--Resource allocation is crucial for the performance optimization of cloud-assisted multi-agent intelligence. Traditional methods often overlook agents' diverse computational capabilities and complex operating environments, leading to inefficient and unfair resource distribution. T o address this, we open-sourced Fair-Synergy, an algorithmic framework that utilizes the concave relationship between the agents' accuracy and the system resources to ensure fair resource allocation across fleet intelligence. We extend traditional allocation approaches to encompass a multidimensional machine learning utility landscape defined by model parameters, training data volume, and task complexity. We evaluate Fair-Synergy with advanced vision and language models such as BERT, VGG16, MobileNet, and ResNets on datasets including MNIST, CIF AR-10, CIF AR-100, BDD, and GLUE. We demonstrate that Fair-Synergy outperforms standard benchmarks by up to 25% in multi-agent inference and 11% in multi-agent learning settings. Also, we explore how the level of fairness affects the least advantaged, most advantaged, and average agents, providing insights for equitable fleet intelligence.

The CyberGuy Kurt Knutsson joins'Fox & Friends' to discuss the U.S.-Saudi investment summit and the debate over regulation as artificial intelligence continues to advance. What happens when one of the world's richest companies decides to go all-in on artificial intelligence? If you're Meta Platforms CEO Mark Zuckerberg, it means launching superclusters so large they could rival the footprint of Manhattan. Recently, Zuckerberg unveiled plans to invest "hundreds of billions of dollars" into next-generation AI infrastructure, including some of the largest compute clusters the world has ever seen. Meta's first supercluster, called Prometheus, is slated to go live in 2026.

Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing text, reason and bargain with human counterparts, translate texts between languages, learn how to take decisions to maximize certain outcomes, etc. Today, machines have revolutionized the detection of cancer, the prediction of protein structures, the design of drugs, the control of nuclear fusion reactors etc. Although these capabilities are still in their infancy, it seems clear that their continued refinement and application will result in a technological impact on nearly all social and economic areas of human activity, the likes of which we have not seen before. In this article, I will share my view as to how AI will likely impact asset management in general and I will provide a mental framework that will equip readers with a simple criterion to assess whether and to what degree a given fund really exploits deep learning and whether a large disruption risk from deep learning exist.

Investing in deep learning (DL) is a major decision that requires understanding of each phase of the process, especially if you're considering AI at the edge. Below are practical tips to help you make a more informed decision about DL technology and the composition of your AI cluster. For the purposes of this article, let's define the terms we'll be using: Neural Network: Artificial neural networks are computing systems inspired by the organic neural networks found in human and other animal brains, where nodes (artificial neurons) are connected (artificial synapses) to work together. To enable deep learning of an artificial neural network, your team must curate huge quantities of data into a designated structure, then feed that training dataset into a DL framework. Once the DL framework is trained, it has learned what inputs lead to what logical conclusion.

Waiting for scripts to terminate is an absolute pet peeve of mine. Not only is it a waste of time but the constant context-switching is distracting and tiring. One grievous offender is the ML experiment, which lasts anywhere from a few minutes to a few days. A fixture of the development lifecycle, the ML experiment takes place any time you want to evaluate your model on a new set of hyperparameters (as in hyperparameter optimization), explore new features (feature selection), or try out a new model architecture. In a way, features and model architectures are simply glorified forms of hyperparameters.

When Ben Fielding and Harry Grieve met at the kick-off weekend of an accelerator program in March 2020, they were told it would be the last time they'd see each other for some time. Grieve and Fielding had joined Entrepreneur First's six-month program, where would-be founders mix with one another in the hope of launching a startup together. The pair clicked immediately and hunkered down throughout the pandemic while working on their new business Gensyn. The startup connects machine learning researchers with the computing power they need to train AI models. Gensysn has just raised $6.5 million, following on from a previously unannounced $1.1 million pre-seed raise.

For self-driving cars and other applications developed using AI, you need what's known as'deep learning', the core concepts of which emerged in the '50s. This requires training models based on similar patterns as seen in the human brain. This, in turn, requires a large amount of compute power, as afforded by TPUs (Tensor Processing Units) or GPUs (Graphics Processing Units) running for lengthy periods. However, cost of this compute power is out of reach of most AI developers, who largely rent it from cloud computing platforms such as AWS or Azure. Well, one approach is that taken by UK startup Gensyn.

Fresh off its rebrand last October, Meta (née Facebook) is putting muscle behind its vision of a metaversal future with a massive new AI supercomputer called the AI Research SuperCluster (RSC). Meta says that RSC will be used to help build new AI models, develop augmented reality tools, seamlessly analyze multimedia data and more. The supercomputer's first phase is already operational, and it is scheduled for full build-out by mid-year. HPCwire is estimating that the final system will weigh in at over 220 Linpack petaflops. For storage, the system is equipped with 175PB of Pure Storage FlashArray, 10PB of Pure Storage FlashBlade and 46PB of cache storage housed in Penguin Computing Altus servers.

If you've ever attempted to automate business processes, reduce risk by enforcing regulatory compliance, or ensure physical safety and security in the workplace, you may have run into repetitive tasks that are expensive to scale by using a human workforce. However, when you looked into using AI, you discovered that you'd need a fast internet connection to make the system work. What if you could automatically monitor video streams in real-time? What if you could do this while keeping your video data private? What if you could do all this without even having an internet connection?

Did you miss a session from the Future of Work Summit? Fifty-three percent of enterprise technology leaders are worried they will run out of computing power in the next decade -- one of several challenges hindering organizations as they look to scale up artificial intelligence initiatives, according to a new report by SambaNova Systems. With AI and ML becoming ubiquitous across industries, it has the same potential to refactor the Fortune 500 as the internet has had over the past several decades. But as the AI revolution accelerates, there's a burgeoning gulf between the haves and the have-nots. That is, a growing number of top companies have figured out how to deploy AI initiatives at scale, gaining a competitive edge against businesses that have yet to do so.