Lifeboat Foundation

BMIs using intracortical electrodes, such as Utah arrays, are particularly adept at sensing fast changing (millisecond-scale) neural activity from spatially localized regions (1 cm) during behavior or stimulation that is correlated to activity in such spatially specific regions, for example, M1 for motor and V1 for vision. Intracortical electrodes, however, struggle to track individual neurons over longer periods of time, for example, between subsequent recording sessions^15,16. Consequently, decoders are typically retrained every day¹⁵. A similar neural population identification problem is also present with an ultrasound device, including from shifts in the field of view between experiment sessions. In the current study, we demonstrated an alignment method that stabilizes image-based BMIs across more than a month and decodes from the same neurovascular populations with minimal, if any, retraining. This is a critical development that enables easy alignment of a previous days’ models to a new day’s data and allows decoding to begin with minimal to no new training data. Much effort has focused on ways to recalibrate intracortical BMIs across days that do not require extensive new data^{18,19,20,21,22,23}. Most of these methods require identification of manifolds and/or latent dynamical parameters and collecting new neural and behavioral data to align to these manifolds/parameters. These techniques are, to date, tailored to each research group’s specific applications with varying requirements, such as hyperparameter tuning of the model²³ or a consistent temporal structure of data²². They are also susceptible to changes in function in addition to anatomy. For example, ‘out-of-manifold’ learning/plasticity alters the manifold²⁴ in ways that many alignment techniques struggle to address. Finally, some of the algorithms are computationally expensive and/or difficult to implement in online use²².

Contrasting these manifold-based methods, our decoder alignment algorithm leverages the intrinsic spatial resolution and field of view provided by fUS neuroimaging to perform decoder stabilization in a way that is intuitive, repeatable and performant. We used a single fUS frame (∼ 500 ms) to generate an image of the current session’s anatomy and aligned a previous session’s field of view to this single image. Notably, this did not require any additional behavior for the alignment. Because we only relied upon the anatomy, our decoder alignment is robust, can use any off-the-shelf alignment tool and is a valid technique so long as the anatomy and mesoscopic encoding of relevant variables do not change drastically between sessions.

It remains an open question as to how much the precise positioning of the ultrasound transducer during each session matters for decoder performance, especially out-of-plane shifts or rotations. In these current experiments, we used linear decoders that assumed a given image pixel is the same brain voxel across all aligned data sessions. To minimize disruptions to this pixel–voxel relationship, we performed image alignment within the 2D plane. As we could only image a 2D recording plane, we did not correct for any out-of-plane brain shifts between sessions that would have disrupted the pixel–voxel mapping assumption. Future fUS-BMI decoders may benefit from three-dimensional (3D) models of the neurovasculature, such as registering the 2D field of view to a 3D volume^25,26,27 to better maintain a consistent pixel–voxel mapping.

“We want the robot to ask for enough help such that we reach the level of success that the user wants. But meanwhile, we want to minimize the overall amount of help that the robot needs,” said Allen Ren.

A recent study presented at the 7th Annual Conference on Robotic Learning examines a new method for teaching robots how to ask for further instructions when carrying out tasks with the goal of improving robotic safety and efficiency. This study was conducted by a team of engineers from Google and Princeton University and holds the potential to design and build better-functioning robots that mirror human traits, such as humility. Engineers have recently begun using large language models, or LLMs—which is responsible for designing ChatGPT—to make robots more human-like, but this can also come with drawbacks, as well.

“Blindly following plans generated by an LLM could cause robots to act in an unsafe or untrustworthy manner, and so we need our LLM-based robots to know when they don’t know,” said Dr. Anirudha Majumdar, who is an assistant professor of mechanical and aerospace engineering at Princeton University and a co-author on the study.

Continue reading “Teaching Robots to Ask for Help: A Breakthrough in Enhancing Safety and Efficiency” »

The explosive growth of generative AI over the last year has been truly phenomenal. Kick-started by the public release of ChatGPT (was it really only a year ago?), it’s now everywhere. Keen to ride the wave, every app from Office to eBay has been adding generative capabilities, and growing numbers of us are finding uses for it in our everyday and professional lives.

