Robotic Process Automation (RPA)

What Is Robotic Process Automation (RPA)?

RPA is the process of automating processes in your company. Thanks to software robots, you can significantly save your employees’ time on routine tasks and reduce the risk of human error. Of course, this doesn’t include physical robots. It’s more about software bots that can automate tedious tasks like invoice processing, payroll, purchasing, etc. In other words, RPA simulates human activities and performs duties, allowing your employees to focus on more critical activities, such as interacting with customers or creating strategies to grow your business.

What Processes Can You Automate With RPA?

Client support

RPA tools help you transform the way you interact with your customers. This helps to simplify and speed up the application review process. Rather than forcing your employees to find information manually, you use RPA to automate each validation of data associated with a customer profile and obtain the required data sets, eliminating the need for an employee to switch between applications.

Sales orders

Trading operations often include entering data into CRM systems, updating ERP reporting and searching for orders, etc.RPA can use custom forms for any sales activity by automating tasks such as entering sales orders, invoicing, and more. Moreover, RPA programs help maintain a clean database, improve customer service, and motivate your sales staff for better results.

Invoice processing

Day-to-day invoice processing can be pretty daunting and tiresome. Workers are aware of the hassle involved in dealing with various file formats, inconvenient email attachments, and other time-consuming processes by processing them manually.RPA helps to locate files and analyze employee invoices without human intervention quickly. In addition, billing is by its nature a rule-based process so that automation will show the status to the client.

Hiring employees

Robotic automation is used to generate and submit job offers automatically. It also triggers automatic workflows when an employee account is created. Companies can also use to help the HR team reduce the volume of processed documents by replacing paper copies with electronic filing systems.

RPA Work

RPA is flexible enough to suit businesses of all sizes, from startups to corporate companies. Unlike other forms of automation, RPA has good intelligence to decide exactly if a process should happen. It analyzes the submitted data and makes decisions based on the logical parameters set by the developer.

Programmable bots

These define the established rules, and the programmers should determine the parameters before a given bot can start working. It involves step-by-step process planning and it can take much longer for more complex tasks.

Intelligent bots

These types analyze both historical and current data to understand how employees are performing the process. The robot monitors click mouse movements and actions. After a while, when it has analyzed enough data, the bot will complete the process independently.

Benefits

Modern transformation

Roughly 60% of world-class CEOs say RPA is one of the most important parts of digital transformation. Today RPA adoption is the ideal solution for companies looking to optimize their legacy IT infrastructure to stay competitive

Cost savings

This is the first and one of the crucial benefits of RPA. The big plus is that you don’t have to upgrade or replace existing systems for RPA to work, as it’s software-independent. Robots help you eliminate disparate technologies by reliably connecting all software tools, regardless of function and department, in front and back offices.

Reliable resilience

The global pandemic has shown us the importance of operational resilience in sustaining businesses in difficult times. Developing a robust digital workforce with RPA implementations can help provide additional layers of different circumstances during “uncertain” times.

High accuracy

Despite the digital transformation, employee experience remains essential. Based on data from UiPath, and Forrester Consulting, about 65% of people believe that RPA is a significant change in work and allows employees to interact more with people and pay more attention to meaningful strategic tasks.

Total compliance

Approximately 90% of managers agree that smart automation has exceeded all expectations in its work. Over the past year, there has been a significant interest in robotics and artificial intelligence technologies. RPA intelligent automation is already delivering tremendous value to businesses, and pioneers in general services and other administrative institutions have benefited significantly.

Great Productivity

Performing repetitive tasks often leads to interruptions in employee work. But reallocating them to tasks in which they use high skills can improve their professional experience and increase productivity. Thus, robotic automation can improve your company’s performance and avoid employee burnout due to tedious work.

Cheerful employees

55% of managers say RPA increases employee engagement. The bots help employees interact with customers by performing system work and data entry, reducing call processing times, and improving customer experience by 51%.

Greater scalability

RPA automation allows you to make large-scale business processes more flexible and adaptable to volatile times and changing conditions. In other words, you can handle any workload faster with an enhanced digital workforce.

     Use RPA For My Business

• Insurance

  The bots will use intelligent document processing to extract data from claims forms, damage assessments, and physician statements and automatically update claims files. In this case, RPA makes it much easier to check coverage and sort the settlement notification requirements and payment. Intelligent analytics built into the RPA platform provides real-time dashboards and insights into claims volume, frequency, severity, status, and timing.

• Banking

  Automation with RPA enables banks and financial companies to transform data-intensive manual transactions while following ever-changing regulatory requirements.

  What’s more, organizations can automate new account settings and streamline data collection from internal and external systems for customer verification, welcome emails, and CRM updates with new data.

• Healthcare

  The current health situation has dramatically accelerated digital transformation. RPA, together with Artificial Intelligence, performs almost all information-related activities. It retrieves data, categorizes files, and searches for required contact information. Automated robots are also used to register new patients, work with medical records and enter other important data.

