Artificial Intelligence – Industrial Automation Review – Industrial Automation | Automation Magazine | Manufacturing Automation News & Resource https://industrialautomationreview.com Online Portal on Industrial Automation & Instrumentation Fri, 11 Aug 2023 10:25:32 +0000 en-US hourly 1 https://wordpress.org/?v=5.5.14 Advantech At the Cutting-edge of Industrial Computing https://industrialautomationreview.com/advantech-at-the-cutting-edge-of-industrial-computing/ https://industrialautomationreview.com/advantech-at-the-cutting-edge-of-industrial-computing/#respond Sat, 22 Jul 2023 11:05:54 +0000 https://industrialautomationreview.com/?p=4742 Vincent Chang, Vice President, Advantech Co. Ltd.

Advantech has undergone a significant transformation over the past four decades, evolving from a hardware manufacturer to a provider of a complete range of edge-to-cloud products, including IoT sensing devices, industrial communication devices, edge computing platforms, application software, and cloud platforms. Indeed, Advantech has grown into a global leader in the IoT industry, with a […]

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Vincent Chang, Vice President, Advantech Co. Ltd.


Advantech has undergone a significant transformation over the past four decades, evolving from a hardware manufacturer to a provider of a complete range of edge-to-cloud products, including IoT sensing devices, industrial communication devices, edge computing platforms, application software, and cloud platforms. Indeed, Advantech has grown into a global leader in the IoT industry, with a presence in over 100 countries around the world. In an exclusive interview Vincent Chang – Vice President, Advantech Co. Ltd. lets us have a ringside view of the company’s capabilities, commitments, offerings and its growth strategies to touch the next milestone of its Golden Jubilee replete with accomplishements in a plethora of areas which will personify its predominance in industrial computing. Excerpts:

Q. As a global leader in the industrial computer industry, what do you see as the potential for Advantech in the Indian market?

As the Managing Director of Advantech Asia and Intercontinental Region and General Manager of Advantech India, I can say that Advantech has a strong presence in the Indian market, having invested here for over 30 years. We recognize India, as one of the most important and emerging markets for both Asia and Europe. Our business model is to localize our approach in every market, and we have continued to invest in our India branch.

We believe that there is great potential in the India IoT and Industry 4.0 market, especially in the areas of smart manufacturing, renewable energy, and oil & gas. To this end, we plan to invest in R&D to develop innovative solutions in these areas and expand our business to new regions, supporting our partners in achieving greater success. We see our role as not just providing products and services, but also partnering with our customers to provide comprehensive solutions that meet their specific needs.

Our business model is to localize our approach in every market, and we plan to leverage the advantage of our talented and high-quality Indian employees to support our partners in achieving greater success. Our team’s talent, intelligence, and smart work ethos are some of our greatest assets. Specifically, our India team has a deep understanding of the local market and can provide tailored services and support to our partners in the region.

We are excited to work with local partners to bring our cloud-based software solutions and market-oriented ready-to-use software applications to the India market. These solutions have been successful in Taiwan, China, ASEAN and we believe that they can help Indian companies make sense of the vast amounts of data generated by their devices and sensors. Additionally, we establish a local service center in India to develop new solutions and technologies that meet the unique needs of the market.

Our goal is to empower our customers to implement IIoT technology quickly and effectively, providing a flexible and scalable infrastructure that allows customers to connect their devices and sensors to the cloud. Overall, we believe that Advantech has a bright future in the Indian market, and we are committed to continuing our investment and expansion efforts in the region. We are excited about the opportunities that lie ahead and look forward to working with our partners to drive business success in India.

Q. Why did you decide to invest in cloud-based software solutions and market-oriented ready to use software applications?

Advantech, as a leader in the industrial computer industry, recognized the growing need for cloud-based software solutions and sector-specific applications that could provide our customers with comprehensive solutions tailored to their specific needs.

Market trends and reports indicate that edge computing represents enormous potential value, estimated to reach between $175 billion and $215 billion in edge computing hardware by 2025. The major focus industries for edge computing include transportation, artificial intelligence, retail, healthcare, the public sector and utilities, and global energy and materials. These sectors align with Advantech’s strategic focus on high-potential growth markets for the coming years.

According to a report from Gartner, the market size of edge computing, encompassing hardware, software, and services, is expected to grow to $500 billion by 2025. The ratio of hardware to platform and security software is 1:5, and the demand for application and consulting services, as well as implementation and managed services, is 10 times that of hardware. This rapid growth in demand for cloud-based software solutions and applications presents a significant opportunity for companies like Advantech to offer comprehensive solutions to their customers.

Furthermore, it is expected that 60% of technical service partners will develop partner ecosystems, up from approximately 25% today. To shorten development cycles and expedite go-to-market strategies, low-code and no-code tools will become widely used, with more than 20% of new edge applications being developed using these tools.

In the light of this information, our decision to invest in cloud-based software solutions, referred to as Phase II business, stemmed from the understanding that our customers need more than just hardware solutions. They require software solutions to help them manage and analyze the vast amounts of data generated by their devices and sensors. By offering cloud-based software solutions and domain-focused, low-code and no-code tools, we can provide our customers with comprehensive solutions that meet their specific needs.

We have been offering these solutions for over a decade and have been continuously developing and improving them to address the evolving needs of our customers. These solutions have been very successful in Taiwan, China, and other Asian countries, and we are excited to bring these success cases to the Indian market while collaborating with local partners.

Q. You always emphasize on co-creation methodology in your business model. Why is it so important?

Co-creation has become increasingly important in the ever-evolving IIoT market. With the understanding that software and hardware solutions cannot be developed independently, co-creation enables both aspects to complement each other and remain fully compatible. In fact, it is anticipated that 60% of technical service partners will develop partner ecosystems, a significant increase from the current 25%.
Emphasizing on co-creation in our business model at Advantech allows us to work closely with customers and partners, understanding their specific needs, and developing customized solutions tailored to their requirements. By involving customers and partners in the product development process, we can gain a deeper understanding of their needs and pain points, leading to innovative and customized solutions that are more efficient and cost-effective. Ultimately, this drives business success for our customers.

Building an IIoT ecosystem based on co-creation also helps establish stronger relationships with customers and partners, increasing loyalty and trust in a highly competitive market.
A significant portion of AIoT value contribution comes from domain solutions, which requires close collaboration with external partners, especially Domain-Focused Solution Integrators (DFSI). These partners possess the domain knowledge and expertise necessary to support and service domain customers. Our focus is on specific domain areas in Industrial IoT and Service IoT, including sectors such as iFactory (Industry 4.0), Machine to Intelligence, Energy and Environment, Intelligent Retail, Public Services, iHospital, iEMS (Intelligent Energy Management System), and iLogistics.

Co-creation is essential in the IIoT market, as it allows us to differentiate ourselves by offering tailored solutions that meet our customers’ specific needs. This approach ultimately helps us achieve our goals of continued innovation, global expansion, and becoming an intelligent enabler of a sustainable planet.

Q. With investments such as 5G, Industry 4.0 and Artificial Intelligence, technology is developing at a great pace. Where is Advantech in this technology evolution?

As technology continues to advance rapidly with developments in 5G, Industry 4.0, and Artificial Intelligence, Advantech remains committed to staying at the forefront of this evolving landscape. Recognizing the transformative potential of AI in various industries, we are dedicated to developing tailored solutions for different vertical markets.
Over the next ten years, we envision Advantech as a key player in the IoT market, driving innovation and growth. Our mission is to remain at the cutting edge of 5G, Industry 4.0, and AI development, providing state-of-the-art solutions that enable our customers to excel in their industries. Our extensive experience in creating solutions for diverse sectors will be our most significant advantage during this period.

