A microservices-based strategy for intelligent personal assistants is only beginning to witness the birth of frameworks.
Cognitive Chatbots are becoming more prevalent. On them is created every cognitive app of the future generation for mobile, social, e-commerce, and almost every other solution domain. A cognitive chatbot using AI gives a superior conversational experience. It can interpret user content, purpose, and motives using AI and cognitive technologies and learn to communicate intelligently with people based on programmed systems.
However, they are not a recent phenomenon. The term is often used to describe intelligent software agents that may operate alone or in reaction to events and whose behavior can be dynamically modified by the stakeholders whose interests they represent. Web crawlers, a crucial component of all search engines, are an example of a typical Internet bot.
These technologies allow your applications to see, hear, understand, and interact in more human-like ways. Consequently, combining a chatbot with cognitive services and augmenting them with customized data analytics capabilities may provide your organization with profound insights and discoveries that can be leveraged to make swift choices.
A well-designed intelligent chatbot should be able to comprehend any unstructured data hanging about in your systems, including any raw data. It may aid in modifying client involvement, boosting decision-making, and implementing self-learning capabilities, which can be incredibly useful for corporate operations utilizing this data.
We are all familiar with the agony of attempting to converse with a rudimentary chatbot that can only grasp simple requests in formal English. And when it does not grasp, it responds with meaningless, irrelevant messages, wasting everyone's time before routing the caller to a human operator. These experiences may be distressing and deceiving and are sufficient to permanently drive individuals away. 73% of customers responded that they would not use one again if they had a negative interaction with a chatbot.
What is Cognitive Intelligence?
IBM claims that chatbots enhanced by cognitive computing systems "learn on a large scale, reason with intent, and interact with humans naturally." They use machine learning, natural language processing (NLP), neural networks, sentiment analysis, and pattern recognition technologies to imitate how the human brain operates.
These cognitive technologies may aid in decision-making and do tasks formerly performed by humans, such as planning, learning, and reasoning from insufficient data. Cognitive chatbots are perfectly suited for dealing with complex human requests expressed in conversational language because they can continuously learn from the data they receive and grasp it in the context in which it is supplied.
Some advanced cognitive chatbots go beyond basic text processing to simulate human interaction more accurately. Consequently, the chatbot may react in an even more relevant and smart manner. Voice recognition, sentiment identification, and emotion recognition are included.
Google autocomplete may guess a user's search word, but cognitive intelligence involves more than just making predictions. It includes creating technologies that can manage enormous volumes of data to solve complex problems. Algorithms are necessary for cognitive technologies to uncover hidden but significant relationships between disparate data points. Cognitive intelligence also includes an element of "learning by doing," in which the technology evaluates its successes and failures to improve over time.
Oh, and it must compellingly engage individuals.
That seems like a mix of big data, artificial intelligence (AI), and machine learning; you are accurate. Cognitive intelligence is when all (or some) of these technologies come together to solve problems logically and communicate solutions to others.
Using cognitive intelligence, the music streaming service Pandora may aid you in DJing your next 1980s dance party. Using what the company refers to as a "music genome," Pandora analyzes more than 450 sound characteristics of different songs to predict which music you would like. In other words, the computer utilizes the songs you instruct it to play to generate playlists that teach you a great deal about music and your listening preferences.
In addition, the complexity rises. InvoCom is a firm that provides artificial intelligence (AI) powered chatbot services for organizations that can manage huge volumes of traffic customers. The company's tools have, among other things, assisted healthcare organizations in managing millions of device specifications and small business owners in establishing 401(k) plans. Manually doing these tasks is time-consuming and limited by human capabilities.
In healthcare, cognitive intelligence helps accelerate the detection of rare diseases. The IBM Watson platform can sift through large amounts of health data to identify vital information that doctors and other human analysts often miss. The database comprises information from clinical trials, pharmacological and genetic investigations, and more than a decade's medical literature on 9,000 rare illnesses.
Cognitive Chatbots are meant to Supplement, not Replace
There is no reason for alarm if you fear that smart chatbots will replace human personnel. Employers should use cognitive technology to relieve employees of tasks that can now be automated so that they may devote their time to more engaging initiatives and offer them well-informed guidance to help them make the best decisions.
According to a recent Deloitte survey, incorporating cognitive technology into businesses promotes worker autonomy and creativity and "transforms how organizations get work done by breaking the trade-offs between speed, cost, and quality." It is projected that by the end of 2019, forty percent of large companies will use intelligent chatbots.
