Artificial intelligence (AI) and machine learning (ML) are two terms that are coming to the forefront. More organizations are adopting these new technologies, which are now all around us but not always immediately visible. Businesses with e-commerce sites are taking advantage of the ability to increase sales by recommending products based on product images viewed. The healthcare industry is treating patients faster because machines can recognize patterns in high-resolution medical imagery. Manufacturers are improving quality control and catching small errors before they become major issues. 

With the advent of the internet, it has never been easier than it is now to publish content. Anyone with a smart device and a web-connection can publish on a multitude of platforms catered for that specific purpose. In recent years, a global debate has emerged around the risks faced by internet users, with a specific focus on protecting users from harmful content. A key element of this debate has centered on the role of content moderation to protect users from potentially harmful material. 

There was an experiment conducted in 2017 in London, with 100 lawyers participating in the event. The experiment involved finding accuracy in credit card irregularities in applications sent to the Finance Ombudsman. Where lawyers predicted an accuracy of 66.3%, a trained AI program achieved the accuracy of 86.6%.

Imagine a lawyer doing research for a case. Where humans may take weeks or even months to complete a particular research, AI will complete the research and provide results in a matter of seconds. Here, Artificial Intelligence will be able to perform better than experienced lawyers. 

As Corona virus is spreading heavily in China and across the globe, hospitals are overcrowded and brave doctors are fighting round the clock to contain this pandemic.

Many healthcare institutions are looking forward to implement AI into their organizations to improve their operations and quality of patient care. Many such organizations have already implemented AI tools. With many articles being written on how AI is stealing human jobs, actually AI is re humanizing healthcare  by helping healthcare professionals focus more on interacting with patients rather than spending time on routine tasks.

#MACHINE LEARNING
1. Skyl NLP now supports Bidirectional LSTM Algorithm

A must try out on your dataset where text classification is dependent on context. With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously which leads to increase in the amount of input information available to the network and over.

Artificial intelligence (AI) and machine learning(ML) have helped optimize processes and workflows in many industries. In the healthcare sector, AI is increasingly helping in solving tough  problems and uncovering hidden insights in the data generated by hospitals most of which is unstructured.

Artificial intelligence is finding applications in more and more business use cases across business verticals. The main reason for this popularity is due to the positive return on investment for early adopters of AI.

After working on many AI implementations, here are some thoughts on what works in a typical AI/ML endeavor.

In this article, we will try to give a simple to understand overview on how computer vision systems work.