Our roadmap and AIoT business model are designed to support Advantech’s continuous, sustainable growth in the coming decade through a three-phase approach:

Phase I – Edge Computing & IO Device: This phase includes our traditional product and business units, which encompass edge computing, iEM Design-in, device & IO service, industrial server, Cloud IoT, AI, and video platform.
Phase II – WISE-PaaS Platform & Marketplace: This phase involves the integration of hardware and software, providing services such as WISE STACK edge cloud, AI framework service, insight APM, WISE DeviceOn, and WISE-Edge Plus.
Phase III – Domain Solutions: In this phase, we will collaborate closely with external partners, particularly Domain-Focused Solution Integrators (DFSI), to create tailored solutions for various industries.

Ultimately, our goal is to establish a strong presence in the AIoT market, powered by edge computing and edge AI technology.

Q. What makes you different from other brands in the cloud-based software solutions and hardware bundled software solution packages that you offer?

Advantech stands out from other brands due to our deep understanding of the industry and our customers’ needs. Our solutions are scalable, flexible, and user-friendly, enabling our customers to quickly benefit from IIoT technology. We differentiate ourselves by providing complete edge-to-cloud solutions that include both hardware and software, ensuring seamless integration and functionality to meet our customers’ unique needs.
A key differentiator for Advantech is our WISE-Edge365 solution, a cloud-based platform that provides a comprehensive suite of tools and services for IoT application development, deployment, and management. Recognizing that not everyone has the technical expertise to develop custom solutions, we offer low-code and no-code solutions, making it easier for our customers to develop custom applications without requiring extensive programming knowledge. Our customers can quickly develop and deploy custom IoT applications, leveraging a range of pre-built modules and templates with WISE-Edge365.

Advantech’s WISE-Core Service Architecture is a purely no-code/low-code platform that seamlessly connects with our full range of edge intelligent devices, including sensing devices, gateways, embedded boards, edge computing, IPCs, high computing servers, and domain-focused products such as intelligent service and logistics, and medical computing platforms. This architecture enables customers to easily develop and deploy their industrial applications with limited resource investment.

Our iFactory and iEMS (Intelligent Environmental Monitoring System) solutions are excellent examples of how we leverage low-code and no-code technology to provide cost-effective and efficient solutions for our customers.

Q. You are celebrating your 40th anniversary. Could you tell us shortly what has changed in Advantech after 40 years? What are the goals of the company for the coming years?

Advantech has undergone a significant transformation over the past four decades, evolving from a hardware manufacturer to a provider of a complete range of edge-to-cloud products, including IoT sensing devices, industrial communication devices, edge computing platforms, application software, and cloud platforms. As a result, Advantech has grown into a global leader in the IoT industry, with a presence in over 100 countries around the world. Our future goals are focused on continued innovation, expanding our global presence, and strengthening our partnerships with industry players.

In the coming years, we plan to build an IOT Eco-system & Alliance by Targeted Domains. This will involve enhancing our services and business approach in new regions, such as India, where we see great potential for growth in the smart manufacturing, new energy, and oil & gas markets. We will foster a strong local management team and encourage leadership growth and development among our partners, nurturing a culture of innovation and collaboration to drive business success.

Q. What has changed and will change in business models with the IoT and IIOT era? We are also curious about your prediction.

The IoT and IIoT era have brought significant changes to business models across industries and will continue to do so in the future. These changes include a shift towards more service-oriented models, increased collaboration between companies, and data-driven decision-making. Here are some predictions on how the IoT and IIoT era will continue to impact business models:

Traditional product-based models are giving way to service-oriented models that focus on providing ongoing value to customers. Companies need to offer continuous support and maintenance for IoT devices and systems, which has led to this shift.
As IoT technology becomes more prevalent, companies will increasingly collaborate and form partnerships to develop innovative solutions and address the complex challenges associated with IoT and IIoT implementations.

The massive amount of data generated by IoT and IIoT devices will lead to a greater emphasis on data-driven decision-making. Businesses will leverage analytics and machine learning to optimize operations, improve efficiency, and gain a competitive edge.

With the rise of IoT services and the need for ongoing support, businesses may adopt subscription-based models to generate recurring revenue while providing customers with access to the latest technologies and updates.

As IoT and IIoT devices become more common, concerns about security and privacy will grow. Businesses will need to invest in robust security measures to protect their customers’ data and maintain trust.

The increasing number of connected devices and the need for real-time data processing will drive the growth of edge computing. This trend will lead businesses to invest in edge computing infrastructure and develop new solutions that leverage its capabilities.

The IoT and IIoT era will continue to bring about significant changes in business models across various industries. Companies will need to adapt to these changes by embracing service-oriented models, increasing collaboration, leveraging data-driven decision-making, and focusing on customization, security, and edge computing. By staying ahead of these trends and adapting their business models accordingly, companies can capitalize on the opportunities offered by the IoT and IIoT era and achieve long-term success.

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Robotics to effect paradigm shift in business processes by 2025 https://industrialautomationreview.com/robotics-to-effect-paradigm-shift-in-business-processes-by-2025/ https://industrialautomationreview.com/robotics-to-effect-paradigm-shift-in-business-processes-by-2025/#respond Wed, 10 Mar 2021 09:06:12 +0000 https://industrialautomationreview.com/?p=2930 Robotics to effect paradigm shift in business processes by 2025

Starting from hiring and skill upgradation to creating new job opportunities, there will be a paradigm shift in workplaces in the years to come. It is important to bear in mind that robotics, artificial intelligence, automation and IoT are becoming more prominent in every aspect of our personal lives. Quite obviously, these technological advances have […]

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Robotics to effect paradigm shift in business processes by 2025

Starting from hiring and skill upgradation to creating new job opportunities, there will be a paradigm shift in workplaces in the years to come. It is important to bear in mind that robotics, artificial intelligence, automation and IoT are becoming more prominent in every aspect of our personal lives. Quite obviously, these technological advances have not gone unnoticed, and the impact of automation is increasing at an alarming pace in the workplace.

What can be the ramifications of such developments? Will there be more chatbots and virtual assistants in the years to come? Over a period of time, developments point toward the affirmative, with the World Economic Forum (WEF), in a report, predicting that machines will handle half of all tasks by 2025.

Organisations are already leveraging automation to improve the efficiency of repetitive manual tasks such as data entry and collation. However, this capability is, in effect, a double-edged sword. On the one hand, it minimises human intervention, providing the professionals with an opportunity to focus on more creative work. However, on the flip side, automation of a task can replace its human operator completely. In the above mentioned report, WEF also states that while the robot revolution will create 97 million jobs worldwide, it may also be responsible for eliminating just as many low-skill, low-value jobs.

The emerging situation puts immense pressure on the skills landscape. The rapidity of tech innovations and the rate at which professionals upgrade their skillsets are already causing the skill-gap to become more expansive. Moreover, with the viral outbreak further accelerating the digital transformation within the global business ecosystem, the rate at which existing job roles and skillsets are becoming obsolete has also increased.

Hence, it has become important that organisations find ways to bridge the skill-gap to stay ahead of the curve. Companies can achieve this by either hiring the right human resource with the right skills; or helping their existing employees stay relevant by training them.

The incremental integration of robots with human resource in the workplace has begun, transforming the nature of roles and functions in its wake. The report predicts an estimated 65 per cent of children entering primary school today will ultimately work in entirely new jobs that don’t yet exist. So, as automation has started driving significant changes in the workplaces, employers have also realised the need to create a system capable of supporting interaction between humans and robots.

Hence, it is imperative that HR professionals should identify skill gaps and create an atmosphere of continuous learning and development for all the employees, from the boardroom to the lowest rung. This effectively means that organisations will have to take a holistic approach towards reskilling and upskilling employees by training the workforce with continuously evolving technologies.

