We do a hyperparameter search for LoRA over the following variables: LoRA dropout { 0.0, 0.05, LoRA α is always proportional to the learning rate. We find that LoRA dropout 0.05 is useful for small models (7B, 13B), but not for larger models (33B, We use the same preprocessing of the Super-Natural Instruction dataset as Wang et al. RA finetuning experiments outlined in Section 5. This limits the dataset to 9,209 examples. HH-RLHF This is a human preference dataset about helpfulness and harmlessness.

Science Dinosaurs Teen discovers Australia's oldest dinosaur fossil--almost 70 years ago An early sauropodomorph likely made the 230-million-year-old footprint. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1958, an Australian teenager named Bruce Runnegar uncovered a mysterious dinosaur footprint during a visit to a quarry with school friends. He kept the fossil for years, eventually becoming a paleontologist himself. Over six decades later, the prehistoric print is now ready for its close-up.

Winter Storm Fern death toll climbs to 34 after brutal freeze batters the US... and meteorologists warn even colder weather is on the way Top lawyer, event planner and pilot identified as three of six killed in private jet crash while taking'girls' trip' to Paris Insidious secret life of promiscuous neurosurgeon found dead in his $2.5m mansion'He has no loyalty': The bitter secret fallout between One Direction star Harry Styles and his former bandmates - as insiders reveal for the first time what really happened at Liam Payne's funeral Nicola Peltz was raised by billionaire'bully' Nelson who became the most feared investor on Wall Street before starting his own dynasty with his 10 children Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Influencer shares haunting 911 call after crash that killed her son known for viral'Okay Baby' video Matthew Stafford's wife Kelly shares emotional moment NFL star returned home after heartbreaking playoff defeat Martha Stewart breaks political silence after being urged by teenage granddaughter: 'Things must change' Insiders reveal the REAL misstep that got Kristi Noem humiliatingly ditched by Trump... and the weak excuse she's peddling to try and save herself Defiant Trump dismisses Alzheimer's fears as he struggles to recall name of disease in interview READ MORE: Evolution debate reignited after'missing human link' is found A new AI app is helping to rewrite the evolution of flight. The app, developed by researchers from the University of Edinburgh, has been used to analyse footprints made by dinosaurs more than 200 million years ago. The results show that several tracks share'uncanny' features with both extinct and modern birds. According to the researchers, this suggests that birds could have originated 60 million years earlier than we thought.

This year, hype around AI really exploded, and so did concerns about AI's environmental footprint. We also saw some surprising biotech developments. It's been a busy and productive year here at . We published magazine issues on power, creativity, innovation, bodies, relationships, and security . We hosted 14 exclusive virtual conversations with our editors and outside experts in our subscriber-only series, Roundtables, and held two events on MIT's campus. And we published hundreds of articles online, following new developments in computing, climate tech, robotics, and more.

Transfer learning from the model trained on large datasets to customized downstream tasks has been widely used as the pre-trained model can greatly boost the generalizability. However, the increasing sizes of pre-trained models also lead to a prohibitively large memory footprints for downstream transferring, making them unaffordable for personal devices. Previous work recognizes the bottleneck of the footprint to be the activation, and hence proposes various solutions such as injecting specific lite modules. In this work, we present a novel memory-efficient transfer framework called Back Razor, that can be plug-and-play applied to any pre-trained network without changing its architecture. The key idea of Back Razor is asymmetric sparsifying: pruning the activation stored for back-propagation, while keeping the forward activation dense. It is based on the observation that the stored activation, that dominates the memory footprint, is only needed for backpropagation. Such asymmetric pruning avoids affecting the precision of forward computation, thus making more aggressive pruning possible. Furthermore, we conduct the theoretical analysis for the convergence rate of Back Razor, showing that under mild conditions, our method retains the similar convergence rate as vanilla SGD. Extensive transfer learning experiments on both Convolutional Neural Networks and Vision Transformers with classification, dense prediction, and language modeling tasks show that Back Razor could yield up to 97% sparsity, saving 9.2x memory usage, without losing accuracy.

Recent methods in ergodic coverage planning have shown promise as tools that can adapt to a wide range of geometric coverage problems with general constraints, but are highly sensitive to the numerical scaling of the problem space. The underlying challenge is that the optimization formulation becomes brittle and numerically unstable with changing scales, especially under potentially nonlinear constraints that impose dynamic restrictions, due to the kernel-based formulation. This paper proposes to address this problem via the development of a scale-agnostic and adaptive ergodic coverage optimization method based on the maximum mean discrepancy metric (MMD). Our approach allows the optimizer to solve for the scale of differential constraints while annealing the hyperparameters to best suit the problem domain and ensure physical consistency. We also derive a variation of the ergodic metric in the log space, providing additional numerical conditioning without loss of performance. We compare our approach with existing coverage planning methods and demonstrate the utility of our approach on a wide range of coverage problems.

With the ever-growing adoption of artificial intelligence (AI), AI-based software and its negative impact on the environment are no longer negligible, and studying and mitigating this impact has become a critical area of research. However, it is currently unclear which role environmental sustainability plays during AI adoption in industry and how AI regulations influence Green AI practices and decision-making in industry. We therefore aim to investigate the Green AI perception and management of industry practitioners. To this end, we conducted a total of 11 interviews with participants from 10 different organizations that adopted AI-based software. The interviews explored three main themes: AI adoption, current efforts in mitigating the negative environmental impact of AI, and the influence of the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD). Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability. Monitoring and mitigation of AI's environmental impact were very limited. Only one participant monitored negative environmental effects. Regarding applied mitigation practices, six participants reported no actions, with the others sporadically mentioning techniques like prompt engineering, relying on smaller models, or not overusing AI. Awareness and compliance with the EU AI Act are low, with only one participant reporting on its influence, while the CSRD drove sustainability reporting efforts primarily in larger companies. All in all, our findings reflect a lack of urgency and priority for sustainable AI among these companies. We suggest that current regulations are not very effective, which has implications for policymakers. Additionally, there is a need to raise industry awareness, but also to provide user-friendly techniques and tools for Green AI practices.

Associative memory models are content-addressable memory systems fundamental to biological intelligence and are notable for their high interpretability. However, existing models evaluate the quality of retrieval based on proximity, which cannot guarantee that the retrieved pattern has the strongest association with the query, failing correctness. We reframe this problem by proposing that a query is a generative variant of a stored memory pattern, and define a variant distribution to model this subtle context-dependent generative process. Consequently, correct retrieval should return the memory pattern with the maximum a posteriori probability of being the query's origin. This perspective reveals that an ideal similarity measure should approximate the likelihood of each stored pattern generating the query in accordance with variant distribution, which is impossible for fixed and pre-defined similarities used by existing associative memories. To this end, we develop adaptive similarity, a novel mechanism that learns to approximate this insightful but unknown likelihood from samples drawn from context, aiming for correct retrieval. We theoretically prove that our proposed adaptive similarity achieves optimal correct retrieval under three canonical and widely applicable types of variants: noisy, masked, and biased. We integrate this mechanism into a novel adaptive Hopfield network (A-Hop), and empirical results show that it achieves state-of-the-art performance across diverse tasks, including memory retrieval, tabular classification, image classification, and multiple instance learning.

Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs).