Category: Uncategorized

The transformative impact of Transformers on natural language processing (NLP) and computer vision (CV) is undeniable. Their scalability and effectiveness have propelled advancements across these fields, but the rising complexity of these models has led to soaring computational costs. Addressing this challenge has become a priority, prompting exploration into alternative approaches like Mixture-of-Experts (MoE) architectures,… Read more

Users of Google’s Chrome browser can rest easy knowing that their surfing is secure, thanks in part to cryptographer Joppe Bos. He’s coauthor of a quantum-secure encryption algorithm that was adopted as a standard by the U.S. National Institute of Standards and Technology (NIST) in August and is already being implemented in a wide range… Read more

For IEEE Spectrum readers following telecommunications news in 2024, signals expanding their reach and range animated readers to read more: Including stories on early-stage cellphone “towers” now in low-earth orbit, low-power Wi-Fi implementations reaching out for kilometers, China expanding its satellite broadband constellations into regions of the globe dominated by SpaceX Starlink, and 6G signals… Read more

These WIRED-tested memory sticks are a virtual filing cabinet in your pocket. Read more

If we can’t get to the forest, the forest must come to us, in the form of cities designed around green spaces. Read more

Social media platforms have revolutionized human interaction, creating dynamic environments where millions of users exchange information, form communities, and influence one another. These platforms, including X and Reddit, are not just tools for communication but have become critical ecosystems for understanding modern societal behaviors. Simulating such intricate interactions is vital for studying misinformation, group polarization,… Read more

In today’s world, Multimodal large language models (MLLMs) are advanced systems that process and understand multiple input forms, such as text and images. By interpreting these diverse inputs, they aim to reason through tasks and generate accurate outputs. However, MLLMs often fail at complex tasks because they lack structured processes to break problems into smaller… Read more

Large language models (LLMs) built using transformer architectures heavily depend on pre-training with large-scale data to predict sequential tokens. This complex and resource-intensive process requires enormous computational infrastructure and well-constructed data pipelines. The growing demand for efficient and accessible LLMs has led researchers to explore techniques that balance resource use and performance, emphasizing achieving competitive… Read more

Large language models (LLMs) encounter significant difficulties in performing efficient and logically consistent reasoning. Existing methods, such as CoT prompting, are extremely computationally intensive, not scalable, and unsuitable for real-time applications or limited resources. These limitations restrict their applicability in financial analysis and decision-making, which require speed and accuracy. State-of-the-art reasoning approaches, like CoT, build… Read more

Machine unlearning is driven by the need for data autonomy, allowing individuals to request the removal of their data’s influence on machine learning models. This field complements data privacy efforts, which focus on preventing models from revealing sensitive information about the training data through attacks like membership inference or reconstruction. While differential privacy methods limit… Read more