Taking the First Steps Toward Enterprise AI

Artificial Narrow Intelligence (ANI) or Weak AI: ANI is designed to perform specific tasks or solve particular problems within a limited domain. It is task-oriented and cannot understand or apply knowledge beyond its designated tasks. Examples include chatbots, recommendation systems, and image recognition systems.

Artificial General Intelligence (AGI) or Strong AI: AGI refers to AI systems with human-like intelligence, understanding, and problem-solving abilities across various domains. It can learn, reason, and adapt to new situations like humans. AGI is still a theoretical concept and has not yet been realized.

Generative AI: Generative AI is a subfield of artificial intelligence that focuses on creating new content, data, or patterns by learning from existing data. It employs algorithms, often based on deep learning techniques, to generate outputs such as images, text, music, or even 3D models. Generative AI can be used to synthesize realistic and creative outputs, enabling applications like art generation, content creation, and data augmentation.

Data Science: Data science plays a crucial role in the development and application of AI, as it involves preprocessing, exploring, and transforming data to create high-quality datasets for training AI models. Data scientists use data-driven approaches to enable AI systems to make better predictions, optimize decision-making, and uncover hidden patterns that ultimately drive innovation and improve performance across various domains.

Machine Learning (ML): ML is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions or decisions based on data. It includes techniques like supervised, unsupervised, and reinforcement learning.

Model: In the context of AI, a model is a computational representation of a system or process designed to predict outcomes or behaviors based on certain inputs. AI models, often based on machine learning algorithms, are trained using data to learn patterns, relationships, or decision rules. They adjust their internal parameters during training to minimize the difference between their predictions and the actual outcomes. Once trained, these models can make predictions, classify information, recognize patterns, or make decisions when presented with new, unseen data. AI models can range from simple linear regressions to complex deep neural networks.

Deep Learning (DL): DL is a subfield of ML that uses artificial neural networks, particularly deep neural networks, to model and solve complex problems. DL is particularly effective in processing large amounts of unstructured data, such as images, audio, and text.

Natural Language Processing (NLP): NLP is a branch of AI that deals with the interaction between computers and human languages. It focuses on enabling machines to understand, interpret, and generate human language in a meaningful and contextually relevant way.

Natural Language Understanding (NLU): NLU is a subset of NLP focused on algorithms that can interpret the meaning of a sentence or document in terms of syntax, grammar, or ontology. Examples of NLU tasks include parsing grammar or parts of speech to score a paper assigned as homework or Named Entity Recognition (NER) to identify specific concepts within a large glossary of terms.

Large Language Model (LLM): LLMs are AI models designed to understand and generate human-like text by learning from vast amounts of textual data. They are based on deep learning architectures, such as transformer networks, and trained on millions or billions of parameters to capture complex linguistic patterns, context, and semantic relationships. These models can perform a wide range of natural language processing tasks, including machine translation, sentiment analysis, text summarization, and conversational AI, demonstrating impressive fluency and comprehension capabilities.

Tuning/Training/Learning: In the context of AI, tuning, training, or learning refers to the process of developing an AI model by exposing it to a set of input data, often labeled with the desired outputs or outcomes. During this process, the model adjusts its internal parameters to minimize the difference between its predictions and the actual or target outputs. The training goal is to enable the AI model to recognize patterns, generalize from the training data, and make accurate predictions or decisions when presented with new, unseen data. This learning process can occur through various techniques, including supervised fine-tuning (SFT), supervised/unsupervised learning, and reinforcement learning (depending on the problem and available data).

Reinforcement Learning with Human Feedback (RLHF): RLHF is a method that combines reinforcement learning and human feedback to train AI models. In this approach, an AI agent learns to make decisions by interacting with an environment and receiving feedback from human experts or evaluators. Human feedback helps guide the learning process by providing valuable insights, preferences, or corrections, which the AI agent can use to improve its decision-making capabilities.

Generative Pre-trained Transformer (GPT): GPT is a type of LLM developed by OpenAI. It is based on the transformer architecture, which utilizes self-attention mechanisms to process and generate text in a parallelized and context-aware manner. The pre-training technique uses vast amounts of textual data from the internet to help models learn and capture complex language patterns, context, and semantics. A GPT model can perform various natural language processing tasks, such as text completion, summarization, translation, and question-answering. GPT models excel so strongly at answering questions that they have been widely adopted for Zero-Shot and Few-Shot learning, where well-engineered prompts can be used to create domain-specific AI without large volumes of training data.

Prompt Engineering: Prompt engineering refers to the practice of carefully crafting the input text (or “prompt”) to guide a model toward generating a desired output. The quality and nature of the input prompt can significantly influence the model’s response. Through skillful prompt engineering, users can maximize the utility of AI language models for various tasks, such as text completion, text generation, text classification, sentiment analysis, and more. It’s a way of indirectly “programming” these models to perform desired tasks without changing the underlying model or its parameters.

Vector Database: A vector database is a specialized database designed to efficiently store, manage, and retrieve high-dimensional vectors, also known as vector embeddings. These vectors often represent complex data such as images, videos, audio, and text in a numerical format suitable for machine learning and AI applications. Vector databases support similarity search operations, allowing users to find vectors most similar to a given query vector. This capability is essential for many AI applications, including recommendation systems, image recognition, and natural language processing.