What are foundation models in generative AI?

There are four basic concepts that form the foundation of artificial intelligence. These concepts help in understanding how AI systems are designed, implemented, and function.

1. Machine learning (ML): machine learning is a subset of AI that involves training algorithms to recognize patterns in data and make predictions or decisions without being explicitly programmed. It enables systems to learn and improve from experience, similar to how humans learn from their experiences. Examples include spam detection in email, recommendation systems in streaming services, and image recognition in social media.
2. NLP: NLP is a field of AI focused on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and respond to human language in a meaningful way. Examples include chatbots, language translation services, and voice-activated assistants like Siri and Alexa.
3. Computer vision: computer vision is the field of AI that trains computers to interpret and make decisions based on visual data. It allows machines to gain high-level understanding from digital images or videos, enabling them to identify and classify objects accurately. Examples include facial recognition systems, autonomous vehicles, and medical imaging diagnostics.
4. Robotics: robotics is a branch of AI that deals with the design, construction, operation, and use of robots. It involves creating intelligent machines that can assist humans in various tasks, often in environments that may be hazardous or difficult for humans to navigate. Examples include industrial robots on assembly lines, drones for delivery and surveillance, and robotic vacuum cleaners.

The core foundation for artificial intelligence lies in its ability to simulate human-centered artificial intelligence and perform tasks that typically require human cognition.
This foundation is built upon these components:
1. Algorithms and models
2. Data
3. Computing power
4. Machine learning
5. Neural networks
6. Natural language processing
7. Computer vision

Creating a foundation model involves a broad range of steps, from data collection to model deployment.

Here’s a high-level overview of the process:

1. Define objectives and scope: clearly define what the foundation model will be used for (e.g., language understanding, image generation). Decide on the scope and limitations of the model, including the types of data it will handle and the specific tasks it will perform.
2. Data collection and preparation: collect large and diverse datasets relevant to the model's objectives. This could include text, images, audio, and video data. Clean and preprocess the data to ensure it is free from errors, inconsistencies, and biases. Enhance the dataset through techniques like augmentation to improve model robustness.
3. Model design: select an appropriate model architecture based on the task. Common architectures include transformers for text (e.g., GPT, BERT) and convolutional neural networks (CNNs) for images. Set the initial parameters, including the number of layers, neurons per layer, activation functions, and learning rate.
4. Training the model: train the model on the collected data using high-performance computing resources. This phase can take a significant amount of time and computational power. Adjust the model on specific, smaller datasets to improve performance on particular tasks. Optimize the model's hyperparameters (e.g., learning rate, batch size) to enhance performance and efficiency.
5. Validation and testing: use a separate validation dataset to periodically test the model during training to ensure it is learning correctly. Evaluate the model on a dedicated test set to assess its performance and generalization capabilities.
6. Deployment and monitoring: deploy the trained model into a production environment where it can be used for real-world applications. Continuously monitor the model's performance and make adjustments as needed. This includes checking for data drift, retraining the model periodically, and ensuring it remains accurate and reliable.
7. Ethical considerations: implement techniques to identify and mitigate biases in the training data and model outputs. Ensure the model's decisions can be understood and explained to end-users and stakeholders. Adhere to relevant regulations and ethical guidelines regarding data privacy and AI usage.

ChatGPT uses a type of artificial intelligence model known as a transformer, specifically the GPT architecture.

You can generate code using generative AI. Multiple AI models and tools are specifically designed for this purpose, leveraging the capabilities of generative AI to assist with coding tasks.

Foundation models are the result of upstream tasks and serve as the basis for downstream tasks in AI development:

1. Upstream work creates versatile, powerful foundation models.
2. Downstream applications build upon these models, saving time and resources compared to developing specialized AI from scratch.
3. This approach allows rapid deployment of AI solutions across industries, as the core capabilities are already in place and only need adaptation for specific use cases.

Generative AI uses algorithms to create new content based on existing data. This process is driven by advanced machine learning techniques, primarily deep learning and neural networks.

Neural networks

Neural network architecture mimics the human brain's structure and function. These networks consist of layers of nodes, or neurons, that process data and learn patterns.

When trained on large datasets, neural networks can generate new content by predicting and assembling elements based on learned patterns.

1. Train the model. The first step in generative AI is training the model using a huge amount of data. For example, a generative AI model designed to create text (such as GPT-3) is trained on diverse text datasets, including books, articles, and websites. The model learns grammar, context, and nuances of language during this phase.
2. Pattern recognition. As the model processes data, it recognizes patterns and relationships within the input data. For text generation, this might include understanding sentence structure, word associations, and contextual relevance. For image generation, it could involve recognizing shapes, colors, and textures.
3. Generate new content. Once trained, the model uses its learned patterns to generate new content. For text, it can write essays, stories, or articles by predicting what comes next based on the initial input. For images, it can create new visuals by blending learned elements in novel ways.

Deep learning

Deep learning, a subset of machine learning, drives the capabilities of neural networks through multiple layers of processing. These layers allow the model to learn complex patterns and representations from large data sets.

• Layered learning. Deep learning models consist of many layers, each extracting higher-level features from the raw input. For example, in image generation, initial layers might detect edges and simple shapes, while deeper layers identify complex structures such as objects and scenes.
• Backpropagation. This algorithmic approach trains deep learning models. It involves adjusting the weights of connections between neurons based on the error rate of the output and expected result.
• High computational power. Deep learning requires substantial computational resources. Specialized hardware such as graphics processing units and tensor processing units handle the intense computations involved in training deep learning models.

Most generative models fall into the following three categories:

• Large language models (LLMs) predict the next word in a sequence based on extensive training data. LLMs are the best for creating textual content and they power many chatbots, writing assistants, and text completion tools.
• Generative adversarial networks (GANs) employ two competing neural networks to produce new content. One network generates content, while the other evaluates it. This back-and-forth process results in increasingly realistic outputs. They work well for creating visual and audio content, such as artificial images or synthetic voices.
• Variational autoencoders (VAEs) compress input into a coded form and then decode it to create a new output. VAEs often produce visual content or code and excel at tasks such as image generation and style transfer.