Sentia Everything You Need To Know About AI Sentia is an innovative CRM solution designed to enhance and optimize customer relationship management through advanced AI features and automation. Sentia is a sophisticated application that integrates seamlessly with existing CRM platforms like Salesforce, offering a modern interface and a suite of over 26 AI-driven features. Its primary goal is to streamline user workflows, automate repetitive tasks, and provide actionable insights, making it an invaluable tool for various teams, including sales, service, marketing, and finance. Sentia - FAQ
Artificial Intelligence (AI) AI refers to the simulation of human intelligence in machines that are designed to think and learn. AI enables computers to perform tasks that typically require human intelligence, such as problem-solving, decision-making, and understanding natural language. Machine Learning (ML) ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make decisions based on data. Rather than being explicitly programmed, ML models improve their performance over time as they are exposed to more data. Deep Learning A type of machine learning that uses artificial neural networks with multiple layers (deep neural networks). It's particularly effective in handling large datasets and complex problems such as image recognition, natural language processing, and speech recognition. Neural Networks A neural network is a series of algorithms that attempt to recognize relationships in a set of data through a process that mimics the way the human brain operates. They are the foundation of deep learning. Natural Language Processing (NLP) NLP is a field of AI that gives machines the ability to understand, interpret, and respond to human language in a valuable way. It involves a combination of linguistics and AI to enable interactions between computers and humans in natural language. Language Model A Language Model (LM) is a type of model used in natural language processing (NLP) that is trained to predict and generate human language. Language models are capable of understanding, interpreting, and generating text based on the input they receive. These models are the foundation of many AI applications, such as text generation, machine translation, and conversational agents.Pretrained Language Models: LMs like GPT (Generative Pretrained Transformer) are pretrained on vast amounts of data to learn the statistical patterns of language. They can then be fine-tuned for specific tasks, such as question answering or summarization. Types of Language Models: Unidirectional: Processes text in one direction (left to right or right to left), predicting the next word based on the previous words.Bidirectional: Considers both the left and right context of a word, as seen in models like BERT (Bidirectional Encoder Representations from Transformers). Language models are at the heart of modern AI systems that understand and generate natural language.LM (Language) An LM is any model that is trained to understand, generate, and predict human language. Language models vary in complexity and size, ranging from small, simple models to more advanced ones. They are used for tasks such as text classification, sentence completion, and machine translation. Large Language Model An LLM is a Large Language Model, which is a specific type of language model distinguished by its massive size. LLMs are trained on extremely large datasets, typically containing billions or even trillions of parameters. This scale allows LLMs to perform far more complex tasks and generate highly nuanced, coherent responses. Examples of LLMs include models like GPT-4, BERT, or T5. Key Differences: Scale: LLMs are much larger than traditional LMs, with a far greater number of parameters and training data.Capability: Due to their scale, LLMs can handle more complex tasks like understanding deeper context, reasoning, and generating high-quality text across a wide range of topics.Performance: LLMs generally outperform smaller LMs in tasks like language understanding, translation, summarization, and conversation generation. LLMs have made significant advancements in AI, allowing for more human-like conversations, advanced reasoning, and more accurate contextual responses. Retrieval Augmented Generation (RAG) RAG is a hybrid approach combining retrieval and generation in natural language processing. It involves retrieving relevant documents or information from a large dataset and using that data to generate contextually accurate and informative responses. RAG models enhance the performance of text generation tasks by pulling in accurate external knowledge, thus improving the factual accuracy of the generated output. Retrieval: The model retrieves relevant information from a knowledge base or dataset (e.g., documents or external sources).Generation: The retrieved information is then used to generate responses, which are more coherent and contextually relevant.RAG is commonly used in applications like question answering, where up-to-date and factual information is needed. Prompt (in AI) A prompt in the context of AI refers to the initial input given to a language model (like GPT) to guide the output it generates. Prompts can be in the form of questions, statements, or specific