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Category: robotics/AI – Page 30

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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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.

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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 , 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.

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An international research team coordinated at KIT (Karlsruhe Institute of Technology) has developed mechanical metamaterials with a high elastic energy density. Highly twisted rods that deform helically provide these metamaterials with a high stiffness and enable them to absorb and release large amounts of elastic energy. The researchers conducted simple compression experiments to confirm the initial theoretical results. Their findings have been published in the journal Nature.

Storage of mechanical energy is required for many technologies, including springs for absorbing energy, buffers for mechanical energy storage, or flexible structures in robotics or energy-efficient machines. Kinetic energy, i.e., motion energy or the corresponding mechanical work, is converted into elastic energy in such a way that it can be fully released again when required.

The key characteristic here is enthalpy—the energy density that can be stored in and recovered from an element of the material. Peter Gumbsch, Professor for at KIT’s Institute for Applied Materials (IAM), explains that achieving the highest possible enthalpy is challenging: “The difficulty is to combine conflicting properties: high stiffness, and large recoverable strain.”

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What happens when technology eliminates scarcity? As our real-world tech oligarchs promise a utopian future with AI reshaping society, we’ll examine what we’re truly sacrificing at the altar of progress.

✉️ Free AI Resources: https://mindfulmachines.ai/
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References.

The Orville: Future Unknown (2022)
https://orville.fandom.com/wiki/Future_Unknown.

The Ones Who Walk Away from Omelas (1973)
https://www.goodreads.com/book/show/92625.The_Ones_Who_Walk_Away_from_Omelas.

The Ones Who Stay and Fight (2018)

Continue reading “The AI Timebomb This Sci-Fi Show Accidentally Predicted” | >

Researchers at the Institute of Automation of the Chinese Academy of Sciences have developed a compact, battery-powered brain stimulation device capable of delivering therapeutic magnetic pulses while a person is walking or performing everyday activities.

Repetitive transcranial magnetic stimulation is used to treat conditions such as depression, stroke-related motor impairment, and other neuropsychiatric disorders. It is also used in cognitive and motor function research.

Existing systems need to be plugged into a power supply and have bulky designs meant for stationary use in . These limitations prevent stimulation during natural movement, such as standing and walking, making at-home or on-the-go treatments impractical.

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A new collaboration between Brown University and TU Delft has brought us closer to interstellar travel using light-powered sails. By combining ultra-thin, highly reflective materials with AI-optimized nanoscale design, researchers created a revolutionary lightsail that’s cheaper, faster to make.

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A phishing-as-a-service (PhaaS) platform named ‘Lucid’ has been targeting 169 entities in 88 countries using well-crafted messages sent on iMessage (iOS) and RCS (Android).

Lucid, which has been operated by Chinese cybercriminals known as the ‘XinXin group’ since mid-2023, is sold to other threat actors via a subscription-based model that gives them access to over 1,000 phishing domains, tailored auto-generated phishing sites, and pro-grade spamming tools.

Prodaft researchers note that XinXin has also been using the Darcula v3 platform for its operations, which indicates a potential connection between the two PhaaS platforms.

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