Chatbots vs Conversational AI +8 Key Differences

Chatbots vs Conversational AI: Which is Right for Your Business? These tools recognize your inputs and try to find responses based on a more human-like interaction. The more training these AI tools receive, the better ML, NLP, and other outputs are used through deep learning algorithms. Conversational AI can power chatbots to make them more […]

What is a machine learning algorithm?

What Is Machine Learning? Definition, Types, and Examples Machine learning refers to the general use of algorithms and data to create autonomous or semi-autonomous machines. Deep learning, meanwhile, is a subset of machine learning that layers algorithms into “neural networks” that somewhat resemble the human brain so that machines can perform increasingly complex tasks. A […]

Conversational AI Platform for Enterprise

Conversational Automation for Enterprises These platforms are tailored to handle the complex communication needs of large-scale organizations, offering scalable, customizable, and integrative solutions. In an increasingly digital world, chatbots have become pivotal tools in enhancing enterprise efficiency and improving employee and customer experience. The best among them meld advanced natural language processing, seamless integration, scalability, […]

Artificial neural networks in supply chain management, a review

A systematic review of machine learning in logistics and supply chain management: current trends and future directions Current processes often depend on unreliable sources of data and outdated IT systems, with coordination limited across functions. To be fair, certain CPG companies consistently follow data-based planning methods, but even they typically optimize decision making at the […]

What is Machine Learning ? and What is the Purpose of ML?

Machine learning Data Science, Algorithms & Automation A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, […]