Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data -- despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1-2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar in a factor graph.

Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to coordinate the learning process, thus introducing potential scalability and security issues. In the literature, server-less federated learning approaches like gossip federated learning and blockchain-enabled federated learning have been proposed to mitigate these issues. In this work, we propose a complete overview of these three techniques proposing a comparison according to an integral set of performance indicators, including model accuracy, time complexity, communication overhead, convergence time, and energy consumption. An extensive simulation campaign permits to draw a quantitative analysis considering both feedforward and convolutional neural network models. Results show that gossip federated learning and standard federated solution are able to reach a similar level of accuracy, and their energy consumption is influenced by the machine learning model adopted, the software library, and the hardware used. Differently, blockchain-enabled federated learning represents a viable solution for implementing decentralized learning with a higher level of security, at the cost of an extra energy usage and data sharing. Finally, we identify open issues on the two decentralized federated learning implementations and provide insights on potential extensions and possible research directions in this new research field.

Artificial intelligence (AI) is unlocking use cases that are transforming industries across a wide swath of the world's economy. From infrastructure that "self-heals" to radically reimagined (and touchless) customer service and experience; from large scale hyper-personalization to automatically created marketing messages and images leveraging Generative AI tools like ChatGPT--it is all a reality today. These AI solutions can powerfully augment and sometimes radically outperform most traditional business roles. This article is a collaborative effort by Joshan Abraham, Jorge Amar, Yuval Atsmon, Miguel Frade, and Tomás Lajous, representing views from McKinsey's Technology, Media & Telecommunications Practice. The impact from these solutions is becoming evident.

The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a considerable social and economic impact on our daily life. This performance relies heavily on the configuration of the network parameters. However, with the massive increase in both the size and complexity of cellular networks, network management, especially parameter configuration, is becoming complicated. The current practice, which relies largely on experts' prior knowledge, is not adequate and will require lots of domain experts and high maintenance costs. In this work, we propose a learning-based framework for handover parameter configuration. The key challenge, in this case, is to tackle the complicated dependencies between neighboring cells and jointly optimize the whole network. Our framework addresses this challenge in two ways. First, we introduce a novel approach to imitate how the network responds to different network states and parameter values, called auto-grouping graph convolutional network (AG-GCN). During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy where each cell considers several local performance metrics to balance its own performance and its neighbors. We evaluate our proposed algorithm via a simulator constructed using real network data. We demonstrate that the handover parameters our model can find, achieve better average network throughput compared to those recommended by experts as well as alternative baselines, which can bring better network quality and stability. It has the potential to massively reduce costs arising from human expert intervention and maintenance.

We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights. As organism networks feature vastly more parameters than simpler architectures, we perform our initial experiments on an arithmetic task as well as on simplified MNIST-dataset classification as a collective. We observe that individual particle networks tend to specialise in either of the tasks and that the ones fully specialised in the secondary task may be dropped from the network without hindering the computational accuracy of the primary task. This leads to the discovery of a novel pruning-strategy for sparse neural networks

Distributed deep learning (DDL) systems strongly depend on network performance. Current electronic packet switched (EPS) network architectures and technologies suffer from variable diameter topologies, low-bisection bandwidth and over-subscription affecting completion time of communication and collective operations. We introduce a near-exascale, full-bisection bandwidth, all-to-all, single-hop, all-optical network architecture with nanosecond reconfiguration called RAMP, which supports large-scale distributed and parallel computing systems (12.8~Tbps per node for up to 65,536 nodes). For the first time, a custom RAMP-x MPI strategy and a network transcoder is proposed to run MPI collective operations across the optical circuit switched (OCS) network in a schedule-less and contention-less manner. RAMP achieves 7.6-171$\times$ speed-up in completion time across all MPI operations compared to realistic EPS and OCS counterparts. It can also deliver a 1.3-16$\times$ and 7.8-58$\times$ reduction in Megatron and DLRM training time respectively} while offering 42-53$\times$ and 3.3-12.4$\times$ improvement in energy consumption and cost respectively.

--This paper presents a novel approach for multi-modal data fusion based on the V ector-Quantized V ariational Autoencoder (VQV AE) architecture. The proposed method is simple yet effective in achieving excellent reconstruction performance on paired MNIST -SVHN data and WiFi spectrogram data. Additionally, the multimodal VQV AE model is extended to the 5G communication scenario, where an end-to-end Channel State Information (CSI) feedback system is implemented to compress data transmitted between the base-station (eNodeB) and User Equipment (UE), without significant loss of performance. The proposed model learns a discriminative compressed feature space for various types of input data (CSI, spectrograms, natural images, etc), making it a suitable solution for applications with limited computational resources. Multimodal fusion is an important aspect of modern artificial intelligence and machine learning systems. It is a process of combining data from multiple sensors to create a comprehensive understanding of the environment. In various applications, such as robotics, autonomous vehicles, and Internet of Things (IoT), multiple sensors are used to capture information from the environment, including vision, audio, lidar, radar, sonar, GPS and more. By combining this data, a more accurate and robust representation of the environment can be created. Multimodal sensor fusion is important because it helps to overcome the limitations of individual sensors and allows for more reliable and robust decision-making. However, compression of multimodal data is also needed for increasing efficiency, decreasing the cost of storage and transmission, and facilitating real-time processing of substantial datasets in a variety of applications. For example, in 5G networks, Channel State Information (CSI) feedback plays a critical role in the communication system.

Late last year, the European Union introduced the Artificial Intelligence Liability Directive (AILD) to "improve the functioning of the internal market by laying down uniform rules for certain aspects of non-contractual civil liability for damage caused with the involvement of AI systems." Bad AI is AI that isn't trustworthy--AI that is based on biased or incomplete data that then, in turn, could perpetuate harmful outcomes. And with AI expecting a compound annual growth rate of 20% by 2030--to reach nearly US $1.4 trillion--the technology, media and telecommunications (TMT) industry has a critical responsibility to not only develop the most trustworthy AI but also model the most trustworthy AI behavior to their business customers and society at large. While AI may have seemed like the stuff of science fiction, it has now entered the realm of reality and offers incredible potential to make businesses more competitive. According to Deloitte's AI Dossier, there are six key ways AI can help businesses create value: But while AI presents amazing potential for business value, AI has an equal amount of potential to go wrong.

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Large language models are computer programs that can analyze and create text. They are trained using massive amounts of text data, which helps them become better at tasks like generating text. Language models are the foundation for many natural language processing (NLP) activities, like speech-to-text and sentiment analysis. These models can look at a text and predict the next word. Examples of LLMs include ChatGPT, LaMDA, PaLM, etc. Parameters in LLMs help the model to understand relationships in the text, which helps them to predict the likelihood of word sequences.