Distributed computing

 Distributed computing

Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers.

The components of a distributed system communicate and coordinate their actions by passing messages to one another in order to achieve a common goal. Three challenges of distributed systems are: maintaining concurrency of components, overcoming the lack of a global clock, and managing the independent failure of components. When a component of one system fails, the entire system does not fail. Examples of distributed systems vary from SOA-based systems to microservices to massively multiplayer online games to peer-to-peer applications. Distributed systems cost more than monolithic architectures, primarily due to increased needs for additional hardware, servers, gateways, firewalls, new subnets, proxies, and so on. Distributed systems can also suffer from fallacies of distributed computing. Conversely, a well-designed distributed system is more scalable, more durable, more changeable, and more fine-tuned than a monolithic application deployed on a single machine. According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant." Serverless technologies fit this definition but the total cost of ownership, and not just the infra cost must be considered.

A computer program that runs within a distributed system is called a distributed program, and distributed programming is the process of writing such programs. There are many types of implementations for the message-passing mechanism, including pure HTTP, RPC-like connectors, and message queues.

Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers, which communicate with each other via message passing.

What are the advantages of distributed computing?

Distributed systems bring many advantages over single system computing. The following are some of them.

Scalability

Distributed systems can grow with your workload and requirements. You can add new nodes, that is, more computing devices, to the distributed computing network when they are needed.

Availability

Your distributed computing system will not crash if one of the computers goes down. The design shows fault tolerance because it can continue to operate even if individual computers fail.

Consistency

Computers in a distributed system share information and duplicate data between them, but the system automatically manages data consistency across all the different computers. Thus, you get the benefit of fault tolerance without compromising data consistency.

Transparency

Distributed computing systems provide logical separation between the user and the physical devices. You can interact with the system as if it is a single computer without worrying about the setup and configuration of individual machines. You can have different hardware, middleware, software, and operating systems that work together to make your system function smoothly.

Efficiency

Distributed systems offer faster performance with optimum resource use of the underlying hardware. As a result, you can manage any workload without worrying about system failure due to volume spikes or underuse of expensive hardware.

Hybrid Computer

 What is a Hybrid Computer?

A hybrid computer is a merger of digital and analog computers. While the analog component frequently functions as a differential equation solver and other mathematically demanding problem solver, the digital component typically acts as the controller and offers logical and numerical operations.

A hybrid computer can perform tasks and offer capabilities found in both digital and analog computers. Developing a combined or hybrid computer model aims to produce a functional device that incorporates the most beneficial aspects of both computer systems. While the digital components of the computer handle the system's logical processes, the analog components of the apparatus are in charge of efficiently processing differential equations.

Features of Hybrid Computer

• Manage large equations: Large equations can be efficiently handled by hybrid computers, which can also generate precise results fast.
• System Ready For Use: Comes with all the connections and cables needed to connect to an analog computer. No further engineering is needed. Link and calculate!
• Proven Performance: The PB250 computer is utilized in over 150 applications and over a hundred hybrid systems.
• Simple Expanded: 64 channel address capabilities built-in and plug-in modular construction. A full range of PB250 peripherals is offered by Hybrid Computer.

Types of Hybrid Computer:

Below are the three types of hybrid computer

1. General-purpose hybrid computer: Hybrid computers with several purposes can be used for a wide range of tasks and problems. Originally, high-speed operating computers or part-time hybrid computers made up the majority of general-purpose hybrid computers.
2. Large electronic hybrid computer: Large electronic hybrid computers were built using hundreds of operating amplifiers between 1960 and 1980. These computers are capable of solving a larger variety of differential equations due to their hybrid construction.
3. Special-purpose hybrid computers: Their programs are embedded in a physical system to carry out tasks like results analyzer, function controller, or subsystem simulator. They are preconfigured to handle the issue at hand.

Agentic AI Technology

 What is agentic AI?

Agentic AI refers to artificial intelligence systems that don’t just react or follow preset rules—they act with autonomy, initiative, and adaptability to pursue goals.

 This form of AI is capable of independently making decisions and taking actions to fulfill objectives in dynamic environments.

Agentic AI is an AI system that combines multiple types of artificial intelligence that, together, make it capable of planning, acting, learning, and improving. Agentic AI systems can:

• Make decisions based on context and changing conditions
• Break down goals into sub-tasks and pursue them independently
• Collaborate with tools and other AI systems to get results
• Reflect and adapt over time to get better results

These new AI capabilities open up vast new applications for AI across every facet of enterprise operations, and have brought AI agents into being. Agentic AI is brainpower that allows AI agents to act independently within unstructured environments—enabling enterprises to expand automation beyond specific, defined tasks and tackle complex, end-to-end processes.