Given its nature, it’s not surprising that content creators, in particular, have found it a powerful addition to their toolset. Marketing agencies, advertising creatives, news organizations and social media influencers have been among the most enthusiastic early adopters.

While it brings great opportunities for improving efficiency and automating manual, repetitive elements of creative work, it also throws up significant challenges. Issues around copyright, spam content, hallucination, the formulaic nature of algorithmic creation and bias all need to be considered by professionals planning on adopting it into their workflow.

When galaxies collide, their supermassive black holes enter into a gravitational dance, gradually orbiting each other ever closer until eventually merging. We know they merge because we see the gravitational beasts that result, and we have detected the gravitational waves they emit as they inspiral. But the details of their final consummation remain a mystery. Now a new paper published on the pre-print server arXiv suggests part of that mystery can be solved with a bit of dark matter.

Just as the famous three-body problem has no general analytical solution for Newtonian gravity, the two-body problem has no general solution in general relativity. So, we have to resort to computer simulations to model how black holes orbit each other and eventually merge.

For binary black holes that are relatively widely separated, our simulations work really well, but when black holes are close to each other things get complicated. Einstein’s equations are very nonlinear, and modeling the dynamics of strongly interacting black holes is difficult.

Researchers report that they have developed a new composite material designed to change behaviors depending on temperature in order to perform specific tasks. These materials are poised to be part of the next generation of autonomous robotics that will interact with the environment.

The new study conducted by University of Illinois Urbana-Champaign civil and environmental engineering professor Shelly Zhang and graduate student Weichen Li, in collaboration with professor Tian Chen and graduate student Yue Wang from the University of Houston, uses computer algorithms, two distinct polymers, and 3D printing to reverse engineer a material that expands and contracts in response to temperature change with or without human intervention.

Continue reading “Researchers engineer a material that can perform different tasks depending on temperature” »

At an MIT event in March, OpenAI cofounder and CEO Sam Altman said his team wasn’t yet training its next AI, GPT-5. “We are not and won’t for some time,” he told the audience.

This week, however, new details about GPT-5’s status emerged.

In an interview, Altman told the Financial Times the company is now working to develop GPT-5. Though the article did not specify whether the model is in training—it likely isn’t—Altman did say it would need more data. The data would come from public online sources—which is how such algorithms, called large language models, have previously been trained—and proprietary private datasets.

A research team led by Professor Sei Kwang Hahn and Dr. Tae Yeon Kim from the Department of Materials Science and Engineering at Pohang University of Science and Technology (POSTECH) used gold nanowires to develop an integrated wearable sensor device that effectively measures and processes two bio-signals simultaneously. Their research findings were featured in Advanced Materials.

Wearable devices, available in various forms like attachments and patches, play a pivotal role in detecting physical, chemical, and electrophysiological signals for disease diagnosis and management. Recent strides in research focus on devising wearables capable of measuring multiple bio-signals concurrently.

However, a major challenge has been the disparate materials needed for each signal measurement, leading to interface damage, complex fabrication, and reduced device stability. Additionally, these varied signal analyses require further signal processing systems and algorithms.

Zero-knowledge proof (ZKP) is a cryptographic tool that allows for the verification of validity between mutually untrusted parties without disclosing additional information. Non-interactive zero-knowledge proof (NIZKP) is a variant of ZKP with the feature of not requiring multiple information exchanges. Therefore, NIZKP is widely used in the fields of digital signature, blockchain, and identity authentication.

Since it is difficult to implement a true random number generator, deterministic pseudorandom number algorithms are often used as a substitute. However, this method has potential security vulnerabilities. Therefore, how to obtain true random numbers has become the key to improving the security of NIZKP.

In a study published in PNAS, a research team led by Prof. Pan Jianwei and Prof. Zhang Qiang from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, and the collaborators, realized a set of random number beacon public services with device-independent quantum random number generators as entropy sources and post-quantum cryptography as identity authentication.

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

Continue reading “Quantum Advantage: A Physicist Explains The Future of Computers” »