instructions. The quality and clarity of the prompt play a crucial role in determining the relevance and quality of the model’s response. Prompt Engineering The process of crafting and refining prompts to ensure that the AI model generates accurate, context-aware, and useful responses. Effective prompts often include clear, concise instructions and, if necessary, context to guide the model's output. Prompts can range from simple questions like "What is the weather today?" to more complex instructions like "Write a 500-word essay on the importance of data privacy." Token Token is the technical term for the unit generative AI models use to create their mathematical maps. People use words and sentences, but AI breaks them down into more uniform-sized tokens — chiefly for reasons of computing efficiency. Multi-modal An AI system that can take input and produce output across different categories of media, typically text, images, audio and video. Context window The short-term memory of a generative AI. The larger the context window, the more information you can "feed" the AI along with a prompt, giving it key ingredients upfront or new data that it might not have access to on the open internet. Hallucination An answer provided by generative AI that sounds plausible but is made up and incorrect. If the program does not have good information to go on, it will still try to answer a question by guessing "next words" that seem to fit. Transformer The "Transformer" part of a GPT refers to the model’s architecture, which is just a fancy way of saying how it processes information. Traditional AI models used to process text one word at a time, in order. Transformers, on the other hand, look at all the words in a sentence at once, which helps them understand the context better. Deepfake Any images, photo or video produced by AI tools designed to fool people into thinking it's real. Chatbots A chatbot is a software application designed to simulate conversation with human users, especially over the Internet. They can be rule-based or powered by NLP and AI to provide more intelligent, conversational responses. Supervised Learning A type of machine learning where the algorithm is trained on labeled data. The model learns from input-output pairs and aims to make predictions based on the input data. Unsupervised Learning In unsupervised learning, the algorithm works on unlabeled data and tries to identify patterns or groupings without specific guidance on what to look for. Reinforcement Learning A type of machine learning where agents take actions in an environment to maximize cumulative reward. The model learns from the outcomes of its actions and adjusts its strategy accordingly. Artificial General Intelligence (AGI) AGI refers to a type of AI that can perform any intellectual task that a human can. It contrasts with narrow AI, which is designed for specific tasks. Often felt by humans (if achieved) to reflect a truly artificial intelligence with free thought. Computer Vision A field of AI focused on enabling machines to interpret and make decisions based on visual data, like identifying objects in images or videos. Data Mining The process of discovering patterns and relationships in large datasets to extract valuable information. It involves techniques from statistics, machine learning, and database systems. Robotics Robotics involves creating machines capable of carrying out tasks, often autonomously, based on AI. AI enhances robots’ ability to learn from their environment and make decisions. Algorithm An algorithm is a set of rules or steps that a machine follows to solve a problem or perform a task. In AI, algorithms are used to process data and make predictions. Big Data Big data refers to extremely large datasets that are too complex to be processed by traditional data-processing tools. AI techniques, particularly machine learning, are often used to analyze big data. Predictive Analytics The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Cognitive Computing Cognitive computing mimics human thought processes in a computerized model. It involves self-learning systems that use data mining, pattern recognition, and natural language processing to solve problems. Bias in AI AI bias occurs when an algorithm produces prejudiced results due to biased training data or flawed model design. It’s important to ensure fairness in AI by identifying and mitigating bias in models. AI Ethics AI ethics refers to the guidelines and moral principles that govern the development and deployment of AI. These include fairness, transparency, privacy, and accountability. Sentiment Analysis A type of data analysis that uses NLP to determine the emotional tone behind a body of text. It’s often used to assess customer opinions, feedback, and reviews. Edge AI Edge AI refers to the deployment of AI algorithms on devices at the edge of the network, such as IoT devices, rather than in centralized cloud environments. This allows for faster processing and reduced latency. Explainable AI (XAI) XAI refers to AI systems that are designed to be interpretable, allowing humans to understand the decision-making process of the models. It’s particularly important for ensuring transparency and trust in AI systems. Artificial Neural Networks (ANN) ANNs