Tens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).


But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.


It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.


The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.


Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.


History of Natural Language Processing


Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.


Early Developments in NLP


Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?


In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.


Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.


Evolution of NLP Techniques and Algorithms


Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:


  • 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
  • 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
  • 1980s – IBM developed an array of NLP-based statistical solutions.
  • 1990s – Recurrent neural networks took center stage.

The Role of Artificial Intelligence and Machine Learning in NLP


Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.


AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.



Key Components of Natural Language Processing


The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.


Syntax Analysis


An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”


Semantic Analysis


Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.


Pragmatic Analysis


A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.


Discourse Analysis


When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.


Speech Recognition and Generation


Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.


Natural Language Processing Techniques


Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.


Rule-Based Approaches


Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.


Statistical Methods


If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.


Neural Networks and Deep Learning


You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.


Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.


Transfer Learning and Pre-Trained Language Models


The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.


Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.


Applications of Natural Language Processing


With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:


  • Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
  • Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
  • Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
  • Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
  • Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.

Challenges and Limitations of Natural Language Processing


Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:


  • Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
  • Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
  • Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
  • Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.

The Future of Natural Language Processing


The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?


  • Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
  • Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
  • Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
  • Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?

The Potential of Natural Language Processing Is Unrivaled


It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.


The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.


If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.

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Wired: Think Twice Before Creating That ChatGPT Action Figure
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 6 min read

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  • Wired, published on May 01st, 2025

People are using ChatGPT’s new image generator to take part in viral social media trends. But using it also puts your privacy at risk—unless you take a few simple steps to protect yourself.

By Kate O’Flaherty

At the start of April, an influx of action figures started appearing on social media sites including LinkedIn and X. Each figure depicted the person who had created it with uncanny accuracy, complete with personalized accessories such as reusable coffee cups, yoga mats, and headphones.

All this is possible because of OpenAI’s new GPT-4o-powered image generator, which supercharges ChatGPT’s ability to edit pictures, render text, and more. OpenAI’s ChatGPT image generator can also create pictures in the style of Japanese animated film company Studio Ghibli—a trend that quickly went viral, too.

The images are fun and easy to make—all you need is a free ChatGPT account and a photo. Yet to create an action figure or Studio Ghibli-style image, you also need to hand over a lot of data to OpenAI, which could be used to train its models.

Hidden Data

The data you are giving away when you use an AI image editor is often hidden. Every time you upload an image to ChatGPT, you’re potentially handing over “an entire bundle of metadata,” says Tom Vazdar, area chair for cybersecurity at Open Institute of Technology. “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.”

OpenAI also collects data about the device you’re using to access the platform. That means your device type, operating system, browser version, and unique identifiers, says Vazdar. “And because platforms like ChatGPT operate conversationally, there’s also behavioral data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

It’s not just your face. If you upload a high-resolution photo, you’re giving OpenAI whatever else is in the image, too—the background, other people, things in your room and anything readable such as documents or badges, says Camden Woollven, group head of AI product marketing at risk management firm GRC International Group.

This type of voluntarily provided, consent-backed data is “a gold mine for training generative models,” especially multimodal ones that rely on visual inputs, says Vazdar.

OpenAI denies it is orchestrating viral photo trends as a ploy to collect user data, yet the firm certainly gains an advantage from it. OpenAI doesn’t need to scrape the web for your face if you’re happily uploading it yourself, Vazdar points out. “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI says it does not actively seek out personal information to train models—and it doesn’t use public data on the internet to build profiles about people to advertise to them or sell their data, an OpenAI spokesperson tells WIRED. However, under OpenAI’s current privacy policy, images submitted through ChatGPT can be retained and used to improve its models.

Any data, prompts, or requests you share helps teach the algorithm—and personalized information helps fine tune it further, says Jake Moore, global cybersecurity adviser at security outfit ESET, who created his own action figure to demonstrate the privacy risks of the trend on LinkedIn.

