Federated learning (FL) mechanisms typically require each client to transfer their weights to a central server, irrespective of how useful they are. In order to avoid wasteful data transfer costs from clients to the central server, we propose the use of consensus based protocols to identify a subset of clients with most useful model weights at each data transfer step. First, we explore the application of existing fluid democracy protocols to FL from a performance standpoint, comparing them with traditional one-person-one-vote (also known as 1p1v or FedAvg). We propose a new fluid democracy protocol named viscous-retained democracy that always does better than 1p1v under the same assumptions as existing fluid democracy protocols while also not allowing for influence accumulation. Secondly, we identify weaknesses of fluid democracy protocols from an adversarial lens in terms of their dependence on topology and/ or number of adversaries required to negatively impact the global model weights. To this effect, we propose an algorithm (FedVRD) that dynamically limits the effect of adversaries while minimizing cost by leveraging the delegation topology.

On his videos, Sergey appears fresh-faced and smiling enthusiastically. Speaking in Russian and sometimes in slightly broken English, he goes through coding challenges step-by-step. His YouTube channel has more than 3,500 subscribers, interested in learning programming languages Python and Unity, or who want to hear more about neural networks, which underlie many artificial intelligence tools. Mr Mandik said Sergey showed not only remarkable developer skills but also "equally unique" skills in teaching. "For me, he is kind of a Mozart."

Artificial Intelligence poses both risks and rewards, but developers should be weary of technologies that could threaten "scary" outcomes, AI technologist says. Men who have trouble finding dates are reportedly turning to artificial intelligence and self-described love guru to craft appealing dating profiles. "My AI prompts and training can turn any guy from zero to hero," Stefan-Pierre Tomlin, a 32-year-old London model and self-described love guru, told South West News Service, according to the New York Post. Tomlin operates a website called Celebrity Love Coach where subscribers can pay between roughly $55 to $150 a month to receive his advice and "support to help you achieve your dating goals," according to the website. Subscribers also receive access to "bespoke" AI to draft appealing dating profiles.

Blurb: Narrates a fictional future where persuasive Artificial General Intelligence (AGI) goes rogue. Inspired in part by the AI Vignettes Project. A fondness for irony will help readers. "AI-powered memetic warfare makes all humans effectively insane." You can't trust any content from anyone you don't know. Phone calls, texts, and emails are poisoned. But the current waste and harm from scammers, influencers, propagandists, marketers, and their associated algorithms are nothing compared to what might happen. Coming AIs might be super-persuaders, and they might have their own very harmful agendas. People being routinely unsure of what's reality is one bad outcome, but there are others worse.

Artificial intelligence has transformed industrial research and development in recent decades during what scientists call "the AI revolution." The technology enables detailed simulations and high-speed modeling that can streamline the journey from drawing board to production line by speeding up or cutting out costly, time-consuming steps to a practical working prototype. But those opportunities bring a new challenge: The simplest simulation package may require hours, days and sometimes weeks of training and configuration – even for users familiar with the software's details and requirements, which often vary from one computing platform to another. The process can cause not just headaches but wasted time and effort for busy engineers and others scrambling to meet tight deadlines. Simulations performed on the Summit supercomputer at Oak Ridge National Laboratory, or ORNL, could help eliminate that problem.

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Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having $O(1)$ global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.

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