There's a new generation of browsers coming to shake up the market and revolutionize the way we use the web--at least, that's how new "AI" browsers like Perplexity's Comet are being pitched to users. But it looks like giving control of your web browsing over to an AI system may be a bit of a gamble, as new research shows that they're at least as susceptible to scams as fleshy humans… possibly more so. Security researchers at Guardio put the AI-powered Comet browser through a series of tests that replicated existing scams and targeted new ones to its "agentic AI" approach. Agentic AI allows you to tell the browser what you want done in plain words, and then the browser acts as an agent on your behalf and performs the actions for you. But Perplexity's AI system seems a bit more trusting than most experienced web users.

Additive Manufacturing (AM) is a promising solution for handling the complexity of fabricating soft robots. However, the AM of hyperelastic materials is still challenging with limited material types. Within this work, pellet-based 3D printing of very soft thermoplastic elastomers (TPEs) was explored. Our results show that TPEs can have similar engineering stress and maximum strain as Ecoflex OO-10. These TPEs were used to 3D-print airtight thin membranes (0.2-1.2 mm), which could inflate up to a stretch of 1320\%. Combining the membrane's large expansion and softness with the 3D printing of hollow structures simplified the design of a bending actuator that can bend 180 degrees and reach a blocked force of 238 times its weight. In addition, by 3D printing TPE pellets and rigid filaments, the soft membrane could grasp objects by enveloping an object or as a sensorized sucker, which relied on the TPE's softness to conform to the object or act as a seal. In addition, the membrane of the sucker was utilized as a tactile sensor to detect an object before adhesion. These results suggest the feasibility of 3D printing soft robots by using soft TPEs and membranes as an interesting class of materials and sensorized actuators, respectively.

There are many remarkable things about octopuses--they're famously intelligent, they have three hearts, their eyeballs work like prisms, they can change color at will, and they can "see" light with their skin. One of the most striking things about these creatures, however, is the fact that each of their eight arms almost seems to have a mind of its own, allowing an octopus to multitask in a manner that humans can only dream about. At the heart of each arm is a structure known as the axial nervous cord (ANC), and a new study published January 15 in Nature Communications examines how the structure of this cord is fundamental to allowing the arms to act as they do. Cassady Olson, first author on the paper, explains to Popular Science that understanding the ANC is crucial to understanding how an octopus's arms work: "You can think of the ANC as equivalent to a spinal cord running down the center of every single arm." Olson explains that "there are many gross similarities [between the ANC and vertebrates' spinal cords]--there is a cell body region, a neuropil region, and long tracts to connect the arms and brains in each."

In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observations of sensorimotor control where an arm localizes and reaches a target. Major contributions of this article are: (i) development of models to capture the mechanical properties of arm musculature, the electrical properties of the arm peripheral nervous system (PNS), and the coupling of PNS with muscular contractions; (ii) modeling the arm sensory system, including chemosensing and proprioception; and (iii) algorithms for sensorimotor control, which include a novel feedback neural motor control law for mimicking target-oriented arm reaching motions, and a novel consensus algorithm for solving sensing problems such as locating a food source from local chemical sensory information (exogenous) and arm deformation information (endogenous). Several analytical results, including rest-state characterization and stability properties of the proposed sensing and motor control algorithms, are provided. Numerical simulations demonstrate the efficacy of our approach. Qualitative comparisons against observed arm rest shapes and target-oriented reaching motions are also reported.

Muscular hydrostats, such as octopus arms or elephant trunks, lack bones entirely, endowing them with exceptional dexterity and reconfigurability. Key to their unmatched ability to control nearly infinite degrees of freedom is the architecture into which muscle fibers are weaved. Their arrangement is, effectively, the instantiation of a sophisticated mechanical program that mediates, and likely facilitates, the control and realization of complex, dynamic morphological reconfigurations. Here, by combining medical imaging, biomechanical data, live behavioral experiments and numerical simulations, we synthesize a model octopus arm entailing ~200 continuous muscles groups, and begin to unravel its complexity. We show how 3D arm motions can be understood in terms of storage, transport, and conversion of topological quantities, effected by simple muscle activation templates. These, in turn, can be composed into higher-level control strategies that, compounded by the arm's compliance, are demonstrated in a range of object manipulation tasks rendered additionally challenging by the need to appropriately align suckers, to sense and grasp. Overall, our work exposes broad design and algorithmic principles pertinent to muscular hydrostats, robotics, and dynamics, while significantly advancing our ability to model muscular structures from medical imaging, with potential implications for human health and care.

A few days after Google and Microsoft announced they'd be delivering search results generated by chatbots -- artificially intelligent software capable of producing uncannily human-sounding prose -- I fretted that our new AI helpers are not to be trusted. After all, Google's own AI researchers had warned the company that chatbots would be "stochastic parrots" (likely to squawk things that are wrong, stupid, or offensive) and "prone to hallucinating" (liable to just make stuff up). The bots, drawing on what are known as large language models, "are trained to predict the likelihood of utterances," a team from DeepMind, the Alphabet-owned AI company, wrote last year in a presentation on the risks of LLMs. "Yet, whether or not a sentence is likely does not reliably indicate whether the sentence is also correct." These chatbots, in other words, are not actually intelligent.

This "octa-glove" lets you pick up objects using octopus-like suckers Artificial suckers inspired by those of an octopus could allow robots to delicately grasp objects that they are usually too clumsy to hold. The technology has been demonstrated in a wearable glove for humans, but researchers say similar devices could one day be used in machine limbs. Octopuses have fine control of around 2000 suckers on their eight limbs, allowing them to dexterously pick up and manipulate objects. Michael Bartlett at Virginia Tech says this ability inspired him to create tiny rubber suckers tipped with flexible membranes that can be activated to create suction and stick to objects. His team created a wearable glove with a sucker and a micro-LIDAR sensor on each fingertip.

Have you ever lost your grip on something that you've dropped into the swimming pool, or worse, toilet? Scientists may have developed a solution to holding onto underwater objects, but it is not primarily intended to help you rescue your iPhone from a watery fate. Researchers at Virginia Tech have developed a glove that will allow divers to get a firm grasp while, for example, rescuing someone or salvaging a shipwreck. The'octa-glove' is inspired by octopus tentacles, and is covered in robotic suckers equipped with sensors that can tell how far away an object is. When the sensors detect a nearby surface, it sends a signal to the controller which will activate the sucker's adhesion.

The main contribution of this paper is a novel sensory feedback control law for an octopus arm. The control law is inspired by, and helps integrate, several observations made by biologists. The proposed control law is distinct from prior work which has mainly focused on open-loop control strategies. Several analytical results are described including characterization of the equilibrium and its stability analysis. Numerical simulations demonstrate life-like motion of the soft octopus arm, qualitatively matching behavioral experiments. Quantitative comparison with bend propagation experiments helps provide the first explanation of such canonical motion using a sensory feedback control law. Several remarks are included that help draw parallels with natural pursuit strategies such as motion camouflage or classical pursuit.

Of all the cool things about octopuses (and there are a lot), their arms may rank among the coolest. Two-thirds of an octopus's neurons are in its arms, meaning each arm literally has a mind of its own. The hundreds of suckers that cover their arms can form strong seals even on rough surfaces underwater. Imagine if a robot could do all that. Researchers at Harvard's Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences (SEAS) and colleagues from Beihang University have developed an octopus-inspired soft robotic arm that can grip, move, and manipulate a wide range of objects.