Read our blog and carry on

LLMs are enabling computers to understand and generate human-like text, making them indispensable in industries ranging from customer service to content creation. The global market for LLMs is expected to skyrocket from $1.59 billion%20market%20size%20in%20terms%20of,79.80%25%20during%202024%2D2030.) in 2023 to $259.8 billion by 2030, with North America alone projected to hit $105.545 million by 2030. The dominance of the top five LLM developers, who currently hold 88.22% of the market revenue,

Ever wonder why so many machine learning (ML) models never see the light of day? Despite their huge potential, only 32% of data scientists say their models usually get deployed. Even more shocking, 43% report that 80% or more of their models never make it into production. This means many businesses miss out on the full value of their AI projects. Imagine a retail company spending months developing a sophisticated customer recommendation system, only for it to never be implemented—losing out on p

Healthcare providers manage an enormous volume of data daily, approximately 137 terabytes, most of which is unstructured. This includes a wide array of formats such as medical images, clinical notes, and genetic test results. Unstructured data, crucial for patient care, poses significant challenges due to its complexity and the varied sizes of its components. The volume of healthcare data is rapidly increasing, fueled by the widespread adoption of electronic health records and advances in medica

Since 2015, Sciforce has been an active contributor to the OHDSI scientific community. Our medical team is consistently at the forefront of OHDSI events, sharing groundbreaking research and driving advancements in health data harmonization that empower better health decisions and elevate care standards. The fall event was no exception: from October 20 to 22 Polina Talapova, Denys Kaduk, and Lucy Kadets were delegated as our company representatives to the Global OHDSI Symposium with more than 440

Current advances in technology are in many ways fueled by the growing flow of data coming from multiple sources and analyzed to create competitive advantage. Both individual users and businesses are switching to a digital system¹, which in turn generates pools of information. In their turn, organizations share data with other companies, giving rise to digital ecosystems that begin to blur traditional industry borders. As the amount of data available grows, the size, diversity, and applications o

If we think of the most successful and widespread applications of machine learning in business, one of the examples would be recommender systems. Each time you visit Amazon or Netflix, you see recommended items or movies that you might like — the product of recommender systems incorporated by these companies. Though a recommender system is a rather simple algorithm that discovers patterns in a dataset, rates items and shows the user the items that they might rate highly, they have the power to b

It is quite often that in our blog we explore intricate connections between state-of-the-art technologies, or explore the mesmerizing depth of a new technique. However, AI or data science is not only bragging about new exciting methods that boost accuracy by 2% (which is a big gain) but about making data and technology work for you. It will help you increase sales, understand your customers, predict future faults in process lines, make an insightful presentation, submit a term project, or have a

The new world we live in gives us more help — and more doubts. Machines stand behind everything — and the scope of this everything is only growing. To what extent can we trust such machines? We are used to relying on them in market trends, traffic management, and maybe even in healthcare. Machines are now analysts, medical assistants, secretaries, and teachers. Are they reliable enough to work as HRs? Psychologists? What can they tell about us? Let’s see how text analysis can analyze your soft s