• Manufacturing

  Like any other field, the manufacturing industry has many tedious administrative tasks. But reducing the need to do this allows workers to focus on other, more critical work. Thus, office automation provides enormous benefits and helps speed up other processes.

• Public Sector

  RPA, in this case, makes it possible to reduce the time spent by employees on routine, monotonous tasks to provide more time for interaction with the public.

  Moreover, RPA also contributes to better data, thereby triggering more efficient management decisions.

• Life Sciences

  Many life science organizations are already actively using RPA bots to accelerate the delivery of new drugs, gain and expand innovations, and optimize manufacturing operations and supply chains. It also helps increase efficiency, improve workflows and empower your team.

• Culture of learning

  RPA helps reduce the need for certain roles, but don’t forget that it also stimulates the growth of new roles to solve more complex tasks. Be ready to improve your culture of learning and innovation as you change positions. Employee training is always essential to a business, as by improving their skills, you can prepare teams for continual shifts in priorities.

• Hard scaling

  Based on Forrester data, 51% of customers say they cannot scale their RPA program due to high costs. according to this research, about 98% of people reported that the robots’ logic requires specific scripts. In addition, 78% of business owners say they have difficulties because their RPA programs require more advanced programming skills.

      Features Essential in RPA Technology?

• Transformation of your company

  Automation bots help save time spent on routine tasks, resulting in employees engaging in more critical strategies.

  Using RPA in combination with artificial intelligence (AI) and other technologies makes it possible to automate your organization and processes completely.

• ROI for RPA

  Based on data from this Institute automated solutions help deliver massive savings of 24% to 35% in labor costs.

  The company can customize its RPA investment for optimal ROI. To maintain the desired level of success, you need to consider and measure metrics throughout your RPA journey.

• Small initial investment

  Robotic automation reduces processing costs by up to 75%. The price of such a solution depends on the number of robots and software components to be deployed. On average, the cost of one bot can reach $5000 to  $15000. In less than a year, most companies already have a positive ROI and potential cost savings.

• No interruptions in work

  Robotic automation doesn’t require any intervention in production systems and uses existing infrastructure without disrupting the operation of the underlying systems.

• Improved scalability

  RPA centers can perform a relatively large number of functions ranging from desktop computers to cloud environments.

• Low code assemblies

   RPA & Business process automation contain low code modules that allow you to take full advantage of robotic automation without the need for additional programming languages.

       Use AI and RPA

        Robotic automation provides the following opportunities for your business:

• It helps you work with large amounts of data and automates all your workflows to save time for your employees.
• It replaces human intervention in robot control and provides optimization of unstructured data.
• Employees will have more time to complete other essential business tasks rather than performing repetitive manual tasks.

       To automate more complex processes, you will need this awesome RPA + AI combo. It                  includes:

• Your workflows where you can’t predict results ahead of time, counting your support calls, product settings, etc.
• AI is used for processes that are vastly different from each other and don’t rely on a clear set of rules, such as purchasing decisions, language translation, etc.

• Marketing and lead generation

  As we know, lead generation is one of the most critical marketing components. Your team adds new data from external sources for leads to the CRM system.

  Most modern CRM platforms have built-in data loading tools. However, another part of them requires manual input of information about each new lead. This increases the likelihood of errors.

  When you implement RPA, workers can quickly import any data from their spreadsheets. It gives teams more time to interact with other customers.

• Payment statement

  The ongoing processing of payrolls is an uphill task for the HR team.

  This often requires a considerable amount of data to be entered, which also leads to errors and causes delays in payment.

  By using RPA for HR processes, your employees can automate payment transactions faster, avoid inaccuracies, and check the consistency of employee data across multiple systems.

• Financial and accounting

  Every end of the month and after quarterly periods are stressful times for the finance departments of any company.

  RPA in finance analyzes past and current market trends to make accurate forecasts of the company’s financial condition. In addition, automated bots download monthly sales data and calculate commission fees.

• Recruitment processes

  Your HR department can receive resumes from various platforms, evaluate their value, and eliminate spam using automated robots.

  What’s more, bots keep track of vital hiring processes from 80% to 90%. It includes checking, evaluating, measuring, and adapting. So, this is also one of the great benefits of RPA.

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Blockchain

What Is Blockchain

Blockchain is a distributed digital ledger that stores data of any kind. A blockchain can record information about Cryptocurrency transactions, NFT ownership, or Defi smart contracts. While any conventional database can store this sort of information, blockchain is unique in that it’s totally decentralized. Rather than being maintained in one location, by a centralized administrator—think of an Excel spreadsheet or a bank database—many identical copies of a blockchain database are held on multiple computers spread out across a network. These individual computers are referred to as nodes.

Blockchain Work

The digital ledger is often described as a “chain” that’s made up of individual “blocks” of data. As fresh data is periodically added to the network, a new “block” is created and attached to the “chain.” This involves all nodes updating their version of the blockchain ledger to be identical.

