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This paper presents an overview of the state of the art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways: to learn representations for linguistic entities; to generate coherent utterances and answer questions; to perform utterance transformations, an illustration being machine translation; to serve as backbone of versatile chatbots, able to answer queries and to perform, at will, a large variety of tasks. These facets of the architecture will be successively presented, which will also allow us to discuss its limitations.
In 2008 Satoshi Nakamoto defined a new model of currencies. The issuance and management of such a currency take place on a peer-to-peer network without central control. Bitcoin is the first cryptographic currency created on this model. It exists since January 2009.As the 7000 other crypto-currencies of the same type created in its wake, it works through what is called a blockchain. This shared file and collectively controlled file by a peer-to-peer network can be used for many other purposes. Many applications are developed and implemented gradually, especially in the world of banking and finance.
Information retrieval covers many applications ranging from document retrieval using Boolean queries to the generation and extraction of precise answers to questions in natural language. It applies to text, images, and audio, and can be interactive, in the form of dialogues with a conversational agent. This article focuses on the crossing of information retrieval and generative AI, known as augmented (response) generation through information retrieval (RAG). RAG assists in the generation of responses from a large language model and information sources that may be private. Large language models and RAG architectures are presented (agentic RAG, GraphRAG, etc.), as are the many strategies that can be followed.
In today's fast-changing world, driven by the explosion in scientific and technological knowledge and the ever-faster evolution of digital tools, intelligence has become an indispensable tool in all business sectors. Its successful implementation requires an appropriate structure, equipped with the most effective Artificial Intelligence (AI) resources and tools, and carried out by qualified “watchers”. This article focuses on the challenges and opportunities of scientific and technological watch (STW) for higher education and research, competitive clusters, companies and local authorities. It also looks at the evolution of the concept of intelligence in the digital age, its different types, sources and tools, and its methodology.
We present attack mechanisms linked to social engineering, such as phishing or smishing techniques. Their prevention in the context of Information Systems (IS) cybersecurity, where human vulnerabilities account for 74% of attacks, exploiting cognitive biases and human weaknesses, based on AI tools, is strategic.Current countermeasures (training, prevention) remain limited. Dynamic and gaming approaches, reinforcing critical thinking, combined with organizational policies and integrating the human element as a key asset in a proactive security strategy, aim to anticipate future threats and yet unknown scenarios.
Artificial intelligence techniques are spreading across a wide range of economic sectors. This article provides an introduction to the basic concepts and definitions of machine learning, which is based on specific algorithms applied to mass data. It looks at the main methods (supervised and unsupervised learning) and illustrates their use in simple cases, with the aim of understanding how these techniques contribute to innovation in different fields such as industry, research, healthcare - and in many areas of our daily lives.
This article begins with the different stages in the deployment of IS in a company: automation and redesign of processes and operations (the operative word being 'organization'), and finally integration of software packages. It then summarizes the difficulties facing big data projects, and concludes with some ways to overcome them.
In this article we propose to frame the issue of expansion of digital technologies and their applications with respect to their ecological implications. We show that this sector is facing critical choices, being on a trajectory hardly compatible with net zero emissions targets set at both French and European levels, and a source of mismanaged wastes. We point the main relevant regulations and their limitations. We conclude that following a “digital sufficiency” is necessary.
Artificial intelligence (AI) has experienced accelerated growth in the digital media field since the 2015s. This article first provides a reminder of the principles, components and techniques of artificial intelligence, in particular machine learning, and deep learning with neural networks. It then proposes a sample of AI applications developed in the field of images in photography, for old films, and in video. In the field of sounds, the article presents some examples related to the automatic processing of speech, 3D audio and music.
Patents currently constitute the best living encyclopedia. They thus constitute a critical documentary fund for companies, but also for researchers and research laboratories. Therefore, patent monitoring is a necessity.Currently many patent sources are available, the majority of them being free, with obviously particularities due to the indexing or coverage of these sources. On the other hand, the number of patents being in strong growth, the user finds himself comforted by a growing number of notices which has led to the development of computerized analytical processing.In this article we present a set of sources (mostly free) as well as examples of patent corpus analysis. From this set we then show how patent information can be effectively used in Technology Watch.
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