There is another bitter truth that HR departments need to focus in light of the ongoing ‘robot revolution’. HR teams will have to figure out which tasks can be automated and how to manage the ramifications while upskilling talent to develop new competencies as specific job roles continue to become obsolete.

Moreover, there is a phenomenal increase in the research and development by organisations and academia in the field of robotics. There are reports that say that robots are expected to displace 20 million human workers worldwide by the year 2030.

Following are some of the robots already in use:

Ballie

Manufacturing country: South Korea | Creator: Samsung

South Korean company, Samsung, launched this small rolling robot assistant that is intended to assist the users around the house at CES 2020. It is designed to comprehend, support and react to the user’s needs around the residence. Sources said that Ballie includes on-device AI capabilities that enable it to be a fitness assistant and a mobile interface that seeks solutions for people’s changing needs.

BellaBot

Manufacturing country: China | Creator: PuduTech 
This Chinese robot was unveiled during the CES 2020. BellaBot is a cat-faced full-dimensional sensory delivery robot developed by PuduTech. The machine is equipped with multi-modal interaction and helps in making food delivery more friendly in manner. It has been developed using Pudu Slam – a multi-sensor fusion SLAM algorithm independently developed by Pudu. The mechanism adopts a combination of various sensors such as vision camera, lidar, IMU, encoder, an RGB-D depth camera and ultrasonic radar.

C-Astra

Manufacturing country: India | Creator: Invento Robotics
Manufactured by Invento Robotics, C-Astra is a smart LiDAR robot that complements the doctors in screening patients as well as disinfecting areas. It is a semi-automatic robot that is also being used to fight coronavirus. It used UVC light to disinfect buildings and thermal cameras to record the temperature of the human body.

Jivaka

Manufacturing country: India | Creator: Parel Workshop
Created by the Parel Workshop of Central Railways of India, Jivaka, is a remote-controlled rover that works as a virtual healthcare worker. This medical-bot performs several activities related to ae patient’s care, such as measuring blood pressure, oxygen saturation level, the temperature of the body, among others.

Vyommitra

Manufacturing country: India | Creator: Indian Space Research Organisation
The Indian space agency, Indian Space Research Organisation, unveiled Vyommitra in 2020, which is a female humanoid robot. It is designed to perform multitasking and is equipped to fly in the first unmanned flight as part of the first manned spaceflight programme (Gaganyaan) of India, which is scheduled for later this year. Further, the robot can also speak two languages and mimic human activities like switch-panel operations and more.

Stretch RE1

Manufacturing country: USA  | Creator: Hello Robot
US-based Hello Robot created the Stretch RE1. It is a lightweight and low-cost mobile robot, equipped with a telescoping arm. The robot is designed for researchers developing robotic applications to help people at residences as well as workplaces. The developers designed this robot for autonomous operations, and it interacts with people by using a low mass, contact-sensitive body. It comprises a gripper, a computer, sensors and software including Python interfaces and ROS integration.

Article by:
Arijit Nag is a freelance journalist who writes on various aspects of the economy and current affairs.
Articles of Arijit Nag

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Industrial Automation Review November-December 2020 https://industrialautomationreview.com/industrial-automation-review-november-december-2020/ https://industrialautomationreview.com/industrial-automation-review-november-december-2020/#respond Thu, 17 Dec 2020 13:07:10 +0000 https://industrialautomationreview.com/?p=2840 Industrial Automation Review November - December 2020

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Industrial Automation Review November - December 2020

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Future of manufacturing to be heavily dependent on AI, 5G https://industrialautomationreview.com/future-of-manufacturing-to-be-heavily-dependent-on-ai-5g/ https://industrialautomationreview.com/future-of-manufacturing-to-be-heavily-dependent-on-ai-5g/#respond Mon, 30 Nov 2020 11:57:18 +0000 https://industrialautomationreview.com/?p=2827 Future of manufacturing to be heavily dependent on AI, 5G

It is quite likely that you have seen mobile phone commercials touting the wonders of 5G and how these networks are now available across the United States. Though to a great extent, this is true – if you have a 5G device and if you’re situated in hot spots in select cities across the country, […]

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Future of manufacturing to be heavily dependent on AI, 5G

It is quite likely that you have seen mobile phone commercials touting the wonders of 5G and how these networks are now available across the United States. Though to a great extent, this is true – if you have a 5G device and if you’re situated in hot spots in select cities across the country, still it is incomplete. In as far as a cellular service is concerned for consumers, 5G remains a work in progress but as a driving technology for manufacturing, the introduction of private (as well as public) networks in the years to come is definitely going to be a game-changer.

Connectivity is the name of the game. Despite 5G being a key part of connectivity for consumers and businesses, manufacturers should focus their efforts on smart connectivity, whether through 5G, Wi-Fi 6, 4G LTE, wired ethernet, or another channel. (At least, 60 per cent of manufacturing executives surveyed think a next generation of Wi-Fi solutions, including future iterations such as Wi-Fi 6, could be alternatives to deliver faster speeds and better performance in the next few years.) What matters most is generating actionable information from a web of connections — from people to systems to sensors to partners.

For some, 5G may end up as only the cellular network that their personal mobile phones run on. While for others, 5G will replace hardwired connections or Wi-Fi networks in factories. It can further be an opportunity to enable IoT, smart factories, and Industry 4.0 with thousands of sensors that monitor equipment, processes, and more, communicating through cutting-edge devices for analysis or connectivity with partners through cloud-based services.

Future of manufacturing on US

While Artificial Intelligence (AI) has already found its way into our private lives, whether through smartphones, intelligent fitness trackers or smart assistants, the manufacturing industry has just started to seriously consider AI integration. The production line of the future, though, will rely heavily on AI for health monitoring and predictive maintenance services, visual inspection systems and optimisation of manufacturing processes.

In the future a factory will be built twice – first virtually, then physically. Moreover, digital representations of production machines continuously fed with live data from the field will be used for health monitoring throughout the entire lifetime of the equipment and will eventually make onsite missions be an exception.

With modularised machines being interconnected through standardised protocols like OPC UA TSN and fixed cable connections being replaced with wireless protocols like 5G, we will also see a transformation on the office floor. In the future, programs running on industrial controllers, edge devices and cloud systems will work more tightly with apps and dashboards and eventually lead to a fusion of the shop floor and the office floor.

The factory of the future will require for its flexible production the help of robots and autonomous handling systems to adapt faster to changing requirements. Although classic programming and teaching of robots are no longer suitable for preparing the system to handle the fast-growing number of different goods, future handling equipment will learn through reinforcement learning and other AI techniques.

Covid impact on manufacturing industry

In 2020, the global smart manufacturing market size was valued at USD 194.7 billion and is expected to experience a CAGR of 10.7 per cent from 2021 to 2027. With the advent of industry 4.0 revolution, there has been a significant transformation in the manufacturing sector in the form of digitisation and automation. An increasing demand for knowledge-based manufacturing and connected supply chains, which are equipped with advanced control, sensing, modelling, and simulation capabilities, are driving the growth of smart manufacturing. In the near future, industrial internet of things (IIoT), cloud technology, and industrial analytics will play a critical role in fostering market growth over the forecast period 2021-2027.

Global industry has been riddled by numerous concerns including intense competition, uncertainties in the supply of raw materials and energy, and exponential operational costs. As a result, industry players are actively looking for ways to reduce costs and make enterprises agile, accelerated, efficient, and compliant with consumer product quality. In the years to come, smart industrial solutions with technological development and enhancements in automation, connectivity, and security will boost the productivity and efficiency. According to a GSMA intelligence report, the industrial IoT connections will reach around 13.8 billion by 2025 and outpace number of IoT connections in consumer sector. The rise in number of IoT connections will foster the connectivity and digitisation among enterprises. Moreover, various aspects of smart manufacturing will offer additional benefits such as real-time optimisation and dynamic production, in turn boosting the demand for smart manufacturing at a rapid rate.

