As a consumer of goods and services, you experience the results of machine learning (ML) whenever the institutions you rely on use ML processes to run their operations. You may receive a text message from a bank requiring verification after the bank has paused a credit card transaction. Or, an online travel site may send you an email that offers personalized accommodations for your next personal or business trip. The work that happens behind the scenes to facilitate these experiences can be difficult to fully realize or appreciate. An important portion of that work is done by the data engineering teams that build the data pipelines to help train and deploy those ML models.

A worldwide lockdown was thrust upon you. While you're already used to sitting on that couch occasionally to'Netflix and chill' after a tiring workweek, this time you don't have other alternatives for entertainment beyond that couch. You're browsing through the content across all OTT platforms and eventually end up binge-watching innumerable shows. All of these suited to your taste, almost tailor-made for you. In the end, you're possibly a satisfied consumer of the content.

The need for real-time decision making is pushing AI closer to "the edge", giving devices the ability to process information and accelerate machine learning tasks locally. Deloitte predicts that in 2020, more than 750 million edge AI chips are expected to be sold. Further, by 2024, the sales of edge AI chips are expected to exceed 1.5 billion, representing more than 20% annual unit sales growth[1]. The reason behind this rapid growth lies in the fact that edge AI chips are increasingly finding their way into consumer market, in addition to enterprise edge devices. Majority of the consumer edge AI chips will be in high-end smartphones, accounting for more than 70% of all consumer edge AI chips currently in use. Edge AI chips enable organisations to increase their ability to not only collect data from connected devices, but also analyse the data and drive data-driven decision making, while avoiding the cost, complexity, and security challenges associated with storing data on the cloud.

Machine Learning might be a department of computer science pointed at empowering computers to memorize unused behaviors based on experimental data. The objective is to plan the algorithms that allow a computer to show the behavior learned from past encounters, instead human interaction. Now we will examine applications of machine learning in cybersecurity and see how the machine learning algorithms offering assistance to us for battle with cyber-attacks. Machine learning (without human interaction) can collect analyze and prepare data. In cybersecurity, this innovation makes a big difference to analyze past cyber-attacks and create individual defense reactions.

Reactive Machines – This AI system doesn't have its memory that is why it cannot store things. The basic example for reactive machines are Deep blue, which is the IBM Chess Program, this example is relevant here because Deep Blue can easily identify the pieces on the chessboard and can easily make the predictions about the game, but doesn't have memory which enables Deep Blue to use its past experience for informing the future ones. It can only analysis the possible moves of both the players and can choose the most strategic move. Limited Memory – This AI system has limited memory because of that, they are able to use their past experience for informing future decisions. Some of its decision-making functions are used in the self-driving car.

In the past decade, supply chain management has skyrocketed across verticals. With e-commerce becoming a critical economy booster, its success depends heavily on an efficient decision-making platform. With advanced technology solutions making steady inroads into businesses every day, the era of supply chain management has evolved. Several companies are thriving today as they capitalize on a robust technology architecture to support large-scale supply chain operations, and are providing analytical insights on route optimization freight tracking and analytics, sales beat optimization. Locus is a state-of-the-art decision-making platform for logistics, optimizing a range of operations to provide consistency, efficiency & transparency.

What is the first thing that comes to a human's mind when we plan to do any of the following? But are we having these conversations anymore, or are we turning to our smart devices instantly? A shift in traditional human behaviour is the biggest driving force behind digitally led intuitive customer experiences. Even before we were so heavily dependent on tech, relationships were built for life. Technology has sure made our lives simpler, but it has also taken away the human element away from some of these relationships. While businesses globally have benefited immensely from the myriad of technology solutions, the one thing they really need to focus on, is customer loyalty.