Google has a monopoly, and that's the official line of the US federal government. In fact, it has two of them, losing two separate antitrust cases that threaten to cripple the tech giant. The Department of Justice has proposed forcing Google to sell or otherwise divest itself of the Chrome browser as its first and preferred remedy. But who would buy it? Unsurprisingly, there are beaucoup business beaus lining up around the block for this browser bachelorette.

On May 13th, during a live event, the artificial-intelligence company OpenAI unveiled the next generation of its technology, GPT-4o, the successor to GPT-3. When OpenAI first released its product to the public in late 2022, as the text-based tool ChatGPT, it nearly single-handedly ushered in the A.I. era. The latest version is far more powerful still. The "o" in the name stands for "omni"; the model can communicate seamlessly across various forms of media at once, including text, audio, and video, receiving prompts in one medium and responding in another. It can maintain a memory of everything you tell it.

Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on FL performance. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In our research paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. Through extensive evaluations of FedAVO on benchmark datasets, we show that FedAVO achieves significant improvement in terms of model accuracy and communication round, particularly with realistic cases of Non-IID datasets. Our extensive evaluation of the FedAVO algorithm identifies the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO, etc.).

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems. This article describes the reusable code repository developed by the RangL team and deployed for the 2022 Pathways to Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning solutions to this particular Challenge seek to optimize the UK's energy transition policy to net zero carbon emissions by 2050. The RangL repository includes an OpenAI Gym reinforcement learning environment and code that supports both submission to, and evaluation in, a remote instance of the open source EvalAI platform as well as all winning learning agent strategies. The repository is an illustrative example of RangL's capability to provide a reusable structure for future challenges.

When I go to Google Photos and search my photos for'skyline', it finds me this picture of the New York skyline I took in August, without me having labelled it! When I search for'cathedral', Google's neural networks find me pictures of cathedrals & churches I've seen. But of course, neural networks aren't magic–nothing is! I recently read a paper, "Explaining and Harnessing Adversarial Examples", that helped demystify neural networks a little for me. The paper explains how to force a neural network to make really egregious mistakes. It does this by exploiting the fact that the network is simpler (more linear!) than you might expect. It's important to understand that this doesn't explain all (or even most) kinds of mistakes neural networks make. There are a lot of possible mistakes!

The vultures of Britain's International Centre for Birds of Prey don't know it, but they're dupes. Every day, the giant birds carefully tend to their eggs, rotating them periodically so they incubate just right. But…take a closer look at that nest. Not every egg in there is made of calcium carbonate, and they don't always contain baby birds. No, at this conservation center, some of those eggs are actually 3-D printed.

Influence Maps have been successfully used in controlling the navigation of multiple units. In this paper, we apply the idea to the problem of simulating a kiting behavior (also known as ¨attack and flee'¨) in the context of real-time strategy (RTS) games. We present our approach and evaluate it in the popular RTS game StarCraft, where we analyze the benefits that our approach brings to a StarCraft playing bot.