The analytics world is best understood by thinking of your toolbox at home. A typical toolbox begins with the basics: hammers, screwdrivers, wrenches, pliers.
Some tasks can be handled by those tools alone – and with your level of DIY skills. However, many tasks, as complexity rises, require more powerful tools and greater expertise. Analytics are no different…
Analytics routinely require the combination of structured & unstructured disparate data sources such as images, video, audio, text, etc. to derive insight. We make use of the appropriate tools when they make the most sense to extract that insight. Our deep experience allows us to know which tools are best for your particular needs. Here are a few of the tools we make use of:
AI
Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.
Machine Learning
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
Neural Nets
Neural networks, or artificial neural networks (ANNs), are machine learning (ML) models that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions. They are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer.
Deep Learning
Deep learning is a subset of machine learning (ML) that uses multi-layered neural networks, called deep neural networks (DNN), to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) in our lives today. The “deep” in deep learning is just referring to the number of layers in a neural network.
A neural network that consists of more than three layers—which would be inclusive of the input and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has three layers is just a basic neural network.
Generative AI
Generative AI refers to deep-learning models that can take raw data—say, all of Wikipedia or the collected works of Rembrandt—and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data.
https://www.ibm.com/topics/artificial-intelligence#The+rise+of+generative+models
Large Language Models
Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.
Foundation Models
Foundation models are singular models that are trained on a huge amount of unlabeled data and then adapted to many applications. The term was first popularized by the Stanford Institute for Human-Centered Artificial Intelligence.
Advanced Analytics
Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning (ML), pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.
https://www.gartner.com/en/information-technology/glossary/advanced-analytics
Predictive Analytics
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.