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Artificial intelligence (AI) has introduced a dynamic shift in various sectors, most notably by deploying autonomous agents capable of independent operation and decision-making. These agents, powered by large language models (LLMs), have significantly broadened the scope of tasks that can be automated, ranging from simple data processing to complex problem-solving scenarios. However, as the capabilities of these agents expand, so do the challenges associated with their deployment and integration.

Within this evolving landscape, a major hurdle has been the efficient management of LLM-based agents. The primary issues revolve around allocating computational resources, maintaining interaction context, and integrating agents with varying capabilities and functions. Traditional approaches often lead to bottlenecks and underutilization of resources, undermining these intelligent systems’ potential efficiency and effectiveness.

A research team from Rutgers University has developed the AIOS (Agent-Integrated Operating System), a pioneering LLM agent operating system designed to streamline the deployment and operation of LLM-based agents. This system is engineered to enhance resource allocation, enable the concurrent execution of multiple agents, and maintain a coherent context throughout agent interactions, optimizing agent operations’ overall performance and efficiency.

Race Speeds Up. Wallets open up. Agi 2025–2029.


Amazon is reportedly planning to spend a whopping $150 billion within the next 15 years on building data centers — a move that will position the tech giant to be able to handle an expected explosion with artificial intelligence applications and other digital services.

The spending spree, earlier reported on by Bloomberg, will also allow Amazon to maintain its top spot in the cloud services market, where it holds roughly twice the share of No. 2 player Microsoft.

“We’re expanding capacity quite significantly,” said Kevin Miller, a vice president at AWS, or Amazon Web Services, Amazon’s cloud computing subsidiary used by upwards of 1.45 million businesses, according to an internal report.

Is anything ever normal in the AI industry?

A doozy of a scoop by the newsletter Nongaap Investing and extensively followed up by Business Insider certainly makes us wonder. The gist is that for a period of time in 2023, the person in charge of OpenAI’s $175 million startup fund appears to have been completely fake — and OpenAI says the documents filed with the California Secretary of State to put the fake person in charge were “completely fabricated.”

Head spinning yet? Us too. OpenAI is almost certainly the hottest startup on the planet right now, and it sounds like someone pulled an extrardinary fast one on it, with unclear goals. And lest you think this is some unimportant position, the person now running the fund is none other than OpenAI’s mercurial CEO, Sam Altman.

Combinatorial optimization problems (COPs) have applications in many different fields such as logistics, supply chain management, machine learning, material design and drug discovery, among others, for finding the optimal solution to complex problems. These problems are usually very computationally intensive using classical computers and thus solving COPs using quantum computers has attracted significant attention from both academia and industry.

Over the past decade, organic luminescent materials have been recognized by academia and industry alike as promising components for light, flexible and versatile optoelectronic devices such as OLED displays. However, it is a challenge to find suitably efficient materials.

To address this challenge, a joint research team has developed a novel approach combining a machine learning model with quantum-classical computational molecular design to accelerate the discovery of efficient OLED emitters. This research was published May 17 in Intelligent Computing.

The optimal OLED emitter discovered by the authors using this “hybrid quantum-classical procedure” is a deuterated derivative of Alq3 and is both extremely efficient at emitting light and synthesizable.