How these new blocks are created is key to why blockchain is considered highly secure. A majority of nodes must verify and confirm the legitimacy of the new data before a new block can be added to the ledger. For a cryptocurrency, they might involve ensuring that new transactions in a block were not fraudulent, or that coins had not been spent more than once. This is different from a standalone database or spreadsheet, where one person can make changes without oversight.

“Once there is consensus, the block is added to the chain and the underlying transactions are recorded in the distributed ledger,” says C. Neil Gray, a partner in the fintech practice areas at Duane Morris LLP. “Blocks are securely linked together, forming a secure digital chain from the beginning of the ledger to the present. Transactions are typically secured using cryptography, meaning the nodes need to solve complex mathematical equations to process a transaction.

Public Blockchains vs Private Blockchains

There are both public and private blockchains. In a public blockchain, anyone can participate meaning they can read, write or audit the data on the blockchain. Notably, it is very difficult to alter transactions logged in a public blockchain as no single authority controls the nodes. A private blockchain, meanwhile, is controlled by an organization or group. Only it can decide who is invited to the system plus it has the authority to go back and alter the blockchain. This private blockchain process is more similar to an in-house data storage system except spread over multiple nodes to increase security.

Cryptocurrency

The most common use of blockchain today is as the backbone of cryptocurrencies, like Bitcoin or Ethereum. When people buy, exchange, or spend cryptocurrency, the transactions are recorded on a blockchain. The more people use cryptocurrency, the more widespread blockchain could become.

“Because cryptocurrencies are volatile, they are not yet used much to purchase goods and services. But that is changing as PayPal, Square and other money service businesses make digital asset services broadly available to vendors and retail customers,” notes Patrick Daugherty, senior partner of Foley & Lardner and lead of the firm’s blockchain task force.

Banking

Beyond cryptocurrency, blockchain is being used to process transactions in fiat currency, like dollars and euros. This could be faster than sending money through a bank or other financial institution as the transactions can be verified more quickly and processed outside of normal business hours.

Asset Transfers

Blockchain can also be used to record and transfer the ownership of different assets. This is currently very popular with digital assets like NFTs, a representation of ownership of digital art and videos.

However, blockchain could also be used to process the ownership of real-life assets, like the deed to real estate and vehicles. The two sides of a party would first use the blockchain to verify that one owns the property and the other has the money to buy; then they could complete and record the sale on the blockchain.

Using this process, they could transfer the property deed without manually submitting paperwork to update the local county’s government records; it would be instantaneously updated in the blockchain.

Smart Contracts

Another blockchain innovation is self-executing contracts commonly called “smart contracts.” These digital contracts are enacted automatically once conditions are met. For instance, a payment for a good might be released instantly once the buyer and seller have met all specified parameters for a deal.

“We see great potential in the area of smart contracts—using blockchain technology and coded instructions to automate legal contracts,” says Gray. “A properly coded smart legal contract on a distributed ledger can minimize, or preferably eliminate, the need for outside third parties to verify performance.”

Supply Chain Monitoring

Supply chains involve massive amounts of information, especially as goods go from one part of the world to the other. With traditional data storage methods, it can be hard to trace the source of problems, like which vendor's poor-quality goods came from. Storing this information on the blockchain would make it easier to go back and monitor the supply chain, such as with IBM’s Food Trust, which uses blockchain technology to track food from its harvest to its consumption.

Voting

Experts are looking into ways to apply blockchain to prevent fraud in voting. In theory, blockchain voting would allow people to submit votes that couldn’t be tampered with as well as would remove the need to have people manually collect and verify paper ballots.

Advantages

1) Because a blockchain transaction must be verified by multiple nodes, this can reduce error. If one node has a mistake in the database, the others would see it’s different and catch the error. In contrast, in a traditional database, if someone makes a mistake, it may be more likely to go through. In addition, every asset is individually identified and tracked on the blockchain ledger, so there is no chance of double spending it (like a person overdrawing their bank account, thereby spending money twice).

2) Using blockchain, two parties in a transaction can confirm and complete something without working through a third party. This saves time as well as the cost of paying for an intermediary like a bank.“It has the ability to bring greater efficiency to all digital commerce, to increase financial empowerment to the unbanked or underbanked populations of the world, and to power a new generation of internet applications as a result,” says Shtylman.

3) Theoretically, a decentralized network, like a blockchain, makes it nearly impossible for someone to make fraudulent transactions. To enter in forged transactions, they would need to hack every node and change every ledger. While this isn’t necessarily impossible, many cryptocurrency blockchain systems use proof-of-stake or proof-of-work transaction verification methods that make it difficult, as well as not in participants’ best interests, to add fraudulent transactions.

4) Since blockchains operate 24/7, people can make more efficient financial and asset transfers, especially internationally. They don’t need to wait days for a bank or a government agency to manually confirm everything.

