Lifeboat Foundation

In parallel to recent developments in machine learning like GPT-4, a group of scientists has recently proposed the use of neural tissue itself, carefully grown to recreate the structures of the animal brain, as a computational substrate. After all, if AI is inspired by neurological systems, what better medium to do computing than an actual neurological system? Gathering developments from the fields of computer science, electrical engineering, neurobiology, electrophysiology, and pharmacology, the authors propose a new research initiative they call “organoid intelligence.”

OI is a collective effort to promote the use of brain organoids —tiny spherical masses of brain tissue grown from stem cells—for computation, drug research and as a model to study at a small scale how a complete brain may function. In other words, organoids provide an opportunity to better understand the brain, and OI aims to use that knowledge to develop neurobiological computational systems that learn from less data and with less energy than silicon hardware.

The development of organoids has been made possible by two bioengineering breakthroughs: induced pluripotent stem cells and 3D cell culturing techniques.

An electrically driven on-chip light source of entangled photon pairs is developed by combining an InP gain section and Si3N4 microrings. A pair generation rate of 8,200 counts s−1 and a coincidence-to-accidental ratio of more than 80 are achieved around the wavelength of 1,550 nm.

Biodegradable devices that generate energy from the same effect behind most static electricity could help power transient electronic implants that dissolve in the body, researchers say.

Implantable electronic devices now help treat everything from damaged hearts to traumatic brain injuries. For example, pacemakers can help keep hearts beating properly, while brain sensors can monitor patients for potentially dangerous swelling in the brain.

However, when standard electronic implants run out of power, they need to be removed lest they eventually become sites of infection. But their surgical removal can result in potentially dangerous complications. Scientists are developing transient implantable electronics that dissolve once they are no longer needed, but these mostly rely on external sources of power, limiting their applications.

A study published in the journal Stem Cell Reports on March 23, led by Dr. Ryosuke Tsuchimochi and Professor Jun Takahashi, examined the effects of combining cell transplantation and gene therapy for axonal outgrowth in the central nervous system. The authors demonstrated the potential of this combinatorial therapy for promoting axonal regeneration in patients with central nervous system injuries.

Stroke and traumatic brain/spinal cord injuries often damage the corticospinal tract (CST), composed of descending axonal tracts from the motor cortex down the spinal cord, that innervates motor neurons to activate skeletal muscles for controlling voluntary movements. Pharmacological and surgical interventions, in conjunction with rehabilitation, can maintain some lost motor functions, but patients with such acute neural injuries often suffer from lifelong severe motor impairment.

Cell replacement therapy—the implantation of new neurons into damaged brain regions —is viewed as a last hope that could help patients recover sufficient motor functions to live a normal life. The research team previously demonstrated that brain tissues transplanted into injured mouse brains could find their way to the CST and spinal cord but believed that further optimization of the host environment was necessary to promote CST reconstruction and functional recovery.

Frequencies of light invisible to the human eye reveal a vast amount of information about our universe. But it took decades for scientists to learn how to view this hidden cosmos.

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Help us welcome Anders Sandberg to the Foresight family! As a Senior Research Fellow in Philosophy, we are proud that he will be joining a fantastic group of Foresight Senior Research Fellows: https://foresight.org/about-us/senior-research-fellows/

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We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo-and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth non-linear functions from small datasets (100 records). Compared with conventional algorithms, such as random forest, support vector machine, and linear regressions, the QCL can offer better predictions with some one-dimensional functions and experimental chemical databases. QCL will potentially help the virtual exploration of new molecules and materials more efficiently through its superior prediction performances.

All the plausible paths to a really great future, involve the development of machine at some point.

Fellow scientists labeled him a crackpot. Now Stuart Hameroff’s quantum consciousness theories are getting support from unlikely places.