Stanford Medicine researchers, after creating an AI-based algorithm to find complex structural variants in the human genome, learned those variants likely contribute to psychiatric disease.
Category: robotics/AI
UC Berkeley’s tiny, bee-inspired flying robot weighs 21 mg, hovers, and hits targets using magnetic fields for potential rescue missions.
Keenon Robotics unveils XMAN-R1 humanoid and new KLEENBOT robots, advancing automation in hospitality and service industries.
Everything is bigger in Texas — including a new housing development with a futuristic vision.
Icon, a 3D technology company, is behind dozens of next-generation 3D-printed homes in the Lone Star State. A YouTube video gives viewers an inside look at the new homes built with robotic construction at the Wolf Ranch development in Georgetown.
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.
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.
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 biases remain rather stable across different business situations but may change as AI evolves from one version to the next.
How do neural networks work? It’s a question that can confuse novices and experts alike. A team from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) says that understanding these representations, as well as how they inform the ways that neural networks learn from data, is crucial for improving the interpretability, efficiency, and generalizability of deep learning models.
With that mind, the CSAIL researchers have developed a new framework for understanding how representations form in neural networks. Their Canonical Representation Hypothesis (CRH) posits that, during training, neural networks inherently align their latent representations, weights, and neuron gradients within each layer. This alignment implies that neural networks naturally learn compact representations based on the degree and modes of deviation from the CRH.
Senior author Tomaso Poggio says that, by understanding and leveraging this alignment, engineers can potentially design networks that are more efficient and easier to understand. The research is posted to the arXiv preprint server.
Heated debate is whether to advance AI to artificial general intelligence (AGI) and only then proceed to superintelligence or just do a straight shot. Here’s the scoop.
AI systems already work their magic in many areas of biomedical science, helping to solve protein structure, discover hidden patterns in the genome and process massive amounts of biological data. Now, an AI-assisted technology developed at the Weizmann Institute of Science and published in Nature Biotechnology may grant researchers and physicians an unprecedented means of peering deep into the body’s tissues by making it possible to simultaneously view more proteins than ever before, in a tissue sample.
“To understand how any particular tissue works, it’s crucial to measure lots of its proteins at the same time,” says Dr. Leeat Keren of Weizmann’s Molecular Cell Biology Department, who headed the research team. “This gives us an idea of which cells are present in the tissue and how they communicate and interact with one another.”
Keren explains that this knowledge is vital to the study of disease processes. Cancerous growths, for example, contain, in addition to tumor cells, various other cell types, including healthy cells of the tissue the tumor is growing on and of the immune system. The cellular makeup of the tumor and how those cell types interact with one another can determine the effectiveness of therapies or be used to predict which patients have a better prognosis and which are likely to develop metastases. Such findings, in turn, can lead to improved personalized treatments.