This article will simplify the fast-changing AI landscape and give you practical knowledge to start using AI effectively and quickly.
Artificial Intelligence (AI)
Artificial intelligence (AI) refers to machines performing tasks that usually need human intelligence, like recognizing speech, understanding language, and making decisions based on data. Read this Peachly AI Modern Advertising Tool 2024
What isn’t considered AI?
- Basic math: Simple arithmetic that doesn’t need advanced learning algorithms.
- Image manipulation: Standard processes like convolution and filtering that don’t involve intelligent decision-making.
- Data entry: Tasks that don’t require complex AI algorithms for analysis or decision-making.
AI Model
An AI model is a program or algorithm that a human has trained and programmed to perform a specific task using certain data.
Some types of AI models are:
| AI Model | Description |
|---|---|
| Rule-Based Systems | Machines follow specific rules coded by humans, using “if-then” statements, resulting in predetermined outcomes. |
| Expert Systems | Machines mimic human expertise by following predetermined rules that simulate the judgment and behavior of a human expert. |
| Machine Learning | Machines learn from experience to perform tasks and improve performance without explicit rules defined by humans. |
Algorithm
An algorithm is like a recipe that tells machines how to do things and make choices. It’s used in all kinds of AI models to help them work properly.
ML Models vs AI Models
Not all AI models are the same. ML models, which are a type of AI, learn and get better over time. But other types, like rule-based and expert AI models, don’t learn or improve as they go.
Thinking about AI’s Impact on Us
Before we get into technical terms, it’s important to understand how AI is impacting our world now and how it might in the future.
AI Ethics
Many people worry about how fast AI is developing and want to make sure it aligns with our values and principles. Here are some important terms to know in this area:
| Term | Simple Explanation |
|---|---|
| Alignment | Making sure AI’s goals match human values and interests. |
| Responsible AI | Using AI technology in an ethical and responsible way. |
| Explainability | Making it clear how AI models make decisions. |
| Black Box | When humans can’t understand how an AI model makes decisions. |
| Singularity | A hypothetical future where AI surpasses human intelligence, causing unpredictable changes in society. |
Responsible AI
Responsible AI means using AI technology ethically and responsibly. It ensures that AI systems are designed and used in ways that respect human rights, diversity, and privacy.
For example, the inputs for an email trigger could include the email body, subject line, sender’s email, sent date, and any tags added.
Explainability
Explainability is important for responsible AI. It means making AI models and their decision-making processes clear and easy to understand.
Black Box
A “black box” AI is when its internal workings and decisions are not easily understood, even by its developers. This raises concerns about trust and accountability.
AI Progression of Intelligence
The ongoing improvement of AI intelligence is not only anticipated but is also a main objective.
So, where does AI intelligence stand now, and where is it headed in the future?
| AI Stage | Description |
|---|---|
| Artificial Narrow Intelligence (ANI) | AI designed for specific tasks like voice recognition or image classification. |
| Artificial General Intelligence (AGI) | AI with human-level intelligence capable of performing a wide range of tasks. |
| Artificial Superintelligence (ASI) | Future AI surpassing human intelligence, performing tasks beyond human comprehension. Singularity falls into this category. |
How AI Models Are Trained
Training and deploying AI models involves four phases:
- Pre-Training: Build a basic foundational model.
- Customization: Adapt the model for a specific task.
- Deployment: Use the model in real-world applications.
- Refinement: Enhance the model’s performance and outputs.
Phase 1: Pre-Training
Most generative AI models are trained on large datasets of complex, unstructured data. Unstructured data is not organized in a predefined way AI Fundamentals.
Unstructured Data vs. Structured Data
Why use unstructured data for training AI models?
- Abundance and Diversity: Unstructured data (like text from websites, books, articles, or images from the internet) is more plentiful and diverse.
- Rich Information: This type of data contains a wide range of human knowledge, language, and visual information, which helps in developing AI models with a broad understanding and the ability to handle various tasks.
Foundation Model
- First Training Level: This initial training creates a “foundation model” or “base model.”
- General-Purpose Design: Foundation models are built to perform a wide range of tasks, incorporating extensive knowledge and capabilities.
- Specific Task Performance: While these models are versatile, they may not be as effective for specific tasks compared to models tailored for those tasks.
Phase 2: Customization
To make an AI model better at specific tasks, it needs extra training, which can involve:
- Specialized Prompts: These are instructions that guide the model to focus on certain tasks or formats without changing its original abilities. There are different methods for this, like:
- Retrieval Augmented Generation: This method enhances the model’s output by pulling in relevant information from outside sources, like a database, to improve its responses.
- Zero Shot Learning: This is when the model is good at doing tasks without specific training, just by following prompts.
- Few Shot Learning: Here, the model is given a few examples to learn from, like a few images of different fruits to recognize various types.
- Fine-Tuning: This involves exposing the model to a new set of data to improve its performance on a specific task. However, too much fine-tuning can make the model too specialized and less useful for new tasks.
- Temperature: This doesn’t change what the model knows but affects how it responds. It adds some randomness to make the model more creative in its responses.
These methods help AI models become better at their jobs without changing them too much or making them too focused on one thing.
Phase 3: Deployment
After training and customization, the AI model is put into action. This can happen in a few ways:
- Private Use: The model is used only within one company for its specific needs.
- Licensing: The model is packaged and sold through an API (a way for different software programs to communicate), like OpenAI’s ChatGPT.
- Open-Source: The model is shared for free, and companies can run it on their own servers. For example, Stability AI’s Stable Diffusion is an open-source model.
Phase 4: Refinement
Once the model is in use, it’s continuously monitored and improved:
- Algorithms are tweaked
- The model is trained with new data to keep it accurate
- New features are added
- Better techniques are developed to handle different tasks or situations
This ensures the model stays effective and up-to-date over time.
ChatGPT’s Training
During its training, OpenAI’s GPT-3.5 AI model used a wide range of sources, including books, Wikipedia, and a large part of the internet known as “CommonCrawl.” This extensive and diverse training data helped make the model knowledgeable and versatile.
AI has significantly changed how businesses work and how people use AI tools. Two important terms in AI are automation and agents. But before we talk about them, let’s understand two other concepts:
Low-Code/No-Code: This is a type of programming that doesn’t need much coding knowledge. It allows businesses to create custom applications quickly without a big development team.
Application Programming Interfaces (APIs): These are like bridges that let different programs talk to each other. They’re crucial for low-code/no-code tools because they connect various apps and services.
Automation and Agents:
Automation: This is all about making tasks easier by letting machines do them. Automation tools link data from different tools and need little manual work once set up. They range from simple tools anyone can use to more complex ones for experienced developers.