Use cases of agentic AI

Streamlining the insurance claims process:-The insurance industry is no stranger to paperwork and manual processes, but agentic AI is rewriting the rules. Insurance companies can leverage this technology to automate much more of the claims process than ever before possible. While people serve as the final approvers, AI agents can work with RPA robots to take on more of the work.

Optimizing logistics and supply chain management:-Every minute counts in the world of logistics and supply chain management. Delays, disruptions, and inefficiencies can ripple through the entire system, costing businesses time and money. Agentic AI is emerging as a powerful tool to tackle these challenges head-on.

 Agentic-AI-powered software agents can analyze vast amounts of data in real-time, optimizing routes, predicting potential bottlenecks, and even adjusting inventory levels based on demand fluctuations. This dynamic optimization can help ensure that goods and services are delivered efficiently, reducing costs and improving customer satisfaction.

Empowering financial decision making:-Agentic AI is also making waves in the financial sector, enabling AI agents to analyze market trends, assess investment opportunities, and even create personalized financial plans for individual clients. Freed from the burden of detailed, data-heavy analysis and report generation, financial advisors can now focus on building relationships and offering strategic guidance.

Beyond investment advice, agentic AI is also transforming how financial institutions manage risk. AI agents can analyze vast amounts of data to surface potential risks and vulnerabilities, helping financial institutions proactively manage their exposure and ensure compliance with regulations. This proactive approach helps minimize losses while strengthening the overall resilience of the financial system.

Accelerating drug discovery and development:-The healthcare industry is undergoing a digital transformation, and agentic AI is playing a pivotal role. For example, some healthcare providers are turning to AI agents to recommend tailored treatment plans based on individual patient data. This personalized approach to healthcare holds the promise of improved patient outcomes and a more efficient use of medical resources.

Agentic AI is also accelerating drug discovery and development by equipping AI agents to rapidly analyze massive datasets, zero in on potential drug targets, and predict their efficacy. This highly expedited process is driving lower development costs while dramatically compressing development cycles.

Transforming customer service and customer support:- Delivering exceptional customer experiences is a top priority for businesses across all industries. Agentic AI is stepping in to enhance customer support with AI agents that handle complex queries, anticipate customer needs, and resolve issues with context-awareness—creating high-quality, always-on support.

Imagine a virtual assistant that not only answers your questions but also proactively offers relevant information and recommendations based on your past interactions. This hyper-personalized service builds brand loyalty by providing customers with a top-notch experience—when and where they need it.

Accelerating and optimizing testing:-Agentic Testing is revolutionizing the software testing field—augmenting human software testers with AI agents across all phases of testing. Testing agents go beyond executing scripts; because they can understand goals and plan actions, they can assist testers in quality-checking requirements, generating test cases, automating manual test cases, and providing real-time, actionable insights into test results. Autonomous AI agents can respond many unpredictable challenges that pervade modern quality assurance (QA) environments

Digital Twin Technology.

 What is digital-twin technology?

A digital twin is a digital replica of a physical object, person, system, or process, contextualized in a digital version of its environment. Digital twins can help many kinds of organizations simulate real situations and their outcomes, ultimately allowing them to make better decisions.

What are the benefits of digital twins?

Improved performance

Real-time information and insights provided by digital twins let you optimize the performance of your equipment, plant, or facilities. Issues can be dealt with as they occur, ensuring systems work at their peak and reduce downtime.

Predictive capabilities

Digital twins can offer you a complete visual and digital view of your manufacturing plant, commercial building, or facility even if it is made up of thousands of pieces of equipment. Smart sensors monitor the output of every component, flagging issues or faults as they happen. You can take action at the first sign of problems rather than waiting until equipment completely breaks down.

Remote monitoring

The virtual nature of digital twins means you can remotely monitor and control facilities. Remote monitoring also means fewer people have to check on potentially dangerous industrial equipment.

Accelerated production time

You can accelerate production time on products and facilities before they exist by building digital replicas. By running scenarios, you can see how your product or facility reacts to failures and make the necessary changes before actual production.

How does a digital twin work?

A digital twin works by digitally replicating a physical asset in the virtual environment, including its functionality, features, and behavior. A real-time digital representation of the asset is created using smart sensors that collect data from the product. You can use the representation across the lifecycle of an asset, from initial product testing to real-world operating and decommissioning.

Digital twins use several technologies to provide a digital model of an asset. They include the following.

Internet of Things

Internet of Things refers to a collective network of connected devices and the technology that facilitates communication between devices and the cloud as well as between the devices themselves. Thanks to the advent of inexpensive computer chips and high-bandwidth telecommunication, we now have billions of devices connected to the internet. Digital twins rely on IoT sensor data to transmit information from the real-world object into the digital-world object. The data inputs into a software platform or dashboard where you can see data updating in real time.

