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These Platforms Will Make Your Bots Language-intelligent
Natural language is a fundamental element of bot technologies. As a result, there has been a direct correlation between the evolution of bot platforms and natural language processing platforms. While the evolution of bot technologies has mostly been driven by messaging platform vendors such as Facebook or WeChat, the main advancements in natural language processing technologies seem to be coming from cloud platform and service providers like Google or IBM. Consequently, most bot developers spend time integrating their front-end bot applications with natural language processing services provided by a different platform.
From a conceptual standpoint, there are two main natural language programming techniques that have become popular with bot technologies: Natural language processing (NLP) and natural language understanding (NLU). Here's a look at their basic features:
The combination of NLP and NLU technologies is becoming increasingly relevant in different software areas today, including bot technologies. While there are many vendors and platforms focused on NLP/NLU technologies, the following technologies are becoming extremely popular within the bot developer community.
Understanding NLP/NLUThe popularity of emerging technologies like bots and artificial intelligence has led to the terms "natural language processing" and "natural language understanding" being used loosely and out of context. Broadly construed, NLP/NLU technologies should include the following elements:
Natural language generation
Natural language generation (NLG) plays an important role in enabling bot technologies to generate meaningful conversations between users and systems. Conceptually, NLG systems are responsible for understanding and maintaining the context of a conversation and then producing language-rich responses as if they were generated by a human. In order to produce language-intelligent responses, NLG techniques leverage elements that simulate human behavior such as beliefs, desires, commitment, intentions, etc.
Some NLP/NLU technologies that will make your bots language-intelligentIn recent years, we have seen impressive progress in NLP/NLU technologies, particularly accelerated by the rise in popularity of technologies like bots, the internet of things (IoT) and artificial intelligence. As a result, several platforms have emerged providing sophisticated NLP/NLU capabilities. Some of the most popular NLP/NLU platforms in the market include these:
Watson Conversation Service
The Watson Developer Cloud provides several services focused on language processing. IBM's Watson Conversation Service (WCS) is specially focused on automating interactions between systems and end users. Utilizing WCS, users can define NLP aspects such as intents and entities, and simulate entire conversations. WCS is typically used in conjunction with other Watson NLP services such as AlchemyLanguage or Natural Language Classifier.
Microsoft's Language Understanding Intelligence Service
Microsoft's Language Understanding Intelligence Service (LUIS) is a component of the Microsoft Cognitive Services (MCS) focused on creating and processing natural language models. LUIS provides a sophisticated toolkit that allows developers to train the platform in new conversation models. LUIS can also be used in conjunction with other text processing APIs in MCS such as linguistic analysis and text analytics. The platform provides a deep integration with the Microsoft Bot Framework and can be used by other bot platforms.
Google Natural Language API
Google Natural Language (NL) API is a recent addition to Google Cloud focused on NLP and NLU capabilities. The NL API enables capabilities such as intent-entity detection, sentiment analysis, content classification and relationship graphs. The NL API also includes sophisticated tooling for training and authoring new NL models. The Google NL platform is actively used by several high-profile services, such as Google Assistant.
Wit.Ai
Wit.Ai is the platform behind the NLP/NLU capabilities of the Facebook Messenger platform. Facebook acquired Wit.Ai in January 2015 and, since then, has rolled out major updates to the platform. One of the best capabilities of Wit.Ai is the sophisticated toolkit that can be used to train the platform in new conversation models as well as monitor the interactions between users and the platform.
Api.Ai
Api.Ai provides a platform that allows developers to design and implement conversational interfaces that can be integrated into external applications like bots. Functionally, Api.Ai includes capabilities such as speech recognition, fulfillment and NLU, as well as a robust management toolkit. Api.Ai provides integration with several bot platforms and is particularly popular within the Slack community.
Alexa Skills Kit
Amazon Alexa can be considered one of the simplest language processing technologies when compared with the other platforms listed in this article. However, the volume of users leveraging Alexa Services on a daily basis also makes it one of the most popular NLP engines in the market. Functionally, the Alexa Skills Kit enables the definition of intents and entities relevant in conversational interactions. One of the greatest advantages of Alexa is its integration with other Amazon Web Services offerings like AWS Lambda.
Recast.AI
Recast.AI is a platform for implementing bot solutions with sophisticated NLP/NLU capabilities. The platform provides developer-friendly interfaces to determine intent and entities in natural language sentences. Additionally, Recast.AI includes a robust toolkit for training and improving NLP models based on user interactions.
Pat
Pat is a newcomer to the NLP/NLU platform market focused on humanizing human-machine interactions. Functionally, Pat deviates from traditional statistical NLP models and focuses on leveraging neural network algorithms to correctly assign meaning to words in a sentence. As a result, the Pat platform is able to correctly analyze extremely complex natural language interactions.
It's just getting startedRegardless of recent developments in NLP/NLU technologies, we are still in the very early stages of the market. In the next few years, we can expect to see new language intelligence techniques that will streamline the conversational models between humans and systems. Bot technologies have been the immediate benefactors of the advancements in NLP/NLU platforms. As NLP/NLU platforms become smarter and more robust, bots will be able to leverage conversations as a new form of user interface for modern technology solutions.
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Chatbots have taken a quantum leap forward in user support, contributing substantially to the emergence of the modern service desk. Even in their earliest form, they heralded the promise of versatile new advances to come, such as sentiment tracking, NLP and machine learning.