While 2020 has shown us how important digitalisation is for the manufacturing industry, the coming years will reveal who is ready for the factory of the future and who is not. Companies that will successfully embrace the challenges and opportunities of a more digital and virtual world will do so with teams of engineers with “domain+” skills i.e., those who are able to combine domain knowledge with (+) expertise in technology and tools from companies like MathWorks. Therefore, companies building and operating industrial equipment need to change their job postings and hire engineers with a completely different profile to be ready for a future in which Industry 4.0 is just the beginning.

Article by:
Arijit Nag is a freelance journalist who writes on various aspects of the economy and current affairs.
Articles of Arijit Nag

 

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Pandemic has added to the importance of AI in days to come https://industrialautomationreview.com/pandemic-has-added-to-the-importance-of-ai-in-days-to-come/ https://industrialautomationreview.com/pandemic-has-added-to-the-importance-of-ai-in-days-to-come/#respond Fri, 13 Nov 2020 12:24:38 +0000 https://industrialautomationreview.com/?p=2729 importance of AI in Pandemic

Artificial Intelligence (AI) was invented several decades ago. Generally, over the years, many people associated AI with only robots. However, now it plays a crucial role in our lives. Today, personal devices, media streaming gadgets, smart cars, and home appliances use artificial intelligence. Lately, various businesses have also started using it to improve customer experience […]

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importance of AI in Pandemic

Artificial Intelligence (AI) was invented several decades ago. Generally, over the years, many people associated AI with only robots. However, now it plays a crucial role in our lives. Today, personal devices, media streaming gadgets, smart cars, and home appliances use artificial intelligence. Lately, various businesses have also started using it to improve customer experience and management functions.

Primarily, AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Interestingly, the term AI can also be used for any machine that exhibits traits associated with a human mind such as learning and problem-solving.

There is a huge misconception prevailing around. The first thing people think when they hear the term artificial intelligence is of robots. This happens because big-budget films and novels weave stories about human-like machines that wreak havoc on earth. Unfortunately, such a perception is far from the truth.

The concept of AI is based on the principle that human intelligence can be defined in a way that a machine can easily imitate it and execute tasks, from the simplest to those that are relatively complex. Among the goals of artificial intelligence are learning, reasoning, and perception.

Along with advances in technology, previous benchmarks that defined artificial intelligence have become outdated. For instance, machines that calculate basic functions or recognise text through optimal character recognition are no longer considered to embody Artificial Intelligence, since these functions are now taken for granted as inherent computer functions.

AI is ever evolving to benefit various industries. AI is formulated using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology, and more.

Interestingly, more often than not algorithms play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.

The applications for artificial intelligence are endless. It can be applied to several sectors and industries. In recent years, especially after the onset of the pandemic, AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.

Common examples of machines with artificial intelligence include computers that play chess and self-driving cars.

Every business strives to offer an enjoyable customer experience and AI does just that. It enables firms to improve their customer service by offering better response time and interaction. AI system assistance includes sales tasks and customer services. It will be more streamlined this year.

Digital marketing experts predict that customer service representatives won’t be required to manage over 85 per cent of customer support communication by December. Numerous organisations can use programs and applications with Artificial Intelligence systems to build brand reputation and loyalty.  Additionally, AI also helps them increase their revenue.

Data is making Artificial Intelligence more versatile. One of the recent Artificial Intelligence innovations this year is Data access enabling ubiquity. Reliable and accurate information helps businesses shift to AI-powered automated decision making. It has cut operational cost, streamlined processes, and improved the research capabilities of many organisations.

For instance, programmers of self-driven car software can access a lot of driving data without driving the vehicles. In the near future, we should witness a rapid increase in the application of Artificial Intelligence in real-world simulations. Over time as AI becomes more sophisticated, it will cause cost-effective and widespread availability of crucial data.

Artificial Intelligence, NLP, and machine learning to process data have a positive effect on augmented analytics. Naturally, more and more companies will start using predictive analytics this year. Essentially, AI would be used in customer service, recruitment, price optimisation, retail sales, and supply chain improvement. Predictive analytics will help businesses use real data to prepare for outcomes and behaviours thus being more proactive.

Today, instant data on current marketing decisions is part of real-time marketing. AI depends on relevant trends and customer feedback to prepare strategies. In the current year, real-time marketing activities is expected to soar, and Artificial Intelligence will drive most of them. Also, it is expected that more companies will apply AI to manage real-time user interactions and satisfy clients.

Now a days many businesses use chatbots to market products and make payments which are efficient in offering exemplary customer service. Many chatbots use data from huge databases but they may not comprehend certain phrases. However, chatbots will be able to match human conversation this year. For instance, AI-driven chatbots can recall some parts of a conversation with a client and make a personalised conversation using them.

Artificial intelligence has a lot of potential and it is one of the most crucial technologies in Industry 4.0 and automation, agriculture, aerospace, construction, logistics, robotics, and connected mobility. The top AI trends this year have been AI customer support and assistance, data access enabling ubiquity, predictive analysis, enhanced customisation, real-time marketing activities, and AI-powered chatbots.

It is expected that by the end of 2024, the global Artificial Intelligence in the medicine market would grow from $ 1,658.27 million in 2017 to $ 15,956.47 million at a compound annual growth rate (CAGR) of 38.19 per cent.

The ability of AI to improve medicine and healthcare facilities is one of the factors largely attributing to the growth of artificial intelligence in medicine market globally.

Fundamentally, AI is changing the core value proposition of security systems. Solutions based on AI go beyond security to capture valuable marketing and sales transaction data, analysing customer patterns and behaviour. AI helps us to “see” in new ways. Practically, it is impossible for humans to consume and accurately monitor the vast amount of video streams and other data available at most businesses, so AI is arriving at a time when we need it the most. Artificial Intelligence can identify objects and actions, classify and organise so that humans can focus on important matters. The technical expertise of AI can take otherwise unmanageable reams of data and turn it into actionable information. It’s a new tool that is nothing short of revolutionary for our industry.

Article by:
Arijit Nag is a freelance journalist who writes on various aspects of the economy and current affairs.
Articles of Arijit Nag

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Leading Embedded Innovations to AIoT Future – Advantech https://industrialautomationreview.com/leading-embedded-innovations-to-aiot-future-advantech/ https://industrialautomationreview.com/leading-embedded-innovations-to-aiot-future-advantech/#respond Fri, 12 Jun 2020 07:50:32 +0000 https://industrialautomationreview.com/?p=2091

As a global leader in the embedded computing market, Advantech is continuously developing cutting edge innovations and joins hands with industry partners to co-create diverse AIoT (Artificial Intelligence of Things) applications. No.1 Worldwide Industrial PC with 34% WW Market Share (2018) 16 manufacturing/repair/logistic centers globally 10 + Years industrial experiences in India with dedicated local sales, engineering, […]

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As a global leader in the embedded computing market, Advantech is continuously developing cutting edge innovations and joins hands with industry partners to co-create diverse AIoT (Artificial Intelligence of Things) applications.

