Here’s Why 5G Technology is the Next Frontier of Connectivity

Here’s Why 5G Technology is the Next Frontier of Connectivity

/in /by

5G Technology is the Next Wave of Unanticipated Innovation Connectivity

The generation is connectivity is shifting to the next level with 5G technology delivering experiences from cloud to clients. 5G networks exploit cloud technologies as it is virtualized and software-driven. In other terms, it is the next generation of mobile internet connectivity offering faster speed, minimized latency, and improved flexibility of wireless service. What it does is that it simplifies mobility with seamless open roaming abilities between cellular and Wi-Fi access. As a mobile user, you can stay connected as you move between outdoor wireless connections and wireless networks inside buildings devoid of any intervention of reauthentication.

Now, What’s the Big Deal About 5g Technology?

The main intention of this technology is to combine cutting-edge network tech and offer networks that are multitudes faster than current networks, with average download speeds (1GBps). This will eventually power a huge rise in Internet of Things (IoT) technology, offering the infrastructure required to conduct large amounts of data permitting for a smarter connected world.

The fifth-generation is now rolling out in major cities, and it’s estimated that by 2024, over 1.5 billion mobile users will be using this technology.

Some of the Benefits of the Technology

  1. High-Speed

This means that real-time streaming will soon be a reality through its networks. What now takes a whopping 26 hours over 3G networks and 6 minutes on 4G to download will only take 3.6 seconds over 5G.

  1. Low Latency

The term “latency” is commonly used in tech articles which simply mean “response time”. To put this in perspective, regard latency as the time that lapses when you click on a YouTube link before it begins streaming a video on your phone. The moment you click you spontaneously sent a request up to the network, where the network responds and begins streaming.

  1. Multiple Device Connection

In principle, this benefit is arguably the most significant of 5G technology. The mantra behind this technology is to connect a far larger number of devices/appliances than a contemporary mobile network. In retrospective, all the multiple devices talked about in Internet of Things (IoT) will smoothly be connected, and each device is getting a fair share of high-speed internet. Again, the network will get optimization to enable devices such as sensors that do not require constant internet connection not to occupy more bandwidth than required.
Here’s Why 5G Technology is the Next Frontier of Connectivity

With That in Mind, Let’s Look at Fifth-generation Services and Use Cases

  • Enhanced Mobile Broadband (eMBB) – Intended to service high densely populated metropolitan centres with downlink speed potentially 1GBps indoors, and 300MBps outdoors. Now, the moment there’s the installation of exceptionally high-frequency millimetre-wave (mmWave) antennas, the technology shall accomplish to reach out throughout the topography. This is according to AT&T that further suggests that since the antennas may not cover a wider area at the moment, it would replace the 4G’s current LTE system with a lower-power omnidirectional antenna offering 50MBps downlink service.
  • Massive Machine-Type Communications (MMTC) – It facilitates the machine-to-machine (M2M) and Internet of Things (IoT) applications on the network without enforcing burdens on other categories of service. Now mMTC is seeking to restore narrow service bands by executing a compartmentalized service tier for devices requiring downlink bandwidth as minimal as 100KBps.
  • Ultra Dependable and Low Latency Communications (URLLC) – This tier would address the self-directed vehicle category. It is where decision time for response to a likely accident is virtually non-existent. Besides that, 5G technology under this case would make it more competitive with satellite. Introducing the prospect of 5G substituting GPS for geolocation, it is still in the discussion phase.

Some of the Advancements of 5G Technology

  • Smart Cities: I know more often than not, you’ve hated trying to use slow apps or internet. The concept of smart cities hasn’t yet taken off. But there have been some pockets of intelligence such as smart parking lots, streetlights, among others. In the future, cities will get connected through IoT. It is to do almost everything from trash pickup and public restrooms cleaning to improving traffic situations.
  • Edge Computing: It refers to as fog computing which allows data to administer as adjacent to the source. Following the high speed, low latency data transmission of 5G, the possibility of edge computing shall be fully achieved in a wider scope of applications
  • Autonomous Vehicles: 5G seemed to be the technology that finally propels them out into the world. The rolling out might not happen overnight. Once it’s available everywhere, they will be no halting the creation of safe autonomous machines.
  • Smart Factories: With the upsurge of smart factories which already exist; 5G will play a pivotal role in its continuous transformation. The connection of the whole chain of product development; feedback collected from the customer will be fed to the designer in real-time. On the other hand, smart factories will develop their application of robots – through AI and machine learning, facilitating real-time decisions.

