This publication is on the computational intelligence of Chat conversational UIs that spans from, NLP, RPA, DNN’s , emergent computing to other paradigms of A.I which also include rule based systems and heuristic definition languages(HDL). The design of the BotLibre conversational UIs is presented as a case study, including future work in improving the knowledge management and database use, from postgre SQL to NoSQL and more sophisticated database clusters. Keywords: conversational UI, botlibre, NLP, Deep Learning, RPA. Introduction A conversational UI is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Animated conversational UI are now common and include full or partial animations based on text. conversational UI are typically used in dialog systems for various practical purposes including customer service or information acquisition. Today, most conversational UI are either accessed via virtual assistants su ch as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. The classic historic early conversational UI are ELIZA and PARRY. More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E. While ELIZA and PARRY were used exclusively to simulate typed conversation, many conversational UI now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the conversational UI Racter. Some more recent conversational UI also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. conversational UI competitions focus on the Turing test or m ore specific goals. conversational UI have been used in applications such as customer service, sales and product education.​ (Bheemaiah, n.d.; Janarthanam 2017; McKay 2013; “Developing and Evaluating Conversational Agents” 2000) Many companies' conversational UI run on messaging apps like Facebook Messenger, WeChat, WhatsApp, LiveChat, Kik, Slack, Line, Telegram, or simply via SMS. They are used for B2C customer service, sales and marketing.​ (Janarthanam 2017; Bheemaiah, n.d.; Oehlman and Blanc 2011) In 2016, Facebook Messenger allowed developers to place conversational UI on their platform. Previous generations of conversational UI were present on company websites, e.g. As kJenn from Alaska Airlines which debuted in 2008 o r Expedia's virtual customer service agent which launched in 2011.​ (Verha gen et al. 2011) The newer generation of conversational UI includes IBM Watson-powered "Rocky", introduced in February 2017 by the New York City-based e-commerce company Rare Carat to provide information to prospective diamond buyers. A SaaS conversational UI business ecosystem has been steadily growing since the F8 Conference when Zuckerberg unveiled that Messenger would allow conversational UI into the app.​ (Bello 2019; Shevat 2017; McTear, Callejas, and Griol 2016) Hello Barbie is an Internet-connected version of the doll that uses a conversational UI provided by the company ToyTalk, which previously used the conversational UI for a range of smartphone-based characters for children. The process of creating a conversational UI follows a pattern similar to the developme nt of a web page or a mobile app. The conversational UI design is the process that defines the interaction between the user and the conversational UI. The conversational UI designer will define the conversational UI personality, the questions that will be asked to the users, and the overall interaction. An important part of the conversational UI design is also centered around user testing. The process of building a conversational UI can be divided into two main tasks: unders tanding the user's intent and producing the co rrect answer. The second task may involve different approaches depending on the type of the response that the conversational UI will generate. To keep conversational UI up to speed with changing company produ cts and services, traditional conversational UI development platforms require ongoing m aintenance. To eliminate these costs, some startups are ex perimenting with Artificial Intelligence to develop self-learning conversa tional UI, particularly in Customer Service applications. The process of building, testing and deploying conversational UI can be done on cloud based conversational UI development platforms offered by cloud Pl atform as a Service providers such as Yekaliva, Oracle Cloud Platf orm, Snatchconversational UI and IBM Watson. These cloud platforms provide Natural Langu age Processing, Artificial Intelligence and Mobile Backend as a Service f or conversational UI development. There are a lot of API's available incase your are building your own conversational UI like these AARC. Malicious conversational UI are frequently used to fill chat rooms with spam and advertisements, by mimicking human behaviour and conversations or to entice people into revealing personal informa tion, such as bank account numbers. Natural Language Processing (Goldberg 2017; Liu 2012; Perez-Marin and Diana 2011)​ Perez-Marin et.al is a good survey on NLP algorithms in conversational UI design. Natural language processing is a field of computer science, artificial intelligence concerned with the interactions between computers and human languages, and, in particular, concerned with programmi ng computers to fruitfully process large natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. Some notably successful NLP systems develo ped in the 1960s were SHRDLU, natural langu age system working in restricted "Blocks worl ds" with restricted vocabularies, and ELIZA, a simulation of a Rog erian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. Starting in the late 1980s there was a revolution in NLP with the introduction of machine learning algorithms for language processing. In recent years, there has been a flurry of results showing deep learning techniques achieving state-of-the-art results i n many natural language tasks, for example in language modeling, parsing, and many others. Since the so-called "Statistical revolution" in t he late 1980s and mid 1990s, much Natural Language Processing research has relied heavily on machine learning. Formerly, many language-processing tasks ty pically involved the direct hand coding of rules, which is not in general robust to natural language variation. In languages such as Turkish or Meitei, a highly agglutinated Indian language such an approach is not possible, as each dictionary entry has thousands of possible word forms. Some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language. Natural language generation Convert information from computer databases or semantic intents into readable human language. Natural language understanding Convert chu nks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantic s which can be derived from natural language expression which usually takes the form of organized notations of natural languages concepts. As a result, the Chomskyan paradigm discouraged the application of such models to language processing. Procedures as a Representation for Data in a Computer Program forUnderstanding Natural Language. Deep Learning (Tur et al. 2018; Bertero, n.d.; Yan 2018) Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts. Most modern deep learning models are based on an artificial neural network, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in DeepBelief Networks and Deep Boltzmann Machines. Importantly, a deep learning process can learn which features to optimally placed in which level on its own. No universally agreed upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth > 2. For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation. Deep learning algorithms can be applied to unsupervised learning tasks. Examples of deep structures that can be train ed in an unsupervised manner are neural history compressors and deep belief networks. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons. Many aspects of speech recognition were taken over by a deep learning method called long short-term memory, a recurrent neural network published by Hochreiter and Schmidhuber in 1997. The 2009 NIPS Workshop on Deep Learning for Speech Recognition was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets might become practical. Multiview deep learning has been applied for learning user p