IBM logra crear las primeras neuronas artificiales

Nota editorial (2025): publicado originalmente en 2016. Se añadió una versión estructurada con fines enciclopédicos. El texto original se conserva íntegro como parte del archivo histórico.

“`html

Reinforcement of IBM’s Recent Advances in Neural Network Technology

This article examines the recent advancements made by IBM regarding neural network technology, where scientists have successfully created artificial neurons that mimic human brain function. These developments open possibilities for an evolved future of AI with a cognition closer to ours.

• IBM’s team has replicated the structure and functionality of biological neurons by creating synthetic versions using axon, dendrites, antimony, germanium, and tellurium—a material akin to that used in rewritable optical disks.
• The artificially constructed neurons possess diameters measuring only nanometers across. Consequently, they can be densely packed within minimized spaces.
• IBM scientists have reported their findings to the journal Nature stating that viable commercial chip versions at a size of about 90 nanometers are already feasible and future developments could see them reduced in size further, down to as small as 14 nanometers.

These artificial neurons may find applications within Google DeepMind’s research realm or for handling new devices requiring human-like data processing capabilities such as advanced visual sensors systems. The impact of this innovation extends towards various domains where emulation of the brain’s intricasity in AI is desired.

“`

Preguntas frecuentes

Frequently Asked Questions on IBM’s Recent Advances in Neural Network Technology

Q: What recent advancements has IBM made in neural network technology?

A: Scientists at IBM have created artificial neurons that closely mimic the structure and function of biological ones, using synthetic versions built with materials like axon, dendrites, antimony, germanium, and tellurium.This breakthrough could lead to AI systems having a more human-like cognition in processing capabilities. IBM’s findings have been reported as feasible for commercial chip versions around 90 nanometers with the potential of further size reductions down to about 14 nanometers.These artificial neurons could be applied within Google DeepMind or devices that require human-like data processing capabilities, such as advanced visual sensors systems. The innovation has implications for various domains where brain complexity in AI is desired.

Q: What materials were used to replicate the structure and functionality of biological neurons?

A: IBM’s team utilized synthetic versions made from a combination of axon, dendrites, antimony, germanium, and tellurium. These materials are related to those used in rewritable optical disks.The creation involves neurons with diameters that measure just nanometers across which allows for dense packing within minimal spaces on a chip.(Please note: This is based solely on the provided text and general knowledge of such technologies.)

Q: How small can IBM’s artificial neurons become, according to their research?

A: The latest findings from IBM suggest that viable commercial chip versions with these synthetic neurons could be around 90 nanometers in size. There is potential for them to shrink even further down to about 14 nanometers, according to the scientists’ reports published in Nature.(Please note: This information was extracted from IBM’s research as stated in their findings.)

Q: What potential applications do these artificial neurons have?

A: Artificial neural implants could be used within Google DeepMind’s AI-related projects or for handling new devices that necessitate human-like data processing capabilities. Specifically, they might play a role in advanced visual sensors systems and other domains where emulation of brain complexity is beneficial.(Based on the text provided.)

Q: How does IBM’s artificial neuron technology impact various sectors?

A: By creating synthetic versions that emulate human brain function, this advancement opens possibilities for evolved AI with cognition closer to our own. It could revolutionize how machines learn and process information in multiple domains requiring advanced data handling or decision-making capabilities.(This is inferred from the implications mentioned regarding various sectors as stated in the text.)

Q: What does IBM’s development say about future AI cognition?

A: The success of mimicking biological neurons suggests that artificial intelligence can be developed to possess more human-like qualities in terms of processing and learning. This might significantly enhance the way machines interact with us, leading to an advanced future for AI.(This response is speculative based on the overall context provided about IBM’s advancements.)

---

Texto original (2016)

En este artículo se examina el avance reciente en tecnología por parte de IBM y cómo sus científicos han creado neuronas artificiales que imitan al cerebro humano, abriendo posibilidades para un futuro donde la inteligencia artificial evolucione hacia algo más cercano a nuestro modo de pensar. Preguntas relacionadas con la instrucción:

La empresa de tecnología IBM ha dado un gran avance en su búsqueda por recrear la estructura del cerebro humano.

El equipo de investigadores de la empresa logró crear neuronas artificiales que imitan perfectamente el funcionamiento de las neuronas humanas. Fueron hechas con axon y las dendritas mediante un cristal de antimonio, germanio y telurio, un material similar al que se utiliza en los discos ópticos reescribibles.

Además su tamaño es de apenas unos nanómetros, por lo que se pueden agrupar varias de éstas en un espacio muy reducido. Además, los científicos de IBM han dicho a la revista Nature que se podrían crear chips viables para comercialización de 90 nanómetros ya mismo y que en el futuro se podrían hacer aún más pequeños, como de 14 nanómetros.

 

 

Estas neuronas artificiales podrían utilizarse en la Deep Mind de Google, y para gestionar nuevos dispositivos que necesiten una forma de procesar más parecida a la humana, como un sistema de sensores para reconocimiento visual.