AI & Machine Learning

The emergence of technologies such as AI and machine learning, along with sophisticated analytics, offers opportunities for smart manufacturers to transform their businesses radically. To succeed, however, companies need to leverage huge amounts of sensor- and device-related data.

As computing power and innovative technological capabilities grow day by day, with AI manufacturers will make a quicker automated decision, realize vaster scalability, and facilitate faster and deeper product development.

Chatbots offer a lot of opportunities for organisations, and they can be fun to interact with if developed correctly. But how do you start with conversational AI and how do you build a good and engaging chatbot? Starting with a chatbot is not easy, as there are many different variables to take into account.

When people say ‘machine learning’ it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble

According to statistics, the majority of brands and businesses will be using the AI-powered Chatbots into their marketing, sales, and service by 2020 so that customers can move down the sales funnel by interacting with the business on their terms.

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], it is undoubtedly the past few

Is AI a fad? AI isn’t a fad, it’s the way to progress. The problems of the future will only be getting harder. After you automate the simple tasks, you’ll want to move on to bigger challenges.

Anomaly detection has been an active research area for several decades, due to its broad applications in a large number of key domains such as risk management, compliance, security, financial surveillance, health and medical risk, and AI safety.

Machine learning systems are very powerful systems. They provide companies with new options in creating new product features, improving existing product features, and improving automation. However, these systems are still fragile, as there are more considerations about how it’s designed, and how data is collected and used to train models.

Many real-world problems involving networks of transactions of various nature and social interactions and engagements are dynamic and can be modelled as graphs where nodes and edges appear over time.

AI and automation are definitely bringing some major changes, both positive and negative, to the workplace and to our private lives. It’s naïve to hope that they will only bring good things but getting ready for any possible bad effects they may have is possible and necessary.