Sure, here's the translation: ```html

在人工智能时代,你还在自己画图表吗?!?

```

Sure, here's the translated text in simplified Chinese within an HTML structure: ```html 免费的工具为您提供更高的工作效率。 ```

Sure, here's the text translated into simplified Chinese while keeping the HTML structure: ```html 🖼 — 一张图片胜过千言万语 ```

Sure, here's the translated text in simplified Chinese: ```html

并不是很原创,但在撰写复杂报告时却非常真实。我特别发现对于图表来说尤其如此:尝试用文字解释下面的树形结构,读者会很难理解,尽管内容很简单。

```
I took 5 seconds to draw it. How much more would it be on an online tool ?

在编程构建图表时速度较慢(无论使用界面还是不使用界面)。我经常使用 draw.io,它简单易用且能完成任务。但是你需要先完成你的设计,更新通常与创建同样复杂。

在前面的树中,如果 D 和 E 最终附加到 B 而不是 C,我可以快速地重新绘制它。在图表软件上,更新它可能会更加复杂。

Sure, here's the translated text in simplified Chinese, keeping the HTML structure: ```html 📝 — 纸张的速度,机器的光滑程度 ```

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html 我们能否兼顾两全? ```

Certainly! Here's the translation of "YES" into simplified Chinese while keeping the HTML structure: ```html 是的 ```

我发现了这个很棒的开源演示:DeTikzify

Sure, here's the translation: 它接受多种类型的输入:来自画布的涂鸦,也可以是图片。

It works out of the box for this simple use case

Sure, here's the translated text in simplified Chinese while maintaining the HTML structure: ```html 它开始简单,但它变得非常深奥 ```

Sure, here's the translation: ```html
⚙️ — 简而言之:它是如何工作的?
```

他们从Arxiv收集了许多图表来构建他