AA - Advanced Analytics

​​​​​​​In the ever-evolving landscape of Artificial Intelligence, a fascinating breed of digital entities has emerged: AI Agents. These agents are not mere lines of code; they are pushing the boundaries of how we are using Generative AI. But what exactly are these agents, and why do we need them?

Databricks Federated Queries enable seamless data querying across multiple sources, eliminating the need for data duplication.

Crosser.io facilitates the integration of OT data into Microsoft Azure, addressing complexities through its low-code platform. Offering extensive data connectors and Azure integration, Crosser.io enables organizations to transform, enrich, and analyze data before transitioning it to Azure, thus simplifying architectures and reducing overall complexity.

Databricks is a leading cloud-based platform that enables data professionals to work together and deliver scalable data solutions. The platform's rapid evolution can make it challenging to stay abreast of new features (check out our insight about Databricks' Data Intelligence Platform to understand Databricks' vision). To ensure you maximize its potential, this overview will introduce recent additions by Databricks, accompanied by practical tips to enhance your data experience.

The rise of Large Language Models (LLMs) like GPT-4, Bard, and Gemini has been a game-changer in AI language processing. These models are powerful, but do they cover the full end of the story? Enter Small LLMs! At first glance, they might seem less capable due to their lower performance on various benchmarks. Yet, businesses are investing in them for good reason. This blog post explores why smaller can sometimes be better, depending on the use case.

Last week OpenAI announced the launch of their GPT Store. Let's have a look at what this GPT Store exactly is and how it can impact your business.

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.