Every individual will agree that 5G brings a lot of value by connecting devices with extremely low power consumption. It also has a little resource usage in the network.

Conclusion

5G Technology bounds to impact major industries from media publishing to healthcare, gaming, automotive, public transport, and utilities. The true purpose of 5G wireless is to provide a global business concept. It is where expenses are minimal and high revenues from services. Consequently, it’s a major leap in technology than to 4G. It’s more than a communication technology but rather a complete digital infrastructure. 5G is the future superhighway which is going to take digital transformation to new heights than never before.

Difference Between Machine Learning and Artificial Intelligence

Difference Between Machine Learning and Artificial Intelligence

/in /by

Get to Understand the Differences between Machine Learning and Artificial Intelligence

Today, the discussion revolving around machine learning and artificial intelligence has turned to be two buzzwords that often seem to be used interchangeably. But in actual sense, they’re not quite the same thing. More often, the two words pop up very commonly when the topic is on Big Data and analytics. Sadly though, there is still a misconception within the public and the media concerning what truly is the difference between the two terms.

Now, below, we will run down through some main differences between AI and ML.

What is Machine Learning?

Scientists have scholarly described ML as the study of computer algorithms that permits computer programs with the ability to automatically “learn.” ML aims to empower machines to learn by themselves using the presented data and make accurate predictions. Primarily, ML is a subset of artificial intelligence.

What is Artificial Intelligence?

Now, AI is the wider concept that constitutes everything from Good Old-Fashioned AI (GOFAI) to the futuristic technologies like deep learning. In another scenario, every time a machine finalizes tasks centred on a set of postulated rules that solve problems (algorithms), such an “intelligent” behaviour refers to AI.

Now, if we decide to take a practical example of let’s say a video game to distinguish the two, where the objective is to manoeuvre through a minefield using a self-navigating car, this is what will happen. Well, at first, the car has no idea what’s the best pathway to follow to avoid the landmines. Say we do simulated runs to gather heaps of data about which pathway works and which does not. The data that can be fed to the machine learning, so it learns from the previous driving experience and utilizes that to steer the car safely.

Now, what if we decide to complicate the problem, let’s say we move the location of the landmines? In this case, the ML algorithm will no longer do a perfect job because it does not know that landmines are existing. What it only knows is what pattern that exists in the pathway that was acquired from the data offered previously, and that’s the same path it will keep on pursuing unless getting with new data for it to learn.

On the other hand, artificial intelligence will take up that data. It also analyzes the reasons why these pathways are changing and codify rules for detection of those (unsafe) spots. It also analyzes how to steer clear from them by leaving a visible trail for that matter. What AI does is learn the facts and applies them similar to how a human brain will work.
Difference Between Machine Learning and Artificial Intelligence

Key Differences between ML and AI

  • AI has two categories; the narrow AI—which is designed to perform specific tasks within a website and “general AI,” which learn and perform tasks anyplace. ML, on the other hand, is confined with “narrow AI based on the latest statistics-based algorithms and models in engineering science.
  • As a result, ML engages procedure statistics, applied computing, and mathematical optimization, while AL draws upon several sciences and technologies: mathematics, linguistics, engineering science, neurobiology, and engineering, among others.
  • AI is concerning making intelligent systems (seeks to reason, capture, plan, learn), encompassing machine intelligence, artificial cognizance, and intelligent communities. On the other hand, ML is machine-regulated feature engineering, trait learning, to mechanically uncover the representations needed for feature recognition, or real-world knowledge (pictures, video), and gadgets knowledge.
  • One formidable AI systems – Watson –applies techniques such as deep learning as the only part very complex collective of techniques, beginning from the applied math technique (Bayesian Illation) to figurative thought. Following the technological scepticisms to ML systems, extraordinary considerations are because of applying ML for lethal autonomous weapons systems.
  • Artificial Intelligence wraps around anything that allows computers to behave like humans. If you bumped to Siri or used it on your phone, you’re already close. Machine learning deals with mining of patterns from data sets. It also finds rules for optimum behaviour and adapts to changes in the environment.