Disadvantages

1) Given that blockchain depends on a larger network to approve transactions, there’s a limit to how quickly it can move. For example, Bitcoin can only process 4.6 transactions per second versus 1,700 per second with Visa. In addition, increasing numbers of transactions can create network speed issues. Until this improves, scalability is a challenge.

2) Having all the nodes working to verify transactions takes significantly more electricity than a single database or spreadsheet. Not only does this make blockchain-based transactions more expensive, but it also creates a large carbon burden on the environment. Because of this, some industry leaders are beginning to move away from certain blockchain technologies, like Bitcoin: For instance, Elon Musk recently said Tesla would stop accepting Bitcoin partly because he was concerned about the damage to the environment.

3) Some digital assets are secured using a cryptographic key, like cryptocurrency in a blockchain wallet. You need to carefully guard this key.“If the owner of a digital asset loses the private cryptographic key that gives them access to their asset, currently there is no way to recover it—the asset is gone permanently,” says Gray. Because the system is decentralized, you can’t call a central authority, like your bank, to ask to regain access.

4) Blockchain’s decentralization adds more privacy and confidentiality, which unfortunately makes it appealing to criminals. It’s harder to track illicit transactions on blockchain than through bank transactions that are tied to a name.

Invest In Blockchain

You can’t actually invest in the blockchain itself, since it’s merely a system for storing and processing transactions. However, you can invest in assets and companies using this technology.“The easiest way is to purchase cryptocurrencies, like Bitcoin, Ethereum, and other tokens that run on a blockchain,” says Gray. Another option is to invest in blockchain companies using this technology. For example, Santander Bank is experimenting with blockchain-based financial products, and if you were interested in gaining exposure to blockchain technology in your portfolio, you might buy its stock. For a more diversified approach, you could buy into an exchange-traded fund ETF that invests in blockchain assets and companies, like the Amplify Transformational Data Sharing ETF (BLOK), which puts at least 80% of its assets in blockchain companies.

Despite its promise, blockchain remains something of a niche technology. Gray sees the potential for blockchain to be used in more situations but it depends on future government policies. “It remains to be seen when and if regulators like the SEC will take action. One thing is evident—the goal will be to protect markets and investors,” he says. Shtylman likens blockchain to the early stages of the internet. “It took about 15 years of having the internet before we saw the first version of Google and over 20 for Facebook. It’s hard to predict where blockchain technology will be in another 10 or 15 years, but much like the internet, it will significantly transform the ways we transact and interact with each other in the future.

Data Science & Analytics

What is Data Analytics

In the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures the broad scope of the field. However, it includes many techniques with many different goals. The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been, and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry.

 Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. 

This information provides new insight from data. Advanced analytics addresses “what if?” questions. The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has been made possible by advances in parallel processing and cheap computational power.

Types of Data Analytics

Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business.

1) Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis, and data visualization. This process provides essential insight into past performance.

2) Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps:

• Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
• Data that is related to these anomalies is collected.
• Statistical techniques are used to find relationships and trends that explain these anomalies.

3) Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and their techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression.

4) Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.

What is the Role of Data Analytics

The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an essential process for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases, or raw sensor data. The key steps in this process are to extract, transform, and load data (often called ETL.) These steps convert raw data into a useful and manageable format. This prepares data for storage and analysis. Data mining is generally the most time-intensive step in the data analysis pipeline.

Data management or data warehousing is another key aspect of a data analyst’s job. Data warehousing involves designing and implementing databases that allow easy access to the results of data mining. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are becoming more common as well.

Statistical analysis allows analysts to create insights from data. Both statistics and machine learning techniques are used to analyze data. Big data is used to create statistical models that reveal trends in data. These models can then be applied to new data to make predictions and inform decision-making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open-source libraries and packages such as TensorFlow enable advanced analysis. The final step in most data analytics processes is data presentation. This step allows insights to be shared with stakeholders. Data visualization is often the most important tool in data presentation. Compelling visualizations can help tell the story in the data which may help executives and managers understand the importance of these insights.

Why Data Analytics is Important

The applications of data analytics are broad. Analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world. One of the earliest adopters in the financial sector. Data analytics has an important role in the banking and finance industries, used to predict market trends and assess risk. Credit scores are an example of data analytics that affects everyone. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce the risk for financial institutions.

The use of data analytics goes beyond maximizing profits and ROI, however. Data analytics can provide critical information for healthcare (health informatics), crime prevention, and environmental protection. These applications of data analytics use these techniques to improve our world. Though statistics and data analysis have always been used in scientific research, advanced analytic techniques and big data allow for many new insights. These techniques can find trends in complex systems. Researchers are currently using machine learning to protect wildlife.

The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding, and improving diagnostic techniques are just a few examples of how data analytics is revolutionizing healthcare. The pharmaceutical industry is also being revolutionized by machine learning. Drug discovery is a complex task with many variables. Machine learning can greatly improve drug discovery. Pharmaceutical companies also use data analytics to understand the market for drugs and predict their sales. The internet of things (IoT) is a field that is used alongside machine learning. These devices provide a great opportunity for data analytics. IoT devices often contain many sensors that collect meaningful data points for their operation. Devices like the Nest thermostat track movement and temperature to regulate heating and cooling. Smart devices like this can use data to learn from and predict your behavior. This will provide advanced home automation that can adapt to the way you live.

