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Hailed by various market research reports as the big tech trend in 2025 — especially in the enterprise — it seems we can’t go more than 12 hours or so without the debut of another way to make, orchestrate (link together), or otherwise optimize purpose-built AI tools and workflows designed to handle routine white collar work.

Yet Emergence AI, a startup founded by former IBM Research veterans and which late last year debuted its own, cross-platform AI agent orchestration framework, is out with something novel from all the rest: a new AI agent creation platform that lets the human user specify what work they are trying to accomplish via text prompts, and then turns it over to AI models to create the agents they believe are necessary to accomplish said work.

This new system is literally a no code, natural language, AI-powered multi-agent builder, and it works in real time. Emergence AI describes it as a milestone in recursive intelligence, aims to simplify and accelerate complex data workflows for enterprise users.

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An international team led by Rutgers University-New Brunswick researchers has merged two lab-synthesized materials into a synthetic quantum structure once thought impossible to exist and produced an exotic structure expected to provide insights that could lead to new materials at the core of quantum computing.

The work, described in a cover story in the journal Nano Letters, explains how four years of continuous experimentation led to a novel method to design and build a unique, tiny sandwich composed of distinct atomic layers.

One slice of the microscopic structure is made of dysprosium titanate, an inorganic compound used in nuclear reactors to trap and contain elusive magnetic monopole particles, while the other is composed of pyrochlore iridate, a new magnetic semimetal mainly used in today’s experimental research due to its distinctive electronic, topological and magnetic properties.

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Researchers harness the power of the world’s most advanced supercomputers to simulate the inner workings of cellular machinery that repairs DNA and helps prevent life-threatening diseases. Sunburn and premature aging are well-known consequences of exposure to ultraviolet (UV) radiation, tobacco s

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At times, the reactions do not produce the intended results, and this is where simulations are used to understand what might have caused the anomalous behavior. Chemistry students are often tasked with running these simulations to learn to think critically and make sense of discoveries.

As the complexity of the process increases, more advanced computing infrastructure is required to carry out these simulations. To understand these reactions at a quantum level, theoretical chemists even use specialized software packages to streamline their research and automate the simulation process. AutoSolvateWeb is just a chatbot but can help even non-experts achieve this level of competence.

AutoSolvateWeb helps compute the dissolving of a chemical, referred to as a solute, into a substance called a solvent. The resultant solution is called the solvate, hence the name. While theoretical chemists use computation software to convert this into simulations that look much like 3D movies, AutoSolvateWeb can achieve the same output through a chatbot-like interface with the user.

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A team of physicists uncovered a strange twist in how superconductors behave when they’re reduced to just a few atomic layers. Using powerful magnetic imaging, they found that superconductivity in ultra-thin materials doesn’t follow the usual rules – it becomes surface-based rather than distribut

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More than 150 million metric tons of propylene are produced annually, making it one of the most widespread chemicals used in the chemical industry.

Propylene is the basis for polypropylene, a polymer used in everything from medical devices to packaging to household goods. But most is produced through steam cracking, a high-energy process that uses heat to break down crude oil into smaller hydrocarbons.

Now, Northwestern University chemists have found a way to create propylene using light. Their findings show that a nanoengineered photoactive catalyst can make propylene directly through a process called nonoxidative propane dehydrogenation (PDH).

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Can we really trust AI to make better decisions than humans? A new study says … not always. Researchers have discovered that OpenAI’s ChatGPT, one of the most advanced and popular AI models, makes the same kinds of decision-making mistakes as humans in some situations—showing biases like overconfidence of hot-hand (gambler’s) fallacy—yet acting inhuman in others (e.g., not suffering from base-rate neglect or sunk cost fallacies).

Published in the Manufacturing & Service Operations Management journal, the study reveals that ChatGPT doesn’t just crunch numbers—it “thinks” in ways eerily similar to humans, including mental shortcuts and blind spots. These remain rather stable across different business situations but may change as AI evolves from one version to the next.

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