What are youth movements demanding in today's climate crisis?


How do AI models generate personalized humor?

arXiv logo

AI models generate personalized humor by analyzing user preferences, interests, and even their sense of humor to create tailored jokes[1]. One approach involves collaborative filtering techniques, identifying users with similar tastes and recommending jokes enjoyed by those users[1]. User preferences can be represented in a matrix, which is then factorized using singular value decomposition (SVD) to estimate user preferences for new jokes and generate personalized recommendations[1].

By analyzing demographics, interests, and facial expressions, AI robo-comedians can adapt their material in real-time to maximize laughter[1]. AI models can also be fine-tuned on relevant datasets to generate jokes for a specific context, such as a sitcom[1]. Furthermore, an AI system called HumorSkills expands on joke topics to find narratives related to the image to grow the number of relatable joke targets[2].


What was Buffett's main topic in the 2025 letter?

title: 'Buffett's 2025 Letter to Shareholders (Full Text)'

Buffett's main topic in the 2025 letter to shareholders was the performance of Berkshire Hathaway in 2024, noting that it exceeded his expectations despite 53% of the 189 operating companies reporting a decline in earnings[1].

[1] coinlive.com

Does your metabolism slowdown as you grow older?

223480bb-1ca3-4de4-88ba-b2fa11b11636

Yes, your metabolism does slow down as you grow older, but not as significantly or as early as commonly believed. According to a study, metabolism peaks around age 1, when infants burn calories 50% faster than adults. After this peak, metabolism gradually declines by about 3% per year until reaching the 20s, where it levels off and remains stable until around age 60. Only then does it start to decline again, at a rate of less than 1% per year[4][6].

Factors like loss of muscle mass and decreased physical activity contribute to this decline, but research indicates that the changes in metabolism are more complex than simply aging. For instance, the study noted that metabolic rates are stable from ages 20 to 60, and lifestyle factors might play a more significant role in weight changes during midlife than a slowing metabolism itself[5][6].

Follow Up Recommendations

Metrics for awareness versus action.

Impressions are a good metric of awareness, of causing awareness, and that's the measure that's used for display ads and social media ads.
KINSHUK JERATH, Ph.D.[1]
Clicks are sort of some kind of action, is a good metric for lower funnel, and that's how search ads are priced.
KINSHUK JERATH, Ph.D.[1]
We don't have better algorithms than anyone else. We just have more data.
Speaker or author name[2]
The standard in the industry for everybody else is, you know, you pay and you hope that you get some.
Speaker or author name[2]
Google reduced the information in the query report, limiting the visibility into specific queries. They describe it as a massive decrease.
MR. DAHLQUIST[4]

How do taxes influence economic inequality?


Why were lightships invented and where were they first used?

title: 'THE BEACON FIRE.'

Lightships were invented because there are many parts of coasts that are unsuitable for lighthouse construction[253].

Robert Hamblin and David Avery combined resources to establish a floating light at The Nore, and subsequently levied a toll for its maintenance[254]. It was first used at The Nore, according to The Lighthouses and Lightships document[254].


Why is climate action urgent now?

Follow Up Recommendations

Who represented the State of Colorado?

The State of Colorado was represented by Jonathan Sallet and Steven Kaufmann from the Colorado Department of Law, CPS/Antitrust Section, located at 1300 Broadway, 7th Floor, Denver, CO 80203. Additionally, William Cavanaugh, Jr. from Patterson, Belknap, Webb & Tyler, LLP, also represented the State of Colorado, with his office at 1133 Avenue of the Americas, Suite 2200, New York, NY 10036. This representation occurred during various proceedings regarding the case against Google[11][6][7][2][1][5][4][9][10][8].

In some instances, the representation included both Jonathan Sallet and William Cavanaugh, Jr. specifically noted for their roles on behalf of not only Colorado but also the State of Nebraska. However, there were mentions of other attorneys, but these were not consistently part of the State of Colorado's representation in this case[3].


Scaling Neural Networks with GPipe

Introduction to GPipe

The paper titled 'GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism' introduces a novel method for efficiently training large neural networks. The increasing complexity of deep learning models has made optimizing their performance critical, especially as they often exceed the memory limits of single accelerators. GPipe addresses these challenges by enabling effective model parallelism and improving resource utilization without sacrificing performance.

Pipeline Parallelism Explained

title: 'Figure 2: (a) An example neural network with sequential layers is partitioned across four accelerators. Fk is the composite forward computation function of the k-th cell. Bk is the back-propagation function, which depends on both Bk+1 from the upper layer and Fk. (b) The naive model parallelism strategy leads to severe under-utilization due to the sequential dependency of the network. (c) Pipeline parallelism divides the input mini-batch into smaller micro-batches, enabling different accelerators to work on different micro-batches simultaneously. Gradients are applied synchronously at the end.'
title: 'Figure 2: (a) An example neural network with sequential layers is partitioned across four accelerators. Fk is the composite forward computation function of the k-th cell. Bk is the back-propagation function, which depends on both Bk+1 from t...Read More

Scaling deep learning models typically requires distributing the workload across multiple hardware accelerators. GPipe specifically focuses on pipeline parallelism, where a neural network is constructed as a sequence of layers, allowing for parts of the model to be processed simultaneously on different accelerators. This approach helps in handling larger models by breaking them into smaller sub-parts, thus allowing each accelerator to work on a segment of the model, increasing throughput significantly.

