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La veille de la cybersécurité

#artificialintelligence

DALL-E can generate images from a few key words--with predictably racist and sexist results. To the casual observer, DALL-E is Silicon Valley's latest miraculous AI creation--a machine learning system that allows anyone to generate almost any image just by typing a short description into a text box. From just a few descriptive words, the system can conjure up an image of cats playing chess, or a teapot that looks like an avocado. It's an impressive trick using the latest advances in natural language processing, or NLP, which involves teaching algorithmic systems how to parse and respond to human language--often with creepily realistic results. Named after both surrealist painter Salvador Dalí and the lovable Pixar robot WALL-E, DALL-E was created by research lab OpenAI, which is well-known in the field for creating the groundbreaking NLP systems GPT-2 and GPT-3.


What is artificial intelligence or artificial intelligence and how does it work?

#artificialintelligence

The full form of AI is Artificial Intelligence or in Hindi it means artificial intelligence or artificial brain. This is such a simulation that machines are given human intelligence, or rather, their brains are so advanced that they can think and work like humans. This is done especially in the computer system itself. There are mainly three processes involved in this process and they are first learning (in which information is put in the mind of machines and they are also taught some rules so that they follow those rules to complete a given task), second is Rezoning (under this, the machines are instructed to follow the rules made to move towards the results so that they can get an approximate or definite conclusion) and the third is Self-Correction. If we talk about the particular application of AI, then it includes expert system, speech recognition and machine vision.


Machines and Influence

arXiv.org Artificial Intelligence

Policymakers face a broader challenge of how to view AI capabilities today and where does society stand in terms of those capabilities. This paper surveys AI capabilities and tackles this very issue, exploring it in context of political security in digitally networked societies. We extend the ideas of Information Management to better understand contemporary AI systems as part of a larger and more complex information system. Comprehensively reviewing AI capabilities and contemporary man-machine interactions, we undertake conceptual development to suggest that better information management could allow states to more optimally offset the risks of AI enabled influence and better utilise the emerging capabilities which these systems have to offer to policymakers and political institutions across the world. Hopefully this long essay will actuate further debates and discussions over these ideas, and prove to be a useful contribution towards governing the future of AI.


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Artificial Intelligence

#artificialintelligence

Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can accomplish activities that would normally need human intelligence. Machines may learn from their experiences, adapt to new inputs, and execute human-like jobs thanks to artificial intelligence (AI). Most AI examples you hear about today rely largely on deep learning and natural language processing, from chess-playing computers to self-driving cars. Computers can be trained to perform certain jobs by processing massive volumes of data and recognizing patterns in the data using these methods. Artificial Intelligence refers to the intelligence displayed by machines. In today's world, Artificial Intelligence has become highly popular. It is the simulation of human intelligence in computers that have been programmed to learn and mimic human actions.


Modeling Strong and Human-Like Gameplay with KL-Regularized Search

arXiv.org Artificial Intelligence

We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predicting human actions but may not match the strength of expert humans, while self-play learning and search techniques (e.g. AlphaZero) lead to strong performance but may produce policies that are difficult for humans to understand and coordinate with. We show in chess and Go that regularizing search policies based on the KL divergence from an imitation-learned policy by applying Monte Carlo tree search produces policies that have higher human prediction accuracy and are stronger than the imitation policy. We then introduce a novel regret minimization algorithm that is regularized based on the KL divergence from an imitation-learned policy, and show that applying this algorithm to no-press Diplomacy yields a policy that maintains the same human prediction accuracy as imitation learning while being substantially stronger.


DeepMind makes bet on AI system that can play poker, chess, Go, and more

#artificialintelligence

DeepMind, the AI lab backed by Google parent company Alphabet, has long invested in game-playing AI systems. It's the lab's philosophy that games, while lacking an obvious commercial application, are uniquely relevant challenges of cognitive and reasoning capabilities. This makes them useful benchmarks of AI progress. In recent decades, games have given rise to the kind of self-learning AI that powers computer vision, self-driving cars, and natural language processing. In a continuation of its work, DeepMind has created a system called Player of Games, which the company first revealed in a research paper published on the preprint server Arxiv.org this week.


Player of Games

arXiv.org Artificial Intelligence

Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants. We introduce Player of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games -- an important step towards truly general algorithms for arbitrary environments. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases. Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning.


Researchers Warn Of 'Dangerous' Artificial Intelligence-Generated Disinformation At Scale - Breaking Defense

#artificialintelligence

A "like" icon seen through raindrops. WASHINGTON: Researchers at Georgetown University's Center for Security and Emerging Technology (CSET) are raising alarms about powerful artificial intelligence technology now more widely available that could be used to generate disinformation at a troubling scale. The warning comes after CSET researchers conducted experiments using the second and third versions of Generative Pre-trained Transformer (GPT-2 and GPT-3), a technology developed by San Francisco company OpenAI. GPT's text-generation capabilities are characterized by CSET researchers as "autocomplete on steroids." "We don't often think of autocomplete as being very capable, but with these large language models, the autocomplete is really capable, and you can tailor what you're starting with to get it to write all sorts of things," Andrew Lohn, senior research fellow at CSET, said during a recent event where researchers discussed their findings.


What is artificial intelligence?

#artificialintelligence

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.