How the White House sees the future of safeguarding AI

Traditionally, large language models powering generative AI tools were available exclusively in the commercial cloud. However, Microsoft has designed a new architecture that enables government agencies to access these language models from Azure Government securely. This ensures that users can maintain the security required for government cloud operations. At AWS, we’re excited about generative AI’s potential to transform public sector organizations of all sizes.

For example, in an AI-powered process automation solution, the entire business process and all underlying systems should be equally secure and compliant – not just the AI-enabled tasks inside of it. Copyright infringement is another unresolved issue with large language models like GPT4. CogAbility’s best-in-class chatbot technology uses 8 different methods https://www.metadialog.com/governments/ to deliver the right answer to each constituent’s question – including carefully-worded responses approved by management where necessary. This data can also be a lucrative target for cyber attackers who seek to steal or manipulate sensitive information. Most AI systems today rely on large amounts of data to learn, predict, and improve themselves over time.

Staff Shortage, Limited Budgets, and Antiquated Systems: The Federal Government’s Need for Conversational AI

Further, an already controversial application of AI, facial recognition, is being used by law enforcement to identify suspects and has recently garnered much attention due to the wrongful arrest of a man in Georgia who was mistaken for a fugitive by Louisiana authorities’ racial recognition technology. With the Gender Shades project revealing the inaccuracies of facial recognition technology for darker-skinned individuals and both the victim and fugitive being Black, this highlights the need to ensure that AI systems, particularly those used in high-risk contexts, are not biased and are accurate for all subgroups. As such, the UK’s Equality and Human Rights Commission has called for suspending facial recognition in policing in England and Wales, with similar action being taken in Washington’s city of Bellingham and Alabama.

The generative AI boom has reached the US federal government, with Microsoft announcing the launch of its Azure OpenAI Service that allows Azure government customers to access GPT-3 and 4, as well as Embeddings. Many people do not understand that when you submit queries and training data to a large language model owned by someone else, you are explicitly giving them the right to use this data in future generations of the technology. For example, although OpenAI’s GPT4 model is much better than earlier versions at generating false positives (aka, “lying”), the model still gets facts wrong 10% of the time.

Ensure Notebooks are Secure

We believe the likelihood for this is high enough to warrant their targeted regulation. Currently there are no broad-based regulations focusing on AI safety, and the first set of legislation by the European Union is yet to become law as lawmakers are yet to agree on several issues. From a political standpoint, a difficulty in gaining acceptance of this policy is the fact that stakeholders will view this as an impediment to their development and argue that they should not be regulated either because 1) it will place an undue burden on them, or 2) they do not fall into a “high-risk” use group. Regulators must balance security concerns with the burdens placed upon stakeholders through compliance. From an implementation standpoint, a difficulty in implementing this policy will be managing the large number and disparate nature of entities, ranging from the smallest startups to the largest corporations, that will be implementing AI systems. Because different stakeholders face unique challenges that may not be applicable in other areas, regulators should tailor compliance to their constituents in order to make the regulation germane to their industry’s challenges.

Effective leadership also means pioneering those systems and safeguards needed to deploy technology responsibly — and building and promoting those safeguards with the rest of the world. My Administration will engage with international allies and partners in developing a framework to manage AI’s risks, unlock AI’s potential for good, and promote common approaches to shared challenges. The unfettered building of artificial intelligence into these critical aspects of society is weaving a fabric of future vulnerability. Policymakers must begin addressing this issue today to protect against these dangers by creating AI security compliance programs. These programs will create a set of best practices that will ensure AI users are taking the proper precautionary steps to protect themselves from attack.

To mitigate the creation of fake news, deep fakes, and the like, regulators in the US and EU are planning to create mechanisms to help end users distinguish the origin of content. While there’s no law yet, the recent executive order in the U.S. requires the Department of Commerce to issue guidance on tools and best practices for identifying AI-generated synthetic content. AI regulation in the US has been under great debate by US policymakers regarding how AI development and usage should be governed and what Secure and Compliant AI for Governments degree of legal and ethical oversight is needed. To date, the US has been strong in empowering existing governmental agencies to ensure law enforcement around AI. Led by Nic Chaillan, the Ask Sage team leverages extensive industry experience to address the unique challenges and requirements of its clients. Currently, over 2,000 government teams and numerous commercial contractors benefit from Ask Sage’s expertise and security features, including data labeling capabilities and zero-trust cybersecurity.

