What is Natural Language Understanding NLU?

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers. It’s time to take a leap and integrate the technology into an organization’s digital security toolbox. This speed enables quicker decision-making and faster deployment of countermeasures. Simply put, NLP cuts down the time between threat detection and response, giving organizations a distinct advantage in a field where every second counts. By analyzing logs, messages and alerts, NLP can identify valuable information and compile it into a coherent incident report. It captures essential details like the nature of the threat, affected systems and recommended actions, saving valuable time for cybersecurity teams.

Automating tasks like incident reporting or customer service inquiries removes friction and makes processes smoother for everyone involved. In a field where time is of the essence, automating this process can be a lifesaver. NLP can auto-generate summaries of security incidents based on collected data, streamlining the entire reporting process.

Keras example for Sentiment Analysis

Natural language processing is the field of study wherein computers can communicate in natural human language. This sentence has mixed sentiments that highlight the different aspects of the cafe service. Without the proper context, some language models may struggle to correctly determine sentiment. AI encompasses the development of machines or computer systems that can perform tasks that typically require human intelligence. On the other hand, NLP deals specifically with understanding, interpreting, and generating human language.

Accordingly, the future of Transformers looks bright, with ongoing research aimed at enhancing their efficiency and scalability, paving the way for more versatile and accessible applications. In the pursuit of RNN vs. Transformer, the latter has truly won the trust of technologists,  continuously pushing the boundaries of what is possible and revolutionizing the AI era. You can foun additiona information about ai customer service and artificial intelligence and NLP. While currently used for regular NLP tasks (mentioned above), researchers are discovering new applications ChatGPT every day. Deployed in Google Translate and other applications, T5 is most prominently used in the retail and eCommerce industry to generate high-quality translations, concise summaries, reviews, and product descriptions. In the phrase ‘She has a keen interest in astronomy,‘ the term ‘keen’ carries subtle connotations. A standard language model might mistranslate ‘keen’ as ‘intense’ (intenso) or ‘strong’ (fuerte) in Spanish, altering the intended meaning significantly.

Future Improvements

Improvements in NLP components can lower the cost that teams need to invest in training and customizing chatbots. For example, some of these models, such as VaderSentiment can detect the sentiment in multiple languages and emojis, Vagias said. This reduces the need for complex training pipelines upfront as you develop your baseline for bot interaction. More sophisticated NLP can allow chatbots to use intent and sentiment analysis to both infer and gather the appropriate data responses to deliver higher rates of accuracy in the responses they provide. This can translate into higher levels of customer satisfaction and reduced cost. Basically, they allow developers and businesses to create a software that understands human language.

Called DeepHealthMiner, the tool analyzed millions of posts from the Inspire health forum and yielded promising results. Similar to machine learning, natural language processing has numerous current applications, but in the future, that will expand massively. Although natural language processing (NLP) has specific applications, modern real-life use cases revolve around machine learning. NLP helps uncover critical insights from social conversations brands have with customers, as well as chatter around their brand, through conversational AI techniques and sentiment analysis. Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs. NLP powers social listening by enabling machine learning algorithms to track and identify key topics defined by marketers based on their goals.

In 2020, OpenAI released the third iteration of its GPT language model, but the technology did not reach widespread awareness until 2022. That year, the generative AI wave began with the launch of image generators Dall-E 2 and Midjourney in April and July, respectively. The excitement and hype reached full force with the general release of ChatGPT that November. In the 1970s, achieving AGI proved elusive, not imminent, due to limitations in computer processing and memory as well as the complexity of the problem. As a result, government and corporate support for AI research waned, leading to a fallow period lasting from 1974 to 1980 known as the first AI winter.

• Large data requirements have traditionally been a problem for developing chatbots, according to IBM’s Potdar.
• Language modeling, or LM, is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence.
• Any bias inherent in the training data fed to Gemini could lead to wariness among users.
• Today, prominent natural language models are available under licensing models.
• We leverage the numpy_input_fn() which helps in feeding a dict of numpy arrays into the model.

