Artificial Intelligence & Machine Learning

Lately, Artificial Intelligence and Machine Learning is a hot topic in the tech industry. Perhaps more than our daily lives Artificial Intelligence (AI) is impacting the business world more. There was about $300 million in venture capital invested in AI startups in 2014, a 300% increase than a year before (Bloomberg). AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI is transiting from just a research topic to the early stages of enterprise adoption. Tech giants like Google and Facebook have placed huge bets on Artificial Intelligence and Machine Learning and are already using it in their products. But this is just the beginning, over the next few years; we may see AI steadily glide into one product after another.


According to Stanford Researcher, John McCarthy, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Simply put, AI’s goal is to make computers/computer programs smart enough to imitate the human mind behaviour.

Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.




Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.

ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict ratio

Artificial intelligence is a serious business.

Integrating AI into any organization is serious work. 

It takes in-depth knowledge, swathes of time, and a dedication to accuracy. Moreover, to implement it successfully, don’t just follow the trends: instead, focus on how AI can add value to your particular business and determine where it’s needed the most.

Then, with the support and experience of a domain specialist, you can put your ideas to work and create long-term value using the demanding field that is artificial intelligence.

Tackling AI in-house is always an option: but to take your business to the next level with AI solutions that enhance your business processes, it can help to partner with an expert.