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Category: robotics/AI – Page 2

Small aquatic robots that assemble into reconfigurable structures on the water

Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site. Their new system, called “FloatForm,” is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart and reassemble into something new, all with minimal human direction.

Each robot, about the size of a dinner plate at 21 centimeters square (8.3 inches square), is a self-contained vessel with its own thrusters, sensors and magnetic latches. Together, they hint at a future in which floating infrastructure could become more adaptive: a temporary platform after an emergency, a market on a canal or a stage that appears for a festival and dissolves when the crowd goes home.

“Our FloatForm project envisions a future where the waterfront becomes a programmable extension of the city, where autonomous boats can self-organize into bridges, platforms, and other useful structures on demand,” says Daniela Rus, the Panasonic Professor of Electrical Engineering and Computer Science at MIT and director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “This kind of distributed robotics opens new possibilities for mobility, emergency response, public space, and infrastructure on water.”

Beyond Agentic AI: The Emergence Of Cognitive AI Ecosystems

In the next decade, AI will likely undergo more significant changes than only becoming more independent; it will also grow more cognitive. AI systems will act as interconnected ecosystems that are capable of contextual awareness, cooperative reasoning, ongoing learning, and adaptive decision-making in almost every facet of society, rather than isolated applications.

Large language models of today are remarkable due to their ability to produce and anticipate information. Persistent memory, multimodal perception, long-term planning, causal reasoning, and self-directed learning within strictly regulated bounds will probably be characteristics of the AI of 2036. Similar to biological neural networks, millions of specialized AI agents will work together to create dynamic intelligence fabrics that continuously optimize national defense, manufacturing, transportation, financial markets, healthcare delivery, and energy grids.

The line between workforce and software will become increasingly hazy. Hundreds of thousands of AI agents working continuously alongside human employees may be employed by organizations as digital workforces. A customized constellation of AI advisers, researchers, legal assistants, financial analysts, engineers, and cybersecurity specialists working around the clock could be present for every knowledge worker. This shift signifies the emergence of an entirely new digital labor force in addition to automation.

Scientists Used Post-Mortem Brain Tissue to Control a Robot

Further reading.

Thumbnail image credit: Paper cited in video.

Unsupervised sensory-motor associative learning by human brain explant in-a-dish enables movement imitation by robot.
https://www.researchsquare.com/articl…

The lab.

LIRMM

Not alive. but not dea: disembodied human brains used for drug testing.
https://www.science.org/content/artic…

#science #brain #technology #news #explained

Continue reading “Scientists Used Post-Mortem Brain Tissue to Control a Robot” | >

Imagination is not escapism, it brings us closer to reality

Einstein called imagination “more important than knowledge,” yet we increasingly treat it as a childhood pastime we outgrow. Philosopher of mind Amy Kind argues that it’s something far more practical: the skill we draw on to make our hardest decisions, read the people around us, and work out who we want to become. Like any skill, it weakens without use — and we’re using it less. Reading for pleasure has nearly halved in two decades, fewer parents play with their children each day, and we increasingly hand our creative work to machines. If we don’t make time to exercise it, we’ll lose the capacity to conceive of things being other than they are, and risk being trapped in the present, unable to imagine a different future, let alone build one.

In recent years, amidst the hustle and bustle of contemporary life, people are devoting considerably less time to imaginative activities. Significantly fewer people are reading for pleasure today compared to 20 years ago, and in just the last decade, significantly fewer parents are making time to play with their children on a daily basis than used to be the case.

This neglect of imagination has been accelerated by the increasing reliance on generative AI tools in both personal contexts and professional contexts. In one recent survey, more than 50% of adults reported interacting with AI tool at least several times a week for personal purposes, often for learning, entertainment, or supporting their children’s education. In another study on business uses of AI, more than half of firms surveyed reported using AI in the creation of new products and services and, more generally, in their at innovation projects. With each passing day, we seem to be increasingly more willing—and perhaps even eager—to outsource our creative and imaginative efforts to machines.

Hybrid AI model cuts financial forecasting error across stocks and crypto

A hybrid artificial intelligence model that combines two well-established deep learning techniques has improved the accuracy of financial market forecasts across major stock indices and so-called cryptocurrency, according to work in the International Journal of Reasoning-based Intelligent Systems.

The researchers designed the model, CLSTM-HN, to address a long-standing problem in financial forecasting: balancing the detection of short-term market movements with the recognition of longer-term trends. The researchers tested the system on publicly available data and achieved a forecasting error 15% to 20% lower than that of conventional long-short-term memory (LSTM) models. They also saw an improvement in the accuracy of predicting whether prices would rise or fall by 10% to 14%.

Financial markets are difficult to predict because prices are volatile, noisy and subject to sudden structural shifts. Traditional statistical approaches often rely on assumptions about market behavior that break down during periods of instability.

Wavelength-multiplexed diffractive optical storage enables massively parallel image retrieval

The explosive growth of data generated by artificial intelligence, cloud computing and modern digital infrastructure is placing increasing pressure on existing information storage technologies. Although magnetic storage systems such as hard disk drives remain the dominant platform for digital storage, they face challenges including rising costs, limited lifespan and relatively slow information retrieval.

To address these challenges, researchers at the University of California, Los Angeles (UCLA) have developed a new optical information storage platform that uses engineered diffractive structures to store and rapidly retrieve thousands of images.

The UCLA team introduced a wavelength-multiplexed diffractive optical storage system composed of multiple passive dielectric layers that are spatially engineered using deep learning. The research is published in the journal Advanced Photonics.

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