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LLMs are a kind of AI model that can handle a lot of text and learn how words are related to each other. They can make new texts based on what they are given or what they are asked to do. They can also do other things with text, such as sorting, summarizing, or changing languages. Some examples of LLMs are GPT-4 and LaMDA, which can make texts that sound natural and interesting.
ANNs can have different shapes and types, depending on what kind of data and task they are dealing with. Convolutional neural networks (CNNs) are a special type of ANNs made for image data. Neurons in CNNs are arranged in three dimensions: height, width, and depth. The depth dimension shows how many filters or feature maps find patterns in the images. Neurons in CNNs are coded using layers that do different things to the input images.
Recurrent neural networks (RNNs) are another special type of ANNs made for data that comes in a sequence, such as text, speech, or time series. RNNs have connections that let the network remember and use information from previous inputs in a memory state. RNNs can learn how the data changes over time and what it means in the whole sequence. RNNs can do tasks such as understanding language, recognizing speech, translating languages, and making texts.
To make the calculations for deep learning faster and easier, graphics processing units (GPUs) have become very useful. GPUs are computer chips that were first made for making computer graphics, but they are also good for deep learning tasks. GPUs can do many calculations at the same time very quickly. GPUs can make the processing of big and high-quality images faster, as well as the training of complex and deep neural networks. GPUs can also make the computation of sound and language models faster, as well as the making and changing of sound signals.
Foundation models (FMs) are deep learning models trained on a lot of data that is not organized or labeled. They can be used for many tasks right away or changed to specific tasks by training them on smaller datasets that have labels. Changing a pretrained FM to do better in a specific task by training it on a smaller dataset is called fine-tuning. Fine-tuning lets the FM learn and adapt to the details and patterns in the smaller dataset.
Generative AI means the amazing abilities of FM-based models to make content, going beyond what earlier AI models could do. FMs can make texts based on what they are given or what they are asked to do, such as GPT-4 or LaMDA. FMs can also make images based on what they are told or described, such as DALL-E or CLIP. FMs can also make sounds from texts or other inputs, such as WaveNet or Tacotron 2. FMs can also do things that are not about making content, such as finding out how people feel based on what they say or finding out what is wrong with someone based on their images or records.
Artificial Intelligence (AI) is the field of computer science that aims to create software capable of performing tasks that typically require human intelligence, such as reasoning, learning, planning, and decision making. It encompasses various techniques and models, including large language models (LLMs) and artificial neural networks (ANNs).
RNNs have recurrent connections that allow the network to store and reuse information from previous inputs in a memory state. RNNs can learn the temporal dependencies and context in the sequential data, and generate outputs that depend on the entire sequence. RNNs can perform tasks such as natural language processing, speech recognition, machine translation, and text generation.
To power the computational demands of deep learning, graphics processing units (GPUs) have become invaluable. Originally developed for computer graphics, GPUs are instrumental in training and running deep learning models efficiently, offering substantial computational power compared to traditional central processing units (CPUs). GPUs can speed up the processing of large and high-resolution images, as well as the training of complex and deep neural networks. GPUs can also accelerate the computation of acoustic and language models, as well as the decoding and synthesis of speech signals.
Generative AI refers to the advanced capabilities of FM-based models to generate content, surpassing earlier AI models. FMs can generate natural language texts based on a given input or context, such as GPT-4 or LaMDA. FMs can also generate images based on a given prompt or description, such as DALL-E or CLIP. FMs can also generate speech from text or other inputs, such as WaveNet or Tacotron 2. FMs can also serve non-generative purposes, such as sentiment classification based on call transcripts or medical diagnosis based on images or records.