Silicon Valley startup Lightmatter has developed a novel computer chip that can speed up artificial intelligence processes and save electricity in the process. The company focuses on using beams of light to move data between computers rather than using electric signals.
Connection speeds are a great matter of concern when it comes to artificial intelligence due to its complex software. This complexity requires the software to be spread over many computers.
The UK is developing a controversial AI tool to predict potential murderers using personal data from convicts, victims, and witnesses. Aimed at preventing crime, the initiative mirrors global trends in AI policing. However, critics warn of bias, privacy issues, and call the project “chilling and dystopian,” raising ethical concerns about pre-crime technology.
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How does the brain work? Where, and when, and why do neurons connect and send their signals? Scientists have created the largest wiring diagram and functional map of an animal brain to date to learn more. Research teams at Allen Institute, @BCMweb and @princeton worked together to map half a billion synapses, over 200,000 cells, and 4km of axons from a cubic millimeter of mouse brain, providing unparalleled detail into its structure and functional properties. The project is part of the Machine Intelligence from Cortical Networks (MICrONS) program, which seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. Research findings reveal key insights into brain activity, connectivity, and structure—shedding light on both form and function—within a region of the mouse visual cortex that plays a critical role in brain health and is often disrupted in neurological conditions such as Alzheimer’s disease, autism, and addiction. These insights could revolutionize our ability to treat neuropsychiatric diseases or study the influence of drugs and other changes on the brain.
This extraordinary achievement begins to reveal the elusive language the brain uses to communicate amongst its millions of cells and the cortical mechanisms of intelligence—one of the holy grails of science.
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Using artificial intelligence shortens the time to identify complex quantum phases in materials from months to minutes, finds a new study published in Newton. The breakthrough could significantly speed up research into quantum materials, particularly low-dimensional superconductors.
The study was led by theorists at Emory University and experimentalists at Yale University. Senior authors include Fang Liu and Yao Wang, assistant professors in Emory’s Department of Chemistry, and Yu He, assistant professor in Yale’s Department of Applied Physics.
The team applied machine-learning techniques to detect clear spectral signals that indicate phase transitions in quantum materials—systems where electrons are strongly entangled. These materials are notoriously difficult to model with traditional physics because of their unpredictable fluctuations.
A machine learning method has the potential to revolutionize multi-messenger astronomy. Detecting binary neutron star mergers is a top priority for astronomers. These rare collisions between dense stellar remnants produce gravitational waves followed by bursts of light, offering a unique opportunit