SMART FRAMEWORKS
Smart Frameworks for the enterprise to deploy AI-based applications into production, at scale.
As we harness the power of data, our Smart Frameworks can unlock new opportunities and drive meaningful change across various sectors. Smart Frameworks pragmatically embrace AI techniques to create mission-critical, scalable and reliable transactional application systems.
BEHIND AI
The general concept behind “Artificial Intelligence” (AI) dates far back to the times of ancient history with traces found in Greek mythology’s Talos, a gigantic bronze mechanical robot-like heroic figure with human intelligence that served as a guardian to the island of Crete against ill-intentioned outsiders and invaders. The origination of modern AI is considered to have transpired in the 1950s alongside the dawn of the computer age. In 1950 Alan Turing published a seminal paper titled “Computing Machinery and Intelligence” where Turing discusses the potential for a scenario where humans create a scenario in which “machines can think.”
MACHINE LEARNING
Some do not consider ML to be AI, but rather a field of computer science. However, the term is commonly used in conjunction with AI. ML strives to educate systems, using structured and/or labeled data, on how to absorb information and perform a specific task without requiring categorical programming. It is a method of data analysis that comprises constructing and amending models that permit programs to “learn” through experience and repetition. Examples of instances wherein ML is utilized include image/speech recognition, financial services, spam/malware email filtering, customer service chatbots, and many more.
DEEP LEARNING
Deep learning is a division of ML that employs numerous “levels” of neural networks, built to function in an unsupervised learning manner that emulates a human brain with the ability to “learn” from vast quantities of data, regardless of whether or not that data is unstructured, unlabeled, missing, or otherwise. Every layer of the neural network contains deep learning algorithms that carry out computations and make forecasts in repetition to learn and progressively boost the precision of the results and recommendations as time goes on. A few examples of instances wherein deep learning is utilized include digital assistants, financial fraud detection, self-driving vehicles, and many more.
​NEURAL NETWORKS
Neural networks are structures of neurons that can adjust with variable data inputs, composed of a sequence of algorithms that seek to identify core connections within a group of data in a procedure that simulates how a human brain might function in order to identify and recognize patterns. “Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems,” stated Pratik Shukla and Roberto Iriondo for Towards AI, a Medium publication.