Natural Language Processing (NLP) has been a field of study for decades, but it has recently gained unprecedented attention due to rapid advancements in deep learning. involves using computers to interpret, understand, and generate human language. In its early days, NLP relied on rule-based systems, which were limited in their ability to handle the nuances of language. NLP However, with the advent of machine learning and deep learning, NLP has made significant strides in recent years. Today, from simple text conversations to advanced videos, the power of AI is constantly trending at the top of all our social media feeds. But what about the other side of the funnel? The AI images we come across in our feeds require terabytes of stock images to make it onto our screens. So how do we get from unique, human-made content to something developed by a computer? The Generative AI Revolution While natural language processing has received tons of attention in the field of AI, generative AI is also making great strides. From creating photorealistic images to writing entire news articles, generative AI has the potential to revolutionize the way we handle content. As this technology continues to develop, companies should feel obliged to integrate AI into their ways of working. By doing so, they can gain a competitive advantage by automating tasks and creating content at scale. However, it's important to approach this technology conscientiously, to make sure it’s being used by the right people for the right reasons. At , we believe in the power of AI to enhance and streamline workflows, but we also understand the importance of using this technology intentionally — making the best queries on the best data — with an action-oriented approach. Lettria The Importance of NLPs This is where NLP comes in, demonstrating where Lettria stands in the AI revolution. The online content that we create and inspire from our wider communities (emails, comments, reviews, voice recordings, etc.) can add up to insurmountable amounts of data. is impossible for a single person, even a large team. Taking a viewpoint that accounts for all this information Data Scientists develop NLP projects to analyze unstructured text data according to specifically calibrated parameters. They often spend many months refining these parameters and developing algorithms that are specifically calibrated to their datasets. The Challenges of NLP Projects Before data teams can even get to the point of annotating and querying their data, they have to compile everything into a single database — often taking into account multiple formats. These are just a couple of the many steps that experts must overcome in the early stages of their projects, and usually it requires multiple people with different skillsets, each working with separate coding languages and toolkits. From a business perspective, the resources required to deploy these in-depth projects can quickly get out of hand. As we often mention, 85% of NLP projects are destined to fail. From the cost of software solutions to the vast amounts of time our data teams require to develop a project pipeline, only 53% of these projects will make it from prototyping to production.¹ The Birth of Lettria A 2019 report by McKinsey & Company found that only 8% of companies were able to successfully scale AI across their organizations.² Lettria was conceived to address these problems by unifying all the siloed steps NLP projects historically require, and democratizing the project so that people from outside the data team can bring their insights, allowing for timelines to be shortened from many months to mere weeks. Since founding Lettria four years ago, the industry has grown, and we’ve adapted our software to think ahead and scale our solution in the direction that companies require. By developing an intricate understanding of the use-cases and applications that frequently inspire NLP projects, Lettria has streamlined the project pipeline to include the final steps and deliver crucial insights within the app. Lettria's Solution From capabilities that automatically scans audio transcripts for clients that deal with call-center data, to coordinating product catalogs so that e-commerce specialists can enhance their product catalogs to enable cross-sells, we’ve automated some of the most common needs that modern businesses face. Voice2CRM No-code platforms have become popular in recent years, and Lettria has taken this approach to enable collaborators with no programming experience to build complex applications, from management to production. It enables businesses to rapidly prototype, test, and deploy their product without the need for multiple developers — democratizing projects — by incorporating outside expertise and directly involving multiple departments in the pipeline. Lettria's Technical Expertise We’ve grown Lettria’s technical expertise over the 4 years of our startup’s history, integrating Linguists and Product Experts to give the most advanced solutions possible. Keeping up with innovative advancements in generative AI, Lettria’s development team is perfectly adapted to the latest trends in deep learning, enabling us to create models that outperform traditional NLP methods. Regarding output, we’ve developed solutions that allow users to automatically generate knowledge graphs and visualize insights, so that managers can rapidly deploy solutions and activate necessary actors in their organizations. We’ve also synchronized our software with your everyday essentials like Shopify, Salesforce and Tableau — allowing sales teams and business intelligence departments to leverage Lettria directly, without sacrificing any of the data team’s valuable time. From our newly revamped user experience to Lettria’s machine learning algorithms scaled hand-in-hand with our Linguistic team, deploying an NLP has never been simpler. Learn More About Lettria Read more about our linguistic work, success stories, and product development strategies on our website. Interested in seeing how our NLP software can work for you? . Get in touch and book a demo today 1. https://www.infoworld.com/article/3639028/why-ai-investments-fail-to-deliver.html 2. https://www.mckinsey.com/~/media/McKinsey/Business Functions/McKinsey Digital/Our Insights/Getting to scale with artificial intelligence/Getting-to-scale-with-artificial-intelligence.ashx The lead image for this article was generated by HackerNoon's via the prompt "ai revolution". AI Image Generator
