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.