Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration, we introduce the Discrete Replica EXchangE Langevin (DREXEL) sampler and its variant with Adjusted Metropolis (DREAM). These samplers use two GDSs at different temperatures and step sizes: one focuses on local exploitation, while the other explores broader energy landscapes. When energy differences are significant, sample swaps occur, which are determined by a mechanism tailored for discrete sampling to ensure detailed balance. Theoretically, we prove both DREXEL and DREAM converge asymptotically to the target energy and exhibit faster mixing than a single GDS. Experiments further confirm their efficiency in exploring non-convex discrete energy landscapes.

We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model's representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Codes and benchmark datasets are available at https://github.com/ArthurLeoM/DRESS-LLM.

Reach for the stars because you might just become one! Drina Shah has a fascination with space exploration and engineering. Engineering and space exploration are two things that Drina Shah finds fascinating. Shah was given the chance to work on the NASA CubeSat Launch Initiative Project while he was in high school. She was one of eight students from her school to become a finalist out of six schools from around the country.

Now, gamers can get their fix without even having to leave the comfort of their bed. Dreams has unveiled The Drift – the UK's first gaming bed featuring a built-in TV, storage for consoles and LED lights. Dreams has unveiled The Drift – the UK's first gaming featuring a built-in TV, storage for console and LED lights The bed comes in three finishes – black faux leather with green or blue piping and grey fabric with black piping. At the foot of the bed is a built-in 32-inch TV with a lifting mechanism. The Drift also has storage space for games consoles, headset holders and USB ports on each side.

Recent techniques for approximating Nash equilibria in very large games leverage neural networks to learn approximately optimal policies (strategies). One promising line of research uses neural networks to approximate counterfactual regret minimization (CFR) or its modern variants. DREAM, the only current CFR-based neural method that is model free and therefore scalable to very large games, trains a neural network on an estimated regret target that can have extremely high variance due to an importance sampling term inherited from Monte Carlo CFR (MCCFR). In this paper we propose an unbiased model-free method that does not require any importance sampling. Our method, ESCHER, is principled and is guaranteed to converge to an approximate Nash equilibrium with high probability. We show that the variance of the estimated regret of ESCHER is orders of magnitude lower than DREAM and other baselines. We then show that ESCHER outperforms the prior state of the art -- DREAM and neural fictitious self play (NFSP) -- on a number of games and the difference becomes dramatic as game size increases. In the very large game of dark chess, ESCHER is able to beat DREAM and NFSP in a head-to-head competition over $90\%$ of the time.

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The ultimate goal, in Dr. Ferrucci's view, is that A.I. becomes a trusted "thought partner," a skilled collaborator at work and at home, making suggestions and explaining them. Elemental Cognition, founded in 2015, is taking measured steps toward that goal with a promising, though unproven, hybrid approach. Its system combines the latest developments in machine learning with a page from the A.I.'s past, software modeled after human reasoning. Newer machine learning programs are remarkable at pattern recognition and predictions, far more powerful than in the "Jeopardy!" They pore through millions of words and word patterns, and generate the most likely interpretations.

As soon as I saw these new artificial intelligence image creation tools, like DALL-E, I wanted to see how well they'd work for generating space and astronomy images. I'm still on the waiting list for DALL-E 2, so I don't have any feedback to give there, but I signed up for Midjourney AI, played around with the free account, and then signed up for a full paid account, so I could test out its capabilities. How well does it work? I'm still learning to craft prompts to get the best results, but the biggest issue is that they're unscientific. If I need a picture of the Space Launch System, it needs to be the actual Space Launch System and not some kind of art deco version of a rocket that looks like it was designed in the 1950s.

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Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists. We often hear that AI is going to automate away or take over all human tasks, including those in art, film, and other creative industries. But this is far from the case. AI is a supplemental tool that artists can use to explore new creative territory.