Why Human like Chat bot is still a thing of the future?

I would start this article by giving 2 examples of chatbot:

1.     Microsoft Tay:

The “Think About You” or Tay was released on Twitter on Mar 23, 2016. But only after 16 hours of its launch, it was shut down as it began to post misogynist, racist & sexually explicit messages.

2.     The Loebner Prize, is one of the most famous & tested method to test which computer programs are most human-like. It is based upon Turing test. In 2013 & 2016, it was won by Mitsuku developed by Steve Worswick. But based upon my experiment with it (dated Jun 18, 2017), though human-like, it still gives me incorrect responses to publically available information (President of India as Pratibha Patil & President of the United States as Barack Obama)

Both these instances reveal that though significant progress has been made in chatbots field, there is a long way to go for chatbots to become fully operational as humans.

Training Artificial Intelligence

Artificial Intelligence is very good at parsing input text – for e.g. Skype’s translator or Google translator. But they are still very distant to understand the semantics of the language – deciphering the meaning of the sentences. Adding to that, there are other issues like human languages are very complex: meaning is spread across levels, from alphabets, to words, to phrases, to sentences; adding to developing a data model to converse like humans. All languages, in my opinion, are abstract-words & are simple ways to explain emotions which machines can’t experience.

The data used to train the bots defines how the bots are going to perform. The Garbage-In-Garbage-Out principle is always valid when we try to curate a bot.

Challenges

In my opinion there are two barriers which are hindering the development of human like chatbot

1.     AI scientist and programmers are less focused upon developing conversational bots, but rather intelligent systems which can provide end-to-end solutions to people’s problems: digital assistant to anticipating & catering to their owner’s wants

2.     Lack of training data, or creating environment where language simulation can be done. For e.g. you may think that internet is an abundant source & can be used to teach chatbots & improve their maturity. But the challenge is that what stands for what is NOT available in a machine-understandable and digestible format.

Way Forward

Chatbots can be a very potent means to resolve problems where expertise requirement is limited & hence a great source for cost cuttings. Hence, enterprises should work progressively to integrate chatbots into their environment.

There has been a great advancement in Deep Learning (see my article on deep learning here). Using Deep Learning along with Reinforcement Learning (I am soon going to write about it) is going to be the easiest way to make human like chatbots which are effective in every environment.