Open-source artificial intelligence is the application of open source practices to the development of artificial intelligence resources

Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems.

Also AI broadly refers to any human-like behavior displayed by a machine or system.

Artificial Intelligence. Open Source Solutions

Artificial Intelligence. Overview

Key AI technologies used in AI development

  • Machine Learning (ML)

    Common machine learning techniques include linear regression, decision trees, support vector machines and neural networks.

  • Natural Language Processing (NLP)

    Natural language processing uses deep learning algorithms to interpret, understand, and gather meaning from text data.

  • Deep Learning (DL)

    Deep learning neural networks form the core of artificial intelligence technologies. Each artificial neuron, or node, uses mathematical calculations to process information and solve complex problems. This deep learning approach can solve problems or automate tasks that normally require human intelligence.

  • Computer Vision

    Computer Vision is an area of artificial intelligence that allows computers and systems to extract meaningful information from digital images, videos and other visual inputs.

  • Generative Models

    Some generative model applications include image generation, image-to-text generation, text-to-image translation, text-to-speech, audio generation, video generation, image and video resolution enhancement, and synthetic data generation.

  • Speech recognition

    Deep learning models can analyze human speech despite varying speech patterns, pitch, tone, language, and accent.

  • Expert Systems

    An expert system is an interactive, reliable, computer-based AI decision-making system that uses facts and heuristics to solve complex decision-making issues. The highest-level human intelligence and expertise solve the most difficult problems in a particular domain.

What is an AI algorithm?

AI algorithms are a set of instructions or rules that allow machines to learn, analyze data, and make decisions based on that knowledge. These algorithms can perform tasks that typically require human intelligence, such as pattern recognition, natural language understanding, problem solving, and decision making.
In any discussion of AI algorithms, it is also important to emphasize the value of using the right data, rather than so much data, when training algorithms.

What is an AI model?

An artificial intelligence (AI) model is a program that analyzes datasets to find patterns and make predictions. AI modeling is the development and implementation of the AI model. AI modeling replicates human intelligence and is most effective when it receives multiple data points.

In simple terms, an AI model is used to make predictions or decisions and an algorithm is the logic by which that AI model operates.

The initial steps to AI modeling

  • Modeling

    After gathering quality data, the user creates an AI model that replicates human intelligence and decision making.

  • Training

    The user provides the AI model quality datasets. The data has three processing phases: training, validation, and testing. Throughout the three phases, the AI model interprets the data to draw conclusions.

  • Inference

    Before this step, the AI model needs to be extensively trained. Once trained, the user provides a live dataset and launches the model for practical usage.

AI models and machine learning

AI models can automate decision-making, but only models capable of machine learning (ML) are able to autonomously optimize their performance over time.

While all ML models are AI, not all AI involves ML. The most elementary AI models are a series of if-then-else statements, with rules programmed explicitly by a data scientist. Such models are alternatively called rules engines, expert systems, knowledge graphs or symbolic AI.

Machine learning models use statistical AI rather than symbolic AI. Whereas rule-based AI models must be explicitly programmed, ML models are “trained” by applying their mathematical frameworks to a sample dataset whose data points serve as the basis for the model’s future real-world predictions.

Categories of Machine Learning (ML) Models:

  • Supervised learning

    A human expert is required to label the training data. Data scientists provide the algorithms with labeled and specific training data. Sample data defines both the inputs and outputs of the algorithm. Convenience – simplicity and lightness of structure. Such a system is useful when predicting a possible limited set of results, dividing data into categories, or combining the results of two other machine learning algorithms.

  • Unsupervised learning

    Does not imply the external existence of “right” or “wrong” answers and therefore does not require labeling. These algorithms independently discover inherent patterns in data sets, group data points into clusters, and make predictions.

  • Semi-supervised learning

    This method combines supervised and unsupervised learning. This method is based on using a small amount of labeled data and a large amount of unlabeled data to train systems. First, the labeled data is used to partially train a machine learning algorithm. After this, the partially trained algorithm itself labels the unlabeled data. This process is called pseudo-labeling. The model is then retrained on the resulting data set without explicit programming.
    The advantage of this method is that you do not need large amounts of labeled data. This is useful when working with data such as long documents that would take too much human time to read and label.

  • Reinforcement learning

    In reinforcement learning, the model learns holistically, through trial and error, by systematically rewarding a correct result (or punishing an incorrect result). Reinforcement models are used to inform offers in social media, algorithmic stock trading, and even in self-driving cars.