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The world is facing a global AI talent shortage, so while there’s a great demand for NLP implementations, the supply of data scientists needed to bring these projects to life are limited. But what if we could democratize NLP, reducing the need for data scientist intervention?
2021 has been a huge year of growth for natural language processing (NLP). As the technology becomes more mainstream, we’re starting to see greater adoption across industries, leading to new and innovative use cases. While this momentum doesn’t seem to be slowing down, NLP doesn’t come without challenges, and to overcome those, the barrier to entry needs to be significantly lower.
The world is facing a global AI talent shortage, so while there’s a great demand for NLP implementations, the supply of data scientists needed to bring these projects to life are limited. This is especially problematic when you consider that NLP pipelines, where the results from a previous task and pre-trained model are used downstream, need to be constantly monitored and tweaked to remain accurate.
But what if we could democratize NLP, reducing the need for data scientist intervention? What if we could put this technology into the hands of a diverse set of users to spark action and innovation in different fields? Thankfully, this is now possible, and it’s one of the factors that will drive NLP growth in the coming year.
No-code solutions, which require no programming experience, are one of the key contributors to the advancement of NLP. Additionally, progress in tuning models and deploying them at scale, and the growth of multimodal tools — for example, using NLP paired with computer vision for more accurate results — will also shine in 2022. But first, we need to understand why.
SEE ALSO: Remember, you’re a bot: Why product managers must be the boss of NLP
Low-code has been a big trend over the last few years. While low-code solutions have certainly made work easier for technologists, that’s only half the battle. Less burden on overworked data scientists is a positive step, but it doesn’t get to the root of the problem. First, there is a very real AI skills gap. Second, data scientists are not always best-suited to do the job.
Alternatively, no-code solutions are gaining steam and will make AI and ML more approachable for users of any competency level. By putting more power in the hands of domain experts, you eliminate the need for highly-coveted programming expertise, making access to NLP more equitable. This is similar to the difference between paying a data scientist to write code and using Excel. Non-technical users now have an entry point to NLP, and that will help the field mature.
Multimodal learning techniques are another area that will take NLP to the next level in the new year. While NLP models are great at processing text, many real-word applications use documents with more complex formats. For example, a law office may have casework that includes visual components, police reports, legal contracts, and other scanned documents. When NLP is used alone for document understanding, the layout and style can be compromised.
Multimodal learning models can learn from both the text in documents via NLP and visual layout through technologies like computer vision. Combining multiple technologies into a certain solution to enable better results will make the technology more accurate, which should give users even more confidence in the tools they’re using, whether a seasoned developer or a domain expert just getting started.
SEE ALSO: What is Data Annotation and how is it used in Machine Learning?
While advances like multimodal learning techniques help render more accurate results, continually tuning models is a necessary undertaking when it comes to NLP. That means continuous monitoring and tweaking over time to prevent model degradation as production environments evolve. Thankfully, we are getting better at this, and the gradual shift from data scientist to domain expert will only help large-scale deployments further.
By enabling domain experts to adjust models, we enable them to correctly tune models to specific situations. For example, a model created to predict heart failure may perform less accurately across different medical centers that have different protocols and processes in place. Considering how models will behave in different environments is crucial to their efficacy. And who better to tune these models than a domain expert — in this case, a medical professional?
No-code, multimodal learning techniques, and improvements with large scale deployments will bolster NLP growth in 2022. With more accurate tools and a broader set of professionals to put NLP to use, it will be exciting to see the new use cases and progress we make over the next 12 months.
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