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While the search for AI’s breakthrough case is underway, the omnipresence of AI Tools is already clear – embedded in our personal devices and set to transform all aspects of our lives. Nevertheless, this rise clashes with the grim reality with which the computer sector is confronted: exponential energy requires that global energy production cannot keep track of.
The computing power that is necessary for AI doubles every 100 days, while the calculation capacity reaches a “extinction event” that is limited by the energy supply that will ultimately force a plateau in computational growth. In response, major technology companies turn to nuclear energy to grow fast -growing AI systems quickly.
The delay in Moore’s law further increases this crisis as the saving scaling of the device approaches physical limits. Unless we build innovative technology that makes energy -efficient computer use possible, the growth of computing power will inevitably stagnate. Instead of concentrating exclusively on incremental optimisations of current architectures, breakthrough innovation from various technological sectors will be needed to maintain sustainable progress.
Hyunjun Park is the CEO of Catalog, a DNA storage and calculation platform. Dionis Minev is a technical leader for business development at Catalog.
Convergence of technologies such as a path forward
The solution lies in the convergence of technologies, in particular new computer paradigms from unconventional areas such as biology, chemistry and optics. As we continue the 21st century, we increasingly recognize the power of biology and the inspiration that we can draw on for radical technological innovation.
The Nobel Prize in Physics of this year underlined this interest by assigning it to inventions and discoveries that make AI possible that were fundamentally inspired by the structure of the brain.
The next generation of computer use
While we continue to explore organically inspired architectures, we must note that the efficiency of the human brain per power unit when performing cognitive tasks is 10,000 times greater than that of generative AI. On a molecular scale this is powered by complex cellular architectures and biochemical reactions that exceed silicone-based operations in energy efficiency and at the same time massively parallel.
For example, a modern supercomputer can perform approximately one Quintillion operations per second. A human cell performs around 1 billion biochemical reactions per second, with trillion cells in the body. This scales to a sextillion reactions per second. Despite these stunning numbers, the energy needed to maintain a human body is lower than they are needed to provide a supercomputer with electricity.
Although this comparison is not computational equivalent, it underlines the remarkable complexity and energy efficiency of biological systems, which inspire the development of emerging technologies such as organic and neuromorf computing.
More practical, organic computer use can use synthetic DNA as a medium for storage and calculation. DNA offers huge data storage density-the volume of a sugar tubus can store the entire library of the long-term congress, which makes it possible to reduce the need for energy-intensive cooling systems. Computing on DNA can use various breakthroughs that can assemble, manipulate, store and read the DNA that the biotechnology industry continues to improve quickly.
Other breakthrough technologies, such as neuromorphic computing, organoid intelligence and photonic computer use, have a similar promise. Neuromorphic systems are based on silicone and designed to simulate the architecture of the brain, achieving very energy -efficient processing by replicating synaptic connections.
Organoid (a simplified version of an organ grown in the lab) Intelligence – a field that is still in its infancy – also tries to use the architecture of the brain, parallel processing options with completely new biological hardware made from cerebral organoids.
Photonic computer use, on the other hand, uses light to perform faster, lower power operations than electronic counterparts. All these approaches are still at an early stage and are confronted with technical challenges that must be overcome. Nevertheless, they offer routes for sustainable computer use that go beyond the energy restrictions of traditional architecture and emphasize the importance of research and development at an early stage.
Unlike incremental improvements in existing systems, they offer the potential for a step-by-step change in energy efficiency that can facilitate a Cambrian explosion applications For the next generation AI.
Overcoming challenges for convergence
Despite the potential, technological convergence is confronted with challenges, including technological adulthood of its components, economic feasibility, potential regulatory and human factors.
For new technologies to achieve large -scale acceptance, they must demonstrate maturity, together with clear value propositions that are financially viable to implement. Organizations can hesitate to fundamentally reconsider their process because of the costs of hiring, training and investments in new infrastructureEspecially if the initial market is too small.
Moreover, some emerging technologies, such as organoid intelligence, can increase ethical considerations. In these cases, the training of the public and guaranteeing transparency around constant research can help reduce. For example, with DNA computing, proactive measures such as screening -DNA sequencies on biosthecurity, for example, are not only tackled with potential regulatory concerns, but also builds trust in this emerging innovation.
A vision for the future
In order to really use the potential of technological convergence, innovation must go beyond the optimization of existing systems and focus on building completely new architectures that are both scalable and energy efficient.
These new systems are not expected to immediately replace or surpass the current technologies. They should also not be considered extensive in their computational activities. After all, the semiconductor industry has had decades to innovate and optimize existing technologies. Instead, they must be considered complementary, finding initial applications in specialized domains that offer unique benefits and can be tested on a scale.
The energy crisis in Computing is a discouraging challenge, but it also creates a crucial opportunity for transformative innovation. By giving priority to convergence and breakthrough architectures, we can achieve scalable, sustainable AI and computer solutions.
The next era of computer use will be powered by innovation, no incremental improvements. The path forward is in radical shifts that use the synergies of multiple fields, so that the digital age continues to evolve in harmony with the energy reality of our planet.
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This article was produced as part of the TechRadarpro expert insight channel, where today we have the best and smartest spirits in the technology industry. The views expressed here are those of the author and are not necessarily those of TechRadarpro or Future PLC. If you are interested in contributing to find out more here: https://www.techradar.com/news/submit-your-story-techradar-pro
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