We hear this buzzword AI, Artificial Intelligence, everywhere we go. AI seems to have appeared out of nowhere in recent years however it has always been around since 1960s. It just was not popular back then because there was not enough computation power nor data.
When AI was first introduced, it was built with a system of conditions such as “If something happens, do this”. But the world we live in has many exceptions and conditions to be hand-coded by humans. It is simply impossible to model the world by writing a list of conditions. Then came perceptron which became the basis for Artificial Neural Network, ANN. In a simple term, perceptron mimics a human neuron cell in a simplified version where it takes the inputs and compute some calculations and returns an output which in turn becomes an input for other neurons. The way machines learned was no longer tied to conditions but rather from the data because data is a great representation that models the world around us better than a set of rules and conditions.
With the development of ANN and explosive growth of data, we now have a capability to recognise objects around us, understand speeches from audio and text, and even translate languages. Some people have trained their machine with data of movie scripts so the machine can come up with a new movie script and some have trained a machine to compose music or draw paintings etc… The application of AI is broad and the task is for people to figure out ways to improve their lives with the help of AI. One of the helpful cases is where AI can detect breast cancer cells from the medical images.
At BCaster, we are using AI on computer vision part to understand what is happening in videos and images. An in-depth understanding of images and videos can provide users the ability to search contents with a high accuracy which in turn offers a seamless user experience. In addition, knowing what is happening in media contents, we could draw statistics to form data-driven decisions such as how visible is a sponsored brand logo in a football match ? What is the effect of presence of logos in certain sports games ? What is the sentiment of festival goers, is there a room for improvements ? The volume of media data is growing more each day but the way to effectively analyse and draw conclusions from them is still limited but in the future there will be more tools available to analyse them. Of course knowing the right question to ask is the first puzzle piece to solve.
AI is here to stay and if anyone wants to learn more about AI at BCaster, contact us via any forms of communications. We are excited to hear from you whether it is your interest to join the team or seeking for ways to integrate BCaster to your business.