This report summarizes a 2023 workshop on software engineering for robotics, highlighting critical challenges in the field. The workshop identified key issues like the simulation-reality gap, integrating machine learning components, and handling the complexity of heterogeneous robot systems. The report proposes several research directions to address these challenges, including developing improved middlewares, architecture description languages, and human-robot interaction models, along with enhancing simulation ecosystems and quality assurance methods. Finally, it emphasizes the need for updated curricula to train future robotics software engineers. The workshop's goal was to foster collaboration and define a research agenda for the next five years.

Source: https://arxiv.org/pdf/2401.12317

NVIDIA CEO Jensen Huang's keynote speech at CES 2025, focusing on the company's advancements in AI and its impact on various industries. The speech highlighted new GPUs (RTX 50 series), AI platforms (Cosmos, Project DIGITS), and software (AI Blueprints, Isaac GR00T, Omniverse Mega) designed to accelerate AI development and deployment across gaming, autonomous vehicles, and robotics. Specific partnerships with major automakers like Toyota were also announced. The overarching theme emphasizes AI's transformative power, particularly in creating agentic AI and physical AI, and the crucial role of NVIDIA's technologies in this revolution. The keynote showcased NVIDIA's ambition to become a key player in the next industrial revolution powered by AI.

This report explores blockchain's potential for climate action and sustainability, dispelling misconceptions about its energy consumption. It highlights blockchain's applications in building a circular economy, particularly through supply chain tracking and product tokenization, improving transparency and efficiency. Furthermore, it showcases blockchain's role in carbon credit management, enhancing monitoring, reporting, and verification (MRV) processes. The report also emphasizes blockchain's capacity to promote democratic participation in climate initiatives by empowering communities and improving data transparency for more informed decision-making. Finally, it addresses upcoming policy initiatives and industry efforts towards sustainable blockchain development.

Source: https://www.blockchain4europe.eu/wp-content/uploads/2024/08/An-Overview-of-Blockchain-for-Climate-Action-and-Sustainability-BC4EU-IOTA-April-2023.pdf

This technical paper from Google describes Spanner, a globally distributed database that enables highly available and consistent data management across multiple datacenters. Spanner uniquely provides externally consistent distributed transactions, a feature that ensures a consistent view of data despite the challenges of distributed systems. This is achieved through a novel time API called TrueTime, which explicitly accounts for clock uncertainty, enabling Spanner to guarantee a globally consistent view of data across all its nodes. The paper explores the architecture, design choices, implementation details, and performance characteristics of Spanner, demonstrating its potential to handle massive datasets and complex operations across continents. The paper concludes by discussing future directions for Spanner, including further optimizations for performance and consistency, as well as the integration of more sophisticated database functionalities.

This research paper introduces a new data structure called the Count-Min Sketch for summarizing large datasets. This method is particularly useful for analyzing data streams, where data arrives continuously and must be processed quickly. The Count-Min Sketch allows for fast and accurate approximations of various functions of interest, such as point queries, range queries, and inner product queries. This approach significantly improves upon existing methods in terms of both space and time complexity, which is particularly relevant for handling massive datasets in applications like network traffic analysis and database monitoring.

This paper provides a thorough and detailed explanation of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs), two popular machine learning architectures used for processing sequential data. The paper starts by deriving the canonical RNN equations from differential equations, establishing a clear foundation for understanding the behaviour of these networks. The paper then explores the concept of "unrolling" an RNN, demonstrating how a long sequence can be approximated by a series of shorter, independent sub-sequences. Subsequently, it addresses the challenges faced when training RNNs, particularly the issues of vanishing and exploding gradients. The paper then meticulously constructs the Vanilla LSTM cell from the canonical RNN, introducing gating mechanisms to control the flow of information within the cell and mitigate the vanishing gradient problem. The paper also presents an extended version of the Vanilla LSTM cell, known as the Augmented LSTM, by incorporating features like recurrent projection layers, non-causal input context windows, and an input gate. Finally, the paper details the backward pass equations for the Augmented LSTM, which are used for training the network using the Back Propagation Through Time algorithm.

Link to the Paper: https://www.sciencedirect.com/science/article/abs/pii/S0167278919305974

This extended abstract presents a novel probabilistic algorithm called HYPERLOGLOG for efficiently estimating the cardinality of massive datasets. It improves upon existing algorithms like LOGLOG by achieving higher accuracy while using significantly less memory. The algorithm is based on the harmonic mean of certain observable quantities, which improves the quality of estimations by effectively reducing variance. The paper also provides a rigorous mathematical analysis of the algorithm’s performance, employing techniques such as poissonization and Mellin transforms, to determine its asymptotic behavior in terms of bias and standard error. Finally, the paper discusses practical considerations for implementing the algorithm, including the use of hash functions, correction for small cardinality issues, and potential optimality compared to other existing algorithms.

Link to the Paper: https://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf

This technical paper details the development and release of Llama 2, a family of large language models (LLMs) created by Meta. The paper comprehensively explains the model’s architecture, training process, and safety considerations. Llama 2 builds upon the foundation of Llama 1, employing key improvements such as enhanced data cleaning, a larger training dataset, increased context length, and the use of grouped-query attention. The paper highlights the significant advancements in Llama 2's performance and safety, particularly in tasks requiring reasoning and knowledge comprehension. The authors also conduct extensive analysis on dataset contamination, demonstrating that Llama 2's performance is not significantly impacted by data overlap between training and evaluation sets.

Link to the full report is here: https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf