Large Language Models LLMs: Definition, How They Work, Types The Motley Fool

Below are some of the benefits LLMs deliver to companies that leverage their capabilities. The most common types of LLMs are language representation, zero-shot model, multimodal, and fine-tuned. While these four types of models have much in common, their differences revolve around their ability to make predictions, the type of media they’re trained on, and their degree of customization. Typically, such predictive AI projects demand heavy involvement by experienced machine learning experts and a lengthy project lifecycle to define the requirements, prepare the data, train a model, evaluate it and integrate it for deployment.

Datadog President Amit Agarwal on Trends in…

• Building a team of data scientists, machine learning engineers and AI ethicists is ideal.
• However, if the limit is set too low then the LLM may struggle to generate the desired output.
• Smaller datasets encouraged more memorization, but as dataset size increased, models shifted toward learning generalizable patterns.
• “It is trying to relate your math question to previous examples of math questions that it has seen before.”

Qwen-1.5-7B-chat is available for use today via a web interface over at huggingface.co, while the larger models can be downloaded to run locally. Why you can trust TechRadarWe spend hours testing every product or service we review, so you can be sure you’re buying the best. We’ve picked one foundation LLM as best overall and selected individual models from a range of foundational models for each category.

However, even these smaller platforms may be sufficient for this type of reduced LLM or even other ANN models that might have similar computational and storage reductions. The significant reduction in storage requirements and similar reduction in computational requirements means that suitably compact LLMs can work on microcontrollers, including those designed for the Internet of Things (IoT). This AI edge computing can allow for local control or reduce the amount of data or frequency of communication with the cloud. Organizations are more likely to implement a portfolio of models, each selected to suit a specific scenario. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. I’m no lawyer or legal expert, but I would highly expect such research to be cited in the numerous ongoing lawsuits between AI providers and data creators/rights owners.

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The MoE architecture allows Mistral’s models to handle large-scale workloads with fewer computational resources while maintaining strong performance across diverse applications. A Large Language Model (LLM) is form of artificial intelligence trained using massive sets of data to allow the model to recognize and generate text across a wide range of tasks. LLMs are build upon machine learning concepts using a type of neural network known as a Transformer Model. While an LLM could be used for the same purpose, the LQM takes a different approach. LLMs are trained on broad, unstructured internet data, which can include information about encryption algorithms and vulnerabilities.

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I evaluated each tool’s pricing by evaluating their free versions and identifying the cost of paid plans, both in terms of actual pricing and the computational resources you’d need to run them. Trained on dialogues and social media discussions, Falcon comprehends conversational flow and context, allowing it to deliver highly relevant responses that take into account what you’ve said in the past. In essence, the longer you interact with Falcon, the better it “knows you” and the more use you can gain from it. This doesn’t happen all the time, of course, but it happens often enough that there is a great deal of concern with trust in LLMs and their outputs. Many people have also noticed that they’re terrible with numbers, often being unable to even count the words in their own output. Narrow AI is about to get a lot wider thanks to the LLMs, according to Amit Prakash, co-founder and CTO of business analytics software and services provider ThoughtSpot.

Best value LLM

In the case of a customer support bot, you probably don’t need advanced intelligence allowing users to have the long philosophical conversations that you might with something like GPT-4o as its way out of scope for what you intend to use it for. To further enhance its chat capabilities, Qwen-1.5 can accept and respond in an impressive 35 languages and can offer translation services in over 150 others. Like with other LLMs, the number of tokens for inputs and outputs depend on the language being used as some have a higher token-to-character ratio. In November 2023, GitHub Copilot was updated to use the GPT-4 model to further improve its capabilities.

Bottom Line: Large Language Models Are Revolutionizing Technology

For example, a GPT-3 model could be fine-tuned on medical data to create a domain-specific medical chatbot or assist in medical diagnosis. Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output. Like LLMs, SLMs can understand natural language prompts and respond with natural language replies. They are built using streamlined versions of the artificial neural networks found in LLMs.

In addition, there will be a far greater number and variety of LLMs, giving companies more options to choose from as they select the best LLM for their particular artificial intelligence deployment. Similarly, the customization of LLMs will become far easier and more specific, which will allow each piece of AI software to be fine-tuned to be faster, more efficient, and more productive. As LLMs are deployed, you must consider their ethical impact, particularly the risk of bias. Conduct regular audits to detect and address biases, ensuring that AI models serve all users fairly.

