NLP vs NLU vs. NLG: What’s the Difference?
Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. To get started with NLU, beginners can follow steps such as understanding NLU concepts, familiarizing themselves with relevant tools and frameworks, experimenting with small projects, and continuously learning and refining their skills. NLU models are evaluated using metrics such as intent classification accuracy, precision, recall, and the F1 score. These metrics provide insights into the model’s accuracy, completeness, and overall performance. This streamlines the support process and improves the overall customer experience. Ambiguity arises when a single sentence can have multiple interpretations, leading to potential misunderstandings for NLU models.
- The idea is to break down the natural language text into smaller and more manageable chunks.
- It is best to compare the performances of different solutions by using objective metrics.
- However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer.
- Human interaction allows for errors in the produced text and speech compensating them by excellent pattern recognition and drawing additional information from the context.
We’ve seen that NLP primarily deals with analyzing the language’s structure and form, focusing on aspects like grammar, word formation, and punctuation. On the other hand, NLU is concerned with comprehending the deeper meaning and intention behind the language. To have a clear understanding of these crucial language processing concepts, let’s explore the differences between NLU and NLP by examining their scope, purpose, applicability, and more.
When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills. For instance, the address of the home a customer wants to cover has an impact on the underwriting process since it has a relationship with burglary risk. NLP-driven machines can automatically extract data from questionnaire forms, and risk can be calculated seamlessly. However, NLU lets computers understand “emotions” and “real meanings” of the sentences.
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A subfield of artificial intelligence and linguistics, NLP provides the advanced language analysis and processing that allows computers to make this unstructured human language data readable by machines. It can use many different methods to accomplish this, from tokenization, lemmatization, machine translation and natural language understanding. Ultimately, we can say that natural language understanding works by employing algorithms and machine learning models to analyze, interpret, and understand human language through entity and intent recognition. This technology brings us closer to a future where machines can truly understand and interact with us on a deeper level. NLG is another subcategory of NLP that constructs sentences based on a given semantic. After NLU converts data into a structured set, natural language generation takes over to turn this structured data into a written narrative to make it universally understandable.
By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly. Instead, machines must know the definitions of words and sentence structure, along with syntax, sentiment and intent. It’s a subset of NLP and It works within it to assign structure, rules and logic to language so machines can “understand” what is being conveyed in the words, phrases and sentences in text. NLU can understand and process the meaning of speech or text of a natural language. To do so, NLU systems need a lexicon of the language, a software component called a parser for taking input data and building a data structure, grammar rules, and semantics theory.
NLP uses computational linguistics, computational neuroscience, and deep learning technologies to perform these functions. NLP and NLU are closely related fields within AI that focus on the interaction between computers and human languages. It includes tasks such as speech recognition, language translation, and sentiment analysis. NLP serves as the foundation that enables machines to handle the intricacies of human language, converting text into structured data that can be analyzed and acted upon. NLU is the ability of a machine to understand and process the meaning of speech or text presented in a natural language, that is, the capability to make sense of natural language.
While computational linguistics has more of a focus on aspects of language, natural language processing emphasizes its use of machine learning and deep learning techniques to complete tasks, like language translation or question answering. Natural language processing works by taking unstructured data and converting it into a structured data format. It does this through the identification of named entities (a process called named entity recognition) and identification of word patterns, using methods like tokenization, stemming, and lemmatization, which examine the root forms of words.
The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning. Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings. Natural language understanding systems let organizations create products or tools that can both understand words and interpret their meaning.
They consist of nine sentence- or sentence-pair language understanding tasks, similarity and paraphrase tasks, and inference tasks. NLU, the technology behind intent recognition, enables companies to build efficient chatbots. In order to help corporate executives raise the possibility that their chatbot investments will be successful, we address NLU-related questions in this article.
For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc. NLU algorithms often operate on text that has already been standardized by text pre-processing steps. NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, nlu and nlp understanding the input, or generating a response. In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input.[13] Instead of phrase structure rules ATNs used an equivalent set of finite state automata that were called recursively. ATNs and their more general format called “generalized ATNs” continued to be used for a number of years.
For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. These approaches are also commonly used in data mining to understand consumer Chat GPT attitudes. In particular, sentiment analysis enables brands to monitor their customer feedback more closely, allowing them to cluster positive and negative social media comments and track net promoter scores.
What is Natural Language Processing?