For the riskiest systems—such as those that could cause catastrophic damage if misused—we might need a “safety case” regime, where companies make an affirmative case to a regulator that these systems do not pose unacceptable risks to society. Much like in other safety-critical industries, such as pharmaceuticals, aviation, and nuclear power, it should be the companies’ responsibility to prove their products are safe enough, for example, via a broad range of safety evaluations. The role of regulators should be to probe the evidence presented to them and determine what risks are acceptable. Flipping the script—disallowing only those models that have been proved unsafe by the regulator—appears inappropriate, as the risks are high and industry has far more technical expertise than the regulator. Effective frontier AI regulation would require that developers of the most capable systems make a substantial effort, using a significant amount of resources, to understand the risks their systems might pose—in particular by evaluating whether their systems have dangerous capabilities or are insufficiently controllable. These risk assessments should receive thorough external scrutiny from independent experts and researchers and inform regulatory decisions about whether new models are deployed and, if so, with what safeguards.

The Protect AI platform provides Application Security and ML teams the visibility and manageability required to keep your ML systems and AI applications secure from unique AI vulnerabilities. Whether your organization is fine tuning an off-the-shelf Generative AI foundational model, or building custom ML models, our platform empowers your entire organization to embrace a security-first approach to AI. Deepset has long been committed to helping organizations navigate the evolving AI regulatory landscape.

Further, once adversaries develop an attack, they may exercise extreme caution in their application of it in order to not arouse suspicion and to avoid letting their opponent know that its systems have been compromised. In this respect, there may be no counter-indications to system performance until after the most serious breach occurs. As content filters are drafted into these battles, there will be strong incentives both to attack them and to generate tools making these attacks easier to execute. Adversaries have already seen the power of using digital platforms in pursuit of their mission. ISIS organically grew an international following and successfully executed a large-scale recruitment program using social media.

As creators of this technology, Microsoft is careful in ensuring that the ethical use of AI is being practiced and that other organizations developing derivative products on top of this technology do the same. With Azure Open AI, organizations can enable sensitivity controls in their Azure tenant to maintain data security by ensuring it’s isolated to your Azure tenant. Mitigating the risks means improving your AI literacy – and you must be ready to get your hands dirty. You need to understand how it functions and test it, then educate people on how to use it and how not to use it to ensure your workforce can safely use it. If organizations aren’t mindful of these challenges, it will affect real people involved in data-driven decisions derived from these results.

In some settings, attacks on physical objects may require larger, coarser attack patterns. This is because these physical objects must first be digitized, for example with a camera or sensor, to be fed into the AI algorithm, a process that can destroy finer level detail. However, even with this digitization requirement, attacks may still be difficult to perceive. The “attack turtle” that is incorrectly classified as a rifle in the example shown below is one such example of a physical attack that is nearly invisible. On the other end of the visibility axis are “imperceivable” attacks that are invisible to human senses.

When you use public chatbots like ChatGPT, anything you put in can become accessible outside your data environment. Or, as you go deeper into AI like Microsoft Copilot, you can establish proper data security practices by enabling access controls and proper visibility into what your users can access to ensure that AI is only doing what you want. To tackle the challenges of the ethical use of AI, it’s then critical for organizations to include all communities involved – more importantly, minorities and other communities – not only in the research phase but also in the governance aspect of their programs. While generative AI like ChatGPT or diffusion models for imaging like DALL-E open various opportunities for efficiency and productivity, they can also be used to spread misinformation. This raises complex human social factors that require policy and possibly regulatory solutions. Or, more relevantly, resembling the online personal data collection situation in the late 2000s, when nearly everyone would collect all they could lay their hands on.

Responsible AI is our overarching approach that has several dimensions such as ‘Fairness’, ‘Interpretability’, ‘Security’, and ‘Privacy’ that guide all of Google’s AI product development. (xxix)    the heads of such other agencies, independent regulatory agencies, and executive offices as the Chair may from time to time designate or invite to participate. (f)  To facilitate the hiring of data scientists, the Chief Data Officer Council shall develop a position-description library for data scientists (job series 1560) and a hiring guide to support agencies in hiring data scientists. (ix)    work with the Security, Suitability, and Credentialing Performance Accountability Council to assess mechanisms to streamline and accelerate personnel-vetting requirements, as appropriate, to support AI and fields related to other critical and emerging technologies.

