AI & Machine Learning

The future is here: machine learning and AI allow you to automate the ineffable! … if only you knew how to spot a good use case. Here is a neat trick you can use to identify tasks that are perfect for machine learning and AI.

Data poisoning is a special type of adversarial attack, a series of techniques that target the behavior of machine learning and deep learning models.
If applied successfully, data poisoning can provide malicious actors backdoor access to machine learning models and enable them to bypass systems controlled by artificial intelligence algorithms.

RPA is a game-changer for the enterprise market in our current reality. The technology enables organizations to deploy virtual workers that can execute any logical actions that have historically been a drag on human productivity.

By employing the right AI technology for your business, you can accelerate growth. But business leaders should not forcefully include AI in their operations; instead, they should find specific workflows in which AI can provide maximum value.

There is a need of an ethical framework that will enable to address the many ethical, social and economic implications of AI. At the same time it should ensure not to hinder innovation, technological development and competitiveness in the process. In addition, AI needs the skills of both men and women in order to be as humane as possible.

Reinforcement Learning (RL) is an increasing subset of Machine Learning and one of the most important frontiers of Artificial Intelligence. This article is a high-level structural overview of many classical RL algorithms.

Data Scientists must follow change and innovation, so AutoML can become a very useful friend of theirs if they start using it properly. If they automate boring tasks, they will likely have more time to spend analyzing information, that is the real goal of a Data Scientist.

This article presents and an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Classification

Successful automation of tasks requires a clear understanding of the nuances related to completing each task; applying this filter will make or break the effectiveness of an AI solution.

Digital transformation is our current generation’s attempt to transform in the face of the Fourth Industrial Revolution. However, the sad truth is that 70 percent of all digital transformations still fail today

One of the fertile areas for quantum computing is AI (Artificial Intelligence), which relies on processing huge amounts of complex datasets. There is also a need to evolve algorithms to allow for better learning, reasoning and understanding.