Knowledge base

The generative AI field is a fast-paced, continuously changing landscape that is dominated by the United States and China. To combat this dominance, European-based companies are increasingly recognizing the importance of creating their own Large Language Models (LLMs) trained on European data. This allows for the development of models fluent in the most widely spoken European languages, as opposed to LLMs only trained on English/American data. Several EU-based companies, often supported by major corporations and governments, have taken up the challenge of developing European LLMs. We investigated one of them called Mistral and explored how their models compare to the well-known American Models.

Using state-of-the-art LLMs to generate a bunch of potential solutions to a complex coding problem, and a verifier to select the best solution, the ability to solve these problems can be increased tremendously. Going forward, the tradeoff between larger models and this generation - verification mechanism will be important for the scalability of these LLM systems. While it looks like AlphaCode2 is already better at solving complex coding problems than most humans, there is still a synergy to be found between human and machine. The role of developers might change from actual hands-on coding to a role that more revolves around instructing the LLMs, safeguarding best practices and explaining the results to the business

By launching Gemini, Google emerged on the Gen AI field as a 600 pound contestant to a sumo wrestling match. With flashy demos and astounding performances on various foundation model benchmarks, Google seems to be back in the race for AI dominance. In this post, we aim to shed a light on what Gemini actually entails and whether or not it is as great as Google wants us to believe.

Introduction If you’ve been working with Power BI semantic models for a while, you may have noticed that things can get very cluttered over time. Especially when you need to compare a set of measures from a time-perspective (current, year-to-date, previous year), or compare results in different currencies.  

This series of insights is intended to keep you updated on the opportunities of generative AI. As new features are launched into the market, we will provide you with our candid opinion and viewpoint on what is going on. By doing this, we hope to assist you in making the best use of your time while being always up to date on the ever-changing generative AI scene.

The first-ever Data & AI World tour was a huge success, and you can expect it to happen again next year in some form (maybe even in Belgium!). Generative AI is a robust ongoing trend, and Databricks utilizes Generative AI to enhance various functionalities and features. This integration has spawned the term "Data Intelligence Platform" by combining Generative AI with a Lakehouse, simplifying data querying, management, and governance processes significantly.

Introduction Microsoft Fabric is the new Microsoft branded analytics platform that integrates data warehousing, data integration and orchestration, data engineering, data science, real-time analytics, and business intelligence into a single product. This new one-stop shop for analytics brings a set of innovative features and capabilities by providing a transformative tool for data professionals and business users.

In this Insight, we will take a closer look at how to place and use Power BI in the Power Platform. We will briefly highlight each component separately after which we will discuss some examples of integrations.

Through easy mapping and robust scenario analysis features, Vena offers a more collaborative and insightful approach to financial planning. The prospects of streamlined financial planning, real-time analysis, and informed decision-making become part of your financial process. Transitioning to Vena will leverage existing resources while embracing a culture of continuous improvement and self-supporting financial transparency.