The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Transform Enterprise Operations with Google’s Latest AI Stack: Smarter Agents, Multimodal Interfaces, Real Business Impact The annual Google Cloud Next event took place in Las Vegas in April, where Google presented its latest innovations for Google Cloud Platform (GCP).
Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.
In recent years, Large Language Models (LLMs) have revolutionised natural language processing by enabling machines to understand and generate human-like text with unprecedented accuracy and coherence. Their applications span across diverse fields such as chatbots and content creation, driving significant advancements in automation and AI-driven solutions. As a result, LLMs have become crucial tools in both academic research and commercial innovation, pushing the boundaries of what AI can achieve. Though, I’m sure you already knew this.
Let’s beat this dead horse one last time: Large Language Models (like GPT, Claude, Gemini, …) have knowledge on a wide range of topics because they’ve been trained on vast amounts of internet data. But once they’re training is complete, their knowledge is fixed. They can’t go for a sneaky little toilet Google-search when they run out of arguments in the middle of a hypothetical discussion with their know-it-all brother-in-law. Or can they?
Machine learning is a powerful technology with the potential to transform businesses and industries. However, for newcomers, it can be a daunting and time-consuming endeavour. The process of learning the fundamentals, setting up the infrastructure, and understanding the intricacies of ML frameworks can be overwhelming.
A screenshot of our chatbot generating personalized marketing messages in Dutch.
An introduction to gen AI in product photography: "Lights, Camera, AI!" With the world of e-commerce expanding rapidly since the pandemic, being able to present your product in an attractive and original way is more important than ever. As customers cannot touch or try your product, letting your product excel in stunning, professional and detailed images is the first step to lure customers into making a purchase. Studies show that 76.5% of customers acknowledge the significant impact of high-quality product photography and its role in influencing their purchase decision. However, getting high quality, professional product pictures does not come for free. Let alone the cost and overhead of setting up a studio and getting your product to the location, professionals can charge on average between 35$-50$ per image to even over 400$ on the high end. A lot of small business owners simply do not have the funds to do this for every product, but even for big players, who have funds, might not have the time to set this all up as their product catalogue changes rapidly.
Introduction Digital transformation is changing the manufacturing industry. More and more companies are incorporating digital technologies into their production processes. The benefits and importance of this is undeniable. However, as those technologies are being introduced, it is important to be aware that they can also introduce new security risks to critical infrastructure.
Visualising Dynamic Pricing modelling to build insights The nice thing about Dynamic Pricing is that it offers a level of explainability. The demo below shows an experiment lasting two weeks in which we are training a dynamic pricing model that is interacting with a fictitious market.