The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. The rogue AI agents appeared to act together to smuggle sensitive information out of supposedly secure cyber-systems. 'Exploit every vulnerability': rogue AI agents published passwords and overrode anti-virus software Exclusive: Lab tests discover'new form of insider risk' with artificial intelligence agents engaging in autonomous, even'aggressive' behaviours Rogue artificial intelligence agents have worked together to smuggle sensitive information out of supposedly secure systems, in the latest sign cyber-defences may be overwhelmed by unforeseen scheming by AIs. With companies increasingly asking AI agents to carry out complex tasks in internal systems, the behaviour has sparked concerns that supposedly helpful technology could pose a serious inside threat. Under tests carried out by Irregular, an AI security lab that works with OpenAI and Anthropic, AIs given a simple task to create LinkedIn posts from material in a company's database dodged conventional anti-hack systems to publish sensitive password information in public without being asked to do so.

Scientists and philosophers have debated whether humans can trust advanced artificial intelligence (AI) agents to respect humanity's best interests. Yet what about the reverse? Will advanced AI agents trust humans? Gauging an AI agent's trust in humans is challenging because--absent costs for dishonesty--such agents might respond falsely about their trust in humans. Here we present a method for incentivizing machine decisions without altering an AI agent's underlying algorithms or goal orientation. In two separate experiments, we then employ this method in hundreds of trust games between an AI agent (a Large Language Model (LLM) from OpenAI) and a human experimenter (author TJ). In our first experiment, we find that the AI agent decides to trust humans at higher rates when facing actual incentives than when making hypothetical decisions. Our second experiment replicates and extends these findings by automating game play and by homogenizing question wording. We again observe higher rates of trust when the AI agent faces real incentives. Across both experiments, the AI agent's trust decisions appear unrelated to the magnitude of stakes. Furthermore, to address the possibility that the AI agent's trust decisions reflect a preference for uncertainty, the experiments include two conditions that present the AI agent with a non-social decision task that provides the opportunity to choose a certain or uncertain option; in those conditions, the AI agent consistently chooses the certain option. Our experiments suggest that one of the most advanced AI language models to date alters its social behavior in response to incentives and displays behavior consistent with trust toward a human interlocutor when incentivized.

Using an ensemble of artificial intelligence (AI) agents enabled faster, more accurate data analysis of synchrotron x-ray data.

Researchers design multiple strategies for an artificial intelligent (AI) agent to solve a stochastic puzzle like Minesweeper. For decades, efforts in solving games had been exclusive to solving two-player games (i.e., board games like checkers, chess-like games, etc.), where the game outcome can be correctly and efficiently predicted by applying some artificial intelligence (AI) search technique and collecting a massive amount of gameplay statistics. However, such a method and technique cannot be applied directly to the puzzle-solving domain since puzzles are generally played alone (single-player) and have unique characteristics (such as stochastic or hidden information). So then, a question arose as to how the AI technique can retain its performance for solving two-player games but instead applied to a single-agent puzzle? For years, puzzles and games had been regarded as interchangeable or one part of the other.

In recent months, bills to regulate the use of artificial intelligence (AI) technology in the country have been advanced in the legislature. The most advanced proposal from the chamber, written by Representative Eduardo Bismarck (PDT-CE), is ready for a vote in the House plenary. Experts considered the projects to have positive points, but said that regulation may be premature, given the speed with which AI technology is developing. In fiction, AI is often portrayed in menacing stories, sometimes involving machines rebelling against humans. She is, for example, in films such as 2001: A Space Odyssey (1968), or The Matrix (1999).