Now, on the further front, we can look at the differences within the following context:

Objectivity

One of the prime goals of AI is to simulate natural intelligence. It is to solve an advanced problem. ML goals are also to make sure the task to maximize the performance of the machine on a particular task.

Functionality

AI main processes result in creating a system to mimic human to respond, behave in exceptional situations. These machines consist of senses as those of humans. So the best offer solutions for tasks which human beings cannot do. ML involves making self-learning algorithms which involve gathering data studying, and then analyzing the data.

Outcome/Output

For AI, the outcome is in intelligence or knowledge, whereas ML results in data.

Conclusion About Machine Learning and Artificial Intelligence

We can arguably conclude that artificial intelligence and machine learning is the future of digital technology. AI has been in existence longer, and its potential is yet to form completely. Therefore, the term ML now offers marketers one new thing to ponder about.

In summary, ML applies the experience to look for the pattern in learning. AI applies the experience to acquire knowledge or facts/skills and how to use that knowledge for new environments. Both ML and AI certainly have a valuable contribution to a business application. Yet ML possesses more adoption for solving the crucial business solutions in the manufacturing industry or smart production.

The Blueprint Behind Accountancy Industry Digital Plan in Singapore

The Blueprint Behind Accountancy Industry Digital Plan in Singapore

/in /by

The Concept Surrounding Accountancy Industry Digital Plan in Singapore

As part of embracing technology in the accountancy industry, the accountancy industry digital plan was developed by the Singapore Accountancy Commission, the Infocomm Media Development Authority (IMDA) and the Institute of Singapore Chartered Accountants. The idea behind the plan is to help small and medium-sized practices (SMPs). It is also for them to adopt technology for greater productivity and competitiveness. In fact, there are three main strategies – improving digital skills and knowledge; enhancing the adoption of technology, and developing accounting technology and inventions. Now, what the plan has done is creating a roadmap to offer guidance to SMPs on the digital solutions to espouse and staff training with each stage of their digital development.

Then, let’s look at the three stages of SMP’s digital development roadmap.

Stage 1: Obtaining Digital Economy Ready

Under this stage, whose aim is to help SMPs get set for Singapore’s digital economy and highlight the crucial digital solutions that enable SMPs to remain competitive. Some of the solutions include Practice Management, Audit Management, and Tax Management to simplify operations and optimize resources.

Stage 2: Developing in the Digital Economy

The stage focuses on digital solutions like Data Analytics for Advisory, and the incorporation of digital solutions for seamless transactions that ensure the merging of data for SMPs to create deeper insights.

Stage 3: Leaping Ahead

This stage recognizes advanced digital technologies that SMPs can execute to provide new innovative services and create more intelligent businesses. These include the use of Artificial Intelligence (AI) enabled digital solutions. It is for business operations and the establishment of services in audit and accounting.

Now, how then can the SMPs get access to the services relating to the accountancy industry digital plan?

Every SMPs have an opportunity to visit the SMP Centre for all-inclusive business diagnosis and advice on adopting digital solutions in their operations. The centre is also a one-stop platform. It serves the requirements of any SMP on its digital journey to exploit technology for greater productivity, growth, and competitiveness. Staff at an SMP can also access and use the online self-assessment toolkit. It is to establish their firm’s stage of digital readiness, ascertain the relevant digital solution and training programmes, and track for available funding schemes.
The Blueprint Behind Accountancy Industry Digital Plan in Singapore

Getting Ready for a Digital Future

It’s imperative to know that as much as tools are essential in this digital era, the human component remains significant in comprehending and using them. Not having the right talent to make use of evolving technologies and tools may lead to unsuccessful sector transformation. Now, the signing of the accounting industry digital plan target to heighten the digital abilities of local SMPs in two main areas:

  • Creating certification courses in RPA that are customized for local SMPs
  • Reinforcing SMPs in the adoption of audit software to automate and streamline their audit workflow.

