Chatbots are believed to be in the early development stage of Conversational AI.

1. 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

2. 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? Let’s 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 wasn’t exactly viable then. Slowly machine learning methods
came into effect to channel more data and code. Then came google’s
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.

3. 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 3–4% 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.

4. 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
user’s 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

5. 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 “she’s gotten good
enough at sensing a user’s emotional state that she can even help with
relationship breakups”. Xiaoice is “much more” than just a chatbot as
described by Microsoft.
Melody
Melody’s objective is to help both doctors and patients. By focusing on
the medical assistant space, they’ve built a conversational bot that can
give highly-customized and situation-appropriate responses to a
patient’s 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 it’s an online
learning system where the weights are constantly changed on usage.
Sensely, HealthTap, and Koko are examples of other chatbots that

6. focus on the same goal. Melody is currently available on Baidu’s 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

7. 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 Facebook’s 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, Facebook’s 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/

8. Related hashtages- #chatboat #AI #conversationalAI #Deeplearning
#cleverboat
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