  • No.1 Worldwide Industrial PC with 34% WW Market Share (2018)
  • 16 manufacturing/repair/logistic centers globally
  • 10 + Years industrial experiences in India with dedicated local sales, engineering, and RMA team

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Fortinet Introduces Self-Learning Artificial Intelligence Appliance for Sub-Second Threat Detection https://industrialautomationreview.com/fortinet-self-learning-artificial-intelligence/ https://industrialautomationreview.com/fortinet-self-learning-artificial-intelligence/#respond Wed, 18 Mar 2020 05:26:11 +0000 https://industrialautomationreview.com/?p=2020 Artifical intelligence

FortiAI Leverages Deep Neural Networks to Automate Threat Detection and Remediation, Expanding Fortinet’s Artificial Intelligence -driven Security Offerings “Fortinet has invested heavily in FortiGuard Labs cloud-based Artificial Intelligence – driven threat intelligence, allowing us to detect more threats, more quickly and more accurately. FortiAI takes the artificial intelligence knowledge from FortiGuard Labs and packages it specifically […]

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Artifical intelligence

FortiAI Leverages Deep Neural Networks to Automate Threat Detection and Remediation, Expanding Fortinet’s Artificial Intelligence -driven Security Offerings

“Fortinet has invested heavily in FortiGuard Labs cloud-based Artificial Intelligence – driven threat intelligence, allowing us to detect more threats, more quickly and more accurately. FortiAI takes the artificial intelligence knowledge from FortiGuard Labs and packages it specifically for on-premises deployments. This gives customers the power of FortiGuard Labs directly in their environment, with self-learning AI to identify, classify and investigate sophisticated threats in sub-seconds.”

News Summary

Fortinet® (NASDAQ: FTNT), a global leader in broad, integrated and automated cybersecurity solutions, today announced FortiAI, a first-of-its-kind on-premises appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time consuming, manual security analyst tasks. FortiAI’s Virtual Security Analystô  embeds one of the industry’s most mature cybersecurity artificial intelligence – developed by Fortinet’s FortiGuard Labs – directly into an organization’s network to deliver sub-second detection of advanced threats.

Organizations Face an Uphill Battle

Security architects confront many challenges when it comes to discovering and remediating threats, including:

  • Cybercriminals are becoming more sophisticated. While traditional cyber threats continue, sophistication of advanced attacks – often enabled by artificial intelligence, machine learning and open source communities – are increasing. As a result, organizations and their defenses are challenged to keep pace with threat evolution.
  • The attack surface is expanding. Millions of new applications, growing cloud adoption and the increase in connected devices are creating billions of edges that security teams need to properly protect and manage. Organizations are challenged to keep pace with the threat volume resulting from many potential entry points.
  • Security teams are constrained due to the cyber skills shortage. The cybersecurity industry faces a skills gap that has become a top emerging risk for organizations. There are not enough skilled professionals available to properly triage, investigate and respond to the growing number of threats – potential and actual – making it easier for cybercriminals to outpace legacy security processes and tools.

Self-Learning AI Adapts Organizations’ Threat Protection

To address these challenges faced by security professionals today, Fortinet is unveiling FortiAI Virtual Security Analystôto accelerate threat remediation. FortiAI handles many of the time consuming, manual tasks currently expected of security professionals, preserving their time for higher value security functions. FortiAI’s self-learning capabilities continue to get smarter once deployed in an organization’s network.

FortiAI leverages Deep Learning known as Deep Neural Networks, which mimic neurons in the human brain, to make complex decisions based on its scientific analysis of threats specific to the organization where it is deployed. As FortiAI’s artificial intelligence continues to mature, organizations benefit from having FortiAI’s Virtual Security Analystô effectively transform and adapt threat protection.

FortiAI Levels the Playing Field

Fortinet’s Deep Neural Networks (DNN) approach enables FortiAI to revolutionize threat protection by:

  • Automating time-consuming manual investigations to identify and classify threats in real time: Organizations using legacy security processes combined with limited security staff find it difficult to perform manual investigations for each threat alert. This creates additional risks including a data breach or security incident due to slow response time. To solve this, FortiAI automates investigations using DNN to identify the entire threat movement and uncover patient zero and all subsequent infections in a sub-second.
  • Transforming security processes for instant detection and remediation of attacks: FortiAI’s Virtual Security Analystô significantly reduces the time organizations are exposed to threats by scientifically analyzing characteristics of threats and generating an accurate verdict to accelerate threat response.
  • Delivering tailored threat intelligence to significantly reduce false positives: False positives are a burden for security analysts to investigate and it is time consuming to determine threats versus non-threats. Through tailored threat intelligence, FortiAI learns new malware features as it adapts to new attacks instantaneously and reduces false positives.

On-premises Protection for Air Gapped Networks

Another key distinction of FortiAI is that it offers on-premises AI suitable for organizations that have air gapped networks. Operational technology environments, government agencies and some large enterprises must adhere to strict compliance regulations and/or security policies that limit their network’s connection to the internet. FortiAI with its self-learning AI model does not require internet connectivity to learn and mature, enabling organizations with closed environments or stringent security policies to stay ahead of threats.

Fortinet’s AI-driven Technologies Automate Threat Protection

Fortinet has a longstanding history of helping customers strengthen their security posture by leveraging artificial intelligence. Some of the existing Fortinet offerings and services, complemented by the new FortiAI, that leverage various forms of AI, such as least squares optimization and Bayesian probability metrics, include:

  • FortiGuard Labs Threat Intelligence: FortiGuard Labs uses proven advanced AI and machine learning to gather and analyze over 100 billion security events every day. This threat intelligence produced by FortiGuard Labs is delivered to customers through its subscription services available for a range of Fortinet’s products, including the flagship FortiGate NGFWs. As a result, customers benefit from artificial intelligence deployed in global labs for faster threat prevention.
  • FortiSandbox: Fortinet is the first security vendor to introduce AI to sandboxing to automate breach protection. FortiSandbox includes two machine learning models to its static and dynamic analysis of zero-day threats, improving the detection of constantly evolving malware, such as ransomware and cryptojacking. Through the use of a universal security language to categorize malware, FortiSandbox also connects discussions between network and security teams, leading to more integrated and improved security operations.
  • FortiEDR: Fortinet’s FortiEDR uses machine learning to automate the endpoint protection against advanced threats with real time orchestrated incident response functionalities. Customers also benefit from more control of network, user and host activity within their environments.
  • FortiInsight: FortiInsight uses machine learning analytics to effectively monitor endpoints, data movements and user activities to detect unusual, malicious behavior and policy violations attributed to insider risk.
  • FortiWeb: To better protect web applications and APIs, FortiWeb applies machine learning to tailor a unique defense for each application. As a result, FortiWeb can quickly block threats while minimizing the false positives that may interfere with end user experience.
  • FortiSIEM: FortiSIEM leverages machine learning to recognize patterns in typical user behavior like location, time of day, devices used and specific servers accessed. FortiSIEM can then automatically notify security operations teams when anomalous activities occur, like concurrent logins from separate locations.

As cyber criminals look to exploit the expanding digital attack surface with sophisticated attacks, the breadth and depth of the Fortinet Security Fabric’s AI-driven technology provides customers with unparalleled threat prevention, detection and response that can be instant and automated.

Supporting Quotes

“Deploying FortiSandbox to protect our organization against zero-day threats was seamless through Fortinet’s Security Fabric platform. FortiSandbox secures our perimeter, client and mail servers, and ultimately is protecting our assets from advanced unknown threats. Leveraging FortiSandbox’s AI-driven capabilities has helped us keep pace with AI-driven threats, all while providing an easy and simplified way to configure and manage our security.”

  • Dario Palermo, System and Network Administrator at Ente Autonomo Volturno

Additional Resources

 

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Digitalisation trends in the APAC aerospace sector https://industrialautomationreview.com/digitalisation-trends-apac-aerospace-sector/ https://industrialautomationreview.com/digitalisation-trends-apac-aerospace-sector/#respond Thu, 05 Dec 2019 07:54:13 +0000 https://industrialautomationreview.com/?p=1845 industrial automation review

The aerospace industry is growing at an exponential rate. In fact, by 2028 it is predicted that upwards of 38,000 aircraft will be in service, a vast increase from the 26,000 being used today. As a result, digitalisation is increasing the reliability and efficiency of aerospace systems across the world. Here, John Young, APAC director […]

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industrial automation review

The aerospace industry is growing at an exponential rate. In fact, by 2028 it is predicted that upwards of 38,000 aircraft will be in service, a vast increase from the 26,000 being used today. As a result, digitalisation is increasing the reliability and efficiency of aerospace systems across the world. Here, John Young, APAC director at automation parts supplier, EU Automation, explains how digitalisation is transforming the aerospace sector in the Asia-Pacific region.

