In the ever-changing world of artificial intelligence, a new AI assistant called Claude has arrived on the scene, and it’s turning heads. Created by a company called Anthropic, Claude is incredibly smart and can understand and communicate with humans in very natural, human-like ways.

What makes Claude so special is the way it has been trained. The folks at Anthropic fed Claude a massive amount of data, which allows it to truly grasp how we humans speak and write. So whether you’re chatting with Claude casually or asking it to tackle some complex task, it can handle it all with impressive skill.

In the realm of artificial intelligence and machine learning, efficient similarity search is a critical component for tasks ranging from recommendation systems to image recognition. In this blog post, we’ll explore the implementation of vector similarity search in Go, utilizing LinGoose framework for indexing and querying vectors in a Qdrant database.

Understanding Vector Similarity Search

Vector similarity search involves finding vectors in a dataset that are most similar to a query vector. This is fundamental in various AI applications where matching or ranking similar items is required. Qdrant, a vector database, provides a robust solution for such searches.

Introduction

Artificial Intelligence has transformed the way we handle data, and one crucial aspect of AI is similarity search. Whether it’s for image recognition, recommendation systems, or natural language processing, finding similar data points quickly and accurately is a common challenge. In this blog post, we will explore a Go code snippet that showcases how to perform efficient vector similarity search using Redis and the Lingoose Go framework, catering to tech-savvy readers interested in both Go programming and AI.

During the Second World War, the German army dominated Europe thanks in part to the cryptographic technology used in communications: the Enigma machine. To be able to decipher its codes, the British relied on a group of mathematicians, physicists, and linguists who worked in a secret location, Bletchley Park, in England. Their work was a true scientific revolution, a fundamental turning point for the Allies’ victory.

Italy has been making significant strides towards achieving the Digital Decade targets and embracing digital transformation. While progress has been made in certain areas, there are still challenges that need to be addressed. In this blog post, we will explore Italy’s advancements in digital infrastructure, the digitalization of businesses, and the need for further efforts in updating advanced digital technologies and digitalizing public services.

Digital Infrastructure

Italy has shown progress in digital infrastructure, with a significant boost in investment through its Recovery and Resilience Plan (RRP). However, when it comes to fixed very high-capacity network (VHCN), Italy still lags behind the EU average, with only 54% of households having access compared to 73% in the EU. Despite a 10 percentage point increase between 2021 and 2022, there is still room for improvement in this area.

Our concentration is a finite resource. I often compare it to a reservoir that provides “fuel” throughout the workday and that must necessarily be recharged through collateral activities. It goes without saying that it is up to us to choose how to distribute our energy during the day and how to divide it among different tasks. After all, every activity requires a certain amount of energy to be completed. So far, so clear—but what happens if we use our concentration trying to carry out several activities at the same time? Staying with the engine metaphor, the effect will be that we cover little ground for each activity we are engaged in, and the result will be unsatisfactory and, in the worst case, frustrating.