T5 (Text-To-Text Transfer Transformer) is another versatile model designed by Google AI in 2019. It is known for framing all NLP tasks as text-to-text problems, which means that both the inputs and outputs are text-based. This approach allows T5 to handle diverse functions like translation, summarization, and classification seamlessly. Transformers like T5 and BART can convert one form of text into another, such as paraphrasing, ChatGPT App text rewriting, and data-to-text generation. This is useful for tasks like creating different versions of a text, generating summaries, and producing human-readable text from structured data. To understand the advancements that Transformer brings to the field of NLP and how it outperforms RNN with its innovative advancements, it is imperative to compare this advanced NLP model with the previously dominant RNN model.

Hardware is equally important to algorithmic architecture in developing effective, efficient and scalable AI. GPUs, originally designed for graphics rendering, have become essential for processing massive data sets. Tensor processing units and neural processing units, designed specifically for deep learning, have sped up the training of complex AI models. Vendors like Nvidia have optimized the microcode for running across multiple GPU cores in parallel for the most popular algorithms.

How to apply natural language processing to cybersecurity

From customer relationship management to product recommendations and routing support tickets, the benefits have been vast. In January 2023, Microsoft signed a deal reportedly worth $10 billion with OpenAI to license and incorporate ChatGPT into its Bing search engine to provide more conversational search results, similar to Google Bard at the time. That opened the door for other search engines to license ChatGPT, whereas Gemini supports only Google. At launch on Dec. 6, 2023, Gemini was announced to be made up of a series of different model sizes, each designed for a specific set of use cases and deployment environments. As of Dec. 13, 2023, Google enabled access to Gemini Pro in Google Cloud Vertex AI and Google AI Studio. For code, a version of Gemini Pro is being used to power the Google AlphaCode 2 generative AI coding technology.

Developments in natural language processing are improving chatbot capabilities across the enterprise. This can translate into increased language capabilities, improved accuracy, support for multiple languages and the ability to understand customer intent and sentiment. New data science techniques, such as fine-tuning and transfer learning, have become essential in language modeling. Rather than training a model from scratch, fine-tuning lets developers take a pre-trained language model and adapt it to a task or domain. This approach has reduced the amount of labeled data required for training and improved overall model performance.

Natural language understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input in the form of sentences using text or speech. NLU enables human-computer interaction by analyzing language versus just words. Natural Language Processing (NLP) is a subfield of machine learning that makes it possible for computers to understand, analyze, manipulate and generate human language. You encounter NLP machine learning in your everyday life — from spam detection, to autocorrect, to your digital assistant (“Hey, Siri?”). In this article, I’ll show you how to develop your own NLP projects with Natural Language Toolkit (NLTK) but before we dive into the tutorial, let’s look at some every day examples of nlp. Hugging Face aims to promote NLP research and democratize access to cutting-edge AI technologies and trends.

Deep Learning (Neural Networks)

Companies can then apply this technology to Skype, Cortana and other Microsoft applications. Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services. Practical examples of NLP applications closest to everyone are Alexa, Siri, and Google Assistant.

There are various reasons for this, but one key ingredient is the lack of data skills across the business. We often see an enterprise deploy analytics to different parts of the organization, without coupling that with skills training. The result is that despite the investment, staff are making decisions without key data, which leads to decisions that aren’t as strategic or impactful. Many important NLP applications are beyond the capability of classical computers.

This type of RNN is used in deep learning where a system needs to learn from experience. LSTM networks are commonly used in NLP tasks because they can learn the context required for processing sequences of data. To learn long-term dependencies, LSTM networks use a gating mechanism to limit the number of previous steps that can affect the current step. RNNs can be used to transfer information from one system to another, such as translating sentences written in one language to another. RNNs are also used to identify patterns in data which can help in identifying images.

Voice systems allow customers to verbally say what they need rather than push buttons on the phone. In addition to the interpretation of search queries and content, MUM and BERT opened the door to allow a knowledge database such as the Knowledge Graph to grow at scale, thus advancing semantic search at Google. Understanding search queries and content via entities marks the shift from “strings” to “things.” Google’s aim is to develop a semantic understanding of search queries and content. As used for BERT and MUM, NLP is an essential step to a better semantic understanding and a more user-centric search engine.

Despite language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master. The field of study that focuses on the interactions between human language and computers is called natural language processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia).