Additionally, training NLU models often requires substantial computing resources, which can be a limitation for individuals or organizations with limited computational power. Consider experimenting with different algorithms, feature engineering techniques, or hyperparameter settings to fine-tune your NLU model. Split your dataset into a training set and a test set, and measure metrics like accuracy, precision, and recall to assess how well the Model performs on unseen data. This includes removing unnecessary punctuation, converting text to lowercase, and handling special characters or symbols that might affect the understanding of the language.
The sophistication of NLU and NLP technologies also allows chatbots and virtual assistants to personalize interactions based on previous interactions or customer data. This personalization can range from addressing customers by name to providing recommendations based on past purchases or browsing behavior. Such tailored interactions not only improve the customer experience but also help to build a deeper sense of connection and understanding between customers and brands. In addition, NLU and NLP significantly enhance customer service by enabling more efficient and personalized responses. Automated systems can quickly classify inquiries, route them to the appropriate department, and even provide automated responses for common questions, reducing response times and improving customer satisfaction.
Natural Language Understanding(NLU) is an area of artificial intelligence to process input data provided by the user in natural language say text data or speech data. It is a way that enables interaction between a computer and a human in a way like humans do using natural languages like English, French, Hindi etc. NLP takes input text in the form of natural language, converts it into a computer language, processes it, and returns the information as a response in a natural language.
Think of the classical example of a meaningless yet grammatical sentence “colorless green ideas sleep furiously”. Even more, in the real life, meaningful sentences often contain minor errors and can be classified as ungrammatical. Human interaction allows for errors in the produced text and speech compensating them by excellent pattern recognition and drawing additional information from the context. This shows the lopsidedness of the syntax-focused analysis and the need for a closer focus on multilevel semantics. Across various industries and applications, NLP and NLU showcase their unique capabilities in transforming the way we interact with machines. You can foun additiona information about ai customer service and artificial intelligence and NLP. By understanding their distinct strengths and limitations, businesses can leverage these technologies to streamline processes, enhance customer experiences, and unlock new opportunities for growth and innovation.
NLP-enabled systems are intended to understand what the human said, process the data, act if needed and respond back in language the human will understand. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output. NLU, a subset of NLP, delves deeper into the comprehension aspect, focusing specifically on the machine’s ability to understand the intent and meaning behind the text.
NLU & NLP: AI’s Game Changers in Customer Interaction – CMSWire
NLU & NLP: AI’s Game Changers in Customer Interaction.
Posted: Fri, 16 Feb 2024 08:00:00 GMT [source]
In the world of AI, for a machine to be considered intelligent, it must pass the Turing Test. A test developed by Alan Turing in the 1950s, which pits humans against the machine. A task called word sense disambiguation, which sits under the NLU umbrella, makes sure that the machine is able to understand the two different senses that the word “bank” is used.
Furthermore, NLU enables computer programmes to deduce purpose from language, even if the written or spoken language is flawed. NLU, however, understands the idiom and interprets the user’s intent as being hungry and searching for a nearby restaurant. Behind the scenes, sophisticated algorithms like hidden Markov chains, recurrent neural networks, n-grams, decision trees, naive bayes, etc. work in harmony to make it all possible.
While NLU has challenges like sensitivity to context and ethical considerations, its real-world applications are far-reaching—from chatbots to customer support and social media monitoring. Symbolic AI uses human-readable symbols that represent real-world entities or concepts. Logic is applied in the form of an IF-THEN structure embedded into the system by humans, who create the rules.
Real-world NLU applications such as chatbots, customer support automation, sentiment analysis, and social media monitoring were also explored. Natural language processing and natural language understanding language are not just about training a dataset. The computer uses NLP algorithms to detect patterns in a large amount of unstructured data. Natural language processing is generally more suitable for tasks involving data extraction, text summarization, and machine translation, among others. Meanwhile, NLU excels in areas like sentiment analysis, sarcasm detection, and intent classification, allowing for a deeper understanding of user input and emotions.
The 1960s and 1970s saw the development of early NLP systems such as SHRDLU, which operated in restricted environments, and conceptual models for natural language understanding introduced by Roger Schank and others. This period was marked by the use of hand-written rules for language processing. NLU and NLP have greatly impacted the way businesses interpret and use human language, enabling a deeper connection between consumers and businesses. By parsing and understanding the nuances of human language, NLU and NLP enable the automation of complex interactions and the extraction of valuable insights from vast amounts of unstructured text data.