Massive sparse expert models.

We’re entering a new era in customer service thanks to large language models, says Hardy Myers, senior vice president of business development and strategy for Cognigy, a provider of a conversational AI platform. “We’re likely going to see more solutions like GitHub Co-Pilot where LLMs are able to assist people in meaningful ways,” Ganapathi says. “These tools are not going to get everything right but they will help solve that initial writer’s block. But if you can describe the prompt or problem and the model outputs something, it may give you a good starting point and show you something you can use—even if it’s not entirely what you want.

How AI Can Transform The IT Service Industry In The Next 5 Years

Many large corporations will emerge as key players in the IT service market, driven by their investments in innovative technologies and AI-driven services. This evolution may position these firms at the forefront of the industry, significantly altering the competitive landscape and setting new standards for technological integration and customer service excellence. Virgin Voyages and Slalom Consulting recently teamed up to launch Vivi, the first generative AI-powered “digital human” built on Salesforce, according to Katie Dunlap, Slalom general manager of global Salesforce. Vivi was created to provide a more engaging and realistic customer service chat experience and significantly reduce the time it takes for passengers to find the information they need. Artificial intelligence (AI) has become an essential tool in the contact center to enhance customer service and agent support.

Chatbot AI Implementation: Metrics, Operational Impact

Thus, engaging with them before implementing significant changes and continuing rigorous testing with human involvement is crucial to ensure AI effectively betters your customer interactions. Deploying AI merely to cut costs or appear technologically advanced can backfire, leading to bitter customer experiences and damaging a company’s reputation. AI is a powerful tool that can streamline a company’s processes, ignite creativity or enrich customer experience. However, it’s crucial to maintain a human element when integrating new technology to mitigate risks and make necessary adjustments.

Adopting Innovations

CMSWire’s Marketing & Customer Experience Leadership channel is the go-to hub for actionable research, editorial and opinion for CMOs, aspiring CMOs and today’s customer experience innovators. Our dedicated editorial and research teams focus on bringing you the data and information you need to navigate today’s complex customer, organizational and technical landscapes. To deliver on the customer service potential of smart services, however, financial service firms need purpose-built AI solutions that address specific use cases and drive sustained success. That means it’s essential to implement carefully, with ample thought given to the impact integration will have on employees and customers. A rushed implementation could decrease the quality of customer service and be a blow to employee satisfaction if the focus is on a fast rollout.

• Maintaining the balance of human-bot interaction is crucial to successfully transform your business with AI.
• The tool evaluates inputted data to provide real-time packaging instructions that help reduce fulfillment costs and wasted materials.
• As a result, productivity will likely improve as AI-enhanced tools empower the workforce to plunge swiftly into specific use cases.
• CMSWire’s Marketing & Customer Experience Leadership channel is the go-to hub for actionable research, editorial and opinion for CMOs, aspiring CMOs and today’s customer experience innovators.

The tool evaluates inputted data to provide real-time packaging instructions that help reduce fulfillment costs and wasted materials. Daily Harvest wanted to improve the customer experience, minimize waste, and reduce materials and shipping costs, Williams added. Those considerations are crucial when it comes to things like implementing an AI chatbot for successful conversational outcomes with customers. Nandan has been involved in those implementations in past roles and is looking forward to working with AI now and in the future. With AI tools now proving their worth in real-world customer service scenarios, several common use cases have emerged.

How AI Can Transform The IT Service Industry In The Next 5 Years

With the increasing demand for intelligent, data-driven applications, a vast market of services is emerging to streamline the modernization of legacy applications using AI-powered technologies. As AI also stands to accelerate a shift in education towards denser, faster-paced learning, students can be better positioned to enter the workforce and provide services earlier than anticipated. Below is a look at how intelligent virtual agents and chatbots and uses of AI in the contact center to improve CX today.

Balancing AI With Human Insight

Given the rapid evolution of intelligent services, this scenario isn’t surprising — like cloud, mobile, and other digital frameworks before it, AI eventually will find its niche. One area that showed early promise and has largely lived up to the hype is customer service. Eighty percent of customers now expect AI to improve customer service, and 43 percent of financial service firms are already using AI to personalize the customer experience. Another challenge can be managing internal resistance or a lack of AI expertise within the team.

