A DNA robot that can walk across biological cell membranes is the first one that can control living cells’ behaviour. The researchers who made the robot hope that it could improve cell-based precision medicine.
A team led by Hong-Hui Wang and Zhou Nie from Hunan University, China, has created a synthetic molecular robot that walks along the outer membrane of biological cells. The robot, powered by an enzyme’s catalytic activity, traverses across receptors that act as stepping stones on the cell surface. With each step, the robot activates a signal pathway that regulates cell migration. Driven by the robot’s movement, the cells can reach speeds of 24 μm/hour.
The researchers write that the DNA robot offers, for the first time, an opportunity to accurately and predictably control the nanoscale operations that power a live cell. They suggest that similar molecular machines that guide cell behaviours could play a role in cell-based therapies and regenerative medicine.
Please be sensitive to any artificial intelligence you encounter today. A UK appeals court just ruled that AI systems cannot submit or hold patents, as software is not human and therefore lacks human rights. Several courtrooms around the world have come to the same conclusion, despite the efforts of a very enthusiastic inventor.
Dr. Stephen Thaler has repeatedly filed patents on behalf of his AI, called DABUS. He claims that this AI should be credited for the inventions that it’s helped to produce. But patent offices disagree. After Dr. Thaler refused to resubmit his patents under a real name, the UK Intellectual Property Office pulled him from the registration process.
Our friend Dr. Thaler responded by taking the Intellectual Property Office to court. And predictably, the body rejected his case. So Dr. Thaler made an appeal, and again, he lost.
Samsung thinks it has a better way to develop brain-like chips: borrow existing brain structures. The tech firm has proposed a method that would “copy and paste” a brain’s neuron wiring map to 3D neuromorphic chips. The approach would rely on a nanoelectrode array that enters a large volumes of neurons to record both where the neurons connect and the strength of those connections. You could copy that data and ‘paste’ it to a 3D network of solid-state memory, whether it’s off-the-shelf flash storage or cutting-edge memory like resistive RAM.
Each memory unit would have a conductance that reflects the strength of each neuron connection in the map. The result would be an effective return to “reverse engineering the brain” like scientists originally wanted, Samsung said.
The move could serve as a ‘shortcut’ to artificial intelligence systems that behave like real brains, including the flexibility to learn new concepts and adapt to changing conditions. You might even see fully autonomous machines with true cognition, according to the researchers.
Consciousness: Evolution of the Mind Documentary (2021), a film by Alex Vikoulov, Part I, WHAT IS CONSCIOUSNESS?
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*Based on recent book The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution (2020) by evolutionary cyberneticist Alex M. Vikoulov, available as eBook, paperback, hardcover, and audiobook on Amazon: https://www.amazon.com/Syntellect-Hypothesis-Paradigms-Minds-Evolution/dp/1733426140
The study of consciousness needs to be lifted out of the mysticism that has dominated it. Consciousness is not just a matter of philosophy or spirituality. It’s a matter of hard science. It’s a matter of understanding the brain and the mind — a pattern structure made out of information. It’s also a matter of engineering. If we can understand the functionality of the brain, its neural code, then we can build the same functionality into our computer systems. We should recognize that our brain is not a “stand-alone” information processing organ, though: It acts as a central unit of our integral nervous system with recurrent data exchange within the entire organism and the Universe. Artificial consciousness may be within our grasp, however, not the way many AI researchers envision.
There’s no consensus on what produces consciousness, but everyone regardless of metaphysical views can agree what it is like to be conscious. Given that consciousness is subjectivity, what consciousness is like is what consciousness is.
Different species have a variety of their biological information processors which unsurprisingly results in qualia diversity. All species live in their own unique sensory universes. Consciousness and optimized information-processing are the two sides of one coin. Feeling and thinking are ways we process information, but our emotional sensation is normally faster than a conscious thought.
Contrary to conventional scientific wisdom, conscious minds as macro-level phenomena might have greater influence over the unfolding future than does the sum of their cognitive algorithms that are arguably their micro-level components. That’s why human consciousness is so scientifically elusive. Neuronal circuits supposedly give rise to cognitive modules, and these immaterial cognitive algorithms, in turn, give rise to meta-algorithmic conscious awareness, all in all at least two layers of emergence on top of “tangible” neurons.
Mammalian neural circuits, referred to as the limbic system, are responsible for human emotional intelligence and forming of long-term memories. The main structures of the limbic brain are the hippocampus, amygdala, and hypothalamus. The neocortex, the latest evolutionary addition, is present in primates and now culminated in the human brain with its two large cerebral hemispheres. The neocortex has been responsible for the development of human language, abstract thought, imagination and self-reflective consciousness. The neocortex is flexible enough to allow almost infinite learning abilities. The neocortex is also what has enabled human cultures to develop.
The K. Lisa Yang Center for Bionics has been established thanks to a $24 million donation from philanthropist Lisa Yang, according to an MIT announcement. That’s probably not enough attain the center’s enormously ambitious goals of restoring neural function and rebuilding lost limbs, but it does get the ball rolling and bring together MIT faculty with a variety of specialties toward a common big-picture objective — potentially serving as a much-needed accelerant for disability tech research.
The new research center will fall under the leadership of MIT Media Lab professor Hugh Herr, who is a double amputee himself and has come to be known as a leader in the field of robotic prosthetics. In the MIT announcement, Herr said that he sees this new initiative as an important step toward eliminating physical disabilities altogether.
“The world profoundly needs relief from the disabilities imposed by today’s nonexistent or broken technologies,” Herr said. “We must continually strive towards a technological future in which disability is no longer a common life experience. I am thrilled that the Yang Center for Bionics will help to measurably improve the human experience for so many.”
Reservoir computing, a machine learning algorithm that mimics the workings of the human brain, is revolutionizing how scientists tackle the most complex data processing challenges, and now, researchers have discovered a new technique that can make it up to a million times faster on specific tasks while using far fewer computing resources with less data input.
With the next-generation technique, the researchers were able to solve a complex computing problem in less than a second on a desktop computer — and these overly complex problems, such as forecasting the evolution of dynamic systems like weather that change over time, are exactly why reservoir computing was developed in the early 2000s.
These systems can be extremely difficult to predict, with the “butterfly effect” being a well-known example. The concept, which is closely associated with the work of mathematician and meteorologist Edward Lorenz, essentially describes how a butterfly fluttering its wings can influence the weather weeks later. Reservoir computing is well-suited for learning such dynamic systems and can provide accurate projections of how they will behave in the future; however, the larger and more complex the system, more computing resources, a network of artificial neurons, and more time are required to obtain accurate forecasts.
