Once more, in line with some other posts on AI, I shall endeavour to summarise certain ideas. This will provide newcomers with an overview of the topic. As customary, the explanation will be kept straightforward, devoid of intricate technical jargon.
Now, let’s begin with the fundamental step: ensuring we comprehend the workings of a computer.
How does a computer calculate?
At its core, a computer calculates using a binary system of 0s and 1s, known as bits. Here’s a simple yet detailed explanation of the process:
Data Representation: Everything a computer does starts with data representation. Inside a computer’s memory, data is stored as a series of bits. Each bit can be either 0 (off) or 1 (on).
Arithmetic Logic Unit (ALU): The heart of a computer’s calculations is the Arithmetic Logic Unit, or ALU. It’s a component that performs all arithmetic and logic operations, like addition, subtraction, multiplication, division, and comparisons.
Instructions: To perform calculations, a computer needs instructions. These instructions are in the form of binary code and tell the computer what operations to perform and on which data.
Fetch-Decode-Execute Cycle
Fetch: The computer fetches an instruction from memory. This instruction specifies the operation to be performed.
Decode: The computer decodes the instruction to understand what operation is required and on which data.
Execute: The ALU performs the specified operation on the data.
Registers: The computer uses registers to store temporary data during calculations. These are small, high-speed memory locations built directly into the CPU (Central Processing Unit). Registers store values being worked on, intermediate results, and the final result.
Binary Arithmetic: All arithmetic is done in binary. Addition, for example, works just like it does in decimal, but with only two digits (0 and 1). Carry-over operations are used when the sum of two bits is greater than 1.
Memory: When calculations involve more data than can fit in registers, the computer uses its main memory (RAM) to store data temporarily.
Conditional Statements: Computers can make decisions based on the results of calculations. Conditional statements like “if,” “else,” and “while” allow the computer to execute different instructions depending on whether certain conditions are met.
Control Unit: The Control Unit is responsible for managing the execution of instructions. It ensures that instructions are fetched, decoded, and executed in the correct order.
Output: Finally, the result of the calculation can be stored in memory, displayed on a screen, or used as input for further calculations or operations.
In conclusion, a computer calculates by processing binary data through a series of instructions, using an ALU for arithmetic and logic operations, registers for temporary storage, and control units to manage the process. It’s all built on the foundation of binary representation and the execution of instructions in a precise sequence.
What We Can Call AI and What Not
AI (Artificial Intelligence) refers to computer systems that can perform tasks that typically require human intelligence. These tasks include things like understanding and responding to natural language (like chatbots or voice assistants), recognizing patterns in data (like image or speech recognition), making decisions based on data (like autonomous vehicles), and learning from experience to improve performance.
Non-AI is a software application that does not understand or learn.
In other words, many computer programs do specific tasks really well, but they don’t truly understand or learn. For example, a calculator can perform complex mathematical calculations, but it doesn’t understand the maths—it follows pre-set rules. Similarly, a regular computer program that follows a fixed set of instructions to complete a task isn’t considered AI.
Let’s see in simple words How AI Works
Data Input: AI systems start by taking in data. This data can be in the form of text, numbers, images, sounds, or any other kind of information.
Processing Data: AI uses special algorithms and computer programs to process this data. Algorithms are like recipes that tell the computer what to do with the data.
Learning (Machine Learning): This is a crucial part of AI. In some cases, AI systems can learn from the data. Imagine you show a computer lots of pictures of cats and dogs. Over time, it can learn to tell the difference between them by finding patterns in the pictures.
Making Decisions: Based on the processed data and what it has learned, AI systems can make decisions or predictions. For example, an AI might decide if an email is spam or not based on its content.
Output: Finally, AI systems provide an output or response based on their analysis. This could be generating a human-like text response in a chatbot, driving a car safely, or suggesting movies based on your past choices.
Feedback Loop: Many AI systems can improve over time by getting more data and feedback. For instance, a language translation AI can get better at translating by learning from the mistakes it makes and the corrections people provide.
In essence, AI is like a smart computer system that can do tasks that seem smart, like understanding what you say, recognizing faces in photos, or making decisions based on data. It does this by processing data, learning from it, and using that knowledge to make decisions or perform tasks.
Let’s now elucidate, in straightforward terms, the foundational technology upon which AI is grounded.
AI is based on computer technology. It relies on powerful computers and software programs to mimic human intelligence and perform tasks that typically require human thinking, such as understanding language, recognizing patterns, making decisions, and learning from experience.
Key components of AI technology include
Data Storage: AI systems store and retrieve vast amounts of data. This data can be anything from text and images to numbers and sensor readings.
Algorithms: These are like step-by-step instructions or rules that tell the computer how to process and analyse data. Algorithms are at the core of AI, guiding its decision-making and learning processes.
Processing Power: AI requires high computing power to handle complex calculations quickly. This power comes from advanced computer hardware, including powerful processors and specialised hardware like GPUs (Graphics Processing Units).
Machine Learning: A significant part of AI technology involves machine learning, where computers can learn from data without being explicitly programmed. This learning ability is a fundamental aspect of AI’s intelligence.
Neural Networks: In many AI applications, neural networks are used to simulate the human brain’s way of processing information. These networks consist of interconnected nodes (artificial neurons) that process and transmit data.
Big Data: AI often works with large datasets to find patterns and make predictions. Big data technologies help collect, store, and manage these massive amounts of information.
Natural Language Processing (NLP): NLP technology enables AI to understand and work with human language, making chatbots, language translation, and voice assistants possible.
Computer Vision: This technology allows AI to interpret and make sense of visual data, like images and videos. It’s used in applications such as facial recognition and autonomous vehicles.
AI is built upon the foundation of advanced computers, algorithms, and data processing capabilities. It’s the combination of these technologies that enables AI systems to perform tasks that seem intelligent and human-like, such as understanding speech, recognizing images, and making decisions.
At this point, you’ve heard the term ‘neural network’ mentioned, and it’s often described as a crucial technology in AI. But what precisely is a neural network?
What is a Neural Network?
Think of a neural network as a computer program that tries to simulate the way our brain works when we think and make decisions.
It’s made up of artificial neurons, just like our brain has billions of tiny neurons.
These artificial neurons are organised into layers, typically an input layer, one or more hidden layers, and an output layer.
How Does it Work?
Imagine a neural network as a team of tiny decision-makers. Each neuron takes in information, processes it, and decides whether to “fire” (activate) or not.
Information flows through the network from the input layer to the hidden layers and finally to the output layer.
Neurons in the hidden layers make sense of the information step by step, like solving a puzzle.
As the information travels through the network, the network learns and adjusts its “thinking” based on the results it gets.
What Makes it Important?
Neural networks are essential in AI because they can learn from data. They don’t need to be explicitly programmed for each task.
They excel at tasks like recognizing images, understanding speech, making predictions, and finding patterns in data.
Neural networks have brought about significant advancements in technology, from self-driving cars to voice assistants, making our lives more convenient.
How it helps AI processes
Neural networks are crucial because they allow AI systems to learn and adapt. This means they can get better at their tasks over time.
They help AI make sense of the complex and messy real-world data that traditional computer programs struggle with.
They’re versatile, which means they can be used in various applications, from healthcare to finance, revolutionising many industries.
In simple terms, a neural network is like a team of virtual brain cells that work together to understand and make decisions based on data. They’re important because they can learn and improve, making AI systems smarter and more capable in various tasks, ultimately benefiting us in many aspects of our daily lives.
Bob Mazzei 