Artificial
Intelligence
Our Artificial Intelligence Team
Our Artificial Intelligence team consist of 3 people with degrees in Physics, Mathematics and Computer Science.
Our Artificial Intelligence team specializes in developing machine learning algorithms and technologies which allow machines and computers to learn from data and make decisions. We leverage our expertise in mathematics, physics, and computer science to design, implement, and evaluate AI solutions for a wide range of problems.
We can develop custom solutions for businesses or individuals, as well as provide general advice on the best AI techniques to use for a particular task. We specialize in deep learning, natural language processing, and computer vision. Our team is also experienced in the development of autonomous systems, cognitive architectures, etc.
Frameworks
TenserFlow
TensorFlow is a powerful open source software library for numerical computation, particularly well–suited and frequently used for deep learning and machine learning applications. It makes use of data flow graphs to represent computation, making it easy for developers to construct and execute neural networks with multiple layers of abstraction. It also supports distributed computing and offers a variety of optimization techniques to make the most of available computing resources. With TensorFlow, developers can quickly and easily build, train, and deploy machine learning models with ease. This makes it a great tool for developing and deploying powerful machine learning models for a variety of applications.
Pytorch is a powerful, open source deep learning framework that provides a seamless path from research to production. It is designed to be flexible, user–friendly and extensible, allowing developers to easily build and deploy sophisticated deep learning models. Pytorch is well–suited for rapid prototyping of complex deep learning models, and offers dynamic computational graphs, which make it easier to debug, visualize, and optimize models. Additionally, Pytorch comes with a rich set of tools and libraries, making it easy to integrate with other popular libraries, such as TensorFlow and Keras. This makes it a great choice for quickly building deep learning models with high accuracy and performance.
PyTorch
Machine Learning
We use machine learning to recognize patterns in data. This can include supervised learning, which uses labeled data, or unsupervised learning, which uses unlabeled data. Machine learning can be used to make predictions about future data, identify relationships between variables, and classify data. It can also be used for clustering, which is the process of grouping similar data points together. Machine learning can be used to improve decision–making by providing insights that may not be visible to the human eye.
Deep Learning
Deep learning is a type of machine learning that uses artificial neural networks to model complex patterns in data. It is a subset of artificial intelligence and is based on the concept of how the human brain works and learning through experience. Deep learning algorithms are used in applications such as image and speech recognition, natural language processing, and self–driving cars.
Data Gathering
Data gathering is the process of collecting data from various sources for the purpose of analysis and evaluation. It is the first step in the field of Artificial Intelligence (AI). Data gathering involves collecting data from a variety of sources, such as surveys, interviews, web scraping, and social media. The data gathered may be structured or unstructured, depending on the type of data being collected.
Once the data is gathered, it is then processed and analyzed in order to extract meaningful insights. This is done with the help of algorithms and machine learning techniques. The data is then used to train AI models and develop predictive models that can be used to make decisions and predictions. This data can also be used to create personalized user experiences and services.
Data gathering is an essential part of AI development, as it provides the necessary data that is used to create and train the AI models. Without data, it would be impossible to develop AI–based applications and services. Data gathering can be a time–consuming process, but it is essential in order for AI to be successful.
Modal Development
AI (Artificial Intelligence) is a form of computer science that enables machines to be able to think and act like humans. It is a branch of computer science that focuses on creating intelligent machines that can interact with their environment and respond to changing situations. AI–based systems are designed to learn from their experiences and adapt to new information in order to solve problems.
Modal development is a process of creating models to explain or predict AI behavior. It involves creating models that can represent the behavior of an AI system, such as how it will react to different inputs or how it will respond to different tasks. Modal development is often used to help design more reliable, efficient, and accurate AI systems.
The process of developing models for AI can be divided into two parts: model building and model validation. In model building, the goal is to create a model that accurately describes the behavior of the AI system. This is done by studying the system’s existing behavior, understanding the data that it is processing, and then constructing a model that captures the behavior of the system. In model validation, the goal is to test the model against the actual behavior of the AI system to ensure that it is accurate and reliable.
Model development is a complex process that requires knowledge of AI and computer science. It also requires a deep understanding of the problem that the AI system is trying to solve and how it is interacting with its environment. As such, it requires a team of experts with different backgrounds and expertise to ensure the success of the model development process.
Natural Language Processing
NLP, or natural language processing, is a type of artificial intelligence (AI) that enables computers to understand and process human language. It is a form of computational linguistics, which is the study of how computers can be used to analyze, understand, and generate human language. NLP is used to analyze text, audio, and video data in order to identify and recognize patterns in the data, and to generate insights from the data.
NLP utilizes various techniques such as natural language understanding (NLU), natural language generation (NLG), and natural language understanding and processing (NLUP). NLU involves understanding the meaning of words and phrases, as well as their context and relations among them. NLG involves generating text from an input, such as a query, and NLUP involves understanding the meaning of a sentence or phrase and determining the best course of action based on the input.
NLP can be used in a variety of applications, such as machine translation, automatic summarization, question answering, speech recognition, text classification, and sentiment analysis. It is also used in customer service, where it can help to respond to customer inquiries and support requests. NLP can also be used to improve search engine results, or to improve the accuracy of virtual assistants such as Siri, Alexa, and Google Assistant.