Uncanny Likeness

In some markets, your photos are protected by regulation. In the UK and EU, data-protection regulation including the GDPR offer strong protections, including the right to access or delete your data. At the same time, use of biometric data requires explicit consent.

However, photographs become biometric data only when processed through a specific technical means allowing the unique identification of a specific individual, says Melissa Hall, senior associate at law firm MFMac. Processing an image to create a cartoon version of the subject in the original photograph is “unlikely to meet this definition,” she says.

Meanwhile, in the US, privacy protections vary. “California and Illinois are leading with stronger data protection laws, but there is no standard position across all US states,” says Annalisa Checchi, a partner at IP law firm Ionic Legal. And OpenAI’s privacy policy doesn’t contain an explicit carve-out for likeness or biometric data, which “creates a grey area for stylized facial uploads,” Checchi says.

The risks include your image or likeness being retained, potentially used to train future models, or combined with other data for profiling, says Checchi. “While these platforms often prioritize safety, the long-term use of your likeness is still poorly understood—and hard to retract once uploaded.”

OpenAI says its users’ privacy and security is a top priority. The firm wants its AI models to learn about the world, not private individuals, and it actively minimizes the collection of personal information, an OpenAI spokesperson tells WIRED.

Meanwhile, users have control over how their data is used, with self-service tools to access, export, or delete personal information. You can also opt out of having content used to improve models, according to OpenAI.

ChatGPT Free, Plus, and Pro users can control whether they contribute to future model improvements in their data controls settings. OpenAI does not train on ChatGPT Team, Enterprise, and Edu customer data⁠ by default, according to the company.

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LADBible and Yahoo News: Viral AI trend could present huge privacy concerns, says expert
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 12, 2025 4 min read

Source:


You’ve probably seen them all over Instagram

By James Moorhouse

Experts have warned against participating in a viral social media trend which sees people use ChatGPT to create an action figure version of themselves.

If you’ve spent any time whatsoever doomscrolling on Instagram or TikTok or dare I say it, LinkedIn recently, you’ll be all too aware of the viral trend.

Obviously, there’s nothing more entertaining and frivolous than seeing AI generated versions of your co-workers and their cute little laptops and piña coladas, but it turns out that it might not be the best idea to take part.

There may well be some benefits to artificial intelligence but often it can produce some pretty disturbing results. Earlier this year, a lad from Norway sued ChatGPT after it falsely claimed he had been convicted of killing two of his kids.

Unfortunately, if you don’t like AI, then you’re going to have to accept that it’s going to become a regular part of our lives. You only need to look at WhatsApp or Facebook messenger to realise that. But it’s always worth saying please and thank you to ChatGPT just in case society does collapse and the AI robots take over, in the hope that they treat you mercifully. Although it might cost them a little more electricity.

Anyway, in case you’re thinking of getting involved in this latest AI trend and sharing your face and your favourite hobbies with a high tech robot, maybe don’t. You don’t want to end up starring in your own Netflix series, à la Black Mirror.

Tom Vazdar, area chair for cybersecurity at Open Institute of Technology, spoke with Wired about some of the dangers of sharing personal details about yourself with AI.

Every time you upload an image to ChatGPT, you’re potentially handing over ‘an entire bundle of metadata’ he revealed.

Vazdar added: “That includes the EXIF data attached to the image file, such as the time the photo was taken and the GPS coordinates of where it was shot.

“Because platforms like ChatGPT operate conversationally, there’s also behavioural data, such as what you typed, what kind of images you asked for, how you interacted with the interface and the frequency of those actions.”

Essentially, if you upload a photo of your face, you’re not just giving AI access to your face, but also the whatever is in the background, such as the location or other people that might feature.

Vazdar concluded: “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

While we’re at it, maybe stop using ChatGPT for your university essays and general basic questions you can find the answer to on Google as well. The last thing you need is AI knowing you don’t know how to do something basic if it does takeover the world.

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