What Is NLP? Natural Language Processing Explained
Natural language processing is a branch of AI that enables computers to understand, process, and generate language just as people do — and its use in business is rapidly growing.
Natural language processing definitionNatural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all
While the term originally referred to a system's ability to read, it's since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) — a computer's ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language.
The introduction of transformer models in the 2017 paper "Attention Is All You Need" by Google researchers revolutionized NLP, leading to the creation of generative AI models such as Bidirectional Encoder Representations from Transformer (BERT) and subsequent DistilBERT — a smaller, faster, and more efficient BERT — Generative Pre-trained Transformer (GPT), and Google Bard.
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How natural language processing worksNLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they're processed using grammatical rules, people's real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next. For example, a translation algorithm that recognizes that, in French, "I'm going to the park" is "Je vais au parc" will learn to predict that "I'm going to the store" also begins with "Je vais au." All the algorithm then needs is the word for "store" to complete the translation task.
NLP applicationsMachine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're helping to train the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future.
Chatbots work the same way. They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like "Hey, Alexa." That's why critics say these programs are always listening; if they weren't, they'd never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.
Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.
Rajeswaran V, senior director at Capgemini, notes that Open AI's GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says.
NLP systems that rely on transformer models are especially strong at NLG.
Natural language processing examplesData comes in many forms, but the largest untapped pool of data consists of text — and unstructured text in particular. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:
Whether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:
There's a wide variety of resources available for learning to create and maintain NLP applications, many of which are free. They include:
Here are some of the most popular job titles related to NLP and the average salary (in US$) for each position, according to data from PayScale.
5 Ways Google Is Improving Their Search Engine Features
Over the years google has tackled several deep, technical challenges in order to develop it into what it is today. There is a lot that has gone into building google to be the search engine that it is today. However, the reality is that search is never a solved problem – there is constantly a need to evolve and with that a number of problems arise along the journey.
Here are some of the biggest technological breakthroughs google has made over the years— and how they continue to push the boundaries of innovation — as they build and improve their search engine.
1. Delivering Quality ResultsGoogle places emphasis on ranking information not only on what is relevant but what is likely to be reliable and helpful. This insight is what set the search engine part from day one. Their PageRank algorithm not only takes into account whether words on a page match, but how sites link to one another that reveals which pages are important or authoritative.
Google has also focused on adapting techniques as the web evolves and as technology improves. For example, with the rise in misinformation, they've developed ways to recognize if topics might be more susceptible to unreliable content, like conspiracy theories or medical misinformation. Once they have determined the reliability of the content google readjusts their ranking towards content that is more authoritative.
Google also conducts thousands of quality evaluations every year to make sure they're meeting high bar for quality. They regularly make broad updates to their systems, called core updates, including more specialized updates, like helpful content updates, to continue delivering useful results.
The company also displays notices when topics change or when they don't have high confidence in the quality of results to caution people to approach results with caution. They also invest heavily in information literacy tools to help people check sources and get proper context in order to evaluate findings.
2. Deciphering MeaningOne of Google's core focus is evaluating and understanding information and queries. In the past their systems were built on word matching, but now factors like spelling has become important. In the past, when a spelling mistake was made, web pages with incorrect spelling of the search terms would appear – to tackle this issue google built their first ML system in search.
Google's Knowlegde Graph gave the company insight into how people, places and things in the world relate to each other – providing them with a more humanistic understanding of world.
Large language models like BERT, developed by the Google Research team, helped the team to decipher natural language queries, in order to deliver relevant results across languages used around the world.
These models can take learnings from one language and apply them to others, to return better results in the many languages that Search is offered in. They also built various tools like Google Translate to break assist in breaking down language barriers.
3. Understanding Images, Videos and MoreThrough making use of the latest developments in natural language processing (NLP), in 2008 they launched a feature to search with voice prompts.
Google took this a step further through developing a "hum to search" feature for moments where you have a tune stuck in your mind and want to find the song but don't know the lyrics.
in 2015, advances in computer vision made it possible to search what you see with Lens. The mobile phone camera was transformed into a way to explore and ask questions about the world. For example if you saw a flower you could take a phone and google would give you more information about that flower.
Today people conduct 12 billion visual searches every month with Lens. Last year, the company launched multisearch, which allows users to add text to visual searches.
4. Spotting and Stopping SpamAnyone who has ever looked into their email spam folder can appreciate all the work that goes into keeping that junk out of their inbox. On Search, google has built advanced systems to fight spam. Without their advanced protections, search results would be clogged with completely irrelevant information, phishing attempts and links to malware.
They are constantly developing new techniques and implementing updates to their ranking systems to protect against spam. But spam also adapts and evolves, requiring constant attention.
In recent years, the team have applied new AI-powered techniques to spam detection, which has helped them to keep search results over 99% spam free. This remains a big area of investment for them: as long as people come to Google looking for information, spammers will continue to try and breach protections.
5. Making Search SaferOver the years, the company has maintained a strong commitment to their principles of maximizing access to information, while helping people stay safe and in control. They aim to help people find information that's within the bounds of legal expression, while not inadvertently exposing them to low-quality or harmful content that they haven't asked to see.
