AI Agents

 What is an AI agent?

AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt.

Their capabilities are made possible in large part by the multimodal capacity of generative AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code, and more simultaneously; can converse, reason, learn, and make decisions. They can learn over time and facilitate transactions and business processes. Agents can work with other agents to coordinate and perform more complex workflows.

Key features of an AI agent

Key features of an AI agent As explained above, while the key features of an AI agent are reasoning and acting more features have evolved over time.

• Reasoning: This core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems. AI agents with strong reasoning capabilities can analyze data, identify patterns, and make informed decisions based on evidence and context.
• Acting: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals. This can include physical actions in the case of embodied AI, or digital actions like sending messages, updating data, or triggering other processes.
• Observing: Gathering information about the environment or situation through perception or sensing is essential for AI agents to understand their context and make informed decisions. This can involve various forms of perception, such as computer vision, natural language processing, or sensor data analysis.
• Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning capabilities can identify the necessary steps, evaluate potential actions, and choose the best course of action based on available information and desired outcomes. This often involves anticipating future states and considering potential obstacles.
• Collaborating: Working effectively with others, whether humans or other AI agents, to achieve a common goal is increasingly important in complex and dynamic environments. Collaboration requires communication, coordination, and the ability to understand and respect the perspectives of others.
• Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time. This can involve machine learning techniques, optimization algorithms, or other forms of self-modification.

How do AI agents work?

Every agent defines its role, personality, and communication style, including specific instructions and descriptions of available tools. 

• Persona: A well defined persona allows an agent to maintain a consistent character and behave in a manner appropriate to its assigned role, evolving as the agent gains experience and interacts with its environment.
• Memory: The agent is equipped in general with short term, long term, consensus, and episodic memory. Short term memory for immediate interactions, long-term memory for historical data and conversations, episodic memory for past interactions, and consensus memory for shared information among agents. The agent can maintain context, learn from experiences, and improve performance by recalling past interactions and adapting to new situations.
• Tools: Tools are functions or external resources that an agent can utilize to interact with its environment and enhance its capabilities. They allow agents to perform complex tasks by accessing information, manipulating data, or controlling external systems, and can be categorized based on their user interface, including physical, graphical, and program-based interfaces. Tool learning involves teaching agents how to effectively use these tools by understanding their functionalities and the context in which they should be applied.
• Model: Large language models (LLMs) serve as the foundation for building AI agents, providing them with the ability to understand, reason, and act. LLMs act as the "brain" of an agent, enabling them to process and generate language, while other components facilitate reason and action.

What is a Neural Interface?

 What is a Neural Interface? The Future of Human-Computer Interaction

• Introduction

Imagine controlling a computer without using your hands or your voice. This isn't science fiction it's the reality of neural interfaces. These groundbreaking technologies create a direct communication pathway between you and your external devices, revolutionizing our interactions with technology.

Their primary purpose is to translate neural signals the electric impulses generated by the body into data that machines can understand.

In today's rapidly evolving technological landscape, neural interfaces are poised to transform everything from healthcare to entertainment, making them a crucial area of innovation to watch.

• The Basics of Neural Interfaces

Neural interfaces are bioelectronic systems that create a direct communication pathway between the nervous system and external digital devices. These innovative systems are designed to interact with various parts of the nervous system, including the brain, spinal cord, and peripheral nerves. Their core purpose is to enable direct communication between the nervous system and man-made devices, revolutionizing how we interact with technology.

It's important to note that the terms "neural interfaces," "brain-computer interfaces" (BCIs), and "human-machine interfaces" (HMIs) are often used interchangeably, but there are subtle differences:

• Neural Interfaces: This is the broadest term, encompassing any system that interacts with the nervous system, including the brain, spinal cord, and peripheral nerves. They can be used for a wide range of applications, from medical devices like cochlear implants to advanced prosthetics and even consumer electronics.
• Brain-Computer Interfaces (BCIs): Also known as brain-machine interfaces (BMIs), these specifically refer to systems that establish a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are primarily focused on interpreting brain signals to control external devices.
• Human-Machine Interfaces (HMIs): This is a more general term that can include neural interfaces and BCIs, but also encompasses other forms of interaction between humans and machines, such as traditional input devices like keyboards and touchscreens.

The key distinction is that neural interfaces have a broader scope, potentially interacting with any part of the nervous system anywhere on the body, while BCIs specifically focus on brain-to-device communication. HMIs encompass all forms of human-machine interaction, including but not limited to neural interfaces and BCIs.

