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Category: biological – Page 4

Will Humans Have to Merge with AI to Survive?
What if the only way to survive the AI revolution is to stop being human?
Ray Kurzweil, one of the most influential futurists and the godfather of AI, predicts that humans will soon reach a turning point where merging with AI becomes essential for survival. But what does this truly mean? Will we evolve into superintelligent beings, or will we lose what makes us human?
In this video, we explore Kurzweil’s bold predictions, the concept of the Singularity, and the reality of AI-human integration. From Neuralink to the idea of becoming “human cyborgs,” we examine whether merging with AI is an inevitable step in human evolution—or a path toward losing our biological identity.
Are we truly ready for a world where there are no biological limitations?
Chapters:
Intro 00:00 — 01:11
Ray Kurzweil’s Predictions 01:11 — 02:23
Singularity Is Nearer 02:23 — 04:05
What Does “Merging with AI” Really Mean? 04:05 — 04:35
Neuralink 04:35 — 07:02
Why Would We Need to Merge with AI? 07:02 — 10:04
Human Life After Merging with AI 10:04 — 12:30
Idea of Becoming ‘Human Cyborg’ 12:30 — 14:33
No Biological Limitations 14:33 — 17:24
#RayKurzweil #AI #Singularity #HumanCyborg #FutureTech #ArtificialIntelligence

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A notable aspect of the CL1 is its ability to learn and adapt to tasks. Previous research has demonstrated that neuron-based systems can be trained to perform basic functions, such as playing simple video games. Cortical Labs’ work suggests that integrating biological elements into computing could improve efficiency in tasks that traditional AI struggles with, such as pattern recognition and decision-making in unpredictable environments.

Cortical Labs says that the first CL1 computers will be available for shipment to customers in June, with each unit priced at approximately $35,000.

The use of human neurons in computing raises questions about the future of AI development. Biological computers like the CL1 could provide advantages over conventional AI models, particularly in terms of learning efficiency and energy consumption. The adaptability of neurons could lead to improvements in robotics, automation, and complex data analysis.

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One limitation of producing biofuel is that the alcohol created by fermentation is toxic to the microbes that produce it. Now scientists are closer to overcoming this obstacle.

Researchers from the University of Cincinnati and the U.S. Department of Energy’s Oak Ridge National Laboratory have achieved a breakthrough in understanding the vulnerability of microbes to the alcohols they produce during of plant biomass.

With the national lab’s neutron scattering and simulation equipment, the team analyzed fermentation of the biofuel , an energy-packed alcohol that also can be used as a solvent or chemical feedstock.

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Brain-computer interfaces have enabled people with paralysis to move a computer cursor with their mind and reanimate their muscles with their thoughts. But the performance of the technology — how easily and accurately a BCI user’s thoughts move a cursor, for example—is limited by the number of channels communicating with the brain.

Science Corporation, one of the companies working towards commercial brain-computer interfaces(BCIs), is forgoing the traditional method of sticking small metal electrodes into the brain in favor of a biology-based approach to increase the number of communication channels safely. “What can I stick a million of, or what could I stick 10 million of, into the brain that won’t hurt it?” says Alan Mardinly, Science Corp co-founder.

The answer: Neurons.

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For decades, scientists have relied on electrodes and dyes to track the electrical activity of living cells. Now, engineers at the University of California San Diego have discovered that quantum materials just a single atom thick can do the job—using only light.

A new study, published in Nature Photonics, shows that these ultra-thin semiconductors, which trap electrons in two dimensions, can be used to sense the biological electrical activity of living cells with high speed and resolution.

Scientists have continually been seeking better ways to track the electrical activity of the body’s most excitable cells, such as neurons, heart muscle fibers and pancreatic cells. These tiny electrical pulses orchestrate everything from thought to movement to metabolism, but capturing them in real time and at large scales has remained a challenge.

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This quantum light manipulation breakthrough paves the way for unprecedented technologies.

Scientists from the University of Basel and the University of Sydney successfully manipulated and identified interacting packets of light energy, or photons, with unprecedented precision.

This breakthrough, published in Nature Physics, marks the first-ever observation of stimulated light emission at the single-photon level—a phenomenon first predicted by Albert Einstein in 1916.

By measuring the time delay between photon interactions, researchers demonstrated how photons could become entangled in a “two-photon bound state,” opening up new possibilities for quantum computing and enhanced measurement techniques.

This discovery has profound implications for photonic quantum computing and metrology, particularly in fields like biological microscopy, where high-intensity light can damage delicate samples. Dr. Sahand Mahmoodian, a leading researcher on the project, emphasized that harnessing quantum light could lead to more precise measurements with fewer photons. Meanwhile, tech companies like PsiQuantum and Xanadu are already exploring how this research could contribute to fault-tolerant quantum computing. As scientists refine their ability to manipulate quantum light, the door opens to a future of more powerful computing, ultra-sensitive sensors, and revolutionary advancements in technology.

Learn more.

Continue reading “Scientists open door to manipulating ‘quantum light’” | >

Researchers have developed small robots that can work together as a collective that changes shape and even shifts between solid and “fluid-like” states — a concept that should be familiar to anyone still haunted by nightmares of the T-1000 robotic assassin from “Terminator 2.”

A team led by Matthew Devlin of UC Santa Barbara described this work in a paper recently published in Science, writing that the vision of “cohesive collectives of robotic units that can arrange into virtually any form with any physical properties … has long intrigued both science and fiction.”

Otger Campàs, a professor at Max Planck Institute of Molecular Biology and Genetics, told Ars Technica that the team was inspired by tissues in embryos to try and design robots with similar capabilities. These robots have motorized gears that allow them to move around within the collective, magnets so they can stay attached, and photodetectors that allow them to receive instructions from a flashlight with a polarization filter.

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We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems.

The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster’s supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind’s Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore.

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Rather than simply scaling up models with more parameters and data, they’re drawing inspiration from biological evolution to create more efficient and creative AI systems. The team explains how their Tokyo-based startup, founded in 2023 with $30 million in funding, aims to harness principles like natural selection and emergence to develop next-generation AI.

Continue reading “Can AI Improve Itself?” | >

Researchers have designed a robotic material that transforms like a living organism.

Inspired by embryos, these disk-shaped robots use magnets, motors, and light to shift between rigid and fluid states. The result? A self-healing, shape-shifting system that could change how we build and interact with materials.

Robots That Behave Like Materials

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