The applications of data analytics are seemingly endless. More and more data is being collected every day — this presents new opportunities to apply data analytics to more parts of business, science, and everyday life.

Data Analytics FAQ

What is the role of data analytics?

Data analytics helps individuals and organizations make sense of data. Data analysts  typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.

What are the types of data analytics?

There are various types of data analysis including descriptive, diagnostic, prescriptive, and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.

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Artificial Intelligence, Machine Learning and Deep Learning

• Machine Learning and Deep Learning
• Now that we better understand what AI actually means we can take a closer look at machine learning and deep learning to draw a clear distinction between these two.
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• Artificial Intelligence: a program that can sense, reason, act,, and adapt.
• Machine Learning: algorithms whose performance improves as they are exposed to more data over time
• Deep Learning: a subset of machine learning in which multilayered neural networks learn from vast amounts of data.
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• What is machine learning?
• Machine learning can lead to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for optimal trades. Machine learning requires complex math and a lot of coding to achieve the desired functions and results. Machine learning also incorporates classical algorithms for various kinds of tasks such as clustering, regression, or classification. We have to train these algorithms on large amounts of data. The more data you provide for your algorithm, the better your model gets

Machine learning is a relatively old field and incorporates methods and algorithms that have been around for dozens of years, some of them since the 1960s. These classic algorithms include the Navies Bayes Classifier and the Support Vector Machines both of which are often used in data classification. In addition to classification, there are also cluster analysis algorithms such as the K-Means and tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tone.

The training component of a machine learning model means the model tries to optimize along a certain dimension. In other words, machine learning models try to minimize the error between their predictions and the actual ground truth values.

Deep Learning

Deep learning is a young subfield of artificial intelligence based on artificial neural networks. Since deep learning algorithms also require data in order to learn and solve problems, we can also call it a subfield of machine learning. The terms machine learning and deep learning are often treated as synonymous. However, these systems have different capabilities. Deep learning uses a multi-layered structure of algorithms called the neural network.

Artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve.All recent advances in intelligence are due to deep learning. Without deep learning, we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate would remain primitive and Netflix would have no idea which movies or TV series to suggest.We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and deep learning. This is the best and closest approach to true machine intelligence we have so far because deep learning has two major advantages over machine learning.

DEEP LEARNING IS BETTER THAN MACHINE LEARNING

The first advantage of deep learning over machine learning is the redundancy of feature extraction. Long before we used deep learning, traditional machine learning methods decision trees, SVM, Naïve Bayes classifier, and logistic regression were most popular. These are otherwise known as flat algorithms. In this context “flat” means these algorithms cannot typically be applied directly to raw data such as .csv, images, text, etc. Instead, we require a preprocessing step called feature extraction.

When it comes to deep learning models, we have artificial neural networks, which don’t require feature extraction. The layers are able to learn an implicit representation of the raw data on their own. A deep learning model produces an abstract, compressed representation of the raw data over several layers of an artificial neural network. We then use a compressed representation of the input data to produce the result. The result can be, for example, the classification of the input data into different classes.

During the training process, the neural network optimizes this step to obtain the best possible abstract representation of the input data. Deep learning models require little to no manual effort to perform and optimize the feature extraction process. In other words, feature extraction is built into the process that takes place within an artificial neural network without human input. If you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car's shape, size, windows, and wheels,  extract these features, and give them to the algorithm as input data. The machine learning algorithm would then perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.

 Another major advantage of deep learning, and a key part in understanding why it’s becoming so popular, is that it’s powered by massive amounts of data. The era of big data technology will provide huge amounts of opportunities for new innovations in deep learning. Deep learning models tend to increase their accuracy with the increasing amount of training data, whereas traditional machine learning models such as SVM and Naïve Bayes classifier stop improving after a saturation point. Deep learning models scale better with a larger amount of data. 

Web 4.0 & Web 5.0

Web 4.0 is still a work-in-progress, with no precise description of what it will entail. The symbiotic web is another name for Web 4.0. Interaction between people and robots in symbiosis is the symbiotic web’s dream. Web 4.0 will enable the creation of more sophisticated interfaces, such as mind-controlled interfaces. To put it another way, computers would be adept at understanding the contents of the web and reacting in the form of executing and determining what to execute first in order to load websites quickly, with greater quality and speed, and construct more commanding interfaces. The read-write-execution-concurrency web will be Web 4.0.

It reaches a critical mass of online network engagement that provides global transparency, governance, distribution, participation, and cooperation to vital communities such as industry, politics, society, and others. Web 4.0, often known as webOS, will be a middleware that will eventually act as an operating system (Web 1.0 Web 2.0 Web 3.0 Web 4.0 Web 5.0).