• In addition to a technical focus on securing models, research attention should also focus on creating testing frameworks that can be shared with industry, government, and military AI system operators.
• Whether you need to caption a legislative proceeding, municipal council meeting, press briefing or an ad campaign, our solutions make it easy and cost-effective.
• Additionally, the memorandum will direct actions to counter potential threats from adversaries and foreign actors using AI systems that may jeopardize U.S. security.
• To reduce the chance of bioterrorism attacks, access to systems that could identify novel pathogens may need to be restricted to vetted researchers.

For example, CrowdStrike now offers a generative AI security analyst called Charlotte AI that uses high-fidelity security data in a tight human feedback loop to simplify and speed investigations, and react quickly to threats. While AI is not new, and the government has been working with AI for quite some time, there are still challenges with AI literacy and the fear of what risks AI can bring to organizations. The problem was that when ChatGPT came out and captured the world’s attention, there was widespread sensationalism in this “new” technology.

In the UK, National Health Service (NHS) formed an initiative to collect data related to COVID patients to develop a better understanding of the virus. Through various partnerships, the NHS set up the National COVID-19 Chest Imaging Database (NCCID), an open-source database of chest X-rays of COVID patients across the UK. This initiative aimed to develop deep learning techniques meant to provide better care for hospitalized COVID-19 patients. This includes monitoring weight, height, blood glucose, stress levels, heart rate, etc., and feeding this information to AI healthcare systems, which can notify doctors of any possible risks. There is little doubt that the emerging science of artificial intelligence is continuing to advance and grow, both in capabilities and the number of things that AI is being tasked with doing. And this is happening at lightning speed, despite there being little to no regulation in place to govern its use.

NLP vs NLU: Whats The Difference? BMC Software Blogs

NLU Chatbots can make the support process easier, faster and more convenient for users, support staff and enterprises. 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. However, the challenge in translating content is not just linguistic but also cultural. Language is deeply intertwined with culture, and direct translations often fail to convey the intended meaning, especially when idiomatic expressions or culturally specific references are involved.

Already applied in healthcare, education, marketing, advertising, software development, and finance, they actively permeate the human resources field. For example, for HR specialists seeking to hire Node.js developers, the tech can help optimize the search process to narrow down the choice to candidates with appropriate skills and programming language knowledge. 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. NLP is growing increasingly sophisticated, yet much work remains to be done.

Definition & principles of natural language processing (NLP)

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. 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. However, NLU lets computers understand “emotions” and “real meanings” of the sentences.

GLUE and its superior SuperGLUE are the most widely used benchmarks to evaluate the performance of a model on a collection of tasks, instead of a single task in order to maintain a general view on the NLU performance. They consist of nine sentence- or sentence-pair language understanding tasks, similarity and paraphrase tasks, and inference tasks. Currently, the quality of NLU in some non-English languages is lower due to less commercial potential of the languages. 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.

2 min read – With rapid technological changes such as cloud computing and AI, learn how to thrive in the foundation model era. 5 min read – Governments around the world are taking strides to increase production and use of alternative energy to meet energy consumption demands.

Natural Language Processing

A chatbot using NLP will keep track of information throughout the conversation and learn as they go, becoming more accurate over time. And AI-powered chatbots have become an increasingly popular form of customer service and communication. From answering customer queries to providing support, AI chatbots are solving several problems, and businesses are eager to adopt them. 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. 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.

It aims to highlight appropriate information, guess context, and take actionable insights from the given text or speech data. The tech builds upon the foundational elements of NLP but delves deeper into semantic and contextual language comprehension. Involving tasks like semantic role labeling, coreference resolution, entity linking, relation extraction, and sentiment analysis, NLU focuses on comprehending the meaning, relationships, and intentions conveyed by the language.

NLP vs. NLU: from Understanding a Language to Its Processing

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. Whether it’s simple chatbots or sophisticated AI assistants, NLP is an integral part of the conversational app building process. And the difference between NLP and NLU is important to remember when building a conversational app because it impacts how well the app interprets what was said and meant by users.

