Artificial Intelligence and Large Language Models

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• Algorithm: In computing, an algorithm is a precise list of operations that a Turing machine could do. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages.  https://simple.m.wikipedia.org/wiki/Algorithm
• Artificial Intelligence: The theory and development of computer systems able to perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. https://www.lexico.com/en/definition/artificial%20intelligence
• Deep Learning: In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technology is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
• Imitation Learning: Generally, imitation learning is useful when it is easier for an expert to demonstrate the desired behavior rather than to specify a reward function that would generate the same behavior or to directly learn the policy. The main component of IL is the environment, which is essentially a Markov Decision Process (MDP).  https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
• Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it... learn for themselves.  https://expertsystem.com/machine-learning-definition/
• Neural Network in AI: A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.  https://aws.amazon.com/what-is/neural-network/
• Reinforcement Learning: In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
• Supervised and Unsupervised Learning: In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/

ChatGPT is a language model developed by OpenAI that uses the GPT-3.5 architecture. It is designed to generate human-like responses in natural language conversations. The model is trained on a vast amount of text data from the internet, allowing it to learn patterns, grammar, and contextual information. When given a prompt or a message, ChatGPT analyzes the input, generates possible completions based on its training, and ranks them to select the most appropriate response. It relies on the context provided by previous messages in the conversation to maintain coherence and relevance. The responses are generated by predicting the next word or phrase, taking into account the probability distribution of different options. While ChatGPT is capable of generating impressive and coherent responses, it may sometimes produce incorrect or nonsensical answers, so caution should be exercised when relying on its outputs.

DALL-E 2 is an advanced version of the original DALL-E model developed by OpenAI. It is a cutting-edge artificial intelligence model trained to generate unique and creative images from textual descriptions. DALL-E 2 combines the power of deep learning techniques and generative adversarial networks (GANs) to understand and interpret natural language prompts, enabling it to create highly detailed and realistic images based on those descriptions. This model expands on the capabilities of its predecessor, allowing for more complex and intricate image generation. It has been trained on a vast dataset of images and text, enabling it to generate novel and imaginative visual concepts. DALL-E 2 has the potential to revolutionize the field of computer-generated imagery, providing a powerful tool for artists, designers, and creators.

Bard is a large language model (LLM) chatbot developed by Google AI. It is trained on a massive dataset of text and code and can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Bard is still under development, but it has learned to perform many kinds of tasks.

Bing and Microsoft have teamed up to update the original Bing search engine to include OpenAI's language model and answer questions and requests with a conversational style.  Microsoft Bing claims to be more powerful than ChatGPT and is designed specifically for Internet searching.  

Humata.ai's innovative approach to software development is an invaluable resource for those seeking to simplify their research process and expedite the journey to discovery. This AI-driven Q&A platform represents a bridge between complex data and actionable insights, facilitating the transition from learning to implementing.

I HIGHLY recommend the blog "One Useful Thing" by Ethan Mollick.  I subscribed to it and receive his new posts in my email.  He focuses on AI and its impact on work and education.