The webOS will function similarly to the human brain, implying a vast network of brilliant connections. Although little is known about web 4.0 and its technologies, it is clear that the web is advancing toward becoming an intelligent web by incorporating artificial intelligence. The read-write-execution-concurrency web will be Web 4.0. It reaches a critical mass of online network engagement that provides global transparency, governance, distribution, participation, and cooperation to vital communities such as industry, politics, society, and others. Web 4.0, often known as webOS, will be a middleware that will eventually act as an operating system. The webOS will function similarly to the human brain, implying a vast network of brilliant connections. Although little is known about web 4.0 and its technologies, it is clear that the web is advancing toward becoming an intelligent web by incorporating artificial intelligence.

The read-write-execution-concurrency web will be Web 4.0. It reaches a critical mass of online network engagement that provides global transparency, governance, distribution, participation, and cooperation to vital communities such as industry, politics, society, and others. Web 4.0, often known as webOS, will be a middleware that will eventually act as an operating system (Web 1.0 Web 2.0 Web 3.0 Web 4.0 Web 5.0).

The webOS will function similarly to the human brain, implying a vast network of brilliant connections. Although little is known about web 4.0 and its technologies, it is clear that the web is advancing toward becoming an intelligent web by incorporating artificial intelligence.

Web 5.0 or Society 5.0

The 5th Science and Technology Basic Plan envisioned Society 5.0 as a future society to which Japan should strive. It is divided into four categories: hunting society (Society 1.0), agricultural society (Society 2.0), industrial society (Society 3.0), and information society (Society 3.0). (Society 4.0). Cyberspace (virtual space) and physical space have a high degree of convergence in Society 5.0. (real space). People would access a cloud service (databases) in cyberspace through the Internet in the previous information society (Society 4.0) and search for, retrieve, and analyze information or data (Web 1.0 Web 2.0 Web 3.0 Web 4.0 Web 5.0).

A massive quantity of data from sensors in physical space is gathered in cyberspace in Society 5.0. artificial intelligence (AI) analyses this considerable data in cyberspace, and the findings are sent back to people in physical space in various formats.

The (emotional) connection between humans and machines will be the focus of Web 5.0. based on neurotechnology, many individuals will begin to engage regularly. FOr the time being, the web is “emotionally” neutral, which means it does not recognize the feelings and emotions of its users.

What is web 5.0

Web 5.0, the sensory and emotive Web. Its goal is to create computers that can communicate with humans. For many people, this friendship will become a regular habit. It was normal practice in the information society to gather data over the Internet and have it examined by people. People, things, and systems are all connected in cyberspace in Society 5.0, and the best outcomes achieved by AI surpassing human capabilities are transmitted back into physical space. This process provides new value to industry and society in previously unimaginable ways (Web 1.0 Web 2.0 Web 3.0 Web 4.0 Web 5.0). Japan aspires to be the first country in the world to establish a human-centered society (Society 5.0), in which everyone may live a healthy, active life. They plan to do so by combining innovative technology into a wide range of sectors and social activities and encouraging innovation to generate new value (Web 1.0 Web 2.0 Web 3.0 Web 4.0 Web 5.0).

Web 3.0

What is Web 3.0

Web 3.0 is the next evolution of the Internet, and it’s coming in a big way. This isn’t something that will happen in the distant future. Web 3.0 has already begun to roll out, and it will be fully established within two years.

The driving force behind Web 3.0 is blockchain technology. Blockchain is the same technology that powers Bitcoin. It’s a decentralized ledger system (DLT) that stores data across thousands of computers at once instead of just one server. This allows for more data storage and sharing, which means more efficiency and accessibility for anyone who needs it.

Evolution of Web (1.0 to 2.0 to 3.0)

1. Web 1.0

Web 1.0 began as a static form in the 1960s with only text browsers like ELISA, followed by HTML, which made the pages more visually appealing, and the first visual browsers like Netscape and Internet Explorer.

Web 1.0 marks the first stage in the evolution of the World Wide Web. Previously, there were only a handful of content creators. However, on Web 1.0, most users were content consumers.

Web 1.0 is a content delivery network (CDN) that enables information to be displayed on websites. Best Suited for use as a personal website. They charge the user based on the number of pages viewed. This includes folders that allow users to search for specific information.

2. Web 2.0

The term Web 2.0, which Tom OReilly coined in 2004, refers to the second generation of website models.

Web 2.0 refers to websites worldwide that highlight user-generated content, ease of use, and interoperability for end users.

This does not mean a change in the technical specifications from Web 1.0 but a difference in the design and use of websites. Web 2.0 enables interaction and collaboration in social media chat to create user-generated content in a virtual community. Web 1.0 is a more accurate version of Web 2.0. Web 2.0 is also known as the Social Web.

3. Web 3.0

Web 3.0 first appeared in 2006 in an article by Web 2.0 critic Jeffrey Zeldman with technologies such as AJAX.

