How will AI improve our lives in the years to come? From its inception six decades ago to its recent exponential growth, futurist Ray Kurzweil highlights AI’s transformative impact on various fields and explains his prediction for the singularity: the point at which human intelligence merges with machine intelligence.
Within the vast tapestry of the universe, where the microscopic building blocks of matter intertwine with the cosmic dance of galaxies, lies a story of profound discovery. Venture into a realm where the laws of physics as we know them are both challenged and confirmed, where the invisible forces that hold the very fabric of our reality together are brought into the light. This narrative isn’t born from the pages of a science fiction novel but emerges from the cutting-edge explorations at the heart of quantum physics. At this frontier, scientists embark on a rigorous inquiry to understand the origins of particle mass, revealing insights that connect the infinitesimal to the immense, from the atoms in our bodies to the distant stars.
The CRISPR gene-editing technique has revolutionised biology, but now an even more powerful system called bridge editing could let us completely reshape genomes.
Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier.
THE SINGULARITY IS NEARER: When We Merge With A.I., by Ray Kurzweil ______ A central conviction held by artificial intelligence boosters, but largely ignored in public discussions of the technology, is that the ultimate fulfillment of the A.I. revolution will require the deployment of microscopic robots into our veins. In the short term, A.I. may help us print clothing on demand, help prevent cancer and liberate half of the work force. But to…
DGIST’s Electrical Engineering and Computer Science Professor Jang Jae-eun and Professor Kwon Hyuk-jun and their research team have developed a high-efficiency process technology for next-generation AI memory transistors. The work is published online in Advanced Science.
The team developed a nanosecond pulsed laser-based “selective heat treatment method” and “thermal energy minimization control process technology” to overcome the shortcomings of the high-temperature process of ferroelectric field-effect transistors, which have non-volatile memory characteristics, high-speed operation, low power consumption, long lifetime, and durability.
The new technology process enables the realization of heterojunction structures, which are the core technology of next-generation AI semiconductors.
Hacking my brain implant wouldn’t do much, he asserted, adding, “You might be able to see like some of the brain signals. You might be able to see some of the data that Neuralink’s collecting.”
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Norland Arbaugh did not specify the data that is being collected by Neuralink chip which is almost the size of a coin and contains thousands of electrodes that monitor and stimulate brain activity, as per the company. This information is digitally transmitted to researchers.
Explore the uncanny valley AI phenomenon, its impact on technology and design, and strategies for creating AI that bridges the gap between human and machine.
Convolutional neural networks (CNNs), with their exceptional image recognition capabilities, have performed outstandingly in the field of AI and notably within platforms like ChatGPT. Recently, a team of Chinese researchers from University of Shanghai for Science and Technology have successfully introduced the concept of CNNs into the field of optics and realized convolutional all-optical neural network, bringing revolutionary progress to AI imaging technology.
Led by Prof. Min Gu and Prof. Qiming Zhang from School of Artificial Intelligence Science and Technology (SAIST) at the University of Shanghai for Science and Technology (USST), the research team has developed an ultrafast convolutional optical neural network (ONN), which achieves efficient and clear imaging of objects behind scattering media without relying on the optical memory effect.
This finding was published in the journal Science Advances, in a paper titled “Memory-less scattering imaging with ultrafast convolutional optical neural networks.”
Mathematics suggested that time travel is physically possible – and Kurt Gödel proved it. Mathematician Karl Sigmund explains how the polymath did it.
By Karl Sigmund
