TECH


Photo Credit: Mercedes-Benz

Imagine grinding gravel and bounding over boulders while surrounded by luxury and without even emitting a single molecule of carbon dioxide. This fantasy meets reality with the fully electric Mercedes G-Wagon. 

Read more ... Add new comment

 
 
Photo Credit: Getty Images
 
Small Footprint, Big Potential: Phi-3 boasts a significantly smaller footprint compared to its predecessors. While specific details about its parameter count haven't been disclosed yet, Microsoft claims Phi-3 is capable of performing tasks typically associated with much larger language models (LLMs) like GPT-3.5. This accomplishment highlights advancements in training techniques that allow AI models to achieve high levels of performance with less computational power.
Read more ... Add new comment


Photo Credit: File Photo

In the vast expanse of our solar system, there are worlds beyond our own waiting to be explored, and Titan, Saturn's largest moon, is one such enigmatic destination. In July 2028, NASA will embark on an unprecedented journey to Titan with its Dragonfly mission, a rotorcraft lander designed to unlock the secrets of this intriguing celestial body.

Read more ... Add new comment

 

Photo Credit: Getty Images

Just months after its highly anticipated launch, Tesla is facing a recall of nearly 4,000 Cybertrucks due to a potentially serious issue with the accelerator pedal. This development comes as a setback for the electric vehicle (EV) giant, following the initial hype surrounding the futuristic truck's arrival.
Read more ... Add new comment

Photo Credit: Getty Images
 
Edge Computing
 
Edge computing is another sustainable technology shaping the future of AI. Instead of relying on centralized data centers, edge computing distributes computing power closer to the source of data generation, reducing the need for long-distance data transmission and lowering energy consumption. This approach is particularly beneficial for AI applications that require real-time processing, such as autonomous vehicles and smart infrastructure. By leveraging edge computing, AI systems can deliver faster response times while minimizing their environmental impact.
 
Data Efficiency and Privacy
 
Efforts to improve AI functionality also include optimizing data usage and enhancing privacy protections. Data-efficient learning algorithms, such as federated learning and differential privacy, enable AI models to learn from decentralized data sources without compromising individual privacy. By reducing the amount of data required for training, these techniques not only improve the efficiency of AI systems but also reduce the environmental impact associated with data storage and transmission.
 
Collaborative Research and Innovation
 
Addressing the intersection of AI and sustainability requires collaboration among researchers, industry leaders, policymakers, and environmental organizations. Initiatives such as the Green AI Consortium and the Partnership on AI are fostering interdisciplinary collaboration to develop sustainable AI solutions and promote responsible AI development. By sharing best practices, research findings, and resources, these collaborations accelerate the adoption of sustainable technologies and drive innovation in the field of AI.
 
Composed by: Hedwig Francis mwendwa 

 

Add new comment
Page 3 of 81

REGISTER FOR DAILY NEWSLETTER

Please enable the javascript to submit this form

MEMBER REGISTRATION/LOG IN

RECENT NEWS

AROUND THE CITIES

© 2024 HTopic LLC. All rights reserved.

Use of this site constitutes acceptance of our Terms & Conditions and Privacy Policy