“Technological evolution is the result of our own desire to lead a better life.” ― R.S. Amblee
And rightly so, with the advent of technology, each passing generation has leveraged more and more benefits. Talking of generations, one technology that has touched many of our lives is AI. From maps to mail, from food to sleep, AI is helping us in ways unimaginable. The most common uses of Artificial Intelligence. Siri, Cortana, and Alexa have become common household names. But before we get into how chatbots became the first form of communication to every business, let us define what Chatbots/Virtual Assistants are.
Chatbot or Virtual Assistants is a program that stimulates interaction with humans via text or voice and works as an information exchange interface. Chatbots are deployed over other Virtual Assistant and channels like messaging platforms, apps, websites, etc.
The AI-powered interface improves customer experience and reduces cost in the customer experience front. Chatbots in its initial days were used as information acquisition interfaces, such as extracting product details, general FAQs, or book appointments.
Chatbots were programmed to reply to only a certain set of questions or statements. If the question asked is other than the learned set of responses by the customer, they would fail. But it has evolved, now the Chatbots are smarter, faster, and possess cognitive skills. They are much far from the initial days of being able to answer just simple questions now they can:
– Pay Bills
– Book Tickets
– Order Groceries/products
– Lead Generation
– Store Data
– Provide detailed engagement analysis, etc
Chatbots orchestrate with other chatbots internally and perform much more complicated tasks. They can now even understand not just human language but also user intent. They can understand the user journey and also predict the conversation journey the user would opt for.
Developers use Chatbot platforms to build conversational user interfaces Chatbot/Virtual Assistants. A conversational platform has a developer API and/or software development kit (SDK) so that third parties and/or clients can extend the platform with their customizations and additions. And they can deploy these Chatbots and Virtual assistants on various platforms such as social media, website, messaging applications, and global Virtual Assistant like Google Alexa.
Alan Turing developed the Turing test to examine the ability of the machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. During the test a human evaluator would examine the conversation between a machine and a human, all the while knowing that one of the subjects is a machine but not knowing which. The conversation would be limited to text only. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test.
Evolution of Interactive Agents
ELIZA – 1964-66
Alan Turing’s famous article Computer Machinery and Computation inspired ELIZA, the first Chatbot developed by MIT professor Joseph Weizenbaum in the 1960s. ELIZA was one of the early natural language processing programs designed and developed. In the paper, Turing brought forth the Turing test.
Turing Test was a test for the machine to determine its exhibit behavior intelligence equivalent or indistinguishable to humans. But ELIZA could not pass the Turing test. Eliza could simulate conversation by using a “pattern matching” and substitution methodology giving users an illusion of understanding by the program but had no built-in framework for contextualizing events. Scripts provided directives on interaction.
PARRY – 1972
Kenneth Colby was an American psychiatrist who worked on the theory and application of computer science and Artificial Intelligence to psychology which led to the development of the computer program PARRY. The program implemented a crude model of the behavior of a person with paranoid schizophrenia based on concepts, conceptualizations, and beliefs. It was way more advanced than ELIZA and was described as ELIZA with an attitude. On a survey run by Colby among his psychiatric friends, only 48% could identify that PARRY was not a human. But even PARRY could not pass the Turing test. Later He began the project “Overcoming Depression” that lasted until he died in 2001.
RACTER – 1983
RACTER was a computer program written by William Chamberlain and Thomas Etter. They revealed the existence of it in a book called The Policeman’s Beard Is Half Constructed, which was described as being composed entirely by the program. RACTER was never publicly released.
JABBERWACKY – 1988
After the acceptance of text-based chatbots, JABBERWACKY chatbot was a voice-based program that; learned from sound and other sensory inputs. It was designed and developed by Roller Carpenter in 1988. He developed intending to simulate natural human conversation in an interesting, humorous manner. Carpenter believed that it can be incorporated into objects around the home such as robots or talking pets (an AI-based pet for the human companion), intending both to be useful and entertaining, keeping people’s company. And well Jabberwacky also could not pass the Turing Test.
DR. SBAITSO -1992
Creative Labs developed Dr. Sbaitso as an AI speech Synthesis program for MS-DOS in 1992. It was one of the earliest efforts of AI integration in a voice-operated chatbot. They distributed on various sound cards. The program conversed with users as if it were a real psychologist. Dr. Sbaitso’s responses were mostly like “WHY DO YOU FEEL THAT WAY?” rather than any complicated interaction. If confronted with a phrase it could not understand, it would reply something around the line “THAT’S NOT MY PROBLEM”. Dr. Sbasito could repeat the text out loud after the command “SAY”. And any swearing or abusive behavior from the user would make the program break down in a “Parity Error”. Dr. Sbasitso could not pass the Turing test.
Artificial Linguistic Internet Computer Entity (ALICE) – 1995
A.L.I.C.E(Artificial Linguistic Internet Computer Entity) or Alicebot is a universal language processing chatbot that uses heuristic pattern matching to carry conversations, pioneered by Richard Wallace in 1995.
The program simulates chat experience with a real person on the internet The program works on an XML schema known as artificial intelligence markup language (AIML), which helps specify conversation rules. The program got edited again in java in 1998 and drafted AIML specification in 2001. After that, other developers drafted the open-source code of Alice in different programming languages and deployed them in different dialects.
