Conversational AI vs Chatbot and their evolution within 10 years

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Conversational AI vs Chatbot and their evolution
within 10 years
Chatbots are believed to be in the early
development stage of Conversational AI. While
some may confuse chatbots with Conversational AI,
one must realize that both possess a slight
difference in their abilities. Where the latter
is based on Cognitive Architecture, AGI
orientation, and is generic, the former is based
on Machine Learning, Deep Learning techniques.
Srini Pagidyala, Co-Founder of Aigo.ai in one of
his LinkedIn blogs defines Chatbot as a narrow
or weak AI-oriented, purpose-driven technology.
Srini believes that Conversational AI can serve
as the Universal User Interface by Humanizing
Interactions with
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machines and systems using natural language
abilities while narrow or weak AI provides the
inputs to Conversational AI. The two
technologies are complementary and when they are
combined effectively they can enhance customer
experience and add significant value to both the
customer the company. Chatbots are believed to
be in the early development stage of
Conversational AI. How? Lets dive into
it. Evolution Timeline In the pursuit to adopt
more humane features, not only menial tasks but
modern chatbot tends to be highly specialized.
Chatbots have evolved magnificently to become
more diverse and creative with a combination of
AI. But how did AI capabilities like NLP aid the
evolution? According to a Medium report, in the
early stages of chatbot development, core NLP
methods were used to design them as machine
learning wasnt exactly viable then. Slowly
machine learning methods came into effect to
channel more data and code. Then came googles
2018 groundbreaking paper BERT, helping
researchers transcend all research. One should
understand the fact that unless the initial brute
force and core methods were utilized there would
have never been enough data for the best ML
algorithms today. Across the decade chatbots
have evolved with great abilities.
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ELIZA Eliza is one of the first natural language
processing computer programs created in 1964 by
Joseph Weizenbaum. It was developed at the MIT
Artificial Intelligence Laboratory. Its main
intention was to demonstrate the superficiality
of communication between humans and machines. It
rose to fame while psychiatric patients perceived
it to be human. Eliza simulated the chatbot
experience by using a pattern matching and
substitution methodology. This gave users the
illusion of understanding the part of the
program but had no built-in framework for
contextualizing events. Eliza interacted by
providing scripts. They were written
originally in MAD-Slip (a programming language).
It allowed ELIZA to process user inputs and
engage in discourse following the rules and
directions of the script. It was one of the
first programs capable of attempting the Turing
test. Cleverbot Cleverbot, was created by
British AI scientist Rollo Carpenter. It is a
chatterbot web app that uses artificial
intelligence (AI) to strike conversations with
humans. It approaches the technique with core
natural language processing and fuzzy logic.
Fuzzy logic is utilized to tackle a million
records stored and their utilization in a
heuristic approach. With over 279 million
interactions, about 34 of the data it has
already accumulated Cleverbot is now taking the
next big step and aiming to improve its
efficiency by implementing ML techniques.
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It rose to fame after being featured in the
popular creepypasta ARG web serial Ben Drowned
by Alexander D. Hall. Mitsuku Mitsuku is a
web-based chatbot developed by Pandorabots was
awarded the annual Loebner Prize twice in 2013
and 2016 for being the most human-like chatbot
around. Stamped as a virtual friend, Mitsuku
can answer questions, play games and do tricks at
the users request, and is capable of basic
reasoning. It is also available on Kik
Messenger. The underlying development was done by
Steve Worswick for a good 13 years as he was
frustrated with his I.T support job. The
codebase is around 350,000. It is based on AI/ML
technology which consists of pattern and
template elements. Rose Rose is an
award-winning chatbot created by Brillig
Understanding, Inc. Brillig has based Rose on
the average teenage girl. They have a claim of
giving the chatterbot its own personality but one
understands on usage that it is just set on a
code basis. The main objective here is a
starting base for chatbots to develop in a way
where the chatbot displays its own emotion and
thought process. The program is based majorly on
powerful pattern matching aimed at detecting
meaning and a simple rule layout combined with
C-style general scripting. Xiaoice
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Xiaoice is the AI system developed by Microsoft
STCA in 2014 based on an emotional computing
framework. XiaoIce is uniquely designed as an AI
companion by forming an emotional connection to
satisfy the human need for communication,
affection, and social belonging. Intelligent
quotient (IQ) and emotional quotient (EQ) are
both considered in the system design. The chat
experience is based on Markov Decision Processes
(MDPs) which optimize XiaoIce for long- term
user engagement. The established metric is
Conversation-turns Per Session (CPS). With a
huge user base, it is backed by none other Bill
Gates himself. He has gone so far to claim that
shes gotten good enough at sensing a users
emotional state that she can even help with
relationship breakups. Xiaoice is much more
than just a chatbot as described by
Microsoft. Melody Melodys objective is to help
both doctors and patients. By focusing on the
medical assistant space, theyve built a
conversational bot that can give
highly-customized and situation-appropriate
responses to a patients query. Melody is
designed to save time but also serves as a sort
of stop-gap solution for the worldwide issue of
doctor shortages. The project is headed by none
other than the deep learning pioneer Andrew Ng.
The parent company is Baidu. Melody is based
on advanced deep learning and NLP technologies.
It is said that it continues to learn through
more usage which means its an online learning
system where the weights are constantly changed
on usage. Sensely, HealthTap, and Koko are
examples of other chatbots that
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focus on the same goal. Melody is currently
available on Baidus Doctor App, accessible only
in China. DialoGPT Toward Human-Quality
Conversational Response Generation via
Large-Scale Pretraining DialoGPT is a joint
project between MSR AI and the Microsoft
Dynamics 365 AI Research team to develop
state-of-the-art chatbot systems. The project
provides a foundation for building versatile
open- domain chatbots that can deliver engaging
and natural conversational responses across a
variety of conversational topics, tasks, and
information requests, without resorting to heavy
hand-crafting. It is trained on data gathered
from 147 million Reddit comment chains. The main
code is utilized on the base of hugging face
PyTorch transformers. Meena Meena is a chatbot
that learns to respond sensibly to a given
conversational context. Google has a fancy term
for it which is the neural conversational
model. The training objective is to minimize
perplexity, and the uncertainty of predicting the
next token (in this case, the next word in a
conversation). At the core lies the Evolved
Transformer seq2seq architecture, a Transformer
architecture discovered by evolutionary neural
architecture search to improve its
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main objective, the perplexity. It has around 2.6
billion parameters that have been trained on 2.5
TB of information. Until the recent announcement
of Facebooks Blender, it was the
state-of-the-art system and was ahead of the
entire race with its high SSA scores. The main
aim of Meena is to address the critical flaw in
chatbots of them not making sense. Google plans
to improve the model by reducing its unnecessary
biases which are an aim in making the model
explainable. For the above reason, there is no
current model that can be tested but the paper
can be found here. Blender Blender, Facebooks
latest chatbot in collaboration with ParlAI, is
named for its ability to merge multiple
conversational skills at once. The chatbot is
built from 9.4 billion parameters and trained
using 1.5 billion examples of conversation,
making it so large that it had to be broken up
into pieces in order to handle larger sets of
data. The AI uses what Facebook calls Blended
Skill Talk (BST) to merge various chatbot
abilities. Though there is a large area for
improvement, around 49 of the judges chose
blender over humans and 3 quarters of the panel
chose it over Meena! The main objective of this
chatbot was to target open-domain conversations
while maintaining empathy, knowledge, and
personality. The paper and code can be found in
the below link. Facebook has kept all the data as
open source in hopes of someone improving the
efficiency of the model. Credits/HELP
-https//www.analyticsinsight.net/
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