Like many other industries, digitalisation is transforming the aerospace sector. Currently, there is already an uninterrupted flow of real-time information coming from aircrafts updating ground operations and the pilots on the status of systems, equipment and weather conditions. However, this is simply the beginning of what is possible with the integration of digital technology across the sector.

Artificial intelligence

Across maintenance departments in the industry, data is being monitored and analysed by artificial intelligence (AI) and machine learning systems. In fact, airlines in Asia have already begun implementing AI tools for simulation and data modelling of aircraft.

This information can then be used to decide precisely when an aircraft’s components should be replaced or repaired and when other maintenance is required. This integration has helped to ensure that the lifespan and function of individual parts are fully optimised, and the overall aircraft systems are kept safe.

By using AI to monitor and predict requirements, it is possible to ensure that all required maintenance equipment and parts are ready for when the time is right. Working with global automation part suppliers, like EU Automation, means you have access to any part that may be needed, at any moment in time.

Virtual reality

In recent years, Virtual Reality (VR) alongside big data has pushed the boundaries of predictive maintenance. Since 2016, the aerospace company Airbus has been making use of this technology to help boost Asia’s maintenance, repair and overhaul (MRO) sector inside its Hangar of the Future initiative in Singapore.

VR and augmented reality (AR) technologies are disrupting traditional techniques of aerospace maintenance by allowing engineers to see maintenance activities from new and unexplored angles. This means that new data can be captured, and advanced simulations can be created to train maintenance teams for future procedures, as well as allowing personnel and pilots to view and test virtual replicas of the aircraft equipment before physically handling them.

Cyber Security

One of the downfalls of rapid uptake in digitalisation is the risk of data security and breach of privacy. This uncertainty applies to the aerospace sector especially, where the increasing connectivity of systems is also putting aircraft at risk of hacking and attack from cyber criminals.

Countries in the Asia-Pacific region have been reported to be 80 per cent more likely to be victims of cyber theft as a result of their lack of awareness. Leading suppliers, however, can offer cyber security services and build a safe environment of data security and trust, while also helping organisations to avoid and recover quickly from cyber-attacks.

There is no shortage of digital technologies being used in the aerospace sector. These new and rising innovations are disrupting traditional methods of maintenance, operations and repair by providing experts with more intel about vital parts and the mechanical needs of aircraft. However, much of the vast quantities of data that technology such as AR and VR are producing still need to be kept secure. Only then can the digitalisation of aerospace fully flourish and continue to grow.

 

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Deloitte report: Manufacturing industry opting for robotics and AI in challenging economic landscape https://industrialautomationreview.com/manufacturing-industry-opting-for-robotics-ai-challenging-economic-landscape/ https://industrialautomationreview.com/manufacturing-industry-opting-for-robotics-ai-challenging-economic-landscape/#respond Thu, 05 Dec 2019 06:18:44 +0000 https://industrialautomationreview.com/?p=1840 industrial automation review

The manufacturing industry is turning to robotics and artificial intelligence as well as a range of other strategies to navigate an increasingly challenging economic landscape, according to a report by Deloitte. The management consultancy says that, as the risk of a global economic downturn grows, the industrial manufacturing industry faces a challenging landscape in 2020 […]

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industrial automation review

The manufacturing industry is turning to robotics and artificial intelligence as well as a range of other strategies to navigate an increasingly challenging economic landscape, according to a report by Deloitte.

The management consultancy says that, as the risk of a global economic downturn grows, the industrial manufacturing industry faces a challenging landscape in 2020 replete with trade tensions, muted job growth, supply chain volatility and an ongoing skilled talent shortage.

Yet, despite these headwinds, industry leaders are rapidly developing coping strategies – not only to weather the brewing storm, but to thrive in spite of it.

Paul Wellener, US industrial products and construction practice leader, shares his insights into these and other topics in his 2020 Manufacturing Industry Outlook.

One of the key findings of the report is the way the current business climate is driving manufacturing, with companies entering partnerships with companies that offer complementary technologies in order to build “digital muscle”.

The velocity with which the fourth industrial revolution is progressing is now challenging manufacturers to roll up their sleeves even more to keep the momentum going as they achieve various milestones along their digital journey.

Early successes have increased many companies’ appetites for further digital exploration and investment.

However, the current labor and trade uncertainties within the global manufacturing industry could stall digital progress.

Therefore, in recent months, many companies have shifted their efforts toward digital projects that build agility and scalability to help them to manage risk.

Digital “muscle building” can be one of the leverage points to increase flexibility in global supply chains.

Applying artificial intelligence, cloud computing, advanced analytics, robotics, and additive manufacturing to the value chain can increase visibility and transparency, allowing manufacturers to make faster changes to operations to respond to market-based threats or opportunities.

As manufacturers continue to seek out the bright spots in the global landscape— including emerging markets—their ability to flex production, delivery, and customer support will continue to be important.

Shifts in sourcing (and thus production) are already playing out on the global stage. US imports from China were down 12.7 percent in the first eight months of 2019 versus the same period in 2018.

Meanwhile, US imports from Mexico were up 5.9 percent, and US imports from Vietnam were up 37.4 percent.4 In a matter of months, manufacturers have shifted both sourcing and production to different geographies, seeking tariff-friendly combinations.

For manufacturers, this must be executed precisely given lead times – and even customer approvals – for both original equipment and their highly profitable aftermarket components.

These focal points are likely to continue for 2020, as they support manufacturers’ efforts to build digital into the core of their supply networks and improve risk management in uncertain conditions.

Read the complete report here

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WHAT IS RPA? WHAT IS INTELLIGENT AUTOMATION? A COMPLETE LIST OF AUTOMATION TERMINOLOGY https://industrialautomationreview.com/what-is-rpa-intelligent-automation-complete-list-automation-terminology/ https://industrialautomationreview.com/what-is-rpa-intelligent-automation-complete-list-automation-terminology/#respond Wed, 20 Nov 2019 09:07:58 +0000 https://industrialautomationreview.com/?p=1782 RPA

Not sure what the latest automation acronym means? You’re not alone. The shortening of terms to an abbreviation of letters is meant to make things simpler, but we are all aware it often doesn’t .For anyone stepping into a room of people from an industry which they aren’t part of, it can feel like they […]

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RPA

Not sure what the latest automation acronym means?

You’re not alone.

The shortening of terms to an abbreviation of letters is meant to make things simpler, but we are all aware it often doesn’t .For anyone stepping into a room of people from an industry which they aren’t part of, it can feel like they are speaking an alien language.And, as automation is part of the tech industry — which is probably more guilty than most for creating swathes of acronyms — we have been known to throw one or two into a conversation.

SO, WHAT IS RPA? GETTING TO GRIPS WITH THE DIFFERENT AUTOMATION PROCESS TERMINOLOGY ON YOUR OWN TERMS

Of course, in any purchasing or investigatory situation around automation, the consultant, techie, or account manager will explain the terms. But many of you will want to understand what each acronym means and, more importantly, what each part does before starting out to ensure you know enough to challenge when looking at potential solutions to your problem, and of course, for your own sanity.

As our industry has quite a few acronyms and terms, it may seem a challenge to understand the main ones used in a short period of time. But, here’s some good news. Within the next 20 minutes, you’ll be able to grasp the basic ones. So, when somebody drops CV, DL, or CNN into a conversation — you won’t be confused in thinking they’re talking about a personal profile, slang term, or a news channel, but instead, will be able to put into the context of the automation product you are looking at.