Web 3.0 is a term used to describe many improvements in web usage and the interaction between different paths. The data, in this case, is not owned but shared, and services show various views of web data. (Web 3.0) promises to make “world information” more meaningful than Google’s current design. This includes transforming a network into a database, a step aimed at accessing content through multiple non-browser applications, and the introduction of artificial intelligence, and spatial and 3D networking technologies.

Why is Web 3.0 Important for the Business?

The world is moving towards a future where there will be no borders, we will all be connected, and everything will be virtual. This is what Web 3.0 is all about.

As said earlier, Web 1.0 was all about introducing personal computers and the Internet. Web 2.0 was the era of social media and social networking websites.

Web 3.0 is the next phase of the Internet, where it becomes an entire universe of its own. Web 3.0 is the era of blockchain technology and decentralized applications. This is when we will see the rise of blockchain-based platforms that can decentralize pretty much every aspect of our lives.

Digital information is placed in Web 3.0, blurring the distinction between digital content and physical objects. So, the impact of Web 3.0 on businesses will be to make them more transparent and user-centric. Anything that went wrong regarding user data in corporate governance will change completely.

Benefits Of Web 3.0

Data Ownership

Over the years, technology behemoths controlled and exploited user-generated data. End-users take full ownership of the data using Web 3.0 provided by the blockchain. Data sent over the network is encrypted. Users can choose what information they want to share with businesses and advertising companies and make money from it.

Anti Monopoly And Data Protection

Web 3.0 features include professional and data, protection models. It promotes non-centralized operating systems that keep control over their users’ data. We’re going to see a frontline shift with decentralization and privacy. The monopoly of technology behemoths is over, and data breach incidents will be few and far between now that users have control over how their data is viewed.

Easy Access To Information

One of the main advantages of Web 3.0 is the ability to access data from anywhere, mainly due to the widespread use of smartphones and cloud applications.

The vision is that the user anywhere in the world has as much access to information as possible. This technology aims to expand the concept by allowing devices to collect user data and smartphones to access data on your computer.

Seamless Service

The suspension of accounts and the rejection of distributed services are significantly reduced. Since there is no single predetermined breaking point, the certainties are minimal. The data is stored on distributed nodes for redundancy, and multiple backups prevent server hijacking or failure.

Permissionless Blockchain

Web 3.0 does not need any central power. Anyone can join and participate in the network by creating an address. This eliminates the possibility of blocking users based on gender, income, orientation, geographic location, or other social factors. It also enables the timely and inexpensive transfer of digital assets and assets across borders.

Features of Web 3.0

Many technologies have to unite and come to the fore, which means that the combination of blockchain technology will not be enough. The emerging technologies of our time are becoming the core components of Web 3.0 for the decentralized and Semantic Web.

The following are five essential features that can help us define Web 3.0:

1. Semantic Web

The Semantic Web is the next step in the evolution of the web that enhances the functionality and accessibility of the websites. The Semantic Web allows users to be more precise and effective when searching for content, allowing them to search for data based on exact meaning rather than keywords or page numbers.

2. Artificial Intelligence (AI)

Artificial intelligence is becoming just that – intelligent by combining machines with humans. And it’s doing so exactly when humans need a helping hand to search or explore the web successfully.

Bringing artificial intelligence and natural language processing together with Web 3.0, businesses of all sizes across the globe can use this powerful combination to give their customers faster and more relevant results.

So they aren’t distracted during the critical workflow processes that they must regularly complete as part of their job responsibilities.

3. 3D Graphics

In today’s digital landscape, 3D design is widely used. It’s common to see it used in art, gaming, and animation. Also, web designers are starting to use 3D imagery to make websites more engaging and immersive.

In Web 3.0, three-dimensional design is revolutionizing online work. It’s transforming many industries by providing added 3D graphics to the Internet. These range from museum guides, computer games, eCommerce, and so much more.

4. Connectivity

Web 3.0, or the Semantic Web, is a system in which data link everything—not just any data, but data that machines can understand. This information was previously hidden from visitors, but it has now been made available to employees and users to improve usability.

As a result, a higher level of connectivity is permitted, allowing user experiences to evolve automatically to take advantage of the additional data.

5. Ubiquity

The Internet is becoming faster, and thus internet-connected computers are becoming more efficient. Everything from Bluetooth devices to watches, drones, and lamps is linked to the Internet. The services can be accessed anytime and anywhere using a mobile phone or computer.

The Internet has changed the world for the better in many ways. The Semantic Web is the next step in the evolution of the Internet, and it will ensure that we are always able to take advantage of its benefits and avoid its frustrations.

WorkFlow Automation (part 3) Website Traffic Secrets

What is workflow automation?

Workflow automation is an approach to making the flow of tasks, documents, and information across work-related activities perform independently in accordance with defined business rules. When implemented, this type of automation should be a straightforward process that is executed on a regular basis to improve everyday production. Workflow is a series of activities needed to complete a task. Workflow automation shifts the performance of those activities from humans to software programs. To Automate a workflow an organization first identifies the tasks that make up the job. It next creates the rules and logic that govern how those tasks should be done. Finally, it programs the software with the predefined business rules and logic.

