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Artificial Intelligence (AI)

Self-Aware AI

Self-aware AI refers to artificial intelligence systems that can recognize their own existence, internal states, capabilities, and limitations. Unlike traditional AI systems, which rely on predefined rules and algorithms, self-aware AI is capable of introspection and self-reflection, allowing it to adapt, learn, and make decisions in dynamic and uncertain environments.

Supervised Learning

Supervised learning (SL) in AI is a machine learning approach where a model is trained on a labeled dataset, learning the relationship between inputs and corresponding outputs. The goal is for the model to generalize and make accurate predictions or classifications on new, unseen data.

Synthesia

An AI-driven video synthesis platform that lets users create personalized, hyper-realistic videos with the artificial intelligence technology. The platform uses advanced machine learning techniques to generate videos that appear as if they were filmed with real actors in real environments.

The platform is widely used across industries for various applications, including marketing, e-learning, customer engagement, and internal communications.

 

Tabnine

An AI-powered code completion tool that helps software developers write code more efficiently. Tabnine integrates with popular code editors and IDEs like Visual Studio Code, IntelliJ IDEA, and others, providing predictive suggestions and auto-completions as developers write code.

Tractable

An artificial intelligence company that specializes in computer vision technology for the insurance industry, specifically automating claims processing for auto insurance companies. The company's AI-powered platform allows insurers to assess vehicle damage quickly and accurately by analyzing photos or videos of damaged vehicles.

Training Data

Labeled data used to teach AI models or machine learning algorithms to make proper decisions. It consists of input-output pairs and serves as the foundation for the model to learn patterns and make predictions or classifications. For example, to teach a computer to recognize cars, street signs, and people, it is shown lots of pictures with labels indicating what's in each picture.

Turing Test

A simple method for determining whether a machine can simulate human intelligence. It involves a human judge engaging in natural language conversations with both a machine and another human without knowing which is which, and the machine passes the test if the judge cannot reliably distinguish between them based on their responses.

UX-Driven AI

UX-Driven AI, or User Experience-Driven AI, refers to the approach in artificial intelligence development that prioritizes and emphasizes the user experience throughout the design and implementation of AI systems. It involves integrating user-centric design principles to create AI apps that are intuitive, user-friendly, and aligned with the needs and expectations of the end-users, enhancing overall usability and satisfaction.

Vertex AI

Google Cloud's machine learning (ML) platform that offers a unified environment for building, training, deploying, and managing ML models. It is designed to streamline the entire ML workflow, from data preparation and model development to deployment and monitoring, making it easier for developers and data scientists to create and deploy ML applications at scale.

Virtual Assistant (VA)

A software application or program that provides assistance to users through voice or text interactions. Virtual assistants utilize artificial intelligence, natural anguage processing, and machine learning algorithms to understand and respond to user queries and commands.

Viso Suite

An AI-powered platform that offers solutions for computer vision tasks such as image recognition, object detection, and image classification. It provides developers with tools and APIs to incorporate computer vision capabilities into their applications and systems.

Development by Synergize.digital

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