Without clean, well-organized data, AI models struggle to make effective recommendations. To navigate this, businesses should invest time in cleaning and structuring their data before starting any AI project. Conducting regular data audits and ensuring data privacy and compliance is also essential. As AI technologies automate routine tasks and consolidate workflows, productivity levels are expected to soar within the next few years, potentially reducing the historical advantage of offshoring locations. For example, software coding and QA roles that once required extensive manual labor may be automated, reducing the dependence on offshore contractors for labor-intensive activities.

Those willing to make changes, analyze results and continue to adapt are positioned to come out on top. If a chatbot takes over all of the “little win” conversations, the customer and human service representative lose their “partners in success” relationship. The customer will begin to see their service rep as a bearer of bad news, and their perception of the service received will suffer.

• As the founder of a company that provides AI solutions to e-commerce brands, these are developments I’ve observed firsthand.
• The largest savings have been in dry ice, the cost of which is 3.6% lower since the switch to smaller boxes, he added.
• As with any technological advancement, its integration comes with challenges and opportunities.
• In this case, if AI is implemented as the main point of contact between a business and its customers, it has a negative impact on the operators.

One application of AI and ML I’m seeing is to deliver more personalized shopping experiences. Many consumers today expect more than a generic online shopping interface; they want recommendations and offers that cater to their specific preferences and behaviors. AI can be used to analyze data, including past purchases, browsing habits and even social media activity, to predict what a customer might want to buy next. In terms of service portfolios, there will likely be a shift towards optimizing the value gained from both new and established solutions by efficiently utilizing AI tools, applications and services for specific purposes. Service providers may place more emphasis on building reusable intellectual property (IP) assets, including knowledge bases, solutions customized for particular needs and frameworks aimed at enhancing performance.

AI integration has massive potential to streamline processes and take over many instances of customer service. However, if a customer is mostly in contact with a bot rather than a human being, many of the perks of direct customer service become lost. No matter how human a chatbot can appear, it can’t replicate interpersonal relationships that develop naturally when a customer works with the same operator daily.

On bots, language and making technology disappear

From a design perspective, bots are aligned with the whole concept of messaging-as-a-platform — we could build a bot right into our own messenger using the same simple elements we’d already designed for human-to-human conversation. Bots from multiple platforms have actually been part of Bing search results in some capacity for months now, and more bots and chat platforms may be added in the future, said the source, who asked to remain anonymous. The digital tools we make live in a completely different psychological landscape to the real world. There is no straight line from a tradesman’s hammer he can repair himself to a chatbot designed and built by a design team somewhere in California (or in Dublin, in our case). Unlike most writers in my company, my work does its job best when it’s barely noticed. Control is incredibly important in designing digital tools — most language we see and experience in a product is about affording control and understanding to you, the person using the product — not me, the writer.

Weird, Excellent Twitter Bots Chosen by Twitter’s Best Bot-Makers

My name has so far evaded Silicon Valley, but I doubt it’ll be long before I end up expressing my concerns to an AI-powered Jacob. This might be because of novelty — we might become more comfortable with the virtual, more trusting of it (though this year’s headlines haven’t given us much to trust). But despite the hundreds of movies we’ve made and books we’ve written about robots, introducing personality into technology might not be the way we become more comfortable.

• My name has so far evaded Silicon Valley, but I doubt it’ll be long before I end up expressing my concerns to an AI-powered Jacob.
• The Cortana Skills Kit is now publicly available, with roughly 50 skills that can teach you how to make a Manhattan, order a pizza, or take care of a child.
• As generative AI continues to advance, expect a deluge of new human-named bots in the coming years, Suresh Venkatasubramanian, a computer-science professor at Brown University, told me.
• Amplify your reach, spark real connections, and lead the innovation charge.

It’s one of many museum-collection bots Emerson has made based on Darius Kazemi’s original @MuseumBot. @Mothgenerator uses Javascript to produce striking renderings of imaginary moths, and then tweets them with faux-scientific names to breathe life into them. Bots can either be viewed by individual platform or as an aggregate to display the best bots from all five bot platforms. It was interrupting them, getting in the way of what they wanted (to talk to a real person), even though its interactions were very lightweight.