Even though the commercial usage of these technologies in chatbots is a recent phenomenon, it is gaining momentum. IBM was one of the first companies to offer Watson, an AI system that can answer inquiries, for sale. Watson was first designed to answer questions on the Jeopardy game show. Watson is now committed to assisting businesses by expediting research, boosting relationships, anticipating disruptions, detecting risks, and providing "certain recommendations." Amazon AWS, Microsoft Azure, and Google Cloud are among the major cloud providers that provide cognitive sciences and analytics as part of their services.
The Rise of Cognitive Chatbots
In addition, by being integrated into mobile devices, wearables, and internet of things endpoints, these intelligent personal bots are spreading throughout society and bringing more autonomous intelligence to the physical world. This type of intelligent personal agent is better described as a cognitive chatbot.
This new generation of intelligent personal assistants is paving the way for cloud-based cognitive computing systems such as IBM's Watson that consistently pass the Turing test. Cognitive chatbots can be distinguished from a long line of "chatterbots" by their capacity to employ machine learning to facilitate natural-language conversations. These agents are the foundation of cognitive IoT chatbots as natural language conversational skills of apps such as Siri and Amazon.com's Alexa grow in popularity among consumers.
To design these capabilities as reusable services for deployment in the cloud, mobile, IoT, and other environments, developers must adopt novel ways of thinking, methodologies, and frameworks.
Cognitive microservices must incorporate chatbots at a minimum. The new online service model will incorporate cognitive chatbots into its products to assist consumers. A recent blog post by Yegor Bugayenko stating that "a chatbot is superior to a microservice's user interface" convinced me of this. As I pondered his thesis, it occurred to me that the chatbot will be a kernel that powers the user interface for numerous microservices in the API economy.
The Use of Cognitive Chatbots
Now that we've examined the principles of cognitive intelligence, what about chatbots? How can chatbots "think" their way through complicated problems and arrive at a solution? How should they act? Let's examine three industries where chatbots cognitively assist users in reaching their objectives to better react to such questions.
E-Commerce and Retail
Consider the InvoCom chatbot, which uses a Color Match feature to aid customers in locating cosmetics. You photograph anything whose color you wish to match using Color Match. Following an image analysis, the bot proposes cosmetics, lipsticks, and eyeshadows of a similar hue. If you provide the bot with a photograph of a purple flower, it will show cosmetics with a similar shade of purple. The same applies to images of people from commercials or social media posts.
What lessons can we take from InvoCom? Like a retail salesperson, a chatbot may respond to customer inquiries and aid them in selecting the best product selections. The bot conveys its suggestions to users through diverse visual data. It operates as a product-savvy salesperson who resides in your phone.
Not only the retail industry uses chatbots to fix problems without human intervention. Malaysia Life, an insurance provider, offers a chatbot that uses predictive modeling to help consumers select the best life insurance plan. While communicating with the bot, users answer various health-related and activity-related inquiries (also known as single Chatbots). The single Chatbot then displays the best-suited life insurance product for their needs.
What the Malaysia Life bot does in the background is fascinating. The conversational algorithms of the bot do calculations utilizing an insurance database. It operates in an almost actuarial manner. Single Chatbot demonstrates that chatbots may serve as a front-end complement to the back-end combination of machine learning and AI.
The same is true in the healthcare industry. Consider Sensely, a chatbot that assists individuals in selecting the best medical treatment using Mayo Clinic health data. Using natural language processing, Sensely triages various symptoms (NLP). Patients are ultimately provided with options for self-care or linked with healthcare specialists within a certain insurance network.
In cognitive intelligence, chatbots have several applications, including customer service, product selection, financial security, and medical treatments. Unsurprisingly, many companies are beginning to see the advantages of "thinking" chatbots. A chatbot's ability to analyze data, evaluate repercussions, and offer advice is rapidly becoming the standard by which bots are evaluated.
Few modern organizations will be immune to the development of cognitive technology, but customer service is the industry most likely to be disrupted shortly by cognitive chatbots. People have been communicating with human customer service professionals over the phone for decades, resulting in a massive contact center staff and a lengthy resolution time for client inquiries. Because intelligent chatbots have quick access to a tremendous quantity of contextual data, they enable organizations to greatly accelerate the problem-solving process.
Cognitive chatbots offer significant disruption potential in the financial services sector. In addition to identifying fraud and hacking, the system may also deliver personalized advice based on a customer's financial profile. Cognitive chatbots may alert organizations when someone may be trying to impersonate an account user by identifying anomalies in textual and spoken exchanges (the latter of which uses voiceprint technology). Voiceprint technology may also detect speech trembling, which might indicate fraud.
Cognitive Chatbots Microservices
Other than those connected to the user interface, it may be difficult to find cognitive-chatbot development fundamentals that are sufficiently generalizable to serve a range of use cases. For various bot deployment tactics, they prioritize node-centered communication patterns. The patterns rely on how successfully each bot recognizes, retains, and utilizes information and state variables associated with certain users, channels, and conversations.
