Self-Learning Bots – An Overview…
Artificial Intelligence
Lijo M Loyid January 15, 2018

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.

What Is Artificial Intelligence?

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 mentioned in all major business guest posting site and is expected to simplify and digitize major business processes,  as well as increase self-learning capabilities.

Goals of Artificial Intelligence

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.

How Does a Bot Work?

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.

Manual Process

Robotic Process Automation

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”.

How Does a Bot Help Us?

  • Analysis data and reports it periodically
  • Performs repetitive tasks such as processing a particular type of transaction
  • Imports/exports data between systems
  • Processes swivel chair data entry
  • Enters data to forms, creates, edits and retrieves data from databases
  • Executes email campaigns, follow up, tracking and archiving

 Advantages of Bots

  • Bots increase productivity
  • Produce accurate and high-quality results
  • Rarely makes mistakes and are reliable
  • Work 24 hours without fatigue and breaks
  • A greater quantity of work in lesser time

Challenges in Programming Bots :

The challenges and possible problems in AI-powered applications are:

  • Identifying and perfecting all the algorithms required
  • Collecting significant input data to train the bots
  • Managing bot behavior when input data is new – bots should be made capable to repeat tasks even when the data inputs are unfamiliar. This means the bots should be monitored and trained constantly through improved algorithms.

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.

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Author Bio

Lijo M Loyid is a passionate software developer who loves learning and working with new technologies. While not working, he is can be seen watching movies, reading or travelling.

Explore how AI can transform your organization so that you can improve business productivity and profitability!

Explore how AI can transform your organization so that you can improve business productivity and profitability!