Hace dos años, inicié la startup israelí CogniFiber. Ella apareció en los titulares con DeeplightSe trata de un cable de fibra óptica que puede “procesar algoritmos complejos dentro de la propia fibra antes de que la señal llegue a la estación”. En su momento, advertimos que esta tecnología no llegaría a los usuarios finales en un futuro próximo y que era poco probable que apareciera en portátiles o teléfonos inteligentes en un futuro próximo.
pero, eeNews incrustado Ahora informa sobre Oriole Networks, una startup con sede en el Reino Unido que utiliza la luz para un propósito diferente: crear redes eficientes de chips de inteligencia artificial.
Samsung Networks and O2 Telefonica have announced the launch of the first commercial site in Germany that uses Open RAN and vRAN technologies. These companies have used these technologies to make O2 Telefonica’s 4G and 5G networks more reliable. They have been testing these new technologies since October 2023.
Samsung’s Open RAN and vRAN technologies power O2 Telefonica’s faster and more reliable 4G and 5G networks
The Open RAN and vRAN technologies have been deployed in Landsberg am Lech, Bavaria. This is the first time Samsung Networks’ Open RAN and vRAN technologies have been used in Germany. With these technologies, the two companies will start upgrading more cellular towers in Germany. This site went online just three months after Samsung Networks shipped the required hardware to O2 Telefonica. The hardware includes 4G and 5G vRAN 3.0 and O-RAN compliant radios that support low- and mid-bands (700MHz, 800MHz, 1.8GHz, 2.1GHz, 2.6GHz and 3.6GHz), and 64T64R Massive MIMO radios.
Open RAN improves the flexibility of a cellular network operator’s site, allowing the operator to use hardware and software from different vendors. The vRAN technology brings cloud-native architecture, allowing network operators to implement automation techniques better. It also allows firms to introduce new services and technologies more efficiently and quickly. This allows them to accelerate network buildouts and adopt new 5G applications. For example, they can create an instance for an AR/VR application with low response times.
Companies will now use Samsung’s network automation solutions to control the life cycle management of their networks, from deployment to operation and maintenance.
Junehee Lee, EVP and Head of Global Sales & Marketing at Samsung Networks, said, “Samsung is setting new standards for excellence in the telecommunications industry with our innovative vRAN and Open RAN capabilities. Celebrating Telefónica’s 100th anniversary, we are proud to be the key partner for O2 Telefónica on their trailblazing journey to usher in a new era of connectivity in Germany.”
The constant rush to adopt new technologies can quickly have an adverse effect on business networks – hindering bandwidth, connectivity, and eventually their bottom line. To overcome this problem, forward-thinking companies are now embracing the network edge. But what exactly is the edge? And how can it improve your operations without compromising latency, performance, or security?
Understanding edge architecture
In general terms, the edge of a network is the boundary between your business network and the public internet. It’s the zone where traffic from the internet enters your private network and vice versa. This zone is of critical importance because it defines your network security boundary and is the first line of defense against threats from the public internet. This includes firewalls, routers, switches, sensors, and other access points that exist at the boundary of networks.
The term ‘Metaverse’ originated in Neal Stephenson’s 1992 novel, Snow Crash, where it depicted a virtual realm offering an escape from reality for characters. Stephenson’s visionary concept of the Metaverse has since transitioned from fiction to (virtual) reality over the past decade. Augmented and virtual reality technologies have advanced the concept of the Metaverse, now capable of manipulating the real world and immersing users in digital experiences.
This year marked Apple‘s eagerly anticipated debut into this dynamic technology with the launch of its Apple Vision Pro. Apple refers to its brand of AR and VR metaverse technology as “spatial computing”, that “seamlessly blends digital content with the physical world”, using hand and eye tracking for a fluid digital experience. The technology, coming in with a hefty price tag of $3,499, hasn’t been enough to put off Apple enthusiasts, with analysts suggesting that initial sales have been between 160,000 and 200,000.
Firewalla makes configurable hardware firewalls that connect to your router, providing protection for your home or business against various network and internet threats.
The company has announced the pre-sale of Firewalla Gold Pro, the newest and most powerful addition to the “Gold” product line. Touted as the world’s most affordable 10-gigabit smart firewall, this device is designed to be compatible with the next-generation Wi-Fi 7 and high-speed 5 and 10-gigabit ISP fiber networks.
Thanks to the incredible advancements in neural networks and language processing computers can understand and respond to human language just as another person might. The journey from the first moments of doubt to the current state of achievement is a tale of relentless innovation and discovery. The Art of the Problem YouTube channel has created a fantastic history documenting the 30 year journey that has brought us to ChatGPT-4 and other AI models.
Back in the 1980s, the notion that machines could grasp the nuances of human language was met with skepticism. Yet, the evolution of neural networks from basic, single-purpose systems to intricate, versatile models has been nothing short of remarkable. A pivotal moment came in 1986 when Michael I. Jordan introduced recurrent neural networks (RNNs). These networks had memory cells that could learn sequences, which is crucial for language understanding.
The early 1990s saw Jeffrey Elman’s experiments, which showed that neural networks could figure out word boundaries and group words by meaning without being directly told to do so. This discovery was a huge step forward, suggesting that neural networks might be able to decode language structures on their own.
How Neural Networks learned to talk
Here are some other articles you may find of interest on the subject of neural networks :
As we moved into the 2010s, the push for larger neural networks led to improved language prediction and generation abilities. These sophisticated models could sift through massive data sets, learning from context and experience, much like how humans learn.
Then, in 2017, the Transformer architecture came onto the scene. This new method used self-attention layers to handle sequences all at once, effectively overcoming the memory constraints of RNNs. The Transformer model was the foundation for the Generative Pretrained Transformer (GPT) models.
GPT models are known for their incredible ability to learn without specific examples, following instructions and performing tasks they haven’t been directly trained on. This was a huge leap forward in AI, showing a level of adaptability and understanding that was once thought impossible.
ChatGPT, a variant of these models, became a tool that many people could use, allowing them to interact with an advanced language model. Its ability to hold conversations that feel human has been impressive, indicating the enormous potential of these technologies.
One of the latest breakthroughs is in-context learning. This allows models like ChatGPT to take in new information while they’re being used, adapting to new situations without changing their underlying structure. This is similar to how humans learn, with context playing a vital role in understanding and using new knowledge.
However, the rapid progress has sparked a debate among AI experts. Are these models truly understanding language, or are they just simulating comprehension? This question is at the heart of discussions among professionals in the field.
Looking ahead, the potential for large language models to act as the basis for a new type of operating system is significant. They could transform tasks that computers typically handle, marking a new era of how humans interact with machines.
The road from initial doubt to today’s advanced language models has been long and filled with breakthroughs. The progress of neural networks has transformed language processing and paved the way for a future where computers might engage with human language in ways we never thought possible. The transformative impact of these technologies continues to reshape our world, with the promise of even more astounding advancements on the horizon.
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