Chatbots are not just the helpdesk assistants of the future, they are the helpdesks of the future. As a company that makes chatbots and has access to a tonne of data that repeatedly shows how chatbots are more effective than your typical customer service agent, we at Kommunicate believe that there will be only one channel of communication between you and your customers in the future -- chatbots. As more and more millennials become paying customers who prefer to talk to a computer rather than a real human being, adding a chatbot to your website is not just common sense, it is a matter of survival. But what if you already have a chatbot involved, which is taking care of all those customer conversations, doing a bit of lead generation for you, handling customer support, and even helping out the sales team. You must have got it all figured out and would now just have to sit back, sip iced tea (or coffee, if you prefer) and watch your business grow, right?

If you are reading this blog, you will probably be familiar with machine learning or will be interested in learning the same. Machine learning is a subfield of artificial intelligence, where it makes the systems to learn from data and make them capable of taking decisions with minimal human intervention. Now generally, we use the word "model" to indicate this intelligent system. Now, suppose we have a model which is designed to perform a particular task. This task can be anything like, for example, classifying the emails as not spam and spam, or an image classification problem.

In a previous blog post, I covered some of the challenges encountered by security operations centers (SOCs) and how leveraging artificial intelligence (AI) can help alleviate these challenges, including the cybersecurity skills shortage, unaddressed security risks and long dwell times. According to ISACA's State of Cybersecurity Report, 78 percent of respondents expect the demand for technical cybersecurity roles to increase in the future. The report also mentions that the effects of the skills shortage are going to get worse. This is where AI can step in and help lighten the load considerably. During a time of tight budgets and IT spend, there is no doubt that any new expenditures must have solid business justifications.

If you have been in machine learning for quite some time then you must be developing models to attain high accuracy, as accuracy is the prime metric to compare models, but what if I tell you that model evaluation does not always consider accuracy only. When we have to evaluate a model we do consider accuracy but what we majorly focus on is how much robust our model is, how will it perform on a different dataset and how much flexibility it has to offer. Accuracy, no doubt, is an important metric to consider but it does not always give the full picture. What we mean when we say that the model is robust is that it has realized and learned about the data in a correct and desirable manner, hence the predictions made by it are close to the actual values. Due to the enormous mathematical techniques involved and uncertain nature of data, it may happen that the model results in better accuracy but fails to realize the data properly and hence performs poorly when the data is varied.

Attention is currently considered the most important currency of the web, but there is another, equally important factor amplifies attention: trust. Trust is what enables meaningful engagement between the content creator and consumer. It is hard to earn, easy to lose, and impossible to purchase. The reading experience has never been more important for publishers – alongside with creating great content, it's how they build their brand and their audience. A great reading experience is what enables loyalty and trust, and creates the engagement levels that in turn are able to generate revenue and attract brands.