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Chinese researchers have developed an open-source “brain-on-chip” interface system, which is the first of its kind in the world. The system can instruct a robot to avoid obstacles, track, and grasp through “mind control,” the Science and Technology Daily reported on Wednesday.

The interface system was co-developed by research teams from Tianjin University and Southern University of Science and Technology.

The system uses an artificial brain cultivated in vitro – such as a “brain-like organ,” which can interact with external information through encoding, decoding and stimulus-feedback when coupled with electrode chips, according to the report.

Terahertz technology has the potential to address the growing need for faster data transfer rates, but converting terahertz signals to various lower frequencies remains a challenge. Recently, Japanese researchers have devised a novel approach to both up-and down-convert terahertz signals within a waveguide. This is achieved by dynamically altering the waveguide’s conductivity using light, thereby creating a temporal boundary. Their breakthrough could lead to advancements in optoelectronics and improved telecommunications efficiency.

As we plunge deeper into the Information Age, the demand for faster data transmission keeps soaring, accentuated by fast progress in fields like deep learning and robotics. Against this backdrop, more and more scientists are exploring the potential of using terahertz waves to develop high-speed telecommunication technologies.

However, to use the terahertz band efficiently, we need frequency division multiplexing (FDM) techniques to transmit multiple signals simultaneously. Of course, being able to up-convert or down-convert the frequency of a terahertz signal to another arbitrary frequency is a logical prerequisite to FDM. This has unfortunately proven quite difficult with current technologies. The main issue is that terahertz waves are extremely high-frequency waves from the viewpoint of conventional electronics and very low-energy light in the context of optics, exceeding the capabilities of most devices and configurations across both fields. Therefore, a radically different approach will be needed to overcome current limitations.

The brain is the most complex organ ever created. Its functions are supported by a network of tens of billions of densely packed neurons, with trillions of connections exchanging information and performing calculations. Trying to understand the complexity of the brain can be dizzying. Nevertheless, if we ever hope to understand how the brain works, we need to be able to map neurons and study how they are wired.

Now, publishing in Nature Communications, researchers from Kyushu University have developed a new AI tool, which they call QDyeFinder, that can automatically identify and reconstruct individual neurons from images of the mouse brain. The process involves tagging neurons with a super-multicolor labeling protocol, and then letting the AI automatically identify the neuron’s structure by matching similar color combinations.

By Gitta Kutyniok

The recent unprecedented success of foundation models like GPT-4 has heightened the general public’s awareness of artificial intelligence (AI) and inspired vivid discussion about its associated possibilities and threats. In March 2023, a group of technology leaders published an open letter that called for a public pause in AI development to allow time for the creation and implementation of shared safety protocols. Policymakers around the world have also responded to rapid advancements in AI technology with various regulatory efforts, including the European Union (EU) AI Act and the Hiroshima AI Process.

One of the current problems—and consequential dangers—of AI technology is its unreliability and subsequent lack of trustworthiness. In recent years, AI-based technologies have often encountered severe issues in terms of safety, security, privacy, and responsibility with respect to fairness and interpretability. Privacy violations, unfair decisions, unexplainable results, and accidents involving self-driving cars are all examples of concerning outcomes.

This is a recording of the AGI23 Conference, Day 1, June 16th 2023, Stockholm. This video shows the following tutorial: Test and Evaluation First Principles for General Learning Systems, led by Tyler Cody.

SingularityNET was founded by Dr. Ben Goertzel with the mission of creating a decentralized, democratic, inclusive, and beneficial Artificial General Intelligence (AGI). An AGI is not dependent on any central entity, is open to anyone, and is not restricted to the narrow goals of a single corporation or even a single country.

The SingularityNET team includes seasoned engineers, scientists, researchers, entrepreneurs, and marketers. Our core platform and AI teams are further complemented by specialized teams devoted to application areas such as finance, robotics, biomedical AI, media, arts, and entertainment.

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