In today’s world, we can see automation enabling users to accomplish a wide range of tasks everywhere, such as checking luggage at the airport, ordering food, booking a hotel room etc. AI is being applied in various fields and is improving at a rapid pace due to programs that perform automated tasks on a human’s behalf. Applications of AI currently range from sentiment analysis to self-driving cars, the simplest being the photo tag suggestions that appear on Facebook photos. Google, Bing and other search engines also employ AI through web crawlers, which are examples of sophisticated bots.
The term Artificial Intelligence (AI) has been around since Allen Newell, Herbert Simon, and Cliff Shaw wrote the Logic Theorist, the first artificial intelligence program made to mimic the problem-solving skills of a human being in the 1950s. Developing systems that equal or exceed human intelligence is at the core of artificial intelligence. While simple bots can automate tasks and increase efficiency, to take care of advanced activities that require intelligent and informed decision making. Technologies like Robotic Process Automation (RPA) includes intelligent automation using real-time self-learning techniques like predictive analytics and cognitive computing. It encompasses Artificial intelligence, machine learning and speech recognition, thereby creating what can be called as self-learning bots. The breakthrough technology is expected to simplify and digitize major business processes, and also increase self-learning capabilities.
The goals of AI include deduction, representing knowledge, planning, natural language processing (NLP), perception, learning, etc. The long-term goals of AI research include achieving creativity, social intelligence, and general (human level) intelligence. It should be able to generate accurate responses, processes and rapidly reason like human knowledge in natural language text.
A cognitive bot learns by observing people at work. It is achieved by constantly and repeatedly analyzing the processes, corrections and transactions of the employer by the bot. The bot thus gains the knowledge to process the incoming data by thinking and performing the suitable action, getting smarter and becoming more accurate over time. It automatically extracts the data needed for decision making and continuously learns from the employer’s feedback. It uses NLP, ML, knowledge representation, reasoning, massive parallel computation and Rapid Domain Adaptation.
To create a self-learning bot, one should go beyond basic AI and progress into Machine Learning. Machine Learning uses algorithms to process incoming data, learn about it, and then determine what to do with it. After Machine Learning, the next step is to move into Deep Learning (DL), a more advanced version of Machine Learning. Deep Learning breaks down the language in ways that make ‘human-level’ chatbot conversation seem possible. In this phase, neural networks come into the act and use it to progressively conclude on a single probability of accuracy. As an example, the final output of a neural net might be: “This input is 90% likely to be a support request”.
The challenges and possible problems in AI-powered applications are:
In the coming years, it shouldn’t come as a surprise if business process management gets transformed completely by Robotic Process Automation and Artificial Intelligence.