This is a question that comes up a lot. It makes sense to ask; we are conditioned to think of computers and programs as being entities that follow very specific logic flows, capable of generating detailed records about the paths they take while performing operations. Yet, this is not so in the realm of neural networksA neural network is an AI model inspired by the human brain's structure and function. It consists of layers of interconnected nodes (neurons) that can learn to perform tasks by adjusting the strength of these connections based on data. . Why? Because once trained and operational, they function in many ways similar to the human brain.
Picture yourself confidently advising others never to touch a hot stove. Your conviction is rooted in an experience—a painful burn from years ago. Yet, when it comes to the details of that event, certain specifics have faded:
These particulars are no longer relevant to the wisdom you gained; they have been distilled down to one inferential lesson: a hot stove means potential harm.
Our brains excel at inferring rules and applying them without recalling every detail of the learning experience. This selective memoryRefers to the components or devices where data is stored for immediate use in a computer or related computing device. Memory typically refers to Random Access Memory (RAM), which is the main memory used by a computer to store data temporarily while it is being processed or accessed by the CPU. This memory is volatile, meaning it loses its content when the computer is turned off. is efficient—it allows us to remember what matters most for future decisions while discarding extraneous details. This is how neural networks function. They absorb information from data Data, in everyday terms, refers to pieces of information stored in computers or digital systems. Think of it like entries in a digital filing system or documents saved on a computer. This includes everything from the details you enter on a website form, to the photos you take with your phone. These pieces of information are organized and stored as records in databases or as files in a storage system, allowing them to be easily accessed, managed, and used when needed. and distill it into patterns, much like how you’ve distilled the experience of burning your hand. Neural networks create a complex map of weighted connections from the data, but like our memory, they don’t retain the specifics once they’ve learned the lesson.
So, when you tell someone the stove is hot, you don't need to prove it with the date, time, or weather conditions from your past experience. It's enough to know that touching it is dangerous.
Because we are placing trust in machines, we crave the transparency that is often lacking in what is called a "black boxIn computing and technology, this term refers to a system or component where the inner workings are not visible or understood by the user. The user can see the input and output but not the process or algorithm that transforms the input into the output. This concept is often used in discussions about complex systems like artificial intelligence, where the exact processing methods are not easily discernible. " system. If a neural network determines a patient has a particular disease, doctors and patients understandably want to know why it reached that conclusion.
The complexity of neural networks makes this transparency difficult. They are not equipped to recall every 'weather condition' or 'time of day' from the data they were trained on. They can tell us the 'stove is hot,' but they can't easily recount the details that led to that knowledge.
This challenge and need for specifics has given rise to the field of explainable AIExplainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by human experts. It contrasts with the concept of the 'black box' in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI is crucial for validating and trusting the use of AI algorithms, particularly in critical applications that require accountability and transparency. (XAI Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by human experts. It contrasts with the concept of the 'black box' in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI is crucial for validating and trusting the use of AI algorithms, particularly in critical applications that require accountability and transparency. ), which seeks to bridge the gap between the inferential wisdom of neural networks and the human desire for detailed explanations. The goal is to create models A model in machine learning is a mathematical representation of a real-world process learned from the data. It's the output generated when you train an algorithm, and it's used for making predictions. that can not only predict with high accuracy but can also recount the details of their learning process, akin to recalling the full context of the day you burned your hand on the stove.
The quest continues to find a balance between leveraging the powerful inferential capabilities of neural networks and satisfying our need for detailed, transparent explanations. For now, we accept the mysterious nature of these artificial minds, much as we accept the complexities of our own cognition.
The neural network, like a person who has learned a lesson but can’t recall every detail, holds onto the essence of the experience. And perhaps, in the journey to make AIA branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI can be categorized into narrow or weak AI, which is designed for specific tasks, and general or strong AI, which has the capability of performing any intellectual task that a human being can. more explainable, we might also uncover more about how our own memories and inferences In the context of AI and machine learning, inference refers to the process of using a trained model to make predictions or decisions based on new, unseen data. It is applying the model to derive useful information from data. work.