However, this design has several limitations if you're searching for a broadly applicable environment for developing cognitive-chatbot microservices.
- Monitoring, personalization, interactivity, encryption, and authentication are disregarded.
- It does not give solution-level patterns (such as those associated with chatbot delivery of "next best action" user guidance in recommendation-engine scenarios).
- It does not describe the chatbot's node-level service model (such as the sensors, solvers, and actuators in IoT endpoints).
- It must teach developers how to choose the optimal cognitive skills to apply in various chatbot solution settings (such as natural language processing, sentiment analysis, voice recognition, face recognition, gesture identification, and streaming analytics).
During my research, I uncovered a great functional taxonomy for chatbots by Tully Hansen on GitHub, which meets some of these requirements. Due to its concentration on Twitter bots, it only sometimes solves the need for a broad microservices and cognitive analytics platform.
Expecting a consistent development approach that can accommodate the extensive diversity of cognitive-chatbot deployment methods is, of course, a tall order. In this light, I have recently discovered this amusing and informative master list of themes for analytics-intensive Twitter chatbot applications. Although it provides no suggestions for a single development framework to encompass these and related endeavors, it demonstrates the tremendous innovation that has spread in the chatbot development community.
Directing cognitive chatbot developers to helpful advice and online resources may speed up the creation and deployment of these features. In addition, IBM provides a new experimental solution for creating Watson chatbots powered by AI that may serve as intelligent agents on social media networks.
Understanding of Future Cognitive Chatbot Development
Cognitive intelligence is evolving into a basic technological paradigm, and chatbots serve as its primary launching pad. Future chatbot production will mix machine learning, artificial intelligence, and big data analysis with conversational, engaging front-end chatbot experiences.
Remember having to decide what to type into the Google search bar?
Today, autocomplete technology assists us in picking the most effective search terms. Google performs everything for us, so we do not have to "think" our way through our searches. The search engine is cognitive as opposed to just reactive.
Autocomplete provides a little improvement to the search experience. Its utility, however, indicates a more significant trend: a shift away from technologies meant to react to human inputs and toward those that utilize cognitive intelligence, a paradigm in which technology finds its solutions to user problems.
Chatbots are a crucial testing ground for the development of cognitive intelligence, rapidly becoming the dominant paradigm in intelligence. As big data expands across industries, the most useful chatbots will be those capable of analyzing particular problems and extracting effective solutions.
Cognitive Intelligence for Future Chatbots
Forecasting the future is always risky. This is why we construct bots: to use previous data for future predictions fully. Obviously, to enhance it.
Examine what the leading players in the bot market are doing to better comprehend the future of bots.
Recall how IBM Watson aids in the detection of rare illnesses by medical practitioners. Watson seems to have delighted Apple. Apple was delighted to announce a strategic agreement with IBM to incorporate Watson into iOS business applications.
Even though it's too early to predict how Watson will connect with iOS, consider that Apple has increased the intelligence of Siri, its well-known talking chatbot. Apple invests in chatbots and is the public face of its cognitive intelligence initiatives.
Amazon's only objective is to create a society where chatbots are talkative, intelligent, and driven by sophisticated algorithms. Amazon Lex does this by "putting the power of Alexa within reach of all developers." Amazon Lex allows developers to construct bots that analyze data and give users virtually human-like experiences.
Dialogflow, Google's bot-building tool, allows developers to incorporate natural language processing into nicer chatbots. Participants at the company's 2018 user conference were told that Dialogflow would allow them to "build AI-powered virtual agents'' for contact centers. In other words, Google's cognitive chatbot platform prioritizes the development of bots that can comprehend data and engage in genuine interactions with people.
Microsoft invests extensively in chatbots that integrate AI with conversational abilities. Microsoft's CEO, Satya Nadella, asserts that bots offer a "conversational canvas" via which individuals may do a range of tasks:
Naturally, Microsoft's voice-activated chatbot is named Cortana. Although it is integrated into Windows 10, Android devices may connect.
People want conversational user interfaces and expect rapid technical answers to difficult problems. Cognitive intelligence will not only be an emerging computing paradigm in the immediate future. The computer paradigm is likely the culprit and it is projected that chatbots will dominate this industry.
The development of cognitive chatbots has the potential to severely disrupt several industries. This is due to the chatbot's ability to mimic human interaction and its ongoing use of contextual data, enabling it to provide more precise and relevant responses to the questions it receives. While reducing total operating expenses also improves analytic and predictive capabilities for businesses.
New cognitive chatbot technology, algorithms, and applications are hitting the market astoundingly. Integrated development frameworks will certainly emerge as the area matures and best practices consolidate over the next few years.