Chatbots and have different computational tasks. Seen from a

Chatbots
are becoming essential for users, businesses, software developers, and web
applications. In practice, chatbots continue to evolve and be more and more
familiar with users or clients in a company. There are lots of tasks that have
to be done every day and sometimes the same way, that is the reason that
chatbots have brought a revolutionized electronic administration and management
and will continue doing so especially considering the fact that some tasks need
a period of time 24/7  (hours/days a
week).

There are
simple and complex chatbots that of course use different tools and features and
have different computational tasks. Seen from a technical perspective a chatbot
will have to interact with a person (human) through text or speech, and that is
the link between a simple and complex chatbot, although complex chatbots can
handle unpredictable data and are very difficult to build and may need years to
be finalized.

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Chatbots
usually need to perform difficult tasks computationally because they make
possible real-time transmission of messages between its users (sender and
receiver) although messages within online chats are sometimes short in order to
provide an experience close to a conversation with people. 

It is understandable that not
all bots are created equal. Four main types of chatbots exist, each serving a unique
purpose. The question remains which type of bots are better. The answer is not straightforward
and depends on many factors. All bot types have their weaknesses and strengths,
performing differently under different circumstances.

Brands and businesses can
benefit from chatbots in plenty of ways. Though one of the most important
aspects is long-term capabilities.

Rule-based chatbots, also
known as flow-oriented chatbots, were made to follow a specific order of actions
characterized by a logic tree set up by the bot engineers. So the client gets a
question and follow-up questions, and choices and based on those data the
chatbot at that point goes down the pre-defined path.

So despite the fact that
the client still gets the chance to settle on the choice, he/she should go down
one of the pre-set way, which is very like how you get the chance to change the
situation of a computer game by browsing an assortment of choices. These bots
have a considerable amount of catches/choices/fast answers and small amount of
text input and will keep apologizing when a user asks anything.

Rule based bots are the
least expensive to work since there is less included when contrasted with AI
visit bots. Since these bots are produced moderately quickly, they’re anything
but difficult to run, integrate and usually with small costs.

Nonetheless, rule based
bots are one-dimensional. They do not have the deep learning processes AI bots
have. This makes them unfit to learn after some time, give analytics, and
limits their capacity to improve the client experience.

Despite this, rule-based
bots are yet helpful in case you’re performing basic tasks like educating
clients or setting updates and arrangements.

So the primary distinction
between Artificial Intelligence and rule-based chatbots is that they can deal
with free-text. So the client can essentially enter any sentence and
fundamental contrast here is that the bot will have the capacity to examine
that into an arrangement of parameters and comprehend what the client’s intent
was and along these lines respond as needs be.

Obviously this is fueled by
NLP (Natural Language Processing) and to work precisely, these bot require a
very enhanced back-end that can genuinely deal with anything that is tossed
their direction and this requires a long time of innovation and improvement,
which is the reason there are many NLP/NLU motors out there which are
open-source and owned by a portion of the greatest organizations; wit.ai (Owned
by Facebook), which is currently transforming into a Messenger only innovation
and api.ai (Owned by Google). However obviously, you can manufacture your own
particular NLP motor and begin from something straightforward that can
upgraded, prepared and will advance into A.I. that can impersonate a genuine
human discussion with time.

When you consolidate chatbots
and A.I what is produced is a bot with the ability to learn. They turn out to
be more intelligent after some time as they assemble data and information that
is then stored over their neural network.

These deep learning forms
rely on something many refer to as Natural Language Processing (NLP). NLP
enables artificial intelligence bots to accumulate data, as well as comprehend
the purpose of questions and human context.

Consider NLP the mind of AI
bots. The bots can do everything rule based bots can, in addition to settle on
constant choices and give definite data. They’re ideal for bringing clients
closer to your brand with recommendations custom-made to particular customer
interests.

Artificial intelligence chatbots
can help in sales, deliver quick client support, serve up analytics, and
upgrade your whole client experience. In case that you require a bot that does
everything, AI chatbots are a sure thing.

Hybrid bots combine the two
types of bots and create a semi-intelligent system that is configured of some
NLP capabilities and a per-defined flow. In fact, most of the bots that you
would encounter today, would be hybrid bots. This is due to the fact that
NLP/NLU still has quite a long way to go to completely understand any sentence
with different spellings, and sometimes even accents in applicable languages,
etc. which simply means you still wouldn’t be able to get what you want out of
the bot 100% of the time and this would lead to user frustration, and so a
better user experience would be definitely offered using a hybrid bot.

So what usually happens is
the user still goes through questions/answers with a certain logic tree, but
the bot would also be able to handle free text and reply accordingly in a way,
to get the user back into one of the set paths to achieve the intended
functionality.

These bots are similar to artificially
intelligent bots, allowing the user to interact using free text, but in any
case there is also a human operator observing and takes the conversation over
if the bot is no longer handling the requests in an acceptable manner. These
bots can also be trained, by operators or developers, who may add additional missing
parameters continuously, teaching the bot how to react in the future.

The perfect example for
this is “M”, Facebook’s A.I. Assistant.

The future of chatbots is pretty
clear, because there are many platforms that use it, and with the growth of
different areas in business, technology and many others there will be also a
meaningful growth in chatbot industry.

The design, programming and
architecture of chatbots will going to increase based on some factors which
include the type of usage (Company or person), type of services, type of
information, personality and behavior of the users and many more.