Creating New Technologies – Accountancy Industry Digital Plan

AccTech Centre, instituted by SAC and the Singapore Institute of Technology (SIT), is a resource and elaborate centre that aids the collaboration and catalyzes technology and business inventions in the accountancy sector. Several SMPs will be able to innovate business processes, business models, service delivery, and products through the centre. The SMPs have an opportunity to work with academics, technology partners, and government agencies to research and prototype inventive ideas. The SMPs participant can profit from workshops and symposia on superior accounting technologies, attending training and acquiring certification through SIT.

To attain the right skills on the accounting industry, digital plan features a training roadmap intended at equipping the sector’s human resources with the right skills. At each of the stages, the SMPs acquire digital skills to prepare them for the digital economy. Generally, the training is dissected into wide segments that are basic and advanced skills. Then, they’d acquire general knowledge and comprehension of tech trends and their influence, and boost a mindset change; whereas advanced skills are available for professionals who need deeper knowledge and skills.
The following three stages are the training roadmap for acquiring the right skills.

Stage 1

The tech basics constitute learning about innovation design thinking and RPA and how they influence the industry. In case accountancy professionals are seeking greater expertise in digital skills, they should learn data analysis for audit and investigation.

Stage 2

Then it’s more of practical understanding of fintech and other disruptive technologies like the Internet of Things and cloud computing. For additional advanced skills trained here involves the use of analytics for organizational functioning and visual dashboards for presentations.

Stage 3

The competence sets within this stage involve big data and machine learning for finance. The more advanced skills for professionals will involve learning about artificial intelligence and its influence on the sector.

The practicability of the accountancy industry digital plan is real, but it still faces some challenges. One challenge several SMPs encounter is the relentless grind of the regular business cycle. Some may find it a rather new trend that hasn’t been tested anywhere else and feel reluctant. But the help offered by IDP, there is a fresh stimulus for several SMPs to jump into the bandwagon to be part of the journey.

Conclusion About Accountancy Industry Digital Plan

The prospects of the accountancy industry digital plan are promising in Singapore. Therefore, the plan has been accessed by the relevant authorities and deemed market and cost-effective. Any SMPs aspiring to get into the next frontier techno wise should embrace this solution. It also increases productivity, job redesign, and real-time response abilities. Digital transformation in the accountancy sector and the creation of accountancy talent bounds to provide a competitive edge in the future. Therefore, SMPs has a chance to get their digital accountancy onto the next level. It is by using the available assessment toolkits to roll out.

BFSI Artificial Intelligence: The Future of Banking Services

BFSI Artificial Intelligence: The Future of Banking Services

/in /by

How BFSI Artificial Intelligence Focuses on Providing Personalized Experience to Customers

The financial industry is a data-intensive business, and with the rise in customer-centricity, BFSI artificial intelligence is inevitable in the sector. Tech-savvy customers exposed to progressive technologies expect financial institutions to deliver seamless experiences. Now, the integration of intelligent algorithms for risk alleviation and compliance, fraud detection applications, and anti-money laundering, primarily form the demand for the technology. But before we expound more on the ingenuity of the technology, what’s artificial intelligence in BFSI (Banking, Financial Services & Insurance)?

Artificial intelligence in BFSI is the imitation of human intelligence into machines with the assistance of sophisticated machine learning, cognitive computing, and natural language processing algorithms that assist in customer relationship management.

With that in mind, let’s us delve into some of the ways the financial institutions can improve on customer experience through artificial intelligence.

BFSI Artificial Intelligence: The Future of Banking Services

Reinforce Lending With Predictive Analytics

It’s with a fair amount of certainty that the banking sector is a data-heavy industry, especially with the loan facilitation. The main core business of banks is lending, among other activities, which means that appropriate measures must be in place. The right decisions must to reach upon as data mainly drive it. Now, if you’re in the banking sector and still implement a lengthy-paper-based application process, then you’re missing out a lot. And that’s where predictive analytics comes on board to help streamline the constant customer data mining and analytics processes that go along with lending resulting in expeditious loan completion and more customer interaction.

But what’s all about predictive analytics?

Predictive analytics encompasses the application of data, statistical algorithms, and machine learning techniques to identify the possibility of future outcomes based on historical data. Here’s the thing, we humans cannot predict future outcomes, but BFSI artificial intelligence creeps into historical data by offering the best assessment of what will happen in the future.

