Showing posts from December, 2019

Five predictions for AI and process automation in 2020

The development of artificial intelligence (AI) and robotic process automation (RPA) rose rapidly during 2019. In the coming year, this pace will climb even further. Enticed by the potential for the technologies to streamline workflows and improve customer service, organisations will be undertaking deployments in ever-increasing numbers. At the same time, the capabilities of the technologies will continue to grow at a breakneck pace. Where initially they were limited to assisting with very structured activities, they will increasingly be put to work in areas that until now have required human intervention. During 2020, five key trends will shape the field of AI and RPA. They are: The rise of the RPA robot Current projects involving the deployment of RPA robots have tended to focus on replicating existing tasks that have traditionally been completed by humans. The robots have been able to learn a repetitive task and then complete it much more quickly. While their AI capabilities allow…

The Power of Visualization

A picture really does say a thousand words. “Visualization is really about external cognition, that is, how resources outside the mind can be used to boost the cognitive capabilities of the mind.” — Stuart Card The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. It is often regarded as obvious or unimportant due to its inherently subjective nature. In this article, I hope to dispel some of those thoughts and show you that visualization is incredibly important, not just in the field of data science, but for communicating any form of information.
Visualization Goals Essentially, there are three goals to visualization: Data Exploration — find the unknownData Analysis — check hypothesesPresentation — communicate and disseminateThat is essentially it. However, these terms are pretty vague, and it is thus quite easy to understand why it is so difficult for individuals to master the ar…

Statistics Vs Machine Leaning

Are they both really SAME?

No, they are not the same. If machine learning is just glorified statistics, then architecture is just glorified sand-castle construction.

Machine Learning is built upon Statistics Before we discuss what is different about statistics and machine learning, let us discuss first the similarities.  Machine learning is built upon a statistical framework. This should be overtly obvious since machine learning involves data, and data has to be described using a statistical framework. However, statistical mechanics, which is expanded into thermodynamics for large numbers of particles, is also built upon a statistical framework. The concept of pressure is actually a statistic, and the temperature is also a statistic. If you think this sounds ludicrous, fair enough, but it is actually true. This is why you cannot describe the temperature or pressure of a molecule, it is nonsensical. Temperature is the manifestation of the average energy produced by molecular collisions. …