The authors argue that by utilizing 'micro-batch pipeline parallelism,' GPipe enhances efficiency by splitting each mini-batch into smaller segments called micro-batches. Each accelerator receives one micro-batch and processes it independently, which helps facilitate better hardware utilization compared to traditional methods that may lead to idle processing times on some accelerators due to sequential dependencies between layers[1].

Advantages of Using GPipe

Improved Training Efficiency

title: 'Figure 1: (a) Strong correlation between top-1 accuracy on ImageNet 2012 validation dataset [5] and model size for representative state-of-the-art image classification models in recent years [6, 7, 8, 9, 10, 11, 12]. There has been a 36× increase in the model capacity. Red dot depicts 84.4% top-1 accuracy for the 550M parameter AmoebaNet model. (b) Average improvement in translation quality (BLEU) compared against bilingual baselines on our massively multilingual in-house corpus, with increasing model size. Each point, T(L, H, A), depicts the performance of a Transformer with L encoder and L decoder layers, a feed-forward hidden dimension of H and A attention heads. Red dot depicts the performance of a 128-layer 6B parameter Transformer.'
title: 'Figure 1: (a) Strong correlation between top-1 accuracy on ImageNet 2012 validation dataset [5] and model size for representative state-of-the-art image classification models in recent years [6, 7, 8, 9, 10, 11, 12]. There has been a 36× incr...Read More

GPipe not only maximizes the capacity of large-scale models but also provides substantial improvements in training speed. The paper reports that using GPipe with various architectures yields significant speedups when scaling the number of accelerators. For example, when training an AmoebaNet model, the authors noted that scaling to 8 accelerators enhanced the training efficiency multiple times compared to non-pipelined approaches[1].

Flexibility in Model Structures

One of the standout features of GPipe is its adaptability to various model architectures, such as convolutional neural networks and transformers. GPipe supports different layer configurations and can dynamically adjust to the specific needs of a given architecture. This flexibility provides researchers and practitioners with the tools they need to optimize models for diverse tasks, including image classification and multilingual machine translation, as demonstrated through their experiments on large datasets[1].

Experiments and Findings

title: 'Figure 2: (a) An example neural network with sequential layers is partitioned across four accelerators. Fk is the composite forward computation function of the k-th cell. Bk is the back-propagation function, which depends on both Bk+1 from the upper layer and Fk. (b) The naive model parallelism strategy leads to severe under-utilization due to the sequential dependency of the network. (c) Pipeline parallelism divides the input mini-batch into smaller micro-batches, enabling different accelerators to work on different micro-batches simultaneously. Gradients are applied synchronously at the end.'
title: 'Figure 2: (a) An example neural network with sequential layers is partitioned across four accelerators. Fk is the composite forward computation function of the k-th cell. Bk is the back-propagation function, which depends on both Bk+1 from t...Read More

Through extensive experiments, the authors demonstrate that GPipe can effectively scale large neural networks. They utilized various architectures—including the 557-million-parameter AmoebaNet and a 1.3B-parameter multilingual transformer model—across different datasets like ImageNet and various translation tasks.

The results showed that models trained with GPipe achieved higher accuracy and better performance metrics, such as BLEU scores in translation tasks, compared to traditional single-device training methods. Specifically, they achieved a top-1 accuracy of 84.4% on ImageNet, showcasing the potential of deeper architectures paired with pipeline parallelism[1].

Addressing Performance Bottlenecks

The design of GPipe counters several potential performance bottlenecks inherent in other parallel processing strategies. One major challenge is the communication overhead between accelerators, particularly in synchronizing the gradient updates. GPipe introduces a novel back-splitting technique that minimizes this overhead by allowing gradients to be computed in parallel while ensuring they are updated synchronously at the end of each training iteration. This allows for seamless integration across multiple devices, significantly reducing latency and maximizing throughput[1].

Practical Implementation Considerations

Implementing GPipe requires considerations around factors like memory consumption. The paper discusses how re-materialization, during which activations are recomputed instead of stored, can significantly reduce memory overhead during training. This is particularly beneficial when handling large models that otherwise might not fit into the available capacity of a single accelerator. By applying this strategy, GPipe can manage larger architectures and ensure efficient resource allocation across the various components involved in training[1].

Conclusion

Table 1: Maximum model size of AmoebaNet supported by GPipe under different scenarios. Naive-1 refers to the sequential version without GPipe. Pipeline-k means k partitions with GPipe on k accelerators. AmoebaNet-D (L, D): AmoebaNet model with L normal cell layers and filter size D . Transformer-L: Transformer model with L layers, 2048 model and 8192 hidden dimensions. Each model parameter needs 12 bytes since we applied RMSProp during training.
Table 1: Maximum model size of AmoebaNet supported by GPipe under different scenarios. Naive-1 refers to the sequential version without GPipe. Pipeline-k means k partitions with GPipe on k accelerators. AmoebaNet-D (L, D): AmoebaNet model with L norm...Read More

GPipe represents a significant advancement in the training of large-scale neural networks by introducing pipeline parallelism combined with micro-batching. This innovative framework allows for efficient model scaling while maintaining training performance across different architectures. The approach not only enhances scalability but also provides a flexible and robust solution for tackling modern deep learning challenges efficiently. Researchers and engineers can leverage GPipe to optimize their training regimes, making it a valuable tool in the ever-evolving landscape of artificial intelligence[1].