Artificial intelligence

Artificial intelligence (AI) is the field of computer science that's dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Machine learning (ML) is an AI technique that develops statistical models and algorithms so that computer systems perform tasks without explicit instructions, relying on patterns and inference instead. Digital twin technology uses machine learning algorithms to process the large quantities of sensor data and identify data patterns. Artificial intelligence and machine learning (AI/ML) provide data insights about performance optimization, maintenance, emissions outputs, and efficiencies.

Digital twins compared to simulations

Digital twins and simulations are bothvirtual model-based simulations, but some key differences exist. Simulations are typically used for design and, in certain cases, offline optimization. Designers input changes to simulations to observe what-if scenarios. Digital twins, on the other hand, are complex, virtual environments that you can interact with and update in real time. They are bigger in scale and application.

For example, consider a car simulation. A new driver can get an immersive training experience, learn the operations of various car parts, and face different real-world scenarios while virtually driving. However, the scenarios are not linked to an actual physical car. A digital twin of the car is linked to the physical vehicle and knows everything about the actual car, such as vital performance stats, the parts replaced in the past, potential issues as observed by the sensors, previous service records, and more.

What are the benefits of digital twin technology?

• Enhance supply chain agility and resilience 

Supply chain disruptions have put a spotlight on agility and resilience. A combination of emerging technologies and platforms have made it possible to pursue a digital twin of the physical end-to-end supply chain. With this type of digital twin, companies get visibility into their supply chain, such as lead times, and can make real-time adjustments internally and with their partners.

• Reduce product time to market

With digital twins, companies receive continuous insights into how their products are performing in the field. With these insights, they can iterate and innovate products faster and with more efficiency.

• Enable new business models (i.e., product as a service)

Digital twins sometimes have a secondary benefit if you’re able to think about the possibilities. With more data visibility into products, there could be opportunities for subscriptions and offerings that deliver enhanced service or support to customers.

• Increase customer satisfaction 

Digital twins can support improved customer satisfaction though use cases like predictive maintenance, but because they collect real-time data on the product, they can also enable smoother customer service and repair operations, while informing future product improvements.

• Improve product quality

This benefit comes with time and data collection through digital twins. After initial investments have been made, generational improvements of a product—based on real-world operational data from many digital twins—can inform engineers and designers when developing a new product or version.

• Drive operational efficiency

Digital twins offer the insights necessary to gain those operational efficiencies across the value chain. With process-based digital twins, for example, organizations can bring together different data sets to capture real-time information on asset and production performance. Not only can they see where there might be bottlenecks, but also how potential solutions could impact the overall process.

• Improve productivity

The challenge of employee turnover and retention is nearly universal across industries. When a skilled employee leaves, they almost always take their knowledge with them, creating a barrier that slows productivity. With digital twins, organizations can mitigate some of these challenges through remote monitoring and assistance.

• Inform sustainability efforts

There are opportunities across the value chain to identify sustainability opportunities with digital twins. It can mean swapping out product materials for more sustainable options, reducing carbon emissions or scrap in the manufacturing process, or decreasing the number of service truck rolls.

• Increase data visibility

Digital twins can break down data silos across the enterprise and unlock value across the product (or process) lifecycle. Historical data and real-time data all live in one place.

What is Adaptive AI?

 WHAT IS ADAPTIVE AI?

Adaptive AI is a form of artificial intelligence that learns, adapts, and improves as it encounters changes, both in data and the environment.

While more traditional forms of AI follow set rules and algorithms and tend to fall apart when faced with obstacles, adaptive AI systems can modify their behavior based on their experiences. It adjusts its own code without input from a person, providing a level of adaptability and business resilience not seen before. This enables AI to deliver better outcomes faster. On that note, let's discuss some key characteristics of adaptive AI.

Characteristics of adaptive AI

Some key characteristics that distinguish adaptive AI systems are:

• Ability to learn. Machine learning algorithms allow the technology to process and analyze new information. As a result, adaptive AI can acquire knowledge, identify patterns, and make predictions.
• Adaptability. Adaptive AI systems adjust their algorithms and decision-making processes when they encounter changes in input data or the context in which they operate. This flexibility makes them practical and relevant even in dynamic and unpredictable situations.
• Self-improvement. Systems improve their capabilities over time by analyzing their own performance, identifying weak or inefficient areas, and refining their algorithms in response.
• Problem-solving capabilities. Adaptive AI systems develop sophisticated and nuanced approaches to problems by learning from experience and adapting to new information. These enhanced problem-solving capabilities often surpass traditional AI systems, leading to more innovative solutions to even the most challenging issues.

