Natural Language Processing: recent breakthroughs

Natural Language Processing (NLP) has origins dating back to the 1950s, and the field has evolved significantly over the past few decades. Initially, NLP was heavily focused on linguistic aspects, concentrating on language structures and exploring ways for computers to grasp them. In contrast, today’s advancements leverage Big Data and modern Neural Networks, which, when sufficiently large, can decipher a vast array of relationships within the data. Additionally, these Neural Networks can learn tasks such as classification, prediction, and visualization solely through examples.

Recent progress in NLP is fundamentally linked to the utilization of Neural Networks and Deep Learning techniques. In the past decade, Deep Learning has risen to prominence, forming the foundation for innovations across various AI domains over the last five years. Guus van de Mond, a partner at Squadra and the founder of Machine Learning Company, illustrates Deep Learning with this analogy:

“Deep Learning essentially breaks a problem down into multiple layers. Each layer represents a specific function, defining an abstract model. Layers built upon one another can leverage information from their predecessors. For example, if you aim to train an algorithm to recognize a dog in a picture, the first layer might identify shapes (circles, triangles, etc.). The second could pinpoint eyes (two ovals positioned side by side). The third might recognize a face, and so forth. Ultimately, the algorithm becomes capable of identifying a dog in an image.” This concept can similarly apply to textual data sources, such as sentences.

The introduction of Transformers (e.g., BERT, T5, and GPT-3) has recently revolutionized NLP. These advanced Deep Learning models process data not sequentially (from start to finish) but utilize a mechanism known as attention to evaluate extensive text simultaneously. This innovation has significantly enhanced the models’ comprehension of linguistic context, allowing new models to outperform older ones across various tasks.

A prominent example of such a task is missing word prediction. This technique is advantageous as it facilitates the creation of large datasets by taking extensive text bodies and masking certain words. Researchers aiming to develop a functional model (such as one that answers questions based on a text) initially trained it on a much smaller dataset for this specific aim (a methodology referred to as fine tuning). The AI community was amazed when BERT surpassed all existing AI models on a broad spectrum of NLP tasks!

The most recent breakthrough comes from the GPT-3 model, an extraordinarily powerful framework with 175 billion parameters. It can comprehend English prompts and generate text without needing any examples. Jelmer Wind, a Data Scientist at Machine Learning Company, experimented with GPT-3, asking it to produce a text that counters a human political argument. Remarkably, the GPT-3 model generated a coherent text opposing the argument without having any examples to work from (zero-shot training). This proficiency stems from its enhanced understanding of human language.

At Squadra Machine Learning Company, various NLP applications are currently in use. Examples include PowerText.ai  and PowerEnrich.ai  . PowerText.ai generates unique and SEO-optimized product descriptions for e-commerce platforms, based on the product’s feature data (such as color, capacity, type, etc.). This tool allows wholesalers and retailers to save time and costs on product description copywriting, leading to a significant boost in revenue through improved SEO outcomes.

PowerEnrich.ai serves e-commerce companies by analyzing various text sources and automatically extracting data elements to enrich product information for online presentation. It dramatically reduces the manual effort required for entering large datasets. Leveraging Transformer models, Guus anticipates enhancing creativity in generated texts and achieving greater accuracy with text extraction algorithms.

Given the immense potential, these recent NLP innovations could also be misused for unethical purposes. For instance, the GPT-3 model can be easily influenced to advocate for any stance, no matter how morally questionable, in a manner that closely mimics human writing. Recent models are capable of producing human-like texts that do not necessarily convey the truth. Consequently, access to models like GPT-3 is restricted, necessitating a balance between technological advancements and ethical considerations.

The NLP landscape is rapidly evolving, with new developments emerging almost daily. Squadra Machine Learning Company continuously monitors advancements that could enhance their services and improve problem-solving for their clients. Occasionally, the latest applications are employed to tackle challenges that could not have been addressed just a year ago, illustrating the swift evolution of NLP!

Squadra Machine Learning Company is an innovative Dutch firm that integrates knowledge of business processes, algorithm development, and data visualization to assist clients with Machine Learning algorithms and the implementation of Artificial Intelligence solutions. They possess a proven track record in applying Data Science and AI solutions for suppliers, wholesalers, and retail organizations.