COMPUTER VISION (CV) – EMULATION OF HUMAN VISION

The human eye and visual cortex is an amazing evolutionary system. It gives us the ability to see patterns, shapes, recognize faces and much, much more. Computer vision at its most advanced aims to emulate, or exceed this ability. In order to achieve this, computer vision uses a range of algorithms and machine learning principles to recognize, interpret and understand images.

For computer vision to be effective in daily use it needs to be trained. The training usually takes a form of being fed labelled imagery, example ‘this is a person’ and ‘this is car’, with the more data and variation provided, the more chance computer vision AI has a reference point for future decisions.

In Intelligent Automation, computer vision has a range of use cases from the complex to the simple. In simple use cases, it is used to work with systems to recognize where a button is on a screen and where it needs to click, and in complex use cases, it can be used to recognize when a car is committing a parking violation.

Ultimately, computer vision opens up a whole new set of possibilities for interactions. Providing digital workers with the ability to not only see, but if trained broadly, the ability to recognize the intent of a UI design if a search button is replaced by a magnifying glass, or in a more complex situation mimics the real-life patterns that people usually carry out

DEEP LEARNING (DL)

Deep learning is a subset of machine learning inspired by the structure of the human brain. It differs from machine learning because it learns without the need for human intervention in the process. Where machine learning requires parameters based on descriptions of the input, deep learning uses data on what the object or piece of data is, and how it differs from something else.

For instance, if you got everyone to draw a letter, each person would draw the letter differently. As a human, you can identify the letter regardless of whether a child, or an adult drew it — a machine usually would not understand this.

Deep learning gives the ability for this to be understood, by taking an input of the pattern comparing it with data of what something should look like and based on weights and possibilities within the system — giving an output of what the likely letter is.

Taking an unstructured data set and giving it a likely meaning for a decision based on a probability. An application of this is for email triage, or chatbots in simple applications, or more complex applications in medical condition recognition.

CONVOLUTIONAL NEURAL NETWORKS (CNN)

Convolutional Neural Networks are generally used as an effective means of recognition within videos or images. They use weightings and biases to work out what something is based on taught parameters from data. Think of those squares around objects that recognize a car, a cat, or a dog in recent uses of AI shown on tech programs.

Usually, these images have a probability number written next to them, this is taking the data from within the neurons and feeding out an outcome of it being that. So, a square around a cat, for instance, may have a number of 0.976, meaning out of 1, it is that sure that the thing is a cat.

So, given that’s the usual application, what is the basic principle for how they work?

CNN are a type of fully connected forward neural network. Sounds complex, but essentially what it means is, within this network, all the neurons always move forward and they are all connected. The network takes instruction data which is then used to decide what something is based upon spatial relationships of pixels on a page.

In application, this may mean that it learns a nose and mouth are usually a set distance apart, which is then combined with other information about a person’s face to give a decision whether it is a person with a probability out of 1. By analysing an image bit by bit in this way, CNN can decipher to a degree of likelihood how many people are in an image then feed that information as an output matrix for a decision, or another use.

MACHINE LEARNING (ML)

Until the last decade, machines learnt only by following instructions from a person. This works, but it means that machines were always an extension of people, instead of being autonomous. People recognized this and they also knew that people learnt from experience and didn’t simply follow instructions.

With that in mind, they thought what if machines were actually taught by people, so rather than just following instructions, they can learn to understand and reason with a decision in a similar way a human would.

This idea came to fruition in the concept of machine learning with three key approach types; supervised, unsupervised and reinforcement learning. All with the end of goal of helping machines make decisions either autonomously, or semi-autonomously, to help them adapt to changes they are exposed to and deliver the best results in the shortest time frame — without needing to constantly refer back to a person for direct instruction.

NATURAL LANGUAGE PROCESSING (NLP)

The basic meaning of this acronym is easily understood if you separate the phrase into ‘natural language’ and ‘processing’. The ‘natural language’ part, in this context, means the human language, how we communicate via speech or writing, and the ‘processing’ part is how a computer works on this information. So, Natural Language Processing means how computers can process our language. This is what the acronym means, but how does it achieve this complex feat?

A simple way to understand this is to visualize how a child learns to speak. Firstly, they learn the basic words, then the basic grammar rules, and then they begin to slowly build complexity by learning figures of speech, or other alternative ways to communicate.

Computers learn in much the same way, starting out with simple structures, and ending with trying to understand the irony in a sentence. This can either be taught via a person giving the machine understanding or through feeding large amounts of data via algorithms to give a depth of meaning to the machine of human to human communication.

In automation at this moment, NLP is used to underpin capabilities in chatbots and virtual agents in human conversation. All with the end goal in mind of a machine being able to communicate to the same efficacy as a person.

OPTICAL CHARACTER RECOGNITION / INTELLIGENT OPTICAL CHARACTER RECOGNITION (OCR / IOCR)

Despite living in a digital age, many businesses still work with paper documentation. In order to work with these documents effectively, many businesses will scan and turn the paper documentation into a PDF. On the surface it would appear this could resolve the problem, however, the PDF documentation is not actually turned into digital text, instead, it is an image of the document, a jpg, for instance.

The result of this is the need for people to manually read the document and rekey the data. The technology used to overcome this problem is OCR and for more accurate processing iOCR. So, what does it do? And, what can iOCR do that OCR can’t?

Let’s take an example of an invoice. If the invoice has static information such as the invoice number in the top corner and the cost in the bottom right, OCR can be used effectively with few exceptions to read, understand and digitize the information. However, if the information is not static and fluctuates due to variations in invoices, OCR will flag more exceptions and result in a return to people reading the scanned documents.

Thankfully, iOCR can help in this situation. As iOCR can learn from peoples’ actions, or through pattern recognition, if the document doesn’t vary wildly, the success rate can significantly improve. As it continues to learn by recognizing recurring information patterns, it can see if the product name or invoice number has shifted corners. All of which results in fewer exceptions being flagged and gives people back more time, and in the cases of automation, allows digital workers to perform the whole process.

ARTIFICIAL INTELLIGENCE (AI)

Up until the early 1990s, AI was understood as the general intelligence of machines, meaning they are self-aware and have abilities which equal, or exceed human intelligence.

This was reflected in films from the time such as The Terminator in the 80s, or HAL from Kubricks 2001: A space odyssey in the 60s. Today, AI has taken on a wider meaning, often referred to as ‘applied AI’, AI used in current automation systems and in IT systems is generally used to simulate part of human intelligence in a process.

AI deployed in systems provides the ability for machines to learn, reason and self-correct. This results in a machine which can intake information within a rules-based structure, reason on these rules to meet conclusions based on probabilities and self-correct current trajectory if they believe the current action is going to be unsuccessful.

The ability to apply intelligence to parts of machine interactions gives them the ability to recognize speech, recognize faces via computer vision, or overcome process decisions without needing human intervention.

RECURRENT NEURAL NETWORKS (RNN)

Traditional neural networks have limitations. The major one being they don’t maintain the information, so every time they try to think about something they have to do it from scratch. Recurrent Neural Networks address this challenge by forming loops of networks that allow information to stay within the architecture. Sounds simple, so what does this mean in terms of processing and what can be achieved?

Let’s take an example to explain this using a person and a sequence of context. Think about the following — a dachshund is a type of _______. As a person, it is easy for you to fill the gap in the sentence, or sequence, with dog. This is using information in the sentence in relation to your previous knowledge.

Essentially, this is the logic for how recurrent neural networks use the sequential structure of data to work something out — hence the name recurrent. The operation of a neural network exploits the sequential structure of data to loop information from previous experience and the current input to analyse every element of a sequence.

This means that RNN is made specifically for information that works sequentially, think the text, or speech example above, and many others such as time, sensors or videos. All of which are giving computers and automation the potential to achieve more by being able to use multiple information inputs to work out sequenced data outcomes.