SMARTER CHILD – 2001
Active Buddy, inc developed SmarterChild – a true precursor of Siri. SmarterChild was widely distributed over SMS and instant messaging networks. SmarterChild attracted over 30 million users “Buddies” on AIM(AOL), MSN, and Yahoo Messenger. It allowed a fun messaging interface with quick access to data.
IBM’S WATSON – 2006
In 2006, IBM introduced IBM Watson named after IBM’s founder and first CEO Thomas J. Watson. They designed the program to answer questions on the show Jeopardy. Watson competed in 2011 with the champions Brad Rutter and Ken Jennings won with a cash price of $1million.
In 2013, the program shifted its application to utilization management decisions in the lung cancer treatment at Memorial Sloan Kettering Cancer Center, New York City.
SIRI – 2010
Apple introduced Siri for IOS users in 2010. It is a smart personal assistant that leverages natural language UI for easy engagements. Siri paved the way for all Conversational AI solutions.
Siri uses voice queries and a natural-language user interface to answer queries, give recommendations, and perform actions by delegating requests to a set of Internet services.
The software adapts to users’ language usages, searches, and preferences, with continuing use. It continues to learn with the help of machine learning and artificial intelligence.
GOOGLE NOW -2012
Google inc. launched Google now in 2012 to compete with Apple’s Siri. Like Siri, it can answer questions, performs actions through requests made to a set of web services, and makes recommendations. It’s built for smartphones and has been upgraded to accommodate several features.
Google Now is one of the more aggressive growth methods of Google’s search software. The idea behind Google now is simple, to give news and recommendations before you ask and serve it in an easy to read format.
Alexa, the virtual assistant by Amazon inhabits Amazon Echo devices can perform the same voice commands as Siri through the internet. It can also control several smart devices using herself as a home automation system.
CORTANA – 2014
Cortona is Microsoft’s Chatbot deployed on Windows phone and Windows 10 PCs. It fetches data from the internet through Bing and provides web-based services. It follows text and voice commands. For someone to get started, they must type a question in the search box, or select the microphone and talk to Cortana.
Facebook Messenger Chatbots – 2016
Facebook introduced Messenger Chatbots in 2016. Facebook is a highly popular social media application, with more than 300M million users as of now. It was a smart move by Facebook to commercialize the widely popular social media application for businesses to engage with their customers through automated conversations. In these troubling times, an adaptation of Facebook Messenger Chatbots has been seen by the state governments of India.
WhatsApp Chatbots -2018
Finally, the current trend – WhatsApp Chatbots. In 2018, WhatsApp officially opened its API for its trusted partners like Yellow Messenger to develop Chatbots on its platform. With WhatsApp Business, brands attained the power to connect with their customers on their preferred messaging channel. You can read more about it here.
Chatbots are at the peak or just post-peak on the “Hype Cycle for Artificial Intelligence, 2019. According to Gartner’s 2019 CIO Agenda, 31% of enterprise CIOs have already deployed conversational platforms.
This represents a 48% year-over-year growth in interest and points to conversational platforms taking center stage in enterprises’ adoption of AI. With the adaptation of Chatbots and virtual assistants are on all platforms such as Slack, WeChat, LinkedIn, WhatsApp, etc; we can expect a growth in the market of this space from USD 2.6 billion in 2019 to USD 9.4 billion by 2024.
Industrial Applications of Chatbot
Chatbots are leveraged across industries for a variety of use cases:
- Customer service: Virtual Assistants designed primarily to take over inquiries coming into the customer service desk, reducing the need for human agents and decreasing TAT.
- IT service:— To automate parts of the IT service desk to more quickly and effectively solve routine IT problems and/or reduce the need for IT support staff.
- HR: To automate routine questions and queries coming in HR, for example, vacation time, payslips, entitlements, hour tracking, overtime pay, and rules/regulations in the workplace.
- Sales support: To support salespeople in their work by giving them support in the sales cycle for example lead capturing, nurturing, and providing the data as required.
- Commerce: To offer sales support to customers at the point of sale or up-sale in relevant situations.
- Marketing: For advertising campaign or to support marketing efforts or automation marketing
- Enterprise software frontends: Conversational AI designed as conversational interfaces, making an alternative UI for enterprise software.
- Advisory services: To advise by collecting relevant information through conversation.
Yellow Messenger’s Chatbot Platform
Meet the world’s first cognitive engagement cloud or in simple words Yellow Messenger’s Chatbot Platform for complete engagement automation. Yellow Messenger’s Chatbots are secure, intuitive, and always learning. The Flow Builder feature of the platform supports developers to build bots with more engaging conversations and simplifying the bot building process.
Yellow Messenger’s Cognitive Engagement Cloud is supported by:
- Powerful NLP Engine: The Natural Language Processing (NLP) component handles most of the Artificial Intelligence related tasks across the Yellow Messenger Platform. When the bot is active all messages that the user types, goes through the NLP pipeline. Read more about our NLP engine
- Conversational Insights: Our Sentiment analysis, memory, and context engine helps users with an engaging conversation flow and helps business by providing the user sales funnel data.
- Intelligent Orchestration: Yellow Messenger’s Bot Orchestrator can interface with multiple bots internally and co-reference information to provide the most appropriate responses to the user.
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