Blog • Mercury Data Science

Navigating Data Security in the Age of Large Language Models (LLMs)

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools with the potential to revolutionize various industries.

AI/ML for Health Tech and Medical Devices: Invest Early in Data Science Infrastructure to Create Competitive Advantage

Harnessing the power of AI/ML has the potential to transform health tech and medical device companies by providing better outcomes, better patient and provider satisfaction and engagement, and better insight into product performance and real world evidence.

Designing Novel Proteins with Deep Hallucination

Newly published research provides a valuable method that may be useful for designing unique, functional proteins for therapeutic and diagnostic applications.

A Better Facial Emotion Recognition Model

Major cloud service providers offer off-the-shelf emotion prediction APIs, but accuracy and interpretability often fail to meet the needs of our clients. Read how our team developed a new approach to predict emotions from video. Here we describe the model building process, its current performance, and future considerations.

Eliminating Racial Bias in AI/ML: Solving the Training Data Problem

Learn about the approach and impact of the model we built to predict skin tone from face images and videos using a unique combination of computer vision and machine learning to help organizations monitor for racial biases in large datasets.

7 Emerging Trends in AI for Life Sciences and Healthcare in 2021

Here is what we see as the most valuable high-impact trends in AI/ML for life sciences and healthcare

5 Data Challenges Every Life Sciences and Healthcare Organization Must Solve to Build a Successful Data Science Mission

Five challenges and solutions critical to the success of Data Science in Healthcare and Life Sciences

IN THE NEWS: Innovation Map - Houston data science company expands pandemic-inspired research tool

“Mercury Data Science has taken a tool it originally developed for COVID-19 research and applied it into new areas of research and innovation.”

Feature Engineering: Mining Value From Your Data

Most data is raw and needs to be shaped into usable features that highlight signal and minimize noise. “Feature engineering” is the most important part of real-world data science, paving the path from raw data to valuable insights.

A Working Model is Only the Start of the ML Life Cycle

Why companies need to think ahead about infrastructure for data science.

Good Data Science: The Art of Being Skeptical, to Allow Room for Creativity

Domain and technical knowledge aren’t sufficient criteria when looking for your new data science hire. We have found that a better characterization of a data scientist’s innate potential is the way they balance creativity and skepticism.

News

updates from our team & and thoughts on the world of Data Science.

Navigating Data Security in the Age of Large Language Models (LLMs)

AI/ML for Health Tech and Medical Devices: Invest Early in Data Science Infrastructure to Create Competitive Advantage

Designing Novel Proteins with Deep Hallucination

A Better Facial Emotion Recognition Model

Eliminating Racial Bias in AI/ML: Solving the Training Data Problem

7 Emerging Trends in AI for Life Sciences and Healthcare in 2021

5 Data Challenges Every Life Sciences and Healthcare Organization Must Solve to Build a Successful Data Science Mission

IN THE NEWS: Innovation Map - Houston data science company expands pandemic-inspired research tool

Feature Engineering: Mining Value From Your Data

A Working Model is Only the Start of the ML Life Cycle

Good Data Science: The Art of Being Skeptical, to Allow Room for Creativity