The fascinating world of human communication is built on the intricate relationship between syntax and semantics. While syntax focuses on the rules governing language structure, semantics delves into the meaning behind words and sentences. In the realm of artificial intelligence, NLU and NLP bring these concepts to life. Natural Language Processing, a fascinating subfield of computer science and artificial intelligence, enables computers to understand and interpret human language as effortlessly as you decipher the words in this sentence. From deciphering speech to reading text, our brains work tirelessly to understand and make sense of the world around us. However, our ability to process information is limited to what we already know.
In such cases, salespeople in the physical stores used to solve our problem and recommended us a suitable product. In the age of conversational commerce, such a task is done by sales chatbots that understand user intent and help customers to discover a suitable product for them via natural language (see Figure 6). Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose https://chat.openai.com/ a structure, such as multiple choice questions. Have you ever wondered how Alexa, ChatGPT, or a customer care chatbot can understand your spoken or written comment and respond appropriately? NLP and NLU, two subfields of artificial intelligence (AI), facilitate understanding and responding to human language. Follow this guide to gain practical insights into natural language understanding and how it transforms interactions between humans and machines.
Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant. Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time. NLU skills are necessary, though, if users’ sentiments vary significantly or if AI models are exposed to explaining the same concept in a variety of ways. Let’s illustrate this example by using a famous NLP model called Google Translate. As seen in Figure 3, Google translates the Turkish proverb “Damlaya damlaya göl olur.” as “Drop by drop, it becomes a lake.” This is an exact word by word translation of the sentence. It is quite common to confuse specific terms in this fast-moving field of Machine Learning and Artificial Intelligence.
We’ll also examine when prioritizing one capability over the other is more beneficial for businesses depending on specific use cases. By the end, you’ll have the knowledge to understand which AI solutions can cater to your organization’s unique requirements. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world. Since then, with the help of progress made in the field of AI and specifically in NLP and NLU, we have come very far in this quest.
If a developer wants to build a simple chatbot that produces a series of programmed responses, they could use NLP along with a few machine learning techniques. However, if a developer wants to build an intelligent contextual assistant capable of having sophisticated natural-sounding conversations with users, they would need NLU. NLU is the component that allows the contextual assistant to understand the intent of each utterance by a user. Without it, the assistant won’t be able to understand what a user means throughout a conversation. And if the assistant doesn’t understand what the user means, it won’t respond appropriately or at all in some cases.
Linguistic experts review and refine machine-generated translations to ensure they align with cultural norms and linguistic nuances. This hybrid approach leverages the efficiency and scalability of NLU and NLP while ensuring the authenticity and cultural sensitivity of the content. “We use NLU to analyze customer feedback so we can proactively address concerns and improve CX,” said Hannan. The greater the capability of NLU models, the better they are in predicting speech context. In fact, one of the factors driving the development of ai chip devices with larger model training sizes is the relationship between the NLU model’s increased computational capacity and effectiveness (e.g GPT-3). Currently, the quality of NLU in some non-English languages is lower due to less commercial potential of the languages.
This information can be used for brand monitoring, reputation management, and understanding customer satisfaction. Rasa NLU also provides tools for data labeling, training, and evaluation, making it a comprehensive solution for NLU development. Fine-tuning involves training the pre-trained Model on your dataset while keeping the initial knowledge intact. This way, you get the best of both worlds – the power of the pre-trained Model and the ability to handle your specific task. Entity extraction involves identifying and extracting specific entities mentioned in the text.
In machine learning (ML) jargon, the series of steps taken are called data pre-processing. The idea is to break down the natural language text into smaller and more manageable chunks. These can then be analyzed by ML algorithms to find relations, dependencies, and context among various chunks. Sometimes people know what they are looking for but do not know the exact name of the good.
Keep reading to discover three innovative ways that Natural Language Understanding is streamlining support, enhancing experiences and empowering connections. Keep reading to learn more about the ongoing struggles with ambiguity, data needs, and ensuring responsible AI. This evaluation helps identify any areas of improvement and guides further fine-tuning efforts.
When it comes to natural language, what was written or spoken may not be what was meant. In the most basic terms, NLP looks at what was said, and NLU looks at what was meant. People can say identical things in numerous ways, and they may make mistakes when writing or speaking. They may use the wrong words, write fragmented sentences, and misspell or mispronounce words. NLP can analyze text and speech, performing a wide range of tasks that focus primarily on language structure. However, it will not tell you what was meant or intended by specific language.
This process allows the Model to adapt to your specific use case and enhances performance. Pre-trained NLU models can significantly speed up the development process and provide better performance. You’ll need a diverse dataset that includes examples of user queries or statements and their corresponding intents and entities.
