Artificial intelligence (AI) technologies encompass a wide range of tools and techniques that aim to simulate or replicate human intelligence in machines.
Machine Learning (ML)
Supervised learning Involves training a model on labeled data, where the algorithm learns to make predictions based on input-output pairs; unsupervised learning involves models that are trained on unlabeled data, and the algorithm discovers patterns or structures within the data; In reinforcement learning, agents learn to make decisions by interacting with an environment and receiving rewards or penalties for their actions.
Deep learning is a subset of ML that uses artificial neural networks (ANNs) with multiple layers (deep neural networks) to model complex patterns and representations in data. Algorithms interpret sensory data through a kind of machine perception, labeling, and clustering of raw input. Common architectures include convolutional neural networks (CNNs) for image processing & video recognition tasks and recurrent neural networks (RNNs) for sequential data like time series or natural language.
Natural Language Processing (NLP)
Techniques for processing and understanding human language, including tasks like text classification, sentiment analysis, machine translation, and chatbots. Tokenization breaks text into words, phrases, symbols, or other meaningful elements; sentiment analysis determines the mood or subjective opinions within large amounts of text; chatbots and conversational agents use NLP to facilitate human-computer interactions. Pre-trained language models like BERT, GPT-3, and GPT-4 have revolutionized NLP tasks.
AI techniques for interpreting and understanding visual information from images or videos. Common applications include object detection, image segmentation, and facial recognition.
Robotic Process Automation (RPA)
The use of software robots to automate repetitive and rule-based tasks in business processes. Sensor integration combines various sensor outputs to create a more comprehensive understanding of the environment & path planning algorithms help a robot or drone decide on a path to reach its destination.
Random forests builds multiple decision trees and merges them together to get a more accurate and stable prediction; Gradient boosting machines (GBM) sequentially add predictors to correct errors from prior models.
Principal component analysis (PCA) reduces the number of variables in a dataset while preserving as much variance as possible; t-distributed stochastic neighbor embedding (t-SNE) technique is used for reducing dimensionality specifically designed for visualizing high-dimensional datasets.
Algorithms that provide personalized recommendations based on user behavior and preferences, commonly used in e-commerce and content platforms.
AI in Autonomous Systems
AI techniques for developing self-driving cars, drones, and robots capable of autonomous decision-making. Optimizing AI models also run efficiently on resource-constrained edge devices like smartphones, IoT devices, and drones.
AI in Gaming
Techniques used to create intelligent non-player characters (NPCs), simulate realistic game worlds, and enhance gameplay.
AI in Healthcare, Banking & Finance, and Other Industries
Tailored AI tools and techniques designed for specific domains, such as medical diagnosis, fraud detection, and autonomous vehicles.