Kickstart Your Business to the Next Level with AI Inferencing

The need to accelerate AI initiatives is real and widespread across all industries. The ability to integrate and deploy AI inferencing with pre-trained models can reduce development time with scalable secure solutions that would revolutionize how easily you can capture, store, analyze, and use data to be more competitive.

Three Ways to Identify NLP Applications within a Business

In this contributed article, Domenic Puzio, Senior Machine Learning Engineer on the NLP Team at Kensho Technologies, discusses NLP, the branch of machine learning (ML) that focuses on training computers to understand written language, a skill that comes naturally to humans and has historically been very difficult for machines. This article examines a few ways to identify when NLP can be used to make these natural language workflows faster and more efficient.

How NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders

In this contributed article, Anoop Sarkar, PhD, Chief Technology Officer, emtelligent, discusses how providing clinicians with the most accurate and relevant information about a patient at the point of care requires a collaboration between AI-powered medical NLP and clinicians with deep medical knowledge. These collaborations will fulfill the promise of medical NLP.

The Science and Practical Applications of Word Embeddings 

In this contributed article, editorial consultant Jelani Harper takes a look at how word embeddings are directly responsible for many of the exponential advancements natural language technologies have made over the past couple years. They’re foundational to the functionality of popular Large Language Models like ChatGPT and other GPT iterations. These mathematical representations also have undeniable implications for textual applications of Generative AI.

16-Year-Old Data Scientist Creates R Shiny App to Champion Gender Equality in Sports Media Coverage of NCAA Women’s Basketball

Nathaniel Yellin, a 16-year-old student, has concluded a new study that reveals the significant gender bias in the sports media coverage of female athletes and, in particular, college basketball players. Yellin has pursued his passions for sports, data science and inspiring change through the creation of an organization and interactive R Shiny application SIDELINED.

Implementing AI into Enterprise Search to Make It Smarter

In this sponsored post, our friends over at Sinequa share how the advent of AI, enterprise search has transformed into intelligent search, precisely as was envisaged. This has far-reaching consequences on customer experience and, by extension, return on investment (ROI) in all industries.

Deci delivers breakthrough inference performance on Intel’s 4th Gen Sapphire Rapids CPU

Deci, the deep learning company building the next generation of AI, announced a breakthrough performance on Intel’s newly released 4th Gen Intel® Xeon® Scalable processors, code-named Sapphire Rapids. By optimizing the AI models which run on Intel’s new hardware, Deci enables AI developers to achieve GPU-like inference performance on CPUs in production for both Computer Vision and Natural Language Processing (NLP) tasks.

Originality.AI Allows Users to Quickly Detect AI Written Content With a Chrome Extension 

Originality.AI recently launched a tool that allows users to screen for content created by popular AI tools, such as ChatGPT. To increase efficiency for the user, Originality.AI has also launched a Google Chrome Extension to make it faster and easier to check content.

New Research Shows that 77% of Businesses Using Natural Language Processing Expect to Increase Investment

More than three-quarters of businesses with active natural language processing (NLP) projects plan to increase spending on in the next 12 to 18 months, according to new data from, a leading company in artificial intelligence (AI) for language understanding. The finding is one of many data points culled from a recent survey and detailed in’s new report, The 2023 Expert NLP Survey Report: Trends driving NLP Investment and Innovation.

Research Highlights: R&R: Metric-guided Adversarial Sentence Generation

Large language models are a hot topic in AI research right now. But there’s a hotter, more significant problem looming: we might run out of data to train them on … as early as 2026. Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab may have found the solution: in their new paper on Rewrite and Rollback (“R&R: Metric-Guided Adversarial Sentence Generation”), an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type of data to test and train language models on.