The researchers contend that the results they obtained could be exceeded if a larger number of iterations were allowed. In this hypothetical example from the paper, a homoglyph attack changes the meaning of a translation by substituting visually indistinguishable homoglyphs (outlined in red) for common Latin characters. In these examples, the algorithm is essentially expressing stereotypes, which differs from an example such as “man is to woman as king is to queen” because king and queen have a literal gender definition. Computer programmers are not defined to be male and homemakers are not defined to be female, so “Man is to woman as computer programmer is to homemaker” is biased. The complete model has about 31M trainable parameters for a total size of about 120MiB. In this example, we pick 6600 tokens and train our tokenizer with a vocabulary size of 6600.

Nvidia has pursued a more cloud-agnostic approach by selling AI infrastructure and foundational models optimized for text, images and medical data across all cloud providers. Many smaller players also offer models customized for various industries and use cases. Similarly, the major cloud providers and other vendors offer automated machine learning (AutoML) platforms to automate many steps of ML and AI development. AutoML tools democratize AI capabilities and improve efficiency in AI deployments.

Quantinuum is an integrated software-hardware quantum computing company that uses trapped-ion for its compute technology. It recently released a significant update to its Lambeq open-source Python library and toolkit, named after mathematician Joachim Lambek. Lambeq (spelled with a Q for quantum) is the first and only toolkit that converts sentences into quantum circuits using sentence meaning and structure to determine quantum entanglement. Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community.

Chatbot vs Conversational AI: Differences Explained

Traditional chatbots operate within a set of predetermined rules, delivering answers based on predefined keywords. They have limited capabilities and won’t be able to respond to questions outside their programmed parameters. If traditional chatbots are basic and rule-specific, why would you want to use it instead of AI chatbots? Conversational AI chatbots are very powerful and can useful; however, they can require significant resources to develop. In addition, they may require time and effort to configure, supervise the learning, as well as seed data for it to learn how to respond to questions.

Based on Grand View Research, the global market size for chatbots in 2022 was estimated to be over $5 billion. Further, it’s projected to experience an annual growth rate (CAGR) of 23.3% from 2023 to 2030. This tool is a part of intelligent chatbots that goes through your knowledge base and FAQ pages. It gathers the question-answer pairs from your site and then creates chatbots from them automatically. This solves the worry that bots cannot yet adequately understand human input which about 47% of business executives are concerned about when implementing bots.

For example, conversational AI technology understands whether it’s dealing with customers who are excited about a product or angry customers who expect an apology. For those interested in seeing the transformative potential of conversational AI in action, we invite you to visit our demo page. There, you’ll find a Chat PG comprehensive video demonstration that showcases the capabilities, functionalities, and real-world applications of conversational AI technology. While chatbots continue to play a vital role in digital strategies, the landscape is shifting towards the integration of more sophisticated conversational AI chatbots.

Natural language understanding

In other words, conversational AI enables the chatbot to talk back to you naturally. Users can speak requests and questions freely using natural language, without having to type or select from options. At the same time that chatbots are growing at such impressive rates, conversational AI is continuing to expand the potential for these applications. The AI impact on the chatbot landscape is fostering a new era of intelligent, efficient, and personalized interactions between users and machines.

For this reason, many companies are moving towards a conversational AI approach as it offers the benefit of creating an interactive, human-like customer experience. A recent PwC study found that due to COVID-19, 52% of companies increased their adoption of automation and conversational interfaces—indicating that the demand for such technologies is rising. A chatbot is a computer program that emulates human conversations with users through artificial intelligence (AI). A rule-based chatbot can, for example, collect basic customer information such as name, email, or phone number. Later on, the AI bot uses this information to deliver personalized, context-sensitive experiences.

Chatbot vs. conversational AI: Examples in customer service

However, the truth is, traditional bots work on outdated technology and have many limitations. Even for something as seemingly simple as an FAQ bot, can often be a daunting and time-consuming task. On the contrary, conversational AI platforms can answer requests containing numerous questions and switch from topic to topic in between the dialogue. Because the user does not have to repeat their question or query, they are bound to be more satisfied. In fact, advanced conversational AI can deduce multiple intents from a single sentence and response addresses each of those points. There is only so much information a rule-based bot can provide to the customer.

