Can machines emulate our ability to make intelligent decisions in the real world? That would vastly depend on their ability to process a diverse set of sensory inputs, understand natural language interactions, learn continuously from new environments, and pursue sound reasoning and judgment. While human cognitive abilities are yet to be replicated fully, we reached an inflexion point in early 2016 when AlphaGo, an artificial intelligence (AI) program, defeated Hui, a top-ranked human Go player. After decades of hype, artificial intelligence finally threatens to disrupt every industry, including retail, manufacturing, healthcare and finance. Deep AI is now considered the next big technological shift, akin to the industrial revolution and the wide adoption of the internet, personal computers and smartphones.
The focus on cognitive automation has been brought on by multiple factors, including the explosion of big data, advancements in artificial intelligence (especially deep learning) and growing expertise in natural language processing (NLP), supported by declining technological costs and democratization of AI capabilities. The surge in big data has provided machines access to newer and wider unstructured data sets. Deep learning algorithms try to emulate the self-learning principles of the neurons in the human brain. NLP is being used for learning, understanding and producing human language content. The significant improvement in AI techniques is due to the availability of large unstructured data sets and massive computing power.The cognitive automation revolution has been led by technology firms and adopted by technology-savvy leaders in other industries. The applications range from accurate image recognition to drug discovery, virtual digital assistants, algorithmic trading, fraud detection and even autonomous driving. The surge in successful use cases in diverse industries and functions has garnered traction across firms.
