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Contrastive Twist Learning and Bidirectional SMC Bounds: A New Paradigm for Language Model Control
Large language models (LLMs) have made significant success in various language tasks, but steering their outputs to meet specific properties remains a challenge. Researchers are attempting to solve the problem of controlling LLM generations to satisfy desired characteristics across a wide range of applications. This includes reinforcement learning from human feedback (RLHF), red-teaming techniques, reasoning…
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Optimizing AI Safety and Deployment: A Game-Theoretic Approach to Protocol Evaluation in Untrusted AI Systems
AI Control assesses the safety of deployment protocols for untrusted AIs through red-teaming exercises involving a protocol designer and an adversary. AI systems, like chatbots with access to tools such as code interpreters, become increasingly integrated into various tasks, ensuring their safe deployment becomes more complex. While prior research has focused on building robustly safe…
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Did a Chinese University Hacking Competition Target a Real Victim?
Participants in a hacking competition with ties to China’s military were, unusually, required to keep their activities secret, but security researchers say the mystery only gets stranger from there.
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Es hora de que los CIO piensen en la criptografía postcuántica
Ante la amenaza de que los ordenadores cuánticos rompan los actuales métodos de cifrado, la adopción de la criptografía poscuántica es ahora una prioridad crítica, sobre todo para los sectores que manejan datos confidenciales, según revela un informe reciente. El estado actual y futuro de la cuántica es una de las seis tendencias que recoge…
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Most US Teens Use Generative AI. Most of Their Parents Don’t Know
A news survey shows that AI use among high schoolers has exploded in recent months, as educators and parents struggle to keep up.
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An Extensible Open-Source AI Framework to Benchmark Attributable Information-Seeking Using Representative LLM-based Approaches
With the success of LLMs in various tasks, search engines have begun using generative methods to provide accurate answers with in-line citations to user queries. However, generating reliable and attributable answers, especially in open-ended information-seeking scenarios, poses challenges due to the complexity of questions and the broad scope of candidate-attributed answers. Existing methods typically focus…
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A Systematic Literature Review: Optimization and Acceleration Techniques for LLMs
Large language models (LLMs) have seen remarkable success in natural language processing (NLP). Large-scale deep learning models, especially transformer-based architectures, have grown exponentially in size and complexity, reaching billions to trillions of parameters. However, they pose major challenges in computational resources and memory usage. Even advanced GPUs struggle to handle models with trillions of parameters,…
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MPPI-Generic: A New C++/CUDA library for GPU-Accelerated Stochastic Optimization
Stochastic optimization problems involve making decisions in environments with uncertainty. This uncertainty can arise from various sources, such as sensor noise, system disturbances, or unpredictable external factors. It can real-time control and planning in robotics and autonomy, where computational efficiency is crucial for handling complex dynamics and cost functions in ever-changing environments. The core problem…
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“No hay decisión comercial en SAS que no aproveche nuestra propia tecnología”
Cinco años al pie del cañón en la industria tecnológica, dado el acelerado ritmo de desarrollo y evolución, son un mundo. Jay Upchurch, vicepresidente ejecutivo y Chief Technology Officer (CIO, por sus siglas en inglés) de SAS desde el pasado 2019, es gran conocedor de ello. “En el último lustro han cambiado muchas cuestiones. Cuando…
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NiNo: A Novel Machine Learning Approach to Accelerate Neural Network Training through Neuron Interaction and Nowcasting
In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. Researchers have been exploring advanced optimization techniques to make this process more efficient. Traditionally, adaptive optimizers such as Adam have been used to speed training by adjusting…