They have done this through expanding policy protections for people to remove sensitive personal information from results, and through improving their ranking systems with safety and inclusivity in mind.
An example of this would be their launching of improvements towards reducing unwanted explicit content from ranking highly in Search and updates to blur explicit imagery by default, and ranking improvements to limit the reach of sites that use exploitive practices.
They've also updated policies so that people under the age of 18 can have images of themselves removed from Search, and launched new tools like results about you to make it easy to control how their personal information shows up in search results.
Google Bard Glossary: Your Quick Reference Guide
This Google Bard glossary is designed to be a quick reference guide where you can easily answer any questions you may have about Bard. In the rapidly evolving world of artificial intelligence, Google has introduced "Bard," a cutting-edge conversational AI chat service. For those diving into this technological marvel, understanding its components and implications is crucial. Here's a glossary to help you navigate the world of Google Bard.
1. Bard:An experimental AI chat service developed by Google. It leverages the power of LaMDA to generate text, translate languages, craft creative content, and provide informative answers.
2. LaMDA:A state-of-the-art neural network language model trained on vast datasets of text and code. It's the engine behind Google Bard, aiding in tasks like text generation, language translation, and creative content creation.
3. Generative AI:A branch of AI that specializes in creating new content, be it text, images, or music. Google Bard exemplifies this, offering diverse content creation capabilities.
4. Dialogue System:Computer programs are designed to converse with human users. Google Bard stands as a testament to this technology, facilitating discussions on myriad topics.
5. Natural Language Processing (NLP):A domain of computer science dedicated to understanding and generating human language. Bard employs NLP to comprehend user queries and craft appropriate responses.
6. Machine Learning:A discipline within computer science that empowers computers to learn autonomously, without explicit programming. Bard harnesses machine learning to refine its conversational abilities.
7. Deep Learning:A subset of machine learning, it utilizes artificial neural networks to decipher data patterns. Bard incorporates deep learning to enhance its user interactions.
8. Neural Network:Mathematical models inspired by human brain structures. Bard relies on these networks to process and respond to user inputs.
9. Dataset:Collections of data used to train machine learning models. Bard's proficiency stems from its training on an extensive dataset of text and code.
10. Model:In AI, a model represents systems, like Bard, which mimics human conversation patterns.
11. Training:The act of teaching machine learning models specific tasks. Bard's expertise is honed through training on vast text and code datasets.
12. Evaluation:Assessing a machine learning model's performance. Bard's efficacy is gauged based on its user interactions.
13. Deployment:The act of making a machine learning model accessible for use. Bard is hosted on Google's servers, ready for user interactions.
14. User:Individuals like you, interact with Google Bard, provided they have internet access.
15. Developer:The brains behind software creation or modification. Google Bard is the brainchild of Google's elite AI team.
16. Privacy:Rest assured, your interactions with Bard remain confidential, neither stored nor shared.
17. Security:Safety first! Conversations with Bard are encrypted, ensuring user data protection.
18. Limitations:While promising, Bard, being in development, has its constraints, occasionally misinterpreting queries or providing less-than-perfect responses.
19. Potential:Bard holds promise as a revolutionary tool for communication, learning, and artistic endeavors, bridging global communication gaps.
20. Future:With Google's relentless AI advancements, Bard's future shines bright, potentially evolving into an entity indistinguishable from human conversation.
21. Bias:A predisposition that can inadvertently favor one aspect over another. While Bard is trained on extensive datasets, it's essential to recognize the potential for inherent biases in its responses.
22. Fairness:The principle of impartiality. Bard aims for unbiased responses, but users should be conscious of any unintended slants in its interactions.
23. Responsibility:The duty to act ethically and for the greater good. While Bard is engineered for responsible interactions, users should be aware of its potential misuse.
24. Accountability:The obligation to justify actions. Bard, and its developers at Google, are answerable for its performance and any unintended consequences.
25. Transparency:The commitment to open and clear information sharing. Users can delve into Bard's workings and training methodologies, ensuring clarity in its operations.
26. Trust:The foundation of any AI-human interaction. Bard is designed to be reliable, but users should remain discerning and critical.
27. Safety:Protection from potential harm. Bard prioritizes user safety, but it's vital to be cautious of any misuse that could compromise this.
28. Ethics:The moral compass guiding actions. Bard is built with ethical considerations at its core, but users should remain vigilant about potential ethical dilemmas in its usage.
29. Law:A binding set of rules. Bard is crafted to adhere to legal standards, but users should be informed about any potential legal implications of its use.
30. Regulation:The framework of laws and rules governing a domain. Bard operates within the boundaries set by governing bodies, ensuring its compliance with established norms.
In conclusion, Google Bard is not just a technological marvel; it's a testament to the advancements in AI and the potential it holds for the future. This glossary serves as a primer for enthusiasts and professionals alike, ensuring a comprehensive understanding of this groundbreaking conversational AI.
We hope that you find this Google Bard glossary helpful and informative, if you have any comments, questions or suggestions, please leave a comment below and let us know. You can find out more details about Google Bard over at Google's website.
Image Credit: Mojahid Mottakin
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