Edge Computing: A Game-Changer for the Internet of Things

VujaDE on July 20, 2023

[ad_1]
Edge Computing: A Game-Changer for the Internet of Things

In the era of digital transformation, where the Internet of Things (IoT) has become an integral part of our lives, the demand for real-time data processing and analysis has skyrocketed. Traditional cloud computing models, while effective for many applications, often struggle to meet the stringent latency requirements of IoT devices. This is where edge computing emerges as a game-changer for the IoT ecosystem.

Edge computing refers to the paradigm shift where computational power is moved closer to the edge of the network, i.e., closer to where the data is being generated. Unlike cloud computing, which centralizes the processing and storage in data centers, edge computing allows data to be processed, analyzed, and acted upon locally, at or near the source. This decentralized approach brings numerous benefits, particularly for IoT applications.

Latency reduction is perhaps the most significant advantage that edge computing offers. In scenarios where real-time response is critical, such as autonomous vehicles or industrial automation, the time it takes to transmit data to a remote cloud server and receive a response would be unacceptable. With edge computing, the processing is done locally, resulting in significantly reduced latency and allowing for near-instantaneous decision-making.

Furthermore, edge computing improves reliability and availability. In a cloud-centric model, a loss of connectivity between IoT devices and the cloud server can disrupt the entire system. However, with edge computing, even if there is intermittent or no internet connection, the local edge devices can continue to operate independently since they have their own computational capabilities. This ensures uninterrupted operations and safeguards against single points of failure.

Data privacy and security also benefit from edge computing. By keeping sensitive data closer to its source and processing it locally, edge computing minimizes the risks associated with transmitting vast amounts of data to a remote cloud server. This significantly reduces the attack surface and potential points of vulnerability, making it more difficult for hackers to breach the system.

Edge computing also offers cost savings by optimizing data transmission and storage. Since edge devices filter and preprocess data at the source, only relevant and actionable information is sent to the cloud, minimizing bandwidth usage and storage costs. This makes edge computing an economically viable option, especially for large-scale IoT deployments where data volumes can be enormous.

Despite its advantages, adopting edge computing for IoT applications does come with some challenges. One major hurdle is managing a distributed network of edge devices efficiently. Ensuring seamless communication, software updates, and monitoring across a large number of edge devices can be complex. However, advancements in edge orchestration frameworks and edge management technologies are addressing this challenge, making it easier to deploy, manage, and scale edge computing infrastructure.

As the IoT ecosystem continues to expand, with billions of interconnected devices generating massive amounts of data, edge computing has emerged as a crucial component to meet the demands of this interconnected world. By providing low latency, enhanced reliability, improved security, and cost savings, edge computing is truly a game-changer for the Internet of Things. With technology advancements and industry-wide adoption, we can expect edge computing to revolutionize how IoT applications are built and deployed, bringing us closer to a fully connected and intelligent future.
[ad_2]

Tech Companies’ Remarkable IPO Performance Makes Startups Appear Inadequate

VujaDE on July 20, 2023

Many venture-backed startups are unable or unwilling to go public, causing concerns among backers of venture funds who want to see returns.

Investors are becoming increasingly cautious about investing more capital in the startup landscape without getting some of their prior cash back. However, with expected delays in IPOs and a backlog of highly valued startups, relief is not anticipated in the near future.

The Exchange explores startups, markets and money.

Read it every morning on TechCrunch+ or get The Exchange newsletter every Saturday.

However, there are still some companies going public, and some of these newly public entities have been successful, even if they are venture-backed or related to the tech industry. This success is somewhat embarrassing for traditional software companies, seen as the center of tech and startups.




The Cava public offering (a privately-backed fast casual food with ecommerce elements) was successful, and this week saw the debut of Oddity Tech, a beauty-focused company that emphasizes its use of modern technology to create its products. Both Cava and Oddity priced their IPOs above their final price range and experienced a surge in value after starting to trade.

While food and beauty may not have the same growth and gross margins as traditional tech companies, these IPO successes challenge the notion that only software companies can thrive in the tech industry.

There is ongoing discussion in the tech industry regarding why software companies aren’t more profitable, considering their high-margin recurring revenue. Some argue that the belief in their increasing profitability over time may be incorrect, especially among smaller SaaS firms that struggle to demonstrate operating leverage.

Meituan invests in Zhipu AI, China’s formidable AI competitor

VujaDE on July 20, 2023

Zhipu AI, a prominent challenger to OpenAI in China, has received funding from Meituan, a food delivery giant with a market cap of approximately $100 billion. A subsidiary of Zhipu AI recently gained a 10% stake in the company. The exact funding details have not been disclosed, but Zhipu AI mentioned raising “hundreds of million yuan” in a Series B round last September. Qiming Venture Partners, Legend Capital, and Tsinghua Holdings are among its investors.

Numerous Chinese companies are working on developing large language models (LLMs) that could rival their Western counterparts. Zhipu AI, which originated from Tsinghua University, is one such company. Founded in 2019, it is led by Tang Jie, a professor in the university’s Department of Computer Science and Technology.

Zhipu AI recently open-sourced its bilingual conversational AI model, ChatGLM-6B, which is trained on six billion parameters and claims to be able to perform inferences on a single consumer-grade graphics card, significantly reducing the cost of running an LLM. They have also previously open-sourced a more powerful variant, the GLM-130B, trained on 130 billion parameters. Their chatbot app, ChatGLM, is currently in a closed beta phase primarily targeting academic and industry players.

Meituan’s investment in Zhipu AI comes shortly after their acquisition of Light Years Beyond, another prominent LLM player in China, for $234 million. These investments are expected to enhance Meituan’s AI capabilities while providing the AI firms access to Meituan’s extensive user base of 450 million individuals who utilize their on-demand platform for food delivery, grocery shopping, and hotel bookings.