Personalize Customers’ Banking Experiences

Over time, banks always target customer microsegments and tailor offers to help them differentiate their institutions, build customer engagement, and develop a competitive advantage. But it follows that the advantage is steadily eroding owing to the upsurge of tech-savvy companies that put personalization at the prime center of their business models and optimize it to attain significant performance gains. Now, the true personalization can only be described as a deeper understanding of each customer’s distinct needs and orchestrating a set of tailored experiences across the digital and human channels. The BFSI artificial intelligence next frontiers for banking include customer adoption of voice interfaces and intelligent agents.

BFSI Artificial Intelligence: The Future of Banking Services

The paradigm shift is now more on value addition that communication brings to the customer rather than the mode of delivery. Some of the core components of success personalization include:

  • Drastic pricing transparency such as Progressive Insurance which establishes trust and assurance of utilizing the personal information to help customers in decision making
  • Implementing a golden customer record that integrates purchase data, preferences, and other behavioral information from several engagement systems and data sources into a single view
  • Enhancing omnichannel engagement, which in retail raises lifetime value by a huge margin.

BFSI Artificial Intelligence: The Future of Banking Services

Making Financial Health Recommendations

Now, as we move into the period of data explosion, it follows that more relevant ways of scanning through a huge amount of data are important. That cannot be possible without having the right artificial intelligence tool, and Recommendation Engines is one such tool. Well, what it does is that it filters and ensures that consumer gets to see data that is relevant to their taste and preference while spending minimal time searching for the appropriate data. Today, several banks providing personal financial management simply display customers their categorized spending, which highlights their poor habits of spending. This, therefore, means that with BFSI artificial intelligence taking the center-stage financial institutions need to answer the question of customers’ healthier financial living. And by adopting of machine learning in recommendation systems that scan, search, filter and offer useful information to the customers based on their needs and tastes.

Automation of Administrative Tasks

The revolution of technology now brings about AI technologies, including robotic process automation (RPA), natural language processing (NLP). With the use of these technologies, banks improve on their front-office and back-office processes. The high-volume and repetitive nature of data give artificial intelligence an ideal candidate for intelligent automation. It follows now that banks can communicate and influence information to their customers through chatbots and virtual assistants. Implementation of BFSI artificial intelligence technology detects points of compromise as soon as they occur, thus, assists in fraud detection, protecting customer information. For instance, companies such as MasterCard and WorldPay have for several years detected falsified transaction patterns thanks to artificial intelligence.

More certainly, there are customer’s engagements and retention improvement through automation of many of the administrative tasks done by employees giving them ample time to build customers’ relationship. With the adopting of automated technology, the financial institutions will drastically cut on cost, improve functionality, and increase assistance. Now, systems equipped with AI can exponentially search more data than what humans can and provide a high level of transparency for clients. Customer’s confidence in the bank institutions is paramount and should never be taken for granted.

Conclusion

The financial institution cannot afford to rest on their laurels as they are poised to grab the next opportunity that comes with technology. As they strive to benefit from what BFSI artificial intelligence revolutionary transformation, they have to understand customer behavior. Each financial institution has uphill tasks to ensure that they retain their customers, and one possible way is through adopting artificial intelligence. Despite the financial cost involved in implementation, one can’t shy away forever from technological progress and not encountering it now may be costly in the long run.

An Inside Look at the Future Robotic Process Automation

An Inside Look at the Future Robotic Process Automation

/in /by

The Buzz Behind the Future Robotic Process Automation Software Technology

The computing era has experienced tremendous transformation in decades back, and abundant software developments occurred in operating systems, applications, utilities, you name it. Today, the business operations are leaping into the new technological landscape of what I can only describe as the future robotic process automation. Companies that are not matching into this world of technology have a chance of being left out on the wayside in the future. RPA is the new language of business in the 21st century.

But what is robotic process automation?

Robotic process automation in business perspective is the configuring software to perform work previously done by a human being such as transferring data from numerous input sources. The scope of RPA now shifts from pilot projects to wider adoption from Interactive Voice Response (IVR) development toolkit to real call center executive.

