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Whether you're a seasoned software developer or an enthusiastic newbie dipping your toes into the vast ocean of technology, understanding the ever-evolving jargon is essential. Here, we'll explore some of the most common and important terms in the realms of Artificial Intelligence (AI), data processing, machine learning, and programming.
Starting with the basics, let's talk about the Application Programming Interface (API). Think of APIs as the universal translators for software. They help different programs communicate with each other, even if they are built using different languages or technologies.
Speaking of languages, Python is a prevalent one in the tech world. Loved for its simplicity, readability, and flexibility, it's a go-to for many AI tools.
GitHub is a platform that houses these coding scripts and fosters collaboration between software developers. On the other hand, Google Colab allows users to run Python scripts in the cloud, making sharing and collaboration easier.
For real-time communication between programs, we use webhooks. They send messages or data to another program over the internet, often used to automate processes.
Artificial Intelligence (AI) refers to machines' ability to mimic human intelligence, such as learning, problem-solving, decision-making, and language understanding. Machine Learning (ML) is a subset of AI, focusing on teaching computers to learn from data, without being explicitly programmed.
Under ML, we have Supervised Learning, where the model learns from labeled training data, and Unsupervised Learning, where the model learns to find patterns in the data on its own. Another approach is Reinforcement Learning, where the model learns by trial and error, receiving rewards or punishments for its actions.
Deep Learning (DL), a further subset of ML, uses deep neural networks to learn complex patterns from data. It brings us to the concept of Neural Networks, modeled on the structure and function of the brain. These are a type of machine learning algorithm used extensively in AI.
Data Processing and Feature Engineering are integral steps in preparing data for ML models. Data processing involves cleaning, transforming, and normalizing raw data, while Feature Engineering involves creating new features from the raw data to improve the ML model's performance.
When dealing with language understanding in AI, we often talk about Embedding, a way of representing words as numbers so that machines can understand their meaning and context.
A Large Language Model (LLM), like OpenAI's Generative Pre-trained Transformer (GPT), is trained on an extensive amount of text data and can generate natural-sounding text. A Prompt is a piece of text used to guide the generation of these models.
Overfitting is a common challenge in ML, where a model performs well on the training data but poorly on new, unseen data.
AI and ML require considerable processing power. A Graphics Processing Unit (GPU), is a special type of computer chip designed to handle complex calculations swiftly. Compute Unified Device Architecture (CUDA) is a technology developed by NVIDIA that helps GPUs solve complex problems faster by dividing them into smaller tasks.
Generative Adversarial Network (GAN) is a type of computer program that creates new data, such as images or music, by training two neural networks against each other. A similar creative use of AI is Generative Art, created using a computer program or algorithm to generate visual or audio output.
AI's capabilities also extend to language assessment with tools like the Giant Language model Test Room (GLTR), which helps differentiate between human-written and computer-generated text.
Langchain is a library connecting AI models to external information sources, allowing the creation of chatbots and agents that perform actions on a user's behalf.
Neural Radiance Fields (NeRF) are a type of deep learning model used for various tasks, including image generation and object detection.
Spatial Computing uses technology to add digital information to the physical world, changing how we interact with the world and with each other. This includes augmented reality and virtual reality experiences.
Lastly, Freemium is a term you might often encounter. It refers to tools that offer both free and paid options, typically with minimal but unlimited usage at the free tier and more features in the paid tiers.
This blog merely scratches the surface of the vast technological lexicon. With time and practice, these terms will become second nature. Stay curious and keep exploring!
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