The rules and logic are often a series of if-then statements that act like instructions telling the program what actions to take and how to move from one task to the next. The software uses those rules and logic to perform the series of tasks from start to finish so that humans no longer have to handle the job.

Benefits for businesses

Automation reduces human errors and eliminates many time-consuming and repetitive tasks, such as manual data entry. Organizations with outdated, manual processes cannot reliably scale with labor- and capital-intensive processes. By adding automation, businesses have improved their capacity for scalability.

Workflow automation also benefits businesses in the following ways:

• Creates processes that reduce costs
• Streamlines task management
• Reduces time in a process cycle 
• Decreases errors from manual entries or oversights
• Automates approval and document flows

Benefits for developers and operations

Workflow automation springboards improved releases and clearer communication channels between developers and operations, two traditionally independent areas. It upends common barriers — such as bottlenecks and follow-ups — that result from siloed developer and operations channels.  

Benefits for IT network administration 

Automating workflows creates better administrative oversight across the cloud, network, operating system, and departmental interactivity. Additionally, it adds a critical layer of visualization to better configure, oversee and analyze network health, security, and deficiencies.

Types of workflow automation

There are two types of workflow automation: Business process (BP) and robotic process (RP) workflows.

Business process workflows

 The methodology is how businesses structure processes to best serve customers. It drives business process workflows toward increased efficiency to reach mission-critical business goals. In many cases, workflow automation software is designed with the BPM philosophy. The software automates business process workflows to optimize tasks that were historically performed manually. Excel's autofill and macro features are early examples of workflow automation. 

Robotic process workflows

Today, the software automates robotic processes and is designed for robots to perform work similar to humans. In many cases, bots work alongside IT (as well as in other fields) to support administrative processes. Bots can identify and suggest the most relevant information to solve processing errors. Bots can also mitigate help desk inquiries, and login authentications and intervene in processing errors.

Workflow automation is a technical term for a practice that has been in use for hundreds of years. Assembly lines and manufacturing during the Industrial Revolution and agriculture, thereafter, were some of the first industries to use RPA workflows. Though, these were machine — not software—based.  

Workflow automation use cases

You can apply automation to most types of workflows. For example, you can automate employee onboarding template documents for human resources or set and automate approval workflows for all of your team members.

You’ll find workflow automation software in most fields and industries:

• Marketing: Marketing Operations Processes (MOPS) use workflow automation for marketing campaigns, and customer communication channels and for measuring metrics and marketing analysis.
• Sales: CRM software also provides workflow management. It automates customer communication, form completion and departmental collaboration. For instance, a CRM can automate approval flow notifications and update internal dashboard data when a customer has taken a specific step, such as signing a document or entering information.
• Finance: Automation increases consistencies in payments, moderates compliance requirements, and supports greater accuracy in forecasting and revenue collections. 
• Manufacturing: Workflow automation offsets shifts in supply and business structures. It reduces redundancies and improves quality-control errors. Automating workflows specifically cuts down purchase, budget and supply chain approval and cycle times. Manufacturing can automate workflows in purchase requests, contract management interactions and go-to-market product development projects.
• Information security: IT automation software mitigates security threats with more nimble responsiveness. Incident report automation and integration with existing security tools and guidelines can help IT better manage hybrid and cloud ecosystems. Companies can also automate workflows to monitor cyber threats for increased security.
• IT operations: For in-house network operations, workflow automation helps manage network users across multiple departments, such as sales, finance, legal and administrative teams.
• Systems management: IT as a service (ITaaS) is a type of software that enables managed cloud services for enterprise businesses, which includes workflow programming. Workflow automation software creates centralized controls for configuring, deploying and overseeing business networks. SDN  and SD-WAN  are two distinct IT network management systems for which workflow automation enables greater integrative oversight. Workflow automation lets IT more easily manage these systems in real-time. 

The future of workflow automation

Workflow automation startups are innovating the workflow process to offer standardized network automation, approval processes, and data-syncing across applications with integrated API solutions.

Most significant is a design shift to low-code workflow automation, which broadens the scope of who can create and deploy workflows to include decision-makers and direct collaborators. The result is that organizations may move away from top-down organizational structures to more symmetrical, collaborative systems that hasten process improvements.

AI in workflow automation is another big trend in the enterprise. AI-powered automation lets businesses draw on data patterns and machine learning to employ predictive analytics and insights for improved processes.

These solutions stand to transform the IT network ecosystem, where network administration is considered one of the last manual processes yet to be automated.  

Workflow automation software coming to market today will include features that advance IT’s ability to control and navigate network issues. Visual workflows will gain traction for easier workflow creation and mediation across IT networks. 

Businesses can also count on templatized workflows that can be used as building blocks to manage future automation, as well as integrative features with pre-existing workflow automation.