An Inside Look at the Future Robotic Process Automation

All You Ought to Know on the Revolutions in Robotic Process Automation

The Robotic world is no doubt, undergoing an iconic moment with massive transformations right before our naked eyes. This iconic spell is barely to do with the invasion of evolving technologies such as machine learning, AI, natural language processing, text analytics, among others. Well, the future robotic process automation is charting a new direction and can be divided into four unique phases.

  • Assisted Robotic Process Automation (RPA 1.0)

The idea behind RPA 1.0 is automating various activities and applications running on the user’s desktop. One of the simplest examples is the cutting and pasting of information from one screen to another. This means that long sophisticated processes would certainly be replaced with single mouse clicks, considerably cutting down the time taken to train an agent.

  • Unassisted Robotic Process Automation (RPA 2.0)

The RPA 2.0 software is installed on some machines to run devoid of the requirement for the automation to be attended. In this case, no employee is required to log on to trigger the processes to begin, monitor its performance, and shut down the automation when it’s complete. In the end, the robot can work around the clock and replace human interactions with business processes.

  • Autonomous Robotic Process Automation

The ingenuity in autonomous RPA is built leveraging AI and associated new technologies like cognitive automation, machine learning, and computer vision, among others. The fascination is on the convergence of several technologies which produces abilities that fundamentally elevate business values and gains a cutting benefit to users.

An Inside Look at the Future Robotic Process Automation

  • Cognitive Robotic Process Automation

Cognitive RPA integrates several unique algorithms and technology approaches like data mining, semantic technology, natural language processing, text analytics, and machine language, to name a few. The process makes most of unstructured data such as customer interactions are seamlessly processed, evaluated, and structured further into beneficial insights for the next steps of the process, such as predictive analytics.

During the last few years, the emerging trends as far as the future robotic process automation is concerned are enormous. Majority of enterprise processes are deterministic, repetitive, and manual with over 70 percent can be automated using software robots. Again, processes that possess greater sophistication and need human judgment are automated within 15-20 percent in collaboration between software robots and human workforce. A massive enormous impact is expected from these technologies with an estimation of $5.2 billion and $6.7 trillion by the end of 2025, according to experts.

Here’s How RPA Will Influence Business Processes to Different Industries:

  • Insurance

Most of the insurance companies have to develop a vital and highly profitable business while regulating risks and mitigating costs. Now, as they handle claims-processing, underwriting, and policy quote, their business practices are often overwhelming and competitive. And with the adoption of RPA by several insurance companies, it has added support to assist in automating the entire workflows and streamline their operational activities.

  • Healthcare

The healthcare is one big benefactor of RPA technology incorporating tasks such as patient scheduling, data entry, and billing. Besides that, other facets of the health industry have improved a great deal with RPA software such as:

    • Patient data management
    • Lesser cancellations of appointments
    • Assured patient experience – patients get more value-based care as automation offers shorter wait times for appointments.
  • Banking

The moment the banking embraces the future robotic process automation, they will find ways to efficiently and securely manage the day-to-day processes as RPA software integrates, eliminates human error and allows for ease of access to information. Compliance is one area that RPA software has streamlined in the banking sector as a result of the complexity of international and local laws and regulations. As a result, all documents are easily handled, making audit process less inconvenient and accurate.

An Inside Look at the Future Robotic Process Automation

  • Manufacturing

This is one sector that acknowledged the use of RPA and has now strengthened their supply chain procedures, bridging the gap with the day-to-day activities like quoting and invoicing for particular suppliers alongside account receivables, payables, and the general ledger operations.

  • Customer Service and Support Desk

With the increased demand for better services, the customer service industry has adopted RPA software in the areas of incident management, billing queries, user administration, and updating of records. The software links different systems and apps into one single console, create a unified knowledge-based that’s capable of delivering relevant data in real-time, and automatically set up and run processes.

Conclusion

The cold truth is that future robotic process automation software is the next big thing in the world of digital technology. In spite of RPA still relatively in its infancy, experts believe that it will be a more dependable business tool in the future. However, it shall come with some additional costs from training to recruitments and employee welfare schemes. As many sectors try to deploy these robotic technologies, the benefits are tangible.