For those interested, here is our benchmarking on the top sentiment analysis tools in the market. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models. You can learn more about custom NLU components in the developer documentation, and be sure to check out this detailed tutorial. Implementing NLU comes with challenges, including handling language ambiguity, requiring large datasets and computing resources for training, and addressing bias and ethical considerations inherent in language processing.
This hard coding of rules can be used to manipulate the understanding of symbols. One of the key advantages of using NLU and NLP in virtual assistants is their ability to provide round-the-clock support across various channels, including websites, social media, and messaging apps. This ensures that customers can receive immediate assistance at any time, significantly enhancing customer satisfaction and loyalty. Additionally, these AI-driven tools can handle a vast number of queries simultaneously, reducing wait times and freeing up human agents to focus on more complex or sensitive issues.
Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) all fall under the umbrella of artificial intelligence (AI). Chrissy Kidd is a writer and editor who makes sense of theories and new developments in technology. The first successful attempt came out in 1966 in the form of the famous ELIZA program which was capable of carrying on a limited form of conversation with a user. All these sentences have the same underlying question, which is to enquire about today’s weather forecast. In this context, another term which is often used as a synonym is Natural Language Understanding (NLU).
NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. For example, using NLG, a computer can automatically generate a news article based on a set of data gathered about a specific event or produce a sales letter about a particular product based on a series of product attributes. NLU makes it possible to carry out a dialogue with a computer using a human-based language. This is useful for consumer products or device features, such as voice assistants and speech to text. Bharat Saxena has over 15 years of experience in software product development, and has worked in various stages, from coding to managing a product. With BMC, he supports the AMI Ops Monitoring for Db2 product development team.
A well-liked open-source natural language processing package, spaCy has solid entity recognition, tokenization, and part-of-speech tagging capabilities. The computational methods used in machine learning result in a lack of transparency into “what” and “how” the machines learn. This creates a black box where data goes in, decisions go out, and there is limited visibility into how one impacts the other. What’s more, a great deal of computational power is needed to process the data, while large volumes of data are required to both train and maintain a model. Grammar complexity and verb irregularity are just a few of the challenges that learners encounter. Now, consider that this task is even more difficult for machines, which cannot understand human language in its natural form.
Programming Languages, Libraries, And Frameworks For Natural Language Processing (NLP)
By applying NLU and NLP, businesses can automatically categorize sentiments, identify trending topics, and understand the underlying emotions and intentions in customer communications. This automated analysis provides a comprehensive view of public perception and customer satisfaction, revealing not just what customers are saying, but how they feel about products, services, brands, and their competitors. Humans want to speak to machines the same way they speak to each other — in natural language, not the language of machines. The field of natural language processing in computing emerged to provide a technology approach by which machines can interpret natural language data. In other words, NLP lets people and machines talk to each other naturally in human language and syntax.
NLP and NLU are transforming marketing and customer experience by enabling levels of consumer insights and hyper-personalization that were previously unheard of. From decoding feedback and social media conversations to powering multilanguage engagement, these technologies are driving connections through cultural nuance and relevance. Where meaningful relationships were once constrained by human limitations, NLP and NLU liberate authentic interactions, heralding a new era for brands and consumers alike. The promise of NLU and NLP extends beyond mere automation; it opens the door to unprecedented levels of personalization and customer engagement. These technologies empower marketers to tailor content, offers, and experiences to individual preferences and behaviors, cutting through the typical noise of online marketing.
Natural Language Understanding (NLU) and Natural Language Processing (NLP) are pioneering the use of artificial intelligence (AI) in transforming business-audience communication. These advanced AI technologies are reshaping the rules of engagement, enabling marketers to create messages with unprecedented personalization and relevance. This article will examine the intricacies of NLU and NLP, exploring their role in redefining marketing and enhancing the customer experience. Applications for NLP are diversifying with hopes to implement large language models (LLMs) beyond pure NLP tasks (see 2022 State of AI Report). CEO of NeuralSpace, told SlatorPod of his hopes in coming years for voice-to-voice live translation, the ability to get high-performance NLP in tiny devices (e.g., car computers), and auto-NLP.
Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason. SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items. Before booking a hotel, customers want to learn more about the potential accommodations.
And so, understanding NLU is the second step toward enhancing the accuracy and efficiency of your speech recognition and language translation systems. NLU focuses on understanding human language, while NLP covers the interaction between machines and natural language. NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. NLP attempts to analyze and understand the text of a given document, and NLU makes it possible to carry out a dialogue with a computer using natural language.
