包阅导读总结
1. `Quantum Computing`、`GPUs`、`AI Architecture`、`Cloud Services`、`Future Potential`
2. 文本探讨了量子计算和 GPU 在云服务及 AI 架构中的情况。指出 GPU 在 AI 系统中可能存在过度工程化，未来或融入架构而不被单独讨论。量子计算虽有潜力但仍处未来，实际应用尚远，当前主要是实验性的，利用门槛高，而 GPU 用于云服务相对门槛低。
3.
– 量子计算在云中的情况
– 有未来潜力，但目前仍主要在实验阶段。
– 行业在朝更先进的量子比特和稳定性发展，但对很多组织实用价值尚不明朗。
– 相比 AI 发展较慢，利用需专业知识，门槛高。
– GPU 在 AI 中的情况
– 可能在 AI 系统中存在过度工程化。
– 未来或融入 AI 架构而不再被单独讨论。
– 集成到云服务中可用于扩展现有 AI 操作，门槛相对较低。
思维导图：
文章地址：https://www.infoworld.com/article/3485730/remember-quantum-computing-in-the-cloud.html
文章来源：infoworld.com
作者：InfoWorld
发布时间：2024/8/13 9:00
语言：英文
总字数：765字
预计阅读时间：4分钟
评分：82分
标签：量子计算,生成 AI,GPU（图形处理器）,云计算,技术趋势
以下为原文内容
本内容来源于用户推荐转载，旨在分享知识与观点，如有侵权请联系删除 联系邮箱 media@ilingban.com
However, GPUs are not all that. As I’ve pointed out here many times, GPUs may be overengineering AI systems when commoditized CPUs would do just fine. Indeed, I see a future when GPUs won’t be discussed at all. Instead, they will just bake into AI architectures. I’m not sure why we focused so much on that part of AI architecture in the first place.
Quantum computing’s potential
Quantum computing, while promising, is still mainly in the realm of future potential. The industry is making strides towards more advanced qubits and increased stability. However, the practical utility of these advancements remains over the horizon for many organizations. This timeline, coupled with the steep learning curve and investment required, has positioned quantum computing as a slower-evolving technology compared to AI.
Moreover, the current quantum offerings, often accessed via cloud platforms, are still primarily experimental. They require specialized knowledge to leverage effectively, whereas GPUs integrated into cloud services can be readily used to scale existing AI operations with relatively lower barriers to entry.