Developers can address such challenges by encoding appropriate rules into their NLU systems along with training to apply the grammar rules correctly. Natural Language Understanding is the part of Natural Language Processing that deals with understanding and is, therefore, the most challenging part for a machine to decode. It involves the computer understanding each word’s meaning, analyzing the word as a noun or a verb, its tense and so on. Part-of-speech tagging (POS), a lexicon or a vocabulary and a set of grammatical rules are embedded into the system’s algorithm. This question can be matched with similar messages that customers might send in the future. The rule-based chatbot is taught how to respond to these questions — but the wording must be an exact match.

NLG systems enable computers to automatically generate natural language text, mimicking the way humans naturally communicate — a departure from traditional computer-generated text. NLU enables computers to understand the sentiments expressed in a natural language used by humans, such as English, French or Mandarin, without the formalized syntax of computer languages. NLU also enables computers to communicate back to humans in their own languages. “NLU and NLP have had a huge impact on the customer experience,” said Zheng.

These technologies enable companies to sift through vast volumes of data to extract actionable insights, a task that was once daunting and time-consuming. 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. In this case, NLU can help the machine understand the contents of these posts, create customer service tickets, and route these tickets to the relevant departments.

The LLM then answers the question with a few possibilities that you can dig into and verify. Here’s a crash course on how NLP chatbots work, the difference between NLP bots and the clunky chatbots of old — and how next-gen generative AI chatbots are revolutionizing the world of NLP. Here is a benchmark article by SnipsAI, AI voice platform, comparing F1-scores, a measure of accuracy, of different conversational AI providers. For example, a recent Gartner report points out the importance of NLU in healthcare. NLU helps to improve the quality of clinical care by improving decision support systems and the measurement of patient outcomes. We serve over 5 million of the world’s top customer experience practitioners.

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. NLU allows computer applications to infer intent from language even when the written or spoken language is flawed. Automated responses driven by predetermined patterns of user correspondence are fed into the programing of chatbots to generate default responses for frequently asked queries and questions. It helps reduce response time, optimize human resource deployment and control costs for enterprises.

That means your bot builder will have to go through the labor-intensive process of manually programming every single way a customer might phrase a question, for every possible question a customer might ask. The stilted, buggy chatbots of old are called rule-based chatbots.These bots aren’t very flexible in how they interact with customers. And this is because they use simple keywords or pattern matching — rather than using AI to understand a customer’s message in its entirety. Understanding the differences between these technologies and their potential applications can help individuals and organizations better leverage them to achieve their goals and stay ahead of the curve in an increasingly digital world. Together with NLG, they will be able to easily help in dealing and interacting with human customers and carry out various other natural language-related operations in companies and businesses.

The key distinctions are observed in four areas and revealed at a closer look. A number of advanced NLU techniques use the structured information provided by NLP to understand a given user’s intent. Natural Language Generation(NLG) is a sub-component of Natural language processing that helps in generating the output in a natural language based on the input provided by the user. This component responds to the user in the same language in which the input was provided say the user asks something in English then the system will return the output in English. Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions. Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs.

Humanizing AI, with Ultimate

NLP is a branch of AI that allows more natural human-to-computer communication by linking human and machine language. Artificial Intelligence (AI) is the creation of intelligent software or hardware to replicate human behaviors in learning and problem-solving areas. Worldwide revenue from the AI market is forecasted to reach USD 126 billion by 2025, with AI expected to contribute over 10 percent to the GDP in North America and Asia regions by 2030. With NLP, we reduce the infinity of language to something that has a clearly defined structure and set rules. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission.

Computers thrive at finding patterns when provided with this kind of rigid structure. Natural Language Processing(NLP) is a subset of Artificial intelligence which involves communication between a human and a machine using a natural language than a coded or byte language. It provides the ability to give instructions to machines in a more easy and efficient manner.

• Matching word patterns, understanding synonyms, tracking grammar — these techniques all help reduce linguistic complexity to something a computer can process.
• It encompasses methods for extracting meaning from text, identifying entities in the text, and extracting information from its structure.NLP enables machines to understand text or speech and generate relevant answers.
• From answering customer queries to providing support, AI chatbots are solving several problems, and businesses are eager to adopt them.
• This enhances the customer experience, making every interaction more engaging and efficient.