The critical difference between chatbots and conversational AI is that the former is a computer program, whereas the latter is a type of technology. A few examples of conversational AI chatbots include Siri, Cortana, Alexa, etc. Depending on the sophistication level, a chatbot can leverage or not leverage conversational AI technology. Conversational AI allows your chatbot to understand human language and respond accordingly.

As we’ve seen, the technology that powers rule-based chatbots and AI chatbots is very different but they still share much in common. Using your CRM, product catalogs and product descriptions to train your AI chatbot is one part of a much broader trend on how big data is changing business. Previously only available to enterprise companies, this technology is now available to small and medium-sized businesses (SMBs). When a visitor asks something more complex for which a rule hasn’t yet been written, a rule-based chatbot might ask for the visitor’s contact details for follow-up. Sometimes, they might pass them through to a live agent to continue the conversation.

Chatbots and conversational AI are two very similar concepts, but they aren’t the same and aren’t interchangeable. Chatbots are tools for automated, text-based communication and customer service; conversational AI is technology that creates a genuine human-like customer interaction. You can map out every possible conversational path and input acceptable responses to narrow down the customer’s intention. Diverging from the straightforward, rule-based framework of traditional chatbots, conversational AI chatbots represent a significant leap forward in digital communication technologies. On the other hand, because traditional, rule-based bots lack contextual sophistication, they deflect most conversations to a human agent. This will not only increase the burden of unresolved queries on your human agents but also nullify the primary objective of deploying a bot.

Natural Language Understanding (NLU)

Chatbots are designed for text-based conversations, allowing users to communicate with them through messaging platforms. The user composes a message, which is sent to the chatbot, and the platform responds with a text. Both chatbots and conversational AI are on the rise in today’s business ecosystem as a way to deliver a prime service for clients and customers. However, both chatbots and conversational AI can use NLP and find their application in customer support, lead generation, ecommerce, and many other fields.

When integrated into a customer relationship management (CRM), such chatbots can do even more. Once a customer has logged in, chatbots can be trained to fetch basic information, like whether payment on an order has been taken and when it was dispatched. After the page has loaded, a pop-up appears with space for the visitor to ask a question. Essentially, conversational AI strives to make interactions with machines more natural, intuitive, and human-like through the power of modern artificial intelligence.

They answer visitors’ questions, capture contact details for email newsletters and schedule callbacks for sales and marketing teams to get in touch with clients and prospects. With the chatbot market expected to grow to up to $9.4 billion by 2024, it’s clear that businesses are investing heavily in this technology—and that won’t change in the near future. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. Depending on their functioning capabilities, chatbots are typically categorized as either AI-powered or rule-based.

According to a report by Accenture, as many as 77% of businesses believe after-sales and customer service are the most important areas that will be affected by artificial intelligence assistants. These new virtual agents make connecting with clients cheaper and less resource-intensive. As a result, these solutions are revolutionizing the way that companies interact with their customers. These rule-based chatbots were programmed with a set library of responses, making them reliable for handling straightforward tasks but limited in their ability to manage complex queries or understand nuanced user intent.

It can understand natural language, context, and intent, allowing for more dynamic and personalized responses. Conversational AI systems can also learn and improve over time, enabling them to handle a wider range of queries and provide more engaging and tailored interactions. The goal of chatbots and conversational AI is to enhance the customer service experience. Chatbots are like knowledgeable assistants who can handle specific tasks and provide predefined responses based on programmed rules.

The voice assistant responds verbally through synthesized speech, providing real-time and immersive conversational experience that feels similar to speaking with another person. It may be helpful to extract popular phrases from prior human-to-human interactions. If you don’t have any chat transcripts or data, you can use Tidio’s ready-made chatbot templates. For example, if someone writes “I’m looking for a new laptop,” they probably have the intent of buying a laptop. But if someone writes “I just bought a new laptop, and it doesn’t work” they probably have the user intent of seeking customer support.

Both technologies have unique features and capabilities that contribute to their respective domains and play crucial roles in advancing AI applications. With this basic understanding of what a chatbot is, we can start to differentiate between traditional chatbots and more intelligent conversational AI chatbots. Chatbots are not just online — they can support both vocal and text inputs, too. You can add an AI chatbot to your telephone system via its IVR function if your supplier supports it. Using voice recognition, it can listen to the customer and, through access to its training and CRM data, respond using voice replication technology.