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@Nice_tips_bot shares life advice from Wikihow to brighten your day. @Twoheadlines draws from Google News, automatically combining the subject of one headline with the action of another. Put your brand in front of 10,000+ tech and VC leaders across all three days of Disrupt 2025. Amplify your reach, spark real connections, and lead the innovation charge. Names and identity lift the tools on the screen to a level above intuition. They make us see the tool in all its virtual glory, and place it in an entirely different context to the person using it — and not always a relationship that person asks for or appreciates.

Weird, Excellent Twitter Bots Chosen by Twitter’s Best Bot-Makers

The following decades brought chatbots with names such as Parry, Jabberwacky, Dr. Sbaitso, and A.L.I.C.E. (Artificial Linguistic Internet Computer Entity); in 2017, Saudi Arabia granted citizenship to a humanoid robot named Sophia. In this new era of generative AI, human names are just one more layer of faux humanity on products already loaded with anthropomorphic features. The new generation of chatbots can not only converse in unnervingly humanlike ways; in many cases, they have human names too. In addition to Tessa, there are bots named Ernie (from the Chinese company Baidu), Claude (a ChatGPT rival from the AI start-up Anthropic), and Jasper (a popular AI writing assistant for brands). Many of the most advanced chatbots— ChatGPT, Bard, HuggingChat—stick to clunky or abstract identities, but there are now many new additions to the already endless customer-service bots with real names (Maya, Bo, Dom).

Resisting the urge to give every bot a human identity is a small way to let a bot’s function stand on its own and not load it with superfluous human connotations—especially in a field already inundated with ethical quandaries. Echoing requests from developers making bots for Facebook Messenger, Cheng said the biggest request she got from users of the Microsoft Bot Framework was to improve discoverability. Talk about a bot search engine has included partners like Slack and Facebook Messenger, Cheng said.

The Cortana Skills Kit is now publicly available, with roughly 50 skills that can teach you how to make a Manhattan, order a pizza, or take care of a child. Customizable artificial intelligence services developed out of Microsoft Research, as well as Skype video bots and Skype chat for web pages, were also announced. A desire to create a common bot search engine is an ambition Lili Cheng, general manager of FUSE Labs at Microsoft Research, first shared with VentureBeat last fall. At Build 2017, alongside a stream of news about AI, bots, and Cortana, Microsoft yesterday announced that Skype bots can now be found in Bing search results. Bots in dozens of categories, including travel, entertainment, sports, and news, can be surfaced by entering a phrase like “travel bots” or “entertainment bots” into the search box on Bing.com.

Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation. As part of your account, you’ll receive occasional updates and offers from New York, which you can opt out of anytime. @phasechase is a linguistic game taken out of the classroom and shared on Twitter, a project by Rob Dubbin. @CensusAmericans, a FiveThirtyEight creation that uses data from the U.S. Census Bureau between 2009 and 2013 to produce mini-biographies of anonymous Americans. @poem_exe generates “micropoetry” based on a technique used by the Oulipo poet Raymond Queneau.

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To be understood intuitively is the goal — the words on the screen are the handle of the hammer. As the resident language expert on our product design team, naming things is part of my job. When we began iterating on a bot within our messaging product, I was prepared to brainstorm hundreds of names. But for now, bots with human names are becoming unavoidable.

When Bing surfaces bot search results, each chat app platform is displayed in a dedicated area at the top of the results. Search for bots on Bing today and you’ll find that the selection of highlighted bots may not be the very best bots available, but it’s a start and is far more extensive than anything currently offered by other chat apps. In the last few years, artists and programmers have turned Twitter bots into an internet-native art form, producing bots that are often hilarious, usually weird, and sometimes unexpectedly poetic. But names don’t trigger an action in text-based bots, or chatbots. Even Slackbot, the tool built into the popular work messaging platform Slack, doesn’t need you to type “Hey Slackbot” in order to retrieve a pre-programmed response. White Castle’s Julia, which simply facilitates the purchase of hamburgers and fries, is no one’s idea of a sentient bot.

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• When Bing surfaces bot search results, each chat app platform is displayed in a dedicated area at the top of the results.
• @phasechase is a linguistic game taken out of the classroom and shared on Twitter, a project by Rob Dubbin.
• Talk about a bot search engine has included partners like Slack and Facebook Messenger, Cheng said.
• But names don’t trigger an action in text-based bots, or chatbots.

