user interface to collect data and manipulate applications, just like humans. They interpret, respond, and communicate with other systems to perform various simple and repetitive tasks.

However, the goal of RPA is not to replace information systems but to improve them. The great advantage of this technology is the possibility of adapting the software to changing situations. This allows the bot to take new actions and communicate automatically.

Also Read: RPA For Financial Management Of The Company

What Are The Advantages Of RPA?

In addition to automating routine processes, one of the greatest benefits of RPA is that it offers greater efficiency and eliminates the margin for error, thus reducing operating costs.

Here are some other advantages of RPA technology:

Greater Precision

Manual and repetitive tasks have a higher probability of error, but with task automation, the risk decreases, and the precision increases.

Optimization of Processes

The use of RPA streamlines and simplifies processes. Repetitive tasks, such as filling out forms, collecting data or statistics, or even submitting automatic responses, can be optimized using RPA. As a result, interactions become simpler and faster, and the service is much more efficient.

More Productive Employees

The automation of tasks also reduces the workload of employees, optimizing time and resources. In addition, by simplifying processes, the work routine becomes much more efficient and more active. The fewer processes employees have to worry about, the more time they have to focus on more complex tasks with greater value.

Technology On The Rise

According to Gartner, RPA is growing rapidly – 20-30% every quarter. For this reason, companies must be aware of the importance of this technology for their growth and know-how to get the most out of it to face the digital transformation.

The post RPA – Threat or Opportunity For The Business World? appeared first on TechReviewsCorner.

Before moving further to this post, we need to understand what exactly the term Digital Transformation means.

In 2011, CapGemini collaborated with the MIT Center For Business to determine the mean of digital transformation. The researchers from MIT and Capgemini stated, “The use of technology to radically improve the operations and enhance the businesses’ reach.”

The definition can be broad, but it is clear from the definition itself that digital transformation can create huge impacts on organizations’ operations. We can conclude that the researchers want to say only digital transformation can enhance the business’s reach. If the world connects everything using digital transformation, parallelly, PCB assembly is also getting popularity after digital transformation. The electronic industry is getting a big jump after compiling technology in their manufacturing process and got several solutions in a single go.

To connect Printed Circuit Boards in the technological world, some components have created a network to connect to the world’s rest. Let’s have a deeper look at these components that are transforming the electronic world.

Mobile Phones

We all know how mobile has permeated into influencing the people. The companies are taking advantage of this awesome device and promoting their products through mobile devices. Nowadays the advertisements are also created while keeping mobile in the minds.

Mobile became a foundation tool for companies to better reach the rest of the world without even leaving their seats. The CEOs can connect directly to the potential customers and influence them using mobile devices.

Let’s take an example to understand the thing efficiently if an organization wants some effective solutions in electronic operations and wants to install Cable Harness Assembly to promote their products. They can go with mobile devices and can find several manufacturers in the world. It will give them ease to get smart solutions directly from the best manufacturers in the world.

Cloud-based services

Companies are compiling cloud services to manage data and provide effective solutions to their clients. When clients are searching for Panel Mount Assembly, manufacturers can use cloud-based systems to provide the prototype and 3D model of the device. It creates an easy-to-understand model and helps the manufacturer to prepare some effective strategies in Turnkey PCB assembly.

The Economist found in research that 78% of cloud services have enhanced the manufacturing world’s services and will reach new milestones in the next 5 years. The world is shifted to digitization, and everyone is connected to cloud-based services to find effective solutions in minimal time.

Also, cloud services have enabled manufacturers to enhance organizations’ productivity and create an effective way of promoting the services. Many organizations have felt that cloud services have enhanced the flexibility and agility of the manufacturing process in the company.

IoT (Internet of Things)

IoT is the latest trend in the PCB world, where manufacturers use some effective components while manufacturing the PCB build box. Manufacturers can provide effective solutions to the companies and connect with the rest of the world using IoT components in the manufacturing process of PCB build Box and other PCB assembly processes.