You can spot this conversation AI technology on an ecommerce website providing assistance to visitors and upselling the company’s products. And if you have your own store, this software is easy to use and learns by itself, so you can implement it and get it to work for you in no time. In today’s digitally driven world, the intersection of technology and customer engagement has given rise to innovative solutions designed to enhance communication between businesses and their clients. We predict that 20 percent of customer service will be handled by conversational AI agents in 2022. And Juniper Research forecasts that approximately $12 billion in retail revenue will be driven by conversational AI in 2023. Siri, Google Assistant, and Alexa all are the finest examples of conversational AI technologies.

It harnesses techniques such as deep learning and neural networks to generate realistic and creative outputs. For a small enterprise loaded with repetitive queries, bots are very beneficial for filtering out leads and offering applicable records to the users. Conversational AI platforms feed off inputs and sources such as websites, databases, and APIs.

It uses speech recognition and machine learning to understand what people are saying, how they’re feeling, what the conversation’s context is and how they can respond appropriately. Also, it supports many communication channels (including voice, text, and video) and is context-aware—allowing it to understand complex requests involving multiple inputs/outputs. It is estimated that customer service teams handling 10,000 support requests every month can save more than 120 hours per month by using chatbots. Using that same math, teams with 50,000 support requests would save more than 1,000 hours, and support teams with 100,000 support requests would save more than 2,500 hours per month.

As businesses get more and more support requests, chatbots have and will become an even more invaluable tool for customer service. Make sure to distinguish chatbots and conversational AI; although they are regularly used interchangeably, there is a vast difference between them. Take time to recognize the distinctions before deciding which technology will be most beneficial for your customer service experience.

A Comparison: Conversational AI Chatbot ands Traditional Rule-Based Chatbots

Chatbots and conversational AI are often used synonymously—but they shouldn’t be. Understand the differences before determining which technology is best for your customer service experience. When compared to conversational AI, chatbots lack features like multilingual and voice help capabilities. The users on such platforms do not have the facility to deliver voice commands or ask a query in any language other than the one registered in the system.

In fact, by 2028, the global digital chatbot market is expected to reach over 100 billion U.S. dollars. Rule-based chatbots (otherwise known as text-based or basic chatbots) follow a set of rules in order to respond to a user’s input. Under the hood, a rule-based chatbot uses a simple decision tree to support customers. This means that specific user queries have fixed answers and the messages will often be looped. To say that chatbots and conversational AI are two different concepts would be wrong because they’re very interrelated and serve similar purposes.

In contrast, bots require continual effort and maintenance with text-only commands and inputs to remain up to date and effective. Conversational AI platforms benefit from the malleable nature of their design, carrying out fluid interactions with users. While most enterprises use the terms bots and conversational AI interchangeably, the two technologies have their key differences. In the last few years, bots have presented a new way for organizations to adopt NLP technologies to generate traffic and engagement. Understanding what is a bot and what is conversational AI can go a long way in picking the right solution for your business. And conversational AI chatbots won’t only make your customers happier, they will also boost your business.

In this example by Sprinklr, you can see the exact conversational flow of a rule-based chatbot. Each response has multiple options (positive and negative)—and clicking any of them, in turn, returns an automatic response. This is more intuitive as it can recognize serial numbers stored within their system—requiring it to be connected to their internal inventory system.

Differences between Conversational AI and Generative AI

We’ve all encountered routine tasks like password resets, balance inquiries, or updating personal information. Rather than going through lengthy phone calls or filling out forms, a chatbot is there to automate these mundane processes. It can swiftly guide us through the necessary steps, saving us time and frustration. A customer of yours has made an online purchase and is eagerly anticipating its arrival. Instead of repeatedly checking their email or manually tracking the package, a helpful chatbot comes to their aid.

Stemming from the word “robot”, a bot is basically non-human but can simulate certain human traits. Most people can visualize and understand what a chatbot is whereas conversational AI sounds more technical or complicated. ” The chatbot picks out the phrases “wireless headphones” and “in stock” and follows an instruction to provide a link to the appropriate page.