There’s a new buzzword in computer design circles every year. Thousands of Facebook Messenger, Slack, Skype, Kik, and Telegram bots can now be found by searching Bing.com, a source familiar with the matter has informed VentureBeat. @the_ephemerides pulls images from NASA’s OPUS database to pair with computer-generated text. It was only when we removed the name and took away the first person pronoun and the introduction that things started to improve.

As generative AI continues to advance, expect a deluge of new human-named bots in the coming years, Suresh Venkatasubramanian, a computer-science professor at Brown University, told me. The names are yet another way to make bots seem more believable and real. “There’s a difference between what you expect from a ‘help assistant’ versus a bot named Tessa,” Katy Steinmetz, the creative and project director of the naming agency Catchword, told me. These names can have a malicious effect, but in other instances, they are simply annoying or mundane—a marketing ploy for companies to try to influence how you think about their products. The future of AI may or may not involve a bot taking your job, but it will very likely involve one taking your name.

Secrets of using AI and data to supercharge customer engagement

According to Twilio’s findings, 96% of respondents felt that not digitizing would have hurt their business once the pandemic started. What’s more, 95% also said they plan to continue investing in digital customer engagement even when the pandemic is over. As I see it, the biggest surprise may be that 5% of companies don’t plan to continue investing in digital. It’s hard to imagine that their transformation is complete and that stopping investment makes sense. This fundamentally transforms how businesses operate and engage with their customers …

How 2020 Is Shaping Customer Engagement Now And In The Future

It’s rooted in meaningful interactions, personalized experiences and shared ownership between brand and customer. We’re at an inflection point in which a combination of technologies are coming together that are going to change the way engagement is done between companies, their customers and team members. There’s going to be a convergence of multiple technology waves that started with the internet, mobility and the cloud. Whatever your approach and the tools you use – high tech, low tech, or no tech – customer engagement can take you far. Farther than just about anything else in today’s competitive business environment. Think retail, sports, entertainment, health services/wellness, financial services, travel, and of course, hospitality as a good solid group of those kinds of vertical industries.

• Customers can earn points, vouchers, and customized rewards for making purchases, leaving reviews, and more.
• Brands that embrace this shift will build more than customer bases—they’ll build communities.
• You can also designate a space on your website or create a Facebook group for your customer community.
• Customer-facing employees should not only focus on a customer or potential customer’s expressed requirements, but should also pay attention his or her implied needs.

A million customer conversations with AI agents yielded this surprising lesson

By staying attuned to these technological shifts and integrating them thoughtfully into your customer engagement strategies, you can create more meaningful, efficient and secure interactions with your customers. Customer engagement remains a top challenge for businesses in today’s technological landscape. What will ultimately happen over the coming years is that communications with businesses and consumers will traverse the digital and virtual worlds. The creation of 360-degree engagements will not happen overnight. It will take time for companies to get the technology and personnel resources in place.

Customer engagement: digital and physical

Salesforce Experience Cloud features strong customer loyalty features to help you incentivize purchases and reward customers for engaging with your company. Customers can earn points, vouchers, and customized rewards for making purchases, leaving reviews, and more. In retail, it’s all about figuring out if your customer engagement game is on point. First up, check your conversion rate – it tells you how many peeps are actually buying after checking out your stuff. So, there you have it – tech’s changing the retail game, making it easier than ever to keep your customers engaged and stoked about your brand. I’ve always said that customers should not feel the bumps of your internal processes or fall through the cracks of your customer journey.

Building Lasting Customer Connection: From Engagement To Conversion

Whether you’ve mapped out a customer journey or not, your customers are going through some sort of a journey and you better make sure it’s a smooth one if you want to keep them as customers. If you aren’t happy with the results you see, then evaluate your customer engagement framework to see what steps may be missing. The desirable mode of operation when it comes to customer success is to be operating proactively. This is the crux of what we are looking to determine and measure.

What is natural language processing NLP? Definition, examples, techniques and applications

For instance, if an NLP program looks at the word “dummy” it needs context to determine if the text refers to calling someone a “dummy” or if it’s referring to something like a car crash “dummy.” The end result is the ability to categorize what is said in many different ways. Depending on the underlying focus of the NLP software, the results get used in different ways. It’s such a little thing that most of us take for granted, and have been taking for granted for years, but that’s why NLP becomes so important.

