Artificial intelligence in the agri-food sector [part 2]
Artificial intelligence is revolutionizing the agricultural sector, offering new solutions to address challenges related to sustainability, efficiency and food security. AI can play a key role in managing the agricultural information cycle, supporting more informed and timely decisions through advanced data analysis. In addition, AI can help optimize agricultural processes by improving productivity, environmental monitoring and resource management. Along with the benefits, there are still technical, economic and social challenges that hinder its large-scale adoption. Will these challenges be overcome and will we arrive at more resilient and sustainable models?
Artificial intelligence in the agri-food sector [part 1]
In recent years, we are witnessing a real revolution in the way we grow, process and consume food. Central to this change is the application of artificial intelligence. From the ability to predict weather conditions with greater accuracy to the ability to monitor the status of crops in real time, artificial intelligence is becoming a valuable ally for the entire agri-food sector.Let’s find out the benefits that these technologies can improve food quality, reduce waste, and make the entire agri-food sector more sustainable and efficient.
Knowledge Graphs and Large Language Models (LLMs) Together [part 2].
LLMs are increasingly present in our daily lives: they answer questions, generate texts, summarize information and much more. But despite their amazing ability to deal with natural language, these models have limitations: they can “make up” facts, confuse concepts or lack access to up-to-date or reliable knowledge. And this is where knowledge graphs come in. These structures organize information in a precise and relational way, allowing LLMs to draw on well-organized and verifiable data. We explore how knowledge graphs can become a key ally in improving the accuracy, transparency and reliability of language models, helping them “really know” what they are talking about.
Knowledge Graphs and Large Language Models (LLMs) Together [part 1]
Nowadays we hear all the time about artificial intelligence and, in particular, about large language models, known as Large Language Models or LLMs. These tools, the most famous of which is ChatGPT, are capable of understanding and generating text in a surprisingly natural way and are finding applications in so many areas, from automatic writing to scientific research. One of the most promising uses in recent years is the generation and curation of knowledge graphs, a graph representation of information of interest, where concepts and relationships between them are linked in a structured way with semantic meaning.
Artificial Intelligence in Marketing
Persuade people to take an action, buy a product or access a service (in other words, respond to a “call to action”), making Programmatic Advertising, Marketing Automation and Customer Care even more effective. This is what Artificial Intelligence is and what it is used for in Marketing.
HBIM: 3D reconstructions of ancient buildings
HBIM, which stands for Heritage or Historic Building Information Modeling, is a data-driven technology that aims to capture and represent the architectural, structural and historical features of historic buildings in a three-dimensional digital environment. Unlike traditional BIM, HBIM considers historic buildings as complex entities, taking into account cultural and historical elements as well. The resulting 3D models will not only represent the building of interest but provide a rich source of information of all its elements.
Deep learning: success stories
Deep learning and artificial intelligence have invaded our daily lives. Evidence that these techniques are so widespread is before our eyes through success stories such as digital assistants and self-driving cars.
Deep learning: developments in the 21st century
Deep learning techniques have their roots in previous centuries. In recent decades there have been some really important evolutions that have led to the algorithms we use today. Let’s learn about the evolutions that led to today’s technology.
Deep learning: roots
Several methodologies and algorithms that are part of deep learning have been developed in recent years. But are these techniques actually that modern? Let’s look at a bit of history, starting from the Middle Ages and arriving at the present day, to understand the roots of deep learning and artificial intelligence.
Deep learning: unsupervised and reinforcement learning
Supervised methods require that our example data also provide us with the labels we should predict. However, these learning methods are limited. In some cases we either do not know what we are looking for or the environment itself can give us hints as to how our model should evolve. Unsupervised and reinforcement learning methods address these types of problems.