Key takeaways

1. Adaptive AI continuously learns and evolves from new data and experiences, enabling organizations to dynamically adjust to changing environments, emerging threats, and business disruptions.
2. Unlike traditional, static AI models, adaptive AI closes the loop between data ingestion, model monitoring, and automated retraining, ensuring ongoing accuracy and relevance as conditions evolve.
3. Implementing adaptive AI enhances operational resilience, decision-making, and efficiency, while enabling proactive anomaly detection, predictive analytics, and real-time decision support across IT, security, and business domains.

What is Generative AI?

 WHAT IS GENERATIVE AI?

Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request.

Generative AI relies on sophisticated machine learning models called deep learning models algorithms that simulate the learning and decision-making processes of the human brain. These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions and respond with relevant new content.

AI has been a hot technology topic for the past decade, but generative AI, and specifically the arrival of ChatGPT in 2022, has thrust AI into worldwide headlines and launched an unprecedented surge of AI innovation and adoption. Generative AI offers enormous productivity benefits for individuals and organizations, and while it also presents very real challenges and risks, businesses are forging ahead, exploring how the technology can improve their internal workflows and enrich their products and services. According to research by the management consulting firm McKinsey, one third of organizations are already using generative AI regularly in at least one business function.¹ Industry analyst Gartner projects more than 80% of organizations will have deployed generative AI applications or used generative AI application programming interfaces (APIs) by 2026.

How generative AI works

Generative AI works on the principles of machine learning, a branch of artificial intelligence that enables machines to learn from data. However, unlike traditional machine learning models that learn patterns and make predictions or decisions based on those patterns, generative AI takes a step further — it not only learns from data but also creates new data instances that mimic the properties of the input data.

Across the major generative AI models – discussed in more detail below – the general workflow for putting generative AI to work is as follows:

• Data collection: A large dataset containing examples of the type of content to be generated is collected. For example, a dataset of images for generating realistic pictures, or a dataset of text for generating coherent sentences.

• Model training: The generative AI model is constructed using neural networks. The model is trained on the collected dataset to learn the underlying patterns and structures in the data.

• Generation: Once the model is trained, it can generate new content by sampling from the latent space or through a generator network depending on the model used. The generated content is a synthesis of what the model has learned from the training data.

• Refinement: Depending on the task and application, the generated content may undergo further refinement or post-processing to improve its quality or to meet specific requirements.

The cornerstone of generative AI is deep learning, a type of machine learning that imitates the workings of the human brain in processing data and creating patterns for decision-making. Deep learning models use complex architectures known as artificial neural networks. Such networks comprise numerous interconnected layers that process and transfer information, mimicking neurons in the human brain.

Generative AI by Numbers

• By 2025, generative AI will generate 10 percent of all data. Compare that number to one percent today, and you’ll see its significance!
• An estimated 33.2%compound annual growth rate for AI between 2020 and 2027 suggests that we’re entering an “AI-enhanced” era.
• By 2027, 30% of manufacturers will use generative AI to make product development more effective.

These figures show that generative AI is laying the groundwork for a future that requires less employee training. Yet greater efficiency in creating unique content will be faster and less costly.

Generative AI & Visual Marketing

As colorful art, generative AI is finding its way through various forms of marketing. And when it comes to digital marketing, our story gets even more interesting as we see more and more generative AI applications in content marketing – especially visual marketing.

For example, German automakers have recently tried using generative AI. BMW, for example, uses this art to link many data points in its advertising campaigns. This includes BMW images, descriptions, and content created for each vehicle. 

The company has been instrumental in developing or using AI software to link more than 500,000 photos.

What is Neural Network ?

 What is a Neural Network?

Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn patterns and enable tasks such as pattern recognition and decision-making.

Understanding Neural Networks in Deep Learning

Neural networks are capable of learning and identifying patterns directly from data without pre-defined rules. These networks are built from several key components:

• Neurons: The basic units that receive inputs, each neuron is governed by a threshold and an activation function.
• Connections: Links between neurons that carry information, regulated by weights and biases.
• Weights and Biases: These parameters determine the strength and influence of connections.
• Propagation Functions: Mechanisms that help process and transfer data across layers of neurons.
• Learning Rule: The method that adjusts weights and biases over time to improve accuracy.

How do neural networks work?

The human brain is the inspiration behind neural network architecture. Human brain cells, called neurons, form a complex, highly interconnected network and send electrical signals to each other to help humans process information. Similarly, an artificial neural network is made of artificial neurons that work together to solve a problem. Artificial neurons are software modules, called nodes, and artificial neural networks are software programs or algorithms that, at their core, use computing systems to solve mathematical calculations.