As chatbots evolve, we are seeing a continuum of progress that will soon make it nearly impossible to tell the difference between human and artificial intelligence in service desk and customer service functions. I believe it's enlightening to understand the chatbot journey, as it has evolved from the first generation to next-gen conversational AI that is unsupervised and context-aware.
First-Generation Chatbots Leave Room For Improvement
As chatbots began to evolve, their popularity and ubiquity revealed some deficits. For one, lacking true AI capabilities at this stage, they offered scripted and robotic user experiences. These rule-based chatbots worked acceptably for simple FAQ content, but even at this stage, a new horizon of functionality was opening up: Chatbots could potentially do a lot more. Early versions were also burdened with a long time to value — at least nine to 12 months to build and deploy.
As an emerging technology, chatbots initially called for a specialized skill set requiring data science and engineering expertise. The cost of a dozen or more experts and chatbot-dedicated software engineers, as well as the time required, made first-generation chatbots less cost-effective than they could be.
Traditional chatbots also required manual training, which could take six to nine months and again require engineers and experts. Because they could not learn autonomously, chatbot training was not a one-time event but rather an ongoing, continuous process.
The Demand For Personalization
Thanks to the digital revolution — and to Apple, Google and Amazon driving expectations — today's users expect no less than a consumerized, personalized experience, with services available at the push of a button on any device. This includes contextual understanding at all times. It quickly became obvious that only sophisticated AI could provide that quality of user experience. Organizations working to apply AI to their customer support and service desk risked falling short on key user expectations.
Covid Presents A Demanding New Landscape
Covid-19 has altered the business landscape, perhaps permanently, affecting countless aspects of the work experience itself, including the role of chatbots. Remote work was once reserved for family exigencies, new construction, weather emergencies and so forth. But now, most organizations have had to adopt a remote workforce at blazing speed to survive, let alone thrive and grow.
As a result, the remote office has now emerged as "the new normal." Artificial intelligence, with its capacity to scale support for remote work has swiftly moved to the forefront as an in-demand technology, spurring chatbot evolution toward third-generation capabilities. AI can address the need of remote workers for self-service and enable them to autonomously resolve requests and sustain employee productivity in the pandemic.
In this relentless environment, and to meet rising user expectations, organizations are now leveraging AI and machine learning (ML) into a revolutionary new paradigm of semantic understanding that seamlessly integrates with ticketing, knowledge, and IAM systems.
When a highly scripted robotic chatbot can't predict user intent or engage in meaningful, dynamic dialogue, user interaction suffers. That's why the momentum of evolution is toward a new golden age of voice driven by natural language processing (NLP) to create an intelligent user engagement hub. AI-infused virtual assistants can actually respond to human interaction by predicting and accurately identifying what users want and then formulate personalized, specific responses. They learn from each interaction and preserve information for the human service desk agent.
Satisfying Customers And Users During And After the Pandemic
In the beginning, remote work put heavy pressures on organizations: Wait times expanded from a few hours to days and weeks, call center costs soared and social distancing and changing expectations added their own challenges. Buying more service desk and customer support licenses was not the answer to these problems.
Creating a more agile approach called for out-of-the-box, instantly usable AI. That's why there are now virtual agents and virtual assistants that enable enriched user engagement; concierge solutions and new platforms can understand and do the job autonomously.
Under the pressure of Covid-19, technology has evolved rapidly into conversational AI that not only learns continuously but relies on its own taxonomy and cognitive AI search to provide users with self-service resolutions. This latest generation of AI-driven chatbots uses unsupervised NLP, NLU and NLG to respond to a vast array of user requests couched in complex vocabulary.
The Wave Of The Future: Zero-Day AI On The Fly
As business emerges from the pandemic, expect organizations to continue investing in conversational AI. Most organizations will look to AI to open up new avenues to revenue, cost savings and business growth, as well as nurture innovation and ease the adoption of new business models. Conversational AI allows organizations to cost-effectively retain and expand their user and customer base, engage people in a new business model and compete aggressively.
The outcome of the chatbot evolution is to dramatically diminish or even eliminate the need for historical data, experts and data scientists. The new technology requires no AI training, no complex manuals or professional services and no prep work such as data cleansing. Deploying AI chatbots need not take weeks and months; the solution can actually be found online within hours and immediately start to deliver automated, continuous value.
Chatbots have now arrived in the new AI era. As it comes of age, next-generation AI has evolved to be not a black box but a convenient, transparent, turnkey solution.
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What Is A Chatbot? Everything You Need To Know
Chatbots are most commonly used on business websites. When you have spent a couple of minutes on a website, you can see a chat or voice messaging prompt pop up on the screen. Those are chatbots.
Chatbots had a humble start as computer programs that used keywords and pattern matching to respond to users' questions based on a pre-written script. Modern chatbots use the latest technologies including artificial intelligence (AI), machine learning (ML), natural language understanding (NLU), natural language processing (NLP), etc. To provide human-like responses to queries.
How chatbots work depends upon how they have been programmed or developed. Based on their conversation style, there are primarily two types of chatbots:
Artificial intelligence algorithms are used to build conversational chatbots that use text- and voice-based communication to interact with users. The chatbots, once developed, are trained using data to handle queries from the users.
Your chatbot will be as good as the AI and data that it uses. You must take care that the AI that you use is ethical and unbiased. Also, the training data must be of high quality so that the ML model trains the chatbot properly. Otherwise, the chatbot may perform poorly or unexpectedly.
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