What is LLM(Large Language Models)?

 What are Large Language Models?

Large language models (LLMs) are a category of deep learning models trained on immense amounts of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs are built on a type of neural network architecture called a transformer which excels at handling sequences of words and capturing patterns in text.

LLMs work as giant statistical prediction machines that repeatedly predict the next word in a sequence. They learn patterns in their text and generate language that follows those patterns.

LLMs represent a major leap in how humans interact with technology because they are the first AI system that can handle unstructured human language at scale, allowing for natural communication with machines. Where traditional search engines and and other programmed systems used algorithms to match keywords, LLMs capture deeper context, nuance and reasoning. LLMs, once trained, can adapt to many applications that involve interpreting text, like summarizing an article, debugging code or drafting a legal clause. When given agentic capabilities, LLMs can perform, with varying degrees of autonomy, various tasks that would otherwise be performed by humans.

LLMs are the culmination of decades of progress in natural language processing (NLP) and machine learning research, and their development is largely responsible for the explosion of artificial intelligence advancements across the late 2010s and 2020s. Popular LLMs have become household names, bringing generative AI to the forefront of the public interest. LLMs are also used widely in enterprises, with organizations investing heavily across numerous business functions and use cases.

LLMs are easily accessible to the public through interfaces like Anthropic’s Claude, Open AI’s ChatGPT, Microsoft’s Copilot, Meta’s Llama models, and Google’s Gemini assistant, along with its BERT and PaLM models. IBM maintains a Granite model series on watsonx.ai, which has become the generative AI backbone for other IBM products like watsonx Assistant and watsonx Orchestrate.

How do large language models work?

LLMs form an understanding of language using a method referred to as unsupervised learning. This process involves providing a machine learning model with data sets–hundreds of billions of words and phrases–to study and learn by example. This unsupervised learning phase of pretraining is a fundamental step in the development of LLMs like ChatGPT (Generative Pre-Trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). 

In other words, even without explicit human instructions, the computer is able to draw information from the data, create connections, and “learn” about language. This is called AI inference. As the model learns about the patterns from which the words are strung together, it can make predictions about how sentences should be structured, based on probability. The end result is a model that is able to capture intricate relationships between words and sentences. 

LMMs require lots of resources

Because they are constantly calculating probabilities to find connections, LLMs require significant computational resources. One of the resources they draw computing power from are graphics processing units (GPUs). A GPU is a specialized piece of hardware designed to handle complex parallel processing tasks, making it perfect for ML and deep learning models that require lots of calculations, like an LLM.

If you are tight on resources, LoRA and QLoRA are resource-efficient fine-tuning techniques that can help users optimize their time and compute resources.

Certain techniques can help  compress your models to optimize for speed, without sacrificing accuracy.

LLMs and transformers

GPUs are also instrumental in accelerating the training and operation of transformers–a type of software architecture specifically designed for NLP tasks that most LLMs implement. Transformers are fundamental building blocks for popular LLM foundation models such as ChatGPT, Claude, and Gemini.

A transformer architecture enhances the capability of a machine learning model by efficiently capturing contextual relationships and dependencies between elements in a sequence of data, such as words in a sentence. It achieves this by employing self-attention mechanisms–also known as parameters–that enable the model to weigh the importance of different elements in the sequence, improving its understanding and performance. Parameters define boundaries, and boundaries are critical for making sense of the enormous amount of data that deep learning algorithms must process.

Transformer architecture involves millions or billions of parameters, which enable it to capture intricate language patterns and nuances. In fact, the term “large” in “large language model” refers to the extensive number of parameters necessary to operate an LLM.

LLMs and deep learning

The transformers and parameters that help guide the process of unsupervised learning with an LLM are part of a more broad structure referred to as deep learning. Deep learning is an artificial intelligence technique that teaches computers to process data using an algorithm inspired by the human brain. Also known as deep neural learning or deep neural networking, deep learning techniques allow computers to learn through observation, imitating the way humans gain knowledge. 

The human brain contains many interconnected neurons, which act as information messengers when the brain is processing information (or data). These neurons use electrical impulses and chemical signals to communicate with one another and transmit information between different areas of the brain. 

Artificial neural networks (ANNs)–the underlying architecture behind deep learning–are based on this biological phenomenon but formed by artificial neurons that are made from software modules called nodes. These nodes use mathematical calculations (instead of chemical signals as in the brain) to communicate and transmit information within the model.

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.