However, with the advent of cutting-edge conversational AI solutions like Yellow.ai, these hurdles are now a thing of the past. Gaining a clear understanding of these differences is essential in finding the optimal solution for your specific requirements. Conversation design, in turn, is employed to make the bot answer like a human, instead of using unnatural sounding phrases. From the Merriam-Webster Dictionary, a bot is  “a computer program or character (as in a game) designed to mimic the actions of a person”.

What sets DynamicNLPTM apart is its extensive pre-training on billions of conversations, equipping it with a vast knowledge base. This extensive training empowers it to understand nuances, context, and user preferences, providing personalized and contextually relevant responses. You can foun additiona information about ai customer service and artificial intelligence and NLP. Businesses worldwide are increasingly deploying chatbots to automate user https://chat.openai.com/ support across channels. However, a typical source of dissatisfaction for people who interact with bots is that they do not always understand the context of conversations. In fact, according to a report by Search Engine Journal, 43% of customers believe that chatbots need to improve their accuracy in understanding what users are asking or looking for.

The best part is that it uses the power of Generative AI to ensure that the conversations flow smoothly and are handled intelligently, all without the need for any training. Yellow.ai’s revolutionary zero-setup approach marks a significant leap forward in the field of conversational AI. With YellowG, deploying your FAQ bot is a breeze, and you can have it up and running within seconds. Also, with exceptional intent accuracy, surpassing industry standards effortlessly, DynamicNLPTM is adaptable across various industries, ensuring seamless integration regardless of your business domain. It has fluency in over 135+ languages, allowing you to engage with a diverse global audience effectively.

NeuroSoph is an end-to-end AI software and services company that has over 30 years of combined experience in the public sector. We are highly skilled and knowledgeable experts in AI, data science, strategy, and software. Using NeuroSoph’s proprietary, secure and cutting-edge Specto AI platform, we empower organizations with enterprise-level conversational AI chatbot solutions, enabling more efficient and meaningful engagements. According to Wikipedia, a chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Most chatbots on the internet operate through a chat or messaging interface through a website or inside of an application.

The most common type of chatbot is one that answers questions and performs simple tasks by understanding the conversation’s words, phrases, and context. These basic chatbots are often limited to specific tasks such as booking flights, ordering food, or shopping online. They’re popular due to their ability to provide 24×7 customer service and ensure that customers can access support whenever they need it.

They could also ask the bot technical questions on an information technology (IT) issue instead of having to wait for a reply from their IT team. Babylon Health’s symptom checker uses conversational AI to understand the user’s symptoms and offer related solutions. It can identify potential risk factors and correlates that information with medical issues commonly observed in primary care.

• Conversational AI chatbots are excellent at replicating human interactions, improving user experience, and increasing agent satisfaction.
• The difference between a chatbot and conversational AI is a bit like asking what is the difference between a pickup truck and automotive engineering.
• It effortlessly provides real-time updates on their order, including tracking information and estimated delivery times, keeping them informed every step of the way.
• Both chatbots and conversational AI are on the rise in today’s business ecosystem as a way to deliver a prime service for clients and customers.
• However, both chatbots and conversational AI can use NLP and find their application in customer support, lead generation, ecommerce, and many other fields.

Machines are not the answer to everything but AI’s ability to detect emotion in language also means you can program it to hand over a case to a human if a more personal approach is needed. Popular examples are virtual assistants like conversational ai vs chatbot Siri, Alexa, and Google Assistant. You can sign up with your email address, your Facebook, Wix, or Shopify profile. Follow the steps in the registration tour to set up your website chat widget or connect social media accounts.

AI chatbots don’t invalidate the features of a rule-based one, which can serve as the first line of interaction with quick resolutions for basic needs. Chatbots and voice assistants are both examples of conversational AI applications, but they differ in terms of user interface. With rule-based chatbots, there’s little flexibility or capacity to handle unexpected inputs. Nevertheless, they can still be useful for narrow purposes like handling basic questions. Chatbots are frequently used for a handful of different tasks in customer service, where they can efficiently handle inquiries, provide information, and even assist with problem-solving. The biggest of this system’s use cases is customer service and sales assistance.