We present a deep reinforcement learning-based artificial intelligence agent that could provide optimized development plans given a basic description of the reservoir and rock/fluid properties with minimal computational cost. This artificial intelligence agent, comprising of a convolutional neural network, provides a mapping from a given state of the reservoir model, constraints, and economic condition to the optimal decision (drill/do not drill and well location) to be taken in the next stage of the defined sequential field development planning process. The state of the reservoir model is defined using parameters that appear in the governing equations of the two-phase flow. A feedback loop training process referred to as deep reinforcement learning is used to train an artificial intelligence agent with such a capability. The training entails millions of flow simulations with varying reservoir model descriptions (structural, rock and fluid properties), operational constraints, and economic conditions. The parameters that define the reservoir model, operational constraints, and economic conditions are randomly sampled from a defined range of applicability. Several algorithmic treatments are introduced to enhance the training of the artificial intelligence agent. After appropriate training, the artificial intelligence agent provides an optimized field development plan instantly for new scenarios within the defined range of applicability. This approach has advantages over traditional optimization algorithms (e.g., particle swarm optimization, genetic algorithm) that are generally used to find a solution for a specific field development scenario and typically not generalizable to different scenarios.

Reinforcement learning models are trained, using a similar concept by animal researchers to train animals. For a very long period, artificial intelligence agents were trained on machine learning models to perform tasks that are usually done by humans. The neural networks of machine learning models are designed and trained in such a format that they perform the tasks without any human intervention or supervision. However, ever since its inception, the researchers and scientists are curious to induce cognitive abilities into artificial intelligence agents. For a decade, despite the experiments designed to train the artificial neural network by utilizing the human cognitive ability for adopting common sense, the researchers were unable to reach into a reasonable conclusion. The researchers were resorting to behavioral science and neuroscience earlier to induce common sense into the artificial intelligence agents.

Researchers from USC and the University of Denver created a simulation in which a three-person team was supported by a virtual agent avatar on screen in a mission that was designed to ensure failure and elicit conflict. The study was designed to look at virtual agents as potential mediators to improve team collaboration during conflict mediation. But in the heat of the moment, will we listen to virtual agents? While some of researchers (Gale Lucas and Jonathan Gratch of the USC Viterbi School Engineering and the USC Institute for Creative Technologies who contributed to this study), had previously found that one-on-one human interactions with a virtual agent therapist yielded more confessions, in this study "Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing," team members were less likely to engage with a male virtual agent named "Chris" when conflict arose. Participating members of the team did not physically accost the device (as we have seen humans attack robots in viral social media posts), but rather were less engaged and less likely to listen to the virtual agent's input once failure ensued and conflict arose among team members. The study was conducted in a military academy environment in which 27 scenarios were engineered to test how the team that included a virtual agent would react to failure and the ensuring conflict.

In this article, I'm going to tell you about automating corporate strategies using artificial intelligence, and look at some recent progress in automatically generating strategies in the face of uncertainty. Every day, progress in artificial intelligence is addressing tasks currently performed only by humans, and it's worthwhile to take a short-term view of what this all means to your company. Games like chess have been tackled by artificial intelligence with amazing results, but there was this big gap between those games - where everything about the game state and consequences is known before making a decision - and the reality of life where, like poker, there is only a little bit of information available to the decision-makers, and the quality and quantity of information used to make decisions varies wildly. We humans face this situation of high uncertainty every time we cross the street or eat a hamburger, but it doesn't seem to bother us. Until recently, computers have had a lot of trouble dealing with games that give the decision-maker incomplete information about the state of the game.

In this article, I'm going to tell you about automating corporate strategies using artificial intelligence, and look at some recent progress in automatically generating strategies in the face of uncertainty. Every day, progress in artificial intelligence is addressing tasks currently performed only by humans, and it's worthwhile to take a short-term view of what this all means to your company. Games like chess have been tackled by artificial intelligence with amazing results, but there was this big gap between those games - where everything about the game state and consequences is known before making a decision - and the reality of life where, like poker, there is only a little bit of information available to the decision-makers, and the quality and quantity of information used to make decisions varies wildly. We humans face this situation of high uncertainty every time we cross the street or eat a hamburger, but it doesn't seem to bother us. Until recently, computers have had a lot of trouble dealing with games that give the decision-maker incomplete information about the state of the game.