Help your business get on the right track to analyze and infuse your data at scale for AI. A Chatbot is one of the most advanced forms of interaction between humans and machines. NLU Chatbots represents the evolution of a ‘Question-Answer System’ that leverages Natural Language Processing. It formulates answers to questions in natural language and is widely applied in end-use applications by various enterprises.

For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions. But while playing chess isn’t inherently easier than processing language, chess does have extremely well-defined rules. There are certain moves each piece can make and only a certain amount of space on the board for them to move.

In addition to processing natural language similarly to a human, NLG-trained machines are now able to generate new natural language text—as if written by another human. All this has sparked a lot of interest both from commercial adoption and academics, making NLP one of the most active research topics in AI today. NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently.

And also the intents and entity change based on the previous chats check out below. Here the user intention is playing cricket but however, there are many possibilities that should be taken into account. You can foun additiona information about ai customer service and artificial intelligence and NLP. It is quite common to confuse specific terms in this fast-moving field of Machine Learning and Artificial Intelligence. The above is the same case where the three words are interchanged as pleased.

This is achieved by the training and continuous learning capabilities of the NLU solution. There has been no drop-off in research intensity as demonstrated by the 93 language experts, 54 of which work in NLP or AI, who were ranked in the top 100,000 most-cited scientists in Elsevier BV’s updated author-citation dataset. Here are some of the best NLP papers from the Association for Computational Linguistics 2022 conference. Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) all fall under the umbrella of artificial intelligence (AI).

The application of NLU and NLP technologies in the development of chatbots and virtual assistants marked a significant leap forward in the realm of customer service and engagement. These sophisticated tools are designed to interpret and respond to user queries in a manner that closely mimics human interaction, thereby providing a seamless and intuitive customer service experience. 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. 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.

While human beings effortlessly handle verbose sentences, mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are typically less adept at handling unpredictable inputs. In the lingo of chess, NLP is processing both the rules of the game and the current state of the board. An effective NLP system takes in language and maps it — applying a rigid, uniform system to reduce its complexity to something a computer can interpret. Matching word patterns, understanding synonyms, tracking grammar — these techniques all help reduce linguistic complexity to something a computer can process. NLP and NLU are significant terms for designing a machine that can easily understand the human language, whether it contains some common flaws. With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5).

In AI, two main branches play a vital role in enabling machines to understand human languages and perform the necessary functions. A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand. Instead of relying on computer language syntax, NLU enables a computer to comprehend and respond to human-written text.

These notions are connected and often used interchangeably, but they stand for different aspects of language processing and understanding. Distinguishing between NLP and NLU is essential for researchers and developers to create appropriate AI solutions for business automation tasks. Natural Language Understanding Applications are becoming increasingly important in the business world. NLUs require specialized skills in the fields of AI and machine learning and this can prevent development teams that lack the time and resources to add NLP capabilities to their applications.

Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers. 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.

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. Understanding the sentiment and urgency of customer communications allows businesses to prioritize issues, responding first to the most critical concerns. 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. NLU, on the other hand, is more concerned with the higher-level understanding.

For example, in healthcare, NLP is used to extract medical information from patient records and clinical notes to improve patient care and research. NLP, NLU, and NLG are all branches of AI that work together to enable computers to understand and interact with human language. They work together to create intelligent chatbots that can understand, interpret, and respond to natural language queries in a way that is both efficient and human-like.

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. 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 nlp and nlu and more manageable chunks. These can then be analyzed by ML algorithms to find relations, dependencies, and context among various chunks. As a software and services vendor that has pioneered machine learning and AI in the customer experience space, we have fully embraced the power of LLMs to change the business landscape.

For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines. Sometimes you may have too many lines of text data, and you have time scarcity to handle all that data. NLG is used to generate a semantic understanding of the original document and create a summary through text abstraction or text extraction. 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.

Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities. Semantics is the process of using words and understanding the meaning behind those words. Natural language processing uses algorithms to understand the structure and purpose of sentences. Semantic techniques include word sense disambiguation and named entity recognition. One of the key benefits of generative AI is that it makes the process of NLP bot building so much easier.

The insights gained from NLU and NLP analysis are invaluable for informing product development and innovation. Companies can identify common pain points, unmet needs, and desired features directly from customer feedback, guiding the creation of products that truly resonate with their target audience. This direct line to customer preferences helps ensure that new offerings are not only well-received but also meet the evolving demands of the market. Both technologies are widely used across different industries and continue expanding.