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. NLP 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. 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 Lettria, 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.
The Importance of NLP
This is where NLP comes in and helps explain 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. Taking a viewpoint that accounts for all this information is impossible for a single person, even a large team.
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 have to overcome in the early stages of their projects, and usually, it requires multiple people with different expertise, 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 actually make it from prototyping to production.*
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.* Lettria was conceived to address these problems by unifying all of 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.