5 Amazing Examples Of Natural Language Processing NLP In Practice

Tell it to do so, and it will happily create web pages, applications, and even basic games in any of a number of different programming languages. These include Python, C, and Javascript, some of the most commonly used languages for software development. One of the most challenging and revolutionary things artificial intelligence (AI) can do is speak, write, listen, and understand human language. Natural language processing (NLP) is a form of AI that extracts meaning from human language to make decisions based on the information. This technology is still evolving, but there are already many incredible ways natural language processing is used today. Here we highlight some of the everyday uses of natural language processing and five amazing examples of how natural language processing is transforming businesses.

Nikhil Makhija is a member of MESA International and an SAP Manufacturing Suite expert with more than 16 years of specialized experience in optimizing manufacturing processes with SAP solutions. One caveat here is that although we can do our best to extrapolate what might happen in the future, in reality, no-one has a crystal ball. It’s fair to say that a lot of people who were used to AI conversing at the level of Alexa or Siri were somewhat shocked by how good ChatGPT is. CodeNet is a follow-up to ImageNet, a large-scale dataset of images and their descriptions; the images are free for non-commercial uses. Codex is adept at generating small, simple or repeatable assets, like “a big red button that briefly shakes the screen when clicked” or regular functions like the email address validator on a Google Web Form.

Natural Language Processing: 11 Real-Life Examples of NLP in Action

Further, academic disciplines as varied as computational physics and statistical sociology increasingly rely on custom computer programs to process data. Decreasing the skill required to create these programs would increase the ability of researchers in specialized fields outside computer sciences to deploy such methods and make new discoveries. As the technology continues to develop, Salva sees generative AI coding to expand far beyond its current technological bounds. To do this, AlphaCode will generate as many as a million code candidates, then reject all but the top 1 percent to pass its test cases.

From coders to gabbers

This means using it to automate the low-value, repetitive tasks that previously would have filled much of our time. Taking this approach to the disruptive emergence of AI tools and applications in our industry – whether you’re a computer programmer or a doctor – is the best way to make sure we stay useful and relevant in the age of AI. Coding is a difficult skill to learn let alone master and an experienced coder would be expected to be proficient in multiple programming languages. NLC, in contrast, leverages NLP technologies and a vast database such as CodeNet to enable anyone to use English, or ultimately French or Chinese or any other natural language, to code. Some natural language processing algorithms focus on understanding spoken words captured by a microphone. These speech recognition algorithms also rely upon similar mixtures of statistics and grammar rules to make sense of the stream of phonemes.

• Some natural language processing algorithms focus on understanding spoken words captured by a microphone.
• Moreover, adapting software written for one robot to run on other machines often requires manual modifications.
• In recent years, Google and other companies have developed advanced AI systems capable of writing software based on user prompts.
• One of the most challenging and revolutionary things artificial intelligence (AI) can do is speak, write, listen, and understand human language.

But no matter how prolific your prose, you won’t be using it for complex projects like coding a server-side load balancing program — it’s just too complicated an ask. 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.

Natural Language Programming AIs are taking the drudgery out of coding

The free version detects basic errors, while the premium subscription of $12 offers access to more sophisticated error checking like identifying plagiarism or helping users adopt a more confident and polite tone. The company is more than 11 years old and it is integrated with most online environments where text might be edited. Teaching an AI system to perform a new task usually involves supplying it with a large number of examples that demonstrate how the task should be performed.

What is vibe coding?

A new programmer could easily use a free, “bare bones” coding terminal and be at a little disadvantage. It is argued that the greater the number of potential innovators, the higher the rate of innovation. GPT-3, OpenAI’s industry-leading NLP model, has been used to allow coding a website or app by writing a description of what you want. Some AI scientists have analyzed some large blocks of text that are easy to find on the internet to create elaborate statistical models that can understand how context shifts meanings. A book on farming, for instance, would be much more likely to use “flies” as a noun, while a text on airplanes would likely use it as a verb. The mathematical approaches are a mixture of rigid, rule-based structure and flexible probability.

Lately, though, the emphasis is on using machine learning algorithms on large datasets to capture more statistical details on how words might be used. Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other.

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The term “vibe coding” was coined by computer scientist Andrej Karpathy, co-founder of OpenAI, in 2025 to describe an AI-based development process that turns human instructions into code using natural language inputs. Traditional coding methods require manual line-by-line programming, but vibe coding lets users define their goals and solve problems creatively while the AI system implements the technical details. It is a chatbot powered by the GPT-3.5 large language model (LLM) designed to use generative AI and natural language processing (NLP) to produce text that is almost indistinguishable from that written by humans. Due to its impressive abilities, it quickly went viral and has so far amassed millions of users. 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.

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Google says its newly debuted CaP tool can save time for developers by automatically generating robot configuration code. In recent years, Google and other companies have developed advanced AI systems capable of writing software based on user prompts. Using such AI systems, CaP can generate code that enables a robot to perform tasks specified by the user.

Before a manufacturer can deploy robotic arms in a factory, it has to customize the systems according to its requirements. For example, a manufacturer’s developers might write code that instructs a robot arm to pick up products from a production line and place them in a crate. Despite all of this, it seems that current thinking is that ChatGPT and other NLP technology available today are not going to immediately make all coders, programmers, and software engineers redundant. AI-generated code used in systems subject to FDA 21 CFR Part 11 regulations must be thoroughly validated, documented and audited, including maintaining audit trails, ensuring data integrity and implementing proper access controls. Due to this, ChatGPT (and other current NLP-based tools) still have limited effectiveness when it comes to creating software designed to give us an edge in business or, indeed, to compete with human creativity and ingenuity.