In some cases, these errors can be glaring—or even catastrophic. During the ensuing decade, researchers experimented with computers translating novels and other documents across spoken languages, though the process was extremely slow and prone to errors. In the 1960s, MIT professor Joseph Weizenbaum developed ELIZA, which mimicked human speech patterns remarkably well. As computing systems became more powerful in the 1990s, researchers began to achieve notable advances using statistical modeling methods.

Let’s use an example to show just how powerful NLP is when used in a practical situation. When you’re typing on an iPhone, like many of us do every day, you’ll see word suggestions based on what you type and what you’re currently typing. NLP combines the techniques of statistics with machine learning.

It can also be used to look at the sentiment of large groups and direct group conversations, as offered by Remesh. In fact, researchers who have experimented with NLP systems have been able to generate egregious and obvious errors by inputting certain words and phrases. Getting to 100% accuracy in NLP is nearly impossible because of the nearly infinite number of word and conceptual combinations in any given language. Natural language processing uses artificial intelligence to replicate human speech and text on computing devices. The training set includes a mixture of documents gathered from the open internet and some real news that’s been curated to exclude common misinformation and fake news. After deduplication and cleaning, they built a training set with 270 billion tokens made up of words and phrases.

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Trend analysis shows us that the term deep learning is now being mentioned much more frequently within the computer vision domain than it was ten years ago. We can also use blogs to conduct sentiment analysis and find out whether terms are being described more positively or negatively. NLP is an increasingly common branch of AI, found in everything from smartphones to home kitchens, and involves the ability of computers to understand spoken language and text. Although most uses are fairly pedestrian — Alexa, what is AI? Natural language processing is a lucrative commodity yet has one of the largest environmental impacts out of all the other fields in the artificial intelligence realm. The process used to train, experiment, and fine-tune a natural language process model has been estimated to create on average more CO2 emissions than two Americans annually.

Large dataset news organizations for Dutch AI language model GPT-NL

Now that algorithms can provide useful assistance and demonstrate basic competency, AI scientists are concentrating on improving understanding and adding more ability to tackle sentences with greater complexity. Some of this insight comes from creating more complex collections of rules and subrules to better capture human grammar and diction. Lately, though, the emphasis is on using machine learning algorithms on large datasets to capture more statistical details on how words might be used. Some natural language processing algorithms focus on understanding spoken words captured by a microphone.

• Read on to learn more about NLP, its history and development, and how it’s being used.
• In the 1990s, however, computers became much faster and more capable of doing calculations in seconds, even those that previously took hours or days.
• NLP combines the techniques of statistics with machine learning.
• Similarly, content analysis can be used for cybersecurity, including spam detection.

However, just because an AI program is coherent or as the ability to readily generate information does not mean the machine is sentient. It is not possible for AI to register experiences or feelings because it does not have the ability to think, feel, or perceive the world with a sentient mind. Sentiment analysis has a number of interesting use cases including brand monitoring, competitive research, product analysis, and others.

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At TNO, we use our tools to automatically extract information from documents. We can also make predictions, such as in the foresight domain. Using the Horizon Scanner, we explore and extract from relevant websites, blogs and documents. This allows us to retrieve relevant information and to show trends.

Sometimes, these data sets can have implicit bias thinking that may affect how an AI learns the language and communicates its findings. For now, business leaders should follow the natural language processing space—and continue to explore how the technology can improve products, tools, systems and services. The ability for humans to interact with machines on their own terms simplifies many tasks. Marketers and others increasingly rely on NLP to deliver market intelligence and sentiment trends. Semantic engines scrape content from blogs, news sites, social media sources and other sites in order to detect trends, attitudes and actual behaviors. Similarly, NLP can help organizations understand website behavior, such as search terms that identify common problems and how people use an e-commerce site.

How are the algorithms designed?

In fact, the latter represents a type of supervised machine learning that connects to NLP. These include language translations that replace words in one language for another (English to Spanish or French to Japanese, for example). For example, NLP can convert spoken words—either in the form of a recording or live dictation—into subtitles on a TV show or a transcript from a Zoom or Microsoft Teams meeting.