IoT has enhanced the visibility of the products and operations. All the manufacturers’ components in the PCB assembly are related to IoT nowadays, enhancing its operations. The electronic world is getting connection due to the IoT devices, and they are the most crucial components to transform digitally.

The insights themselves show that IoT is the revolution in the digitization process of the electronic world. Every electronic device is now capable of running billions of network systems through them using IoT.

Robotics

Just imagine a world where every company is using robotic devices to manage some critical operations. Electronic industries are now using robotic devices by compiling AI and Machine Learning into the system. It is creating a better impact on the operations of the companies.

Robotic devices can go with every situation and help manufacturers understand the design and process in the assembly.

Final Words

When the world is moving at this much huge speed, electronic devices are a crucial part in taking this speed to the speed of light. After digitizing the electronic world, everything is getting fast operations, and the companies are getting enormous benefits. The use of smart electronic devices has created a better impact on the world, and everyone can connect themselves to the rest of the world.

The post 4 Components Of Digital Transformation For Electronic Industry appeared first on TechReviewsCorner.

There is no industry that doesn’t use some form of robotics. Storage robots take care of orders and drones deliver packages. Robotic arms perform operations and robotic grading systems evaluate student work.

Robots are everywhere today. We have reached a point where some robotic systems such as B. ATMs and self-service checkouts are so widespread that they are hardly seen as robots.

No doubt we have seen tremendous advances in this area over the past decade. It stands to reason that further developments will change society in the near future. The world of tomorrow will be automated, digitized, and networked.

But what technologies are behind this revolution? Mechanics, intelligence, and networking will have a lasting impact on our future. Let’s look at how these aspects work together to create a society where robots are ubiquitous.

Nowadays, robots also play an important role outside of industrial applications. Robots for use in the private sector, in companies, as military robots as well as autonomous vehicles are making huge strides for profound changes in the respective sectors.

Technology in the background

The key to progress is the further development of sensor technologies. Smaller, more powerful tools have taken the place of sensors with a short-range and cameras with a single resolution. Thanks to modern motion and image processing systems, robots are becoming more autonomous and can perform tasks faster and more precisely.

Also Read: THE REAL DEBATE – WILL AI REPLACE HUMANS?

Some of the robotic technologies currently in use:

Robot view

Systems for optical distance and speed measurement (LIDAR, Light Direction and Ranging):  The light reflected from surfaces in the vicinity is used to create 3D maps of the surroundings 

2D vision:  Flat, calibrated vision that allows the robot to measure length and width, but not height

3D View:  Usually two or more cameras that provide X, Y, and Z-axis information for a more detailed geometric representation

Robotic sound processing

Microphone:  A conversion unit for analog-to-digital conversion can be used in combination with digital signal processing in order to capture the emotional state of a person via the voice.

Robot touch

Capacitive sensors: Capacitive sensor technology, which is already used in touchscreen devices, helps robots to feel different surfaces.

Robot perception

Proximity sensors:  Can be based on capacitive, inductive, optical, sonar, ultrasonic and fiber optic technologies

Robots in motion

Robots appear in many forms. Most famous, however, is the humanoid, popularized in fictional representations, which is modeled on the human body. Despite the impressive developments from engineering companies like Boston Dynamics, it will be many years before robots can mimic the fluid movements of humans.

But compared to the technology that once inspired ‘Robot Dance’, we have made great progress. Here are some examples of interesting developments in natural robotic motion.

AI scanners that predict disease

Scientists from Verily, Alphabet’s life sciences research organization, have developed an AI algorithm that targets heart disease that can predict looking into a patient’s eye. The software analyzes the scans of the eyes and derives properties such as age and blood pressure based on machine learning. This data is then used to predict cardiovascular risk.

Also Read: Artificial Intelligence (AI) In Marketing And Sales

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