Artificial Intelligence

• AI alignment: In the field of artificial intelligence (AI), AI alignment research aims to steer AI systems towards humans' intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances its intended objectives. – Source link
• AI accelerator: high-performance parallel computation machines that efficiently process AI workloads like neural networks. They can be used to train both supervised and unsupervised models, and are often used in conjunction with GPUs. – Source link
• AI Inference vs. Training Models:
  • Trained Models are the products of rigorous learning from historical data. They encapsulate the knowledge acquired during the training phase, storing information about the relationships between inputs and outputs.
  • AI inference involves training models to recognize patterns and make predictions, and is the step where these training models are utilized to process new data. This process is akin to a well-trained human expert making decisions based on their wealth of experience.  – Source link
  • Another way for comparison:  training chips process large datasets to build the model, while inference chips efficiently execute these models, delivering quick and accurate outputs.
• Bidirectional Encoder Representations (BERT): is based on Transformers, a deep learning model in which every output element is connected to every input element, and the weightings between them are dynamically calculated based upon their connection. – Source link
• Categorization in AI: AI data classification is a process where AI systems are trained to categorize data into predefined classes or labels. By learning from patterns in historical data, AI classification sorts through vast amounts of data, creating order from the digital chaos. – Source link
• ChatGPT: which stands for Chat Generative Pre-trained Transformer is a type of generative AI that helps with content creation and information retrieval. It is a conversational AI, which means it creates AI systems that can understand and generate human-like responses in natural language conversations. ChatGPT is trained on large amounts of text data, such as books, articles, and web pages. OpenAI uses a publicly available corpus of web pages called the Common Crawl, which includes billions of web pages. – Source link
• CHIPs in AI:  FPGA and GPU processors can execute an AI algorithm much more quickly than a CPU. GPUs are most often used for initially developing and refining AI algorithms; this process is known as “training.” FPGAs are mostly used to apply trained AI algorithms to real-world data inputs; this is often called “inference.” ASICs can be designed for either training or inference. – Source link
• Convolutional Neural Networks (CNNs): is a type of deep learning algorithm that can learn from data. CNNs are particularly useful for image and video analysis. They can:
  • Identify objects, classes, and categories by finding patterns in images
  • Classify audio, time-series, and signal data
  • Extract features from images and videos
  • Classify or detect objects or scenes – Source link
• DALL-E, DALL·E 2, and DALL·E 3: are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions, called "prompts." – Source link
• Data Labeling: the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. - Source link
• Explainable AI (XAI):  is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result, Explainable AI is used to describe an AI model, its expected impact and potential biases. – Source link

And separately, ChatGPT is not an explainable AI as it is a text-to-text generative AI app that takes text as input and produces text as output. - Source link

• Generative adversarial networks (GANs): are a type of deep learning architecture that use two neural networks to generate new data. The two networks are a generator and a discriminator, and they compete against each other to produce artificial data that looks like real data. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell how "realistic" the input seems.  - Source link
• Generative pretrained transformer (GPT): are a type of large language model (LLM), and a prominent framework for generative artificial intelligence. They are artificial neural networks that are used in natural language processing tasks. GPTs are based on the transformer architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content. – Source link
• Language Model for Dialogue Applications (LaMDA): is a collection of large language models (LLMs) developed by Google for conversational applications. LaMDA is based on Transformer architecture and trained on human dialogue and stories. – Source link
• Large language model (LLM): is a language model notable for its ability to achieve general-purpose language generation. LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. – Source link
• Massive Multitask Language Understanding (MMLU): is a new benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings. - Source link
• Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information. Implementing RAG in an LLM-based question answering system has two main benefits: It ensures that the model has access to the most current, reliable facts, and that users have access to the model’s sources, ensuring that its claims can be checked for accuracy and ultimately trusted. - Source link ...... In a nutshell, RAG, requires a deep dive into its two foundational elements - retrieval models and generative models - the retrieval model acts as a specialized 'librarian,' pulling in relevant information from a database or a corpus of documents. This information is then fed to the generative model, which acts as a 'writer,' crafting coherent and informative text based on the retrieved data. The two work in tandem to provide answers that are not only accurate but also contextually rich.  Source link.
• RAG process: When users ask an LLM a question, the AI model sends the query to another model that converts it into a numeric format so machines can read it. The numeric version of the query is sometimes called an embedding or a vector. The embedding model then compares these numeric values to vectors in a machine-readable index of an available knowledge base. When it finds a match or multiple matches, it retrieves the related data, converts it to human-readable words and passes it back to the LLM. Finally, the LLM combines the retrieved words and its own response to the query into a final answer it presents to the user, potentially citing sources the embedding model found.  Source link
• Reinforcement Learning from AI Feedback (RLAIF): Reinforcement Learning from AI Feedback, or RLAIF, is a hybrid learning approach that integrates classical Reinforcement Learning (RL) algorithms with feedback generated from other AI models. This approach allows the learning agent to refine its actions not only based on rewards from its environment but also on insights garnered from other AI systems, thus enriching the learning process.  - Source link
• Recursive self-improvement (RSI): A process where a system, such as artificial intelligence (AI), writes its own code in repeated cycles of improvement. The system can make adjustments to its own functionality, resulting in improved performance. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. - Source link
• Supervised learning: a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs. - Source link
• Transformer model: a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. - Source link-1 and Source link-2
• Zero-shot learning, few-shot learning and one-shot learning: are all techniques that allow a machine learning model to make predictions for new classes with limited labeled data.  - Source link
  • Zero-Shot Learning: teaching a computer to recognize things it has never seen by giving it a general idea. For instance, training it on various fruits and then asking it to identify a fruit it has never encountered.
  • One-Shot Learning: teaching a computer about something using only one example.