These technologies use machine learning to determine the meaning of the text, which can be used in many ways. Artificial intelligence is becoming an increasingly important part of our lives. However, when it comes to understanding human language, technology still isn’t at the point where it can give us all the answers. Pursuing the goal to create a chatbot that would be able to interact with human in a human-like manner — and finally to pass the Turing’s test, businesses and academia are investing more in NLP and NLU techniques. The product they have in mind aims to be effortless, unsupervised, and able to interact directly with people in an appropriate and successful manner. Semantic analysis, the core of NLU, involves applying computer algorithms to understand the meaning and interpretation of words and is not yet fully resolved.
NLU includes tasks like extracting meaning from text, recognizing entities in a text, and extracting information regarding those entities.NLU relies upon natural language rules to understand the text and extract meaning from utterances. To interpret a text and understand its meaning, NLU must first learn its context, semantics, sentiment, intent, and syntax. Semantics and syntax are of utmost significance in helping check the grammar and meaning of a text, respectively. Though NLU understands unstructured data, part of its core function is to convert text into a structured data set that a machine can more easily consume.
NLP and NLU are significant terms for designing a machine that can easily understand human language, regardless of whether it contains some common flaws. Hence the breadth and depth of “understanding” aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The “breadth” of a system is measured by the sizes of its vocabulary and grammar. The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding,[25] but they still have limited application.
NLG is a software process that turns structured data – converted by NLU and a (generally) non-linguistic representation of information – into a natural language output that humans can understand, usually in text format. NLU’s core functions are understanding unstructured data and converting text into a structured data set which a machine can more easily consume. Applications vary from relatively simple tasks like short commands for robots to MT, question-answering, news-gathering, and voice activation. For many organizations, the majority of their data is unstructured content, such as email, online reviews, videos and other content, that doesn’t fit neatly into databases and spreadsheets. Many firms estimate that at least 80% of their content is in unstructured forms, and some firms, especially social media and content-driven organizations, have over 90% of their total content in unstructured forms.
For example, a restaurant receives a lot of customer feedback on its social media pages and email, relating to things such as the cleanliness of the facilities, the food quality, or the convenience of booking a table online. Another difference between NLU and NLP is that NLU is focused more on sentiment analysis. Sentiment analysis involves extracting information from the text in order to determine the emotional tone of a text. NLP models are designed to describe the meaning of sentences whereas NLU models are designed to describe the meaning of the text in terms of concepts, relations and attributes. Since the 1950s, the computer and language have been working together from obtaining simple input to complex texts. It was Alan Turing who performed the Turing test to know if machines are intelligent enough or not.
Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language. Going back to our weather enquiry example, it is NLU which enables the machine to understand that those three different questions have the same underlying weather forecast query. After all, different sentences can mean the same thing, and, vice versa, the same words can mean different things depending on how they are used.
NLU converts input text or speech into structured data and helps extract facts from this input data. The integration of NLP algorithms into data science workflows has opened up new opportunities for data-driven decision making. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia.
The rest 80% is unstructured data, which can’t be used to make predictions or develop algorithms. NLP has many subfields, including computational linguistics, syntax analysis, speech recognition, machine translation, and more. Also, NLP processes a large amount of human data and focus on use of machine learning and deep learning techniques. Natural language understanding is the first step in many processes, such as categorizing text, gathering news, archiving individual pieces of text, and, on a larger scale, analyzing content. Real-world examples of NLU range from small tasks like issuing short commands based on comprehending text to some small degree, like rerouting an email to the right person based on a basic syntax and decently-sized lexicon. Much more complex endeavors might be fully comprehending news articles or shades of meaning within poetry or novels.
With unstructured content only growing for most organizations, it’s important to have ways to continue to capture, analyze and make sense of this valuable data, and understanding the differences between NLP vs. NLU is a crucial first step. However, NLP, which has been in development for decades, is still limited in terms of what the computer can actually understand. Adding machine learning and other AI technologies to NLP leads to natural language understanding (NLU), which can enhance a machine’s ability to understand what humans say. As it stands, NLU is considered to be a subset of NLP, focusing primarily on getting machines to understand the meaning behind text information.
In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content. Text abstraction, the original document is phrased in a linguistic way, text interpreted and described using new concepts, but the same information content is maintained. NLP consists of natural language generation (NLG) concepts and natural language understanding (NLU) to achieve human-like language processing. Until recently, the idea of a computer that can understand ordinary languages and hold a conversation with a human had seemed like science fiction.
