Spermine, a small but powerful molecule in the body, helps neutralize harmful protein accumulations linked to Alzheimer’s and Parkinson’s. It encourages these misfolded proteins to gather into manageable clumps that cells can more efficiently dispose of through autophagy. Experiments in nematodes show that spermine also enhances longevity and cellular energy production. These insights open the door to targeted therapies powered by polyamines and advanced AI-driven molecular design.
MIT researchers have taken a major step toward making this scenario a reality. They developed microscopic, wireless bioelectronics that could travel through the body’s circulatory system and autonomously self-implant in a target region of the brain, where they would provide focused treatment.
In a study on mice, the researchers show that after injection, these miniscule implants can identify and travel to a specific brain region without the need for human guidance. Once there, they can be wirelessly powered to provide electrical stimulation to the precise area. Such stimulation, known as neuromodulation, has shown promise as a way to treat brain tumors and diseases like Alzheimer’s and multiple sclerosis.
Moreover, because the electronic devices are integrated with living, biological cells before being injected, they are not attacked by the body’s immune system and can cross the blood-brain barrier while leaving it intact. This maintains the barrier’s crucial protection of the brain.
A nonsurgical brain implant enabled through a cell–electronics hybrid for focal neuromodulation.
Photovoltaic devices attached to immune cells travel through the blood to inflamed brain regions.
From the rise of numerical and symbolic computing to the future of AI, this talk traces five decades of breakthroughs and the challenges ahead.
Bill is the author of Berkeley UNIX, cofounder of Sun Microsystems, author of “Why the Future Doesn’t Need Us” (Wired 2000), ex-cleantech VC at Kleiner Perkins, investor in and unpaid advisor to Nodra. AI.
Talk Details.
50 Years of Advancements: Computing and Technology 1975–2025 (and beyond)
I came to UC Berkeley CS in 1975 as a graduate student expecting to do computer theory— Berkeley CS didn’t have a proper departmental computer, and I was tired of coding, having written a lot of numerical code for early supercomputers.
But it’s hard to make predictions, especially about the future. Berkeley soon had a Vax superminicomputer, I installed a port of UNIX and was upgrading the operating system, and the Internet and Microprocessor boom beckoned.
When it comes to training robots to perform agile, single-task motor skills, such as handstands or backflips, artificial intelligence methods can be very useful. But if you want to train your robot to perform multiple tasks—say, performing a backward flip into a handstand—things get a little more complicated.
“We often want to train our robots to learn new skills by compounding existing skills with one another,” said Ian Abraham, assistant professor of mechanical engineering. “Unfortunately, AI models trained to allow robots to perform complex skills across many tasks tend to have worse performance than training on an individual task.”
To solve for that, Abraham’s lab is using techniques from optimal control—that is, taking a mathematical approach to help robots perform movements in the most efficient and optimal way possible. In particular, they’re employing hybrid control theory, which involves deciding when an autonomous system should switch between control modes to solve a task. The research is published on the arXiv preprint server.
A dystopian future where advanced artificial intelligence (AI) systems replace human decision-making has long been a trope of science fiction. The malevolent computer HAL, which takes control of the spaceship in Stanley Kubrick’s film, 2001: A Space Odyssey, is a chilling example.
But rather than being fearful of automation, a more useful response is to consider what types of repetitive human tasks could be safely offloaded to AI, particularly with the advances of large language models (LLMs) that can sort through vast amounts of data, see patterns and make predictions.
Such is the area of research co-authored by Christoph Treude, an Associate Professor of Computer Science at Singapore Management University (SMU). The team explores potential roles for LLMs in annotating software engineering artifacts, a process that is expensive and time-consuming when done manually.
Yay 😁 Robotic utopia here we come!
Mentee Robotics, an Israeli company started by Mobileye co-founder Amnon Shashua, has released a new video of its V3 MenteeBot working in a real warehouse.
The company shared an unedited 18-minute test where two humanoid robots work together in space.
Many in the industry now see long, continuous footage like this as a strong sign of real capability. Mentee says the test was fully autonomous and done without any remote control.
In a preprint published in bioRxiv, Prof. Vadim Gladyshev and a team of researchers have used an artificial intelligence-based system to discover a wide variety of potential interventions, including a drug that significantly improves biomarkers of frailty in mice.
Repurposing previous data
Previous research efforts have created a massive dataset in the form of the Gene Expression Omnibus (GEO), which contains the results of a great many experiments related to potentially disease-modifying drugs, many of which are tissue-specific [1]. These researchers refer to this dataset as a “massive missed opportunity” in aging research, because the vast majority of the experiments in the GEO were unrelated to aging and their data was never investigated in that context.
Extracellular ATP is an environmental cue in bacteria.
Extracellular ATP acts as a signal regulating physiology and immunity in eukaryotes and is elevated during intestinal infection or damage. Tronnet et al. show that extracellular ATP reprograms the transcriptional and metabolic landscapes of gut bacteria, impacting biofilm formation, production of bioactive metabolites, bacterial envelope composition, antimicrobial resistance, and virulence.
A large-scale laboratory screening of human-made chemicals has identified 168 chemicals that are toxic to bacteria found in the healthy human gut. These chemicals stifle the growth of gut bacteria thought to be vital for health. The research, including the new machine learning model, is published in the journal Nature Microbiology.
Most of these chemicals, likely to enter our bodies through food, water, and environmental exposure, were not previously thought to have any effect on bacteria.
As the bacteria alter their function to try and resist the chemical pollutants, some also become resistant to antibiotics such as ciprofloxacin. If this happens in the human gut, it could make infections harder to treat.