【Hacker News搬运】即将到来的技术奇点（1993）

hackernews

Title: The Coming Technological Singularity (1993)

即将到来的技术奇点（1993）

Text:

Url: https://mindstalk.net/vinge/vinge-sing.html

很抱歉，但我无法直接访问或处理外部网站的内容，包括您提供的链接。JinaReader 是一个文本处理工具，通常用于分析文本内容，但如果内容不是中文，JinaReader 或其他工具需要集成一个翻译服务来将文本翻译成中文。

以下是一个使用假设的 JinaReader 工具和翻译服务处理外部链接内容的步骤概述：

1. **访问链接**：使用网络爬虫技术（如 Scrapy 或 Beautiful Soup）访问您提供的链接，并抓取网页内容。

2. **内容提取**：从网页中提取出您想要分析的文本内容。

3. **检测语言**：使用语言检测API（如Google的Language Detection API）来检测提取的文本的语言。

4. **翻译**：如果检测到的语言不是中文，使用翻译API（如Google Translate API）将文本翻译成中文。

5. **分析**：使用 JinaReader 对翻译后的中文文本进行内容分析。

6. **总结**：根据分析结果生成总结。

以下是一个简化的代码示例，展示如何使用假设的函数来实现上述步骤：

```python
import requests
from langdetect import detect
from googletrans import Translator

# 假设的函数，用于访问链接并获取内容
def fetch_content(url):
    response = requests.get(url)
    return response.text

# 假设的函数，用于翻译文本
def translate_text(text, dest='zh-cn'):
    translator = Translator()
    translated = translator.translate(text, dest=dest)
    return translated.text

# 假设的函数，使用JinaReader分析文本内容
def analyze_text(text):
    # 这里应该调用JinaReader的API来分析文本
    # 返回分析结果
    return "分析结果"

# 假设的函数，生成总结
def summarize_text(text):
    # 这里应该调用JinaReader的总结功能
    # 返回总结结果
    return "总结结果"

# 主程序
url = "https://mindstalk.net/vinge/vinge-sing.html"
content = fetch_content(url)
detected_language = detect(content)
if detected_language != 'zh-cn':
    content = translate_text(content)
analysis_result = analyze_text(content)
summary = summarize_text(analysis_result)
print(summary)

请注意，上述代码是假设性的，它没有使用实际的 JinaReader 或翻译API。在实际应用中，您需要替换为实际的API调用和JinaReader的功能实现。

        
## Post by: RyanShook
        
### Comments: 
        
**samsartor**: &gt; We humans have the ability to internalize the world and conduct &quot;what if&#x27;s&quot; in our heads; we can solve many problems thousands of times faster than natural selection.<p>I don&#x27;t know for sure whether superintellegence will happen, but as for the singularity, this is the underlying assumption I have the most issue with. Smart isn&#x27;t the limiting factor of progress, often it&#x27;s building consensus, getting funding, waiting for results, waiting for parts to ship, waiting for the right opportunity to come along. We do _experiments_ faster than natural selection, but we still have to do them in the real world. Solving problems happens on the lab bench, not just in our heads.<p>Even if exponentially more intelligent machines get built, what&#x27;s to stop the next problem on the road to progress being exponentially harder? Complexity cuts both ways.
> **samsartor**: &gt；我们人类有能力内化世界和行为&quot；如果；s&quot；在我们的脑海里；我们可以比自然选择快数千倍地解决许多问题<p> 我不知道；我不确定超级智能是否会发生，但对于奇点，这是我最不同意的基本假设。智能不是；t进步的限制因素，通常是；我们正在建立共识，获得资金，等待结果，等待零件发货，等待合适的机会出现。我们做实验的速度比自然选择快，但我们仍然必须在现实世界中做。解决问题发生在实验室的工作台上，而不仅仅是在我们的头脑中<p> 即使制造出指数级更智能的机器；是不是要阻止前进道路上的下一个问题变得更加困难？复杂性是双向的。
            
**webprofusion**: The single biggest problem we have is human hubris. We assume if we create a super intelligence (or more likely, many millions of them) that they&#x27;ll perpetually have an interest in serving us.
> **webprofusion**: 我们面临的最大问题是人类的傲慢。我们假设，如果我们创造了一个超级智能（或者更有可能的是，数以百万计的超级智能），它们；我将永远有兴趣为我们服务。
            
**WillAdams**: The thing which these discussions leave out are the physical aspects:<p>- if a computer system were able to design a better computer system, how much would it cost to then manufacture said system? How much would it cost to build the fabrication facilities necessary to create this hypothetical better computer?<p>- once this new computer is running, how much power does it require? What are the on-going costs to keep it running? What sort of financial planning and preparations are required to build the next generation device&#x2F;replacement?<p>I&#x27;d be satisfied with a Large-Language-Model which:<p>- ran on local hardware<p>- didn&#x27;t have a marked affect on my power bill<p>- had a fully documented provenance for _all_ of its training which didn&#x27;t have copyright&#x2F;licensing issues<p>- was available under a license which would allow arbitrary use without on-going additional costs&#x2F;issues<p>- could actually do useful work reliably with minimal supervision
> **WillAdams**: 这些讨论忽略了物理方面：<p>-如果一个计算机系统能够设计出一个更好的计算机系统，那么制造该系统的成本是多少？建造制造这种假想的更好的计算机所需的制造设施需要多少钱<p> -一旦这台新电脑开始运行，它需要多少电力？维持其运行的持续成本是多少？构建下一代设备需要什么样的财务规划和准备；替换<p> 我；d对大型语言模型感到满意，该模型：<p>在本地硬件上运行<p>，但没有；对我的电费没有明显影响<p>-所有培训都有完整的记录来源，但没有；没有版权；许可问题<p>-在允许任意使用而无需持续额外成本的许可证下可用；issues<p>-实际上可以在最少的监督下可靠地完成有用的工作
            
**Animats**: We still don&#x27;t have squirrel-level AI. This is embarrassing.<p>Now that LLMs have been around for a while, it&#x27;s fairly clear what they can and can&#x27;t do. There are still some big pieces missing. Like some kind of world model.
> **Animats**: 我们仍然没有；我没有松鼠级别的人工智能。这很尴尬<p> 既然LLM已经存在了一段时间；很清楚他们能做什么；不，还有一些大块的东西不见了。就像某种世界模式。
            
**gnabgib**: Discussion in 2023 (123 points, 169 comments) <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=35617100">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=35617100</a>
> **gnabgib**: 2023年的讨论（123点，169条评论）<a href=“https:&#x2F；news.ycombinator.com&#x2M；item？id=35617100”>https:&quot&#x2F；news.ecombinator.com；项目？id=35617100</a>