are computing systems inspired by the biological neural networks in animal brains. They consist of interconnected layers of nodes that process data and are used in machine learning models. Generative AI Generative AI refers to AI models that can create new content, such as text, images, music, or code. GPT-3, which powers ChatGPT, is an example of a generative AI model. Training Data Training data is the labeled dataset used to train machine learning models. The quality and quantity of training data significantly affect the performance of AI systems. Overfitting Overfitting happens when a machine learning model is too closely aligned to the training data and performs poorly on new, unseen data. This is because the model has learned to memorize the training data instead of generalizing from it. Underfitting Underfitting occurs when a machine learning model is too simple and does not capture the underlying trends in the data, resulting in poor performance on both training and test datasets. Data Labeling Data labeling is the process of tagging or annotating data to make it recognizable by machine learning models. Labeled data is used in supervised learning algorithms. Transfer Learning Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. It speeds up the training process and reduces the need for large datasets. Hyperparameter Tuning Hyperparameters are settings that are not learned from the data but defined before training a machine learning model. Hyperparameter tuning involves adjusting these settings to improve model performance. API (Application Programming Interface) An API allows two systems to communicate and share data. In the context of AI, APIs are often used to integrate AI capabilities, like NLP or computer vision, into applications. iPaaS (Integration Platform as a Service) iPaaS is a platform that facilitates the integration of applications and data across different environments. Sentia Mesh is an example of an iPaaS that enables effortless integration and AI-powered automation.
Agentic AI Agentic AI refers to artificial intelligence systems that exhibit autonomous behavior in decision-making and action-taking, much like a human agent. These systems can act independently in complex environments to achieve specific goals without requiring continuous human intervention. Unlike traditional AI systems that respond to direct inputs and predefined rules, Agentic AI is goal-oriented and capable of Autonomy: Making decisions and executing actions on its own based on the objectives it is given. Adaptability: Adjusting its behavior in response to changes in the environment, often without explicit reprogramming. Problem-Solving: Tackling complex tasks by dynamically interacting with its environment, learning from feedback, and optimizing its actions to achieve a goal. Agentic AI is often used in scenarios such as robotics, autonomous vehicles, and AI-driven systems where the AI must interact with real-world environments and make decisions in real-time. Embodied AI Embodied AI refers to AI systems that are integrated into physical bodies or devices, enabling them to interact with the real world through physical actions. This is commonly seen in robotics, where the AI controls movement, perception, and interaction with objects in its environment. The key distinction is that embodied AI operates not just in a virtual space, but in the physical world. Example: Robots with AI that navigate a room, pick up objects, or assist in healthcare settings. Autonomous AI Autonomous AI is AI that can perform tasks or make decisions without human oversight. It goes beyond simple automation by continuously learning and adapting to new data or environments. Autonomous AI systems often rely on machine learning, and they are designed to handle uncertainty and operate effectively in dynamic environments. Example: Autonomous vehicles that drive on roads, make decisions about speed and route, and avoid obstacles. Cognitive AI Cognitive AI mimics human cognitive processes, such as thinking, reasoning, problem-solving, and learning. This type of AI aims to replicate the way the human brain works to solve problems and make decisions. Cognitive AI systems are often used in applications that require a deeper understanding of context and the ability to simulate human decision-making processes. Example: AI-driven customer support systems that understand and respond to complex customer queries. Reactive AI Reactive AI refers to the simplest form of AI that reacts to specific inputs or conditions without memory or learning capabilities. These systems do not learn from past experiences and only respond to current inputs or situations. Example: A chess-playing AI that considers only the current game state when making decisions, without learning from previous games. Hybrid AI Hybrid AI combines multiple AI techniques or models (e.g., symbolic AI and machine learning) to create a more flexible and effective system. Hybrid AI is used in complex environments where no single approach can fully address the problem, so it blends different methods to achieve optimal outcomes. Example: Systems that combine rule-based logic (symbolic AI) with machine learning to handle tasks that require both structured rules and adaptive learning.