The big strides seen in generative AI are not based on somewhat obscure algorithms, but due to clearly defined generative principles. The resulting concrete implementations have proven themselves in large numbers of applications. We suggest that it is imperative to thoroughly investigate which of these generative principles may be operative also in the brain, and hence relevant for cognitive neuroscience. In addition, ML research led to a range of interesting characterizations of neural information processing systems. We discuss five examples, the shortcomings of world modelling, the generation of thought processes, attention, neural scaling laws, and quantization, that illustrate how much neuroscience could potentially learn from ML research.

Thank you all for helpful reviewing. We clarify our viewpoint with respect to some of your questions. Is the assumption about data spectrum unrealistic? Our formulation is not limited to some specific eigenvalue distributions. The equation shown above p.6 containing f (d 1)

How healthy am I? My immunome knows the score. Made up of 1.8 trillion cells and trillions more proteins, metabolites, mRNA, and other biomolecules, every person's immunome is different, and it is constantly changing. It's shaped by everything we have ever been exposed to physically and emotionally, and powerfully influences everything from our vulnerability to viruses and cancer to how well we age to whether we tolerate certain foods better than others. Yet as critical as the immunome is to each of us, it has remained largely beyond the reach of modern medicine. Now, thanks to a slew of new technologies, understanding this vital and mysterious system is within our grasp, paving the way for powerful new tools and tests to help us better assess, diagnose and treat diseases. On Monday, we published our 2025 edition of Climate Tech Companies to Watch .

What our latest list of Climate Tech Companies to Watch says about this moment. On Monday, we published our 2025 edition of Climate Tech Companies to Watch . This marks the third time we've put the list together, and it's become one of my favorite projects to work on every year. In the journalism world, it's easy to get caught up in the latest news, whether it's a fundraising round, research paper, or startup failure. Curating this list gives our team a chance to take a step back and consider the broader picture. What industries are making progress or lagging behind?

The next time you order a takeaway, it might be flown directly to your door. Today, Deliveroo has launched its first drone delivery service for customers in Ireland. Drones travelling at speeds of up to 50 miles per hour (80 kph) will carry food from restaurants to customers in as little as three minutes. Upon arrival, the drone will hover above the customer's home and gently lower the food to the ground on a tether before returning to the delivery hub. Launching in Blanchardstown, on the outskirts of Dublin, the trial will cover a 1.8-mile (3km) radius, reaching up to 150,000 people.

One key caveat here is that we don't know much about "closed source" models--for these, companies hold back the details of how they work. Instead, we worked with researchers who measured the energy it takes to run open-source AI models, for which the source code is publicly available. But using open-source models, it's possible to directly measure the energy used to respond to a query rather than just guess. We worked with researchers who generated text, images, and video and measured the energy required for the chips the models are based on to perform the task. Even just within the text responses, there was a pretty large range of energy needs.

The takeaway recommendation system is designed to recommend users' future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and increasing merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequence data. However, two main challenges limit the performance of these approaches: (1) how to capture dynamic user preferences on complex geospatial information and (2) how to efficiently integrate spatial-temporal knowledge from graphs and sequence data with low calculation costs. In this paper, we propose a novel spatial-temporal knowledge distillation for takeaway recommendation model (STKDRec) based on the two-stage training process. Specifically, during the first pre-training stage, a spatial-temporal knowledge graph (STKG) encoder is pre-trained to extract the high-order spatial-temporal and collaborative associations within the STKG. During the second STKD stage, a spatial-temporal Transformer is employed to comprehensively model dynamic user preferences on various types of fine-grained geospatial information from a sequence perspective. Furthermore, the STKD strategy is introduced to adaptively fuse the rich spatial-temporal knowledge from the pre-trained STKG encoder and the spatial-temporal transformer while reducing the cost of model training. Extensive experiments on three real-world datasets show that our STKDRec significantly outperforms the state-of-the-art baselines. Our code is available at:https://github.com/Zhaoshuyuan0246/STKDRec.

In the field of emotion analysis, much NLP research focuses on identifying a limited number of discrete emotion categories, often applied across languages. These basic sets, however, are rarely designed with textual data in mind, and culture, language, and dialect can influence how particular emotions are interpreted. In this work, we broaden our scope to a practically unbounded set of \textit{affective states}, which includes any terms that humans use to describe their experiences of feeling. We collect and publish MASIVE, a dataset of Reddit posts in English and Spanish containing over 1,000 unique affective states each. We then define the new problem of \textit{affective state identification} for language generation models framed as a masked span prediction task. On this task, we find that smaller finetuned multilingual models outperform much larger LLMs, even on region-specific Spanish affective states. Additionally, we show that pretraining on MASIVE improves model performance on existing emotion benchmarks. Finally, through machine translation experiments, we find that native speaker-written data is vital to good performance on this task.