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Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, and decision-making. The goal of AI is to create intelligent machines that can assist humans.
Machine Learning is a subset of AI that involves training algorithms on data to improve their performance at certain tasks without explicit programming. As they process more data, the algorithms "learn" patterns and make predictions. For example, machine learning powers facial recognition, product recommendations, and self-driving cars.
Neural Networks are computing systems modeled on the neural networks in the human brain. They are the backbone of deep learning, a modern approach to machine learning. Neural nets have input and output layers, as well as hidden layers that enable learning. The connections between neural layers are weighted, allowing the network to adjust weights and improve its performance on tasks.
Generative Models are a type of machine learning that involves algorithms generating new data points, like images, audio, and text. Examples include generating artificial photos of people who don't exist or creating long passages of human-like text, such as chatbots. Key generative models include generative adversarial networks (GANs) and variational autoencoders (VAEs).
Large Language Models (LLMs) are a class of neural networks that are trained on massive text datasets, allowing them to generate human-like text and engage in conversational dialog. They can understand and generate natural language.LLMs like ChatGPT work by taking in a text prompt and predicting the most likely next words using patterns learned from their training data. Their scale (billions of parameters) allows them to model complex language remarkably well. However, they do not have a deeper understanding of what they are generating.
GPT stands for Generative Pretrained Transformer. GPT models are trained to predict the next word in a sequence by analyzing patterns in massive amounts of text data. Later GPT iterations like GPT-3 and GPT-3.5 have demonstrated increasingly advanced natural language abilities.
Over time, LLMs have grown much larger and more capable. Earlier models like GPT-2 had 1.5 billion parameters, while later models like GPT-3 scaled up to 175 billion parameters, with performance improving dramatically. Recent models are now surpassing 100 trillion parameters. The rapid progress exemplifies the potential of LLMs.
The "Attention is All You Need" paper published in 2017 introduced a novel neural network architecture called the transformer. This paper forms the basis for modern large language models like GPT.
OpenAI's Custom GPTs are specialized versions of the Generative Pre-trained Transformer models that can be fine-tuned on specific datasets to tailor their responses for unique applications or to adhere to particular content guidelines. This customization allows organizations to create AI models that are more closely aligned with their communication styles, technical requirements, or industry-specific knowledge, enhancing the relevance and effectiveness of the model's outputs for their specific use cases.
Travel Through AI's Evolution: Uncover Key Moments on Our Interactive AI Timeline!
For new users of generative AI tools like ChatGPT and others, there are several important precautions to keep in mind to ensure safe, ethical, and effective use. These include:
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