ORCHESTRATION

Orchestration isn’t exactly an acronym. It’s here because it’s an important term within automation that is used regularly and often misunderstood, so we thought it was key to put it in. Orchestration is one way out of three main approaches of managing automation, with the other two being manual and scheduling.

Firstly, the glaringly obvious one; manual. Manual is quite simply a person triggering a job, usually for a specific process or task. The next one is scheduling — the most common technique for people managing automation platforms — works by instructing the digital workers to perform a task every 2 minutes between specified times. Although this is the common approach and is more autonomous than manual — it has its drawbacks.

Namely that once the digital worker has completed the task it will sit idly until the next one. This was the accepted outcome, until the arrival of orchestration. Orchestration leverages data and algorithms to gain an understanding of when the best time would be to perform tasks or assign themselves to other tasks instead of sitting on the bench. This approach delivers peak efficiency and means digital workers aren’t slacking off or being ‘part-timers’.

NATURAL LANGUAGE GENERATION (NLG)

Natural language generation is simply taking data that a machine understands and human can’t, and turning it into language that people can understand. We are surrounded by so much data that it becomes overwhelming and can’t be comprehended by the human mind alone. But, machines can comprehend this information and NLG gives the capabilities to feed it back to people in terms we can grasp.

The way NLG is spoken about above is the more complex end of the spectrum when talking about its wide applications today. A use today would be for something around financial advising for instance. The machine scans the market for data and brings together a stock overview.

For example; Your stocks for (company name ‘A’) today have dropped by (x number of points), your other stocks (‘B’) has gone up (x amount).

From your AI analysis of the market and data, we advise you to sell (A) and invest in (B) due to the predicted achievements of (insert predicted data) rise.

While this is simple, it demonstrates the capabilities currently being used and how the future is heading towards NLG, giving us an understanding of data which we couldn’t possibly compute in our own minds.

PROOF OF VALUE (POV)

In order to explain Proof of Value you need to understand Proof of Concept, or POC. POC is a common term which is used across software products with a simple meaning; proving the concept, or technology works as claimed. In the automation industry, this usually means showing that a process can be automated and a simple one at that.

Within automation, a POC stands as a waste of time – attempting to prove a concept that has been proven time and time again from America to Australia. This is where Proof of Value, or POV, comes in. Proof of Value may just sound like something a marketing committee came up with, but it’s so much more than that.

A POV is about showing that the business case for automation can be delivered at scale for all their business needs. While a POC will look at simple things such as ‘does the technology work as expected?’ and ‘how has it been deployed?’ a POV will scope the business case, the transformation and map, measure, design and forecast the potential outcome with leadership sponsorship.

ROBOTIC PROCESS AUTOMATION (RPA)

Robotic Process Automation or RPA is a term for a piece of software, or a ‘robot’, which carries out tasks and activities within systems, or applications, in the same way, a human would. The software is perceived as a ‘robot’ because it works in a robotic way, completing tasks automatically in the same way a human would. This element of the software is a deviation from previous automation products.

Previous automation products would need modification to applications, or systems in order to carry out processes and tasks. Robotic Process Automation works differently. It interacts with systems and applications utilizing the same interfaces a person does to capture and manipulate the required information for the process.

On top of that, they can work with other methods such as scripts, or web services. The result is a ‘robot’ which can complete an extensive number of repetitive tasks in places where once they were only easily completed by people.

CENTRE OF EXCELLENCE (COE)

The term Centre of Excellence is an acronym with slightly different meaning depending on what industry you find yourself in. Generally speaking, a CoE is usually responsible for providing leadership, best practices, research and support for the rest of the business. In automation, it means the above and more.

A Centre of Excellence (CoE) is vital in any automation deployment to deliver scale and instil an ‘automation first’ mindset. What does that mean in real terms? It means creating the go-to place for employees to gain knowledge and resources on how automation can help their department. Rather than merely setting up a team and assuming success, a CoE must be a place to distribute, reuse and enlighten staff to the possibilities of automation.  The technology lead and developers within a CoE will generally have three main areas to focus on. Firstly, they look at building a pipeline of automations — working out which processes are most suitable and have qualifying potential.

Next, they scope those processes into deployment, being responsible for the execution of delivery — from design to deployment. Before being there to pick up any improvements and support that are needed — which is important in identifying problems and for sharing with the rest of the company experiences of deployments. If allowed to entwine and grow amongst an organization, a CoE can provide lasting automation success.

ENTERPRISE RPA

You don’t use a teaspoon to dig foundations. In the same way, you don’t use simple RPA to automate an entire enterprise. It will be inadequate at dealing with the needs of the organization. Enterprise RPA is built to handle the needs of an organization spanning thousands of employees — with key characteristics to deliver automation at scale.

Unlike simple RPA, or desktop automation tools, Enterprise RPA is not a locally installed solution. No more rooms full of PCs, or locally installed versions on your laptop. Instead, it is built into servers either on-premises or in the cloud, instilling it with the ability to scale and giving the ability for overall control. In this environment, controls, availability and security can be implemented to provide the ability for management of more than one robot at a time and easy auditability.

After all organizations need to know what the bots are doing when they turn down the lights at the end of the day. What’s more, Enterprise RPA has the ecosystem and development structure around it so that it can maintain, reuse and develop automations in a simple, repeatable and reliable manner. In this way, the ‘robots’ or in more advanced AI versions, digital workers, can meet every process perfectly.

INTELLIGENT AUTOMATION (IA)

Robotic Process Automation is the mimic of human actions, Artificial Intelligence is the simulation of human intelligence, and Intelligent Automation is the combination of the two.

It takes the ‘doing’ from RPA and combines it with ‘learning’ from ML and ‘thinking’ from AI to allow the expansion of automation capabilities and possibilities.

IA takes technology such as computer vision, NLP and machine learning and applies it to RPA, allowing the automation of processes that don’t have a rules-based structure. Using IA digital workers can now handle unstructured data and provide answers based on subjective probability.

The result of this is the ability to expand the number of processes that can be automated, from the semi-structured such as an invoice being processed, to the unstructured such as email triage for an organization. But, it goes further than that, supercharging the abilities of RPA through orchestration and the ability to think without requesting human instruction.  Meaning Intelligent Automation gives organizations new efficiency and productivity, and ultimately a new digital workforce to rely on.

NATURAL LANGUAGE CLASSIFICATION (NLC)

Words can have different meanings depending on the context. As a human, we learn how these contexts interlink as we grow up and understand how a word can relate to multiple things depending on how it’s placed. Natural Language Classification is a way of teaching a machine to learn a language which is domain-specific, essentially teaching the machine to understand the context in the same way a human would. Meaning they can understand and respond to words depending on their placement, or meaning in that structure.

An example of this is demonstrated by one of the worlds most recognised brands, Apple. If you take the word Apple on its own you would assume you are referring to the fruit. But, if you are in mobile phone network the word has a different meaning. NLC is used to classify this data with labelling, so when the machine reads the word apple it knows it means a phone type to a phone network, or it could be labelled to mean an apple to a supermarket.

Hopefully, this list of acronyms gives you a real insight into the world of AI and automation. The concept always seems complex, but most of them can at least be partly understood in plain English. Of course, the above doesn’t delve into the deep areas of complex mathematics, – I don’t know about you, but we like to keep our thinking on a business level. After all, automation is about a solution to a problem and by democratizing the use of AI, we hope you can apply these definitions in your quest for the right automation solution for you.

The post WHAT IS RPA? WHAT IS INTELLIGENT AUTOMATION? A COMPLETE LIST OF AUTOMATION TERMINOLOGY appeared first on Industrial Automation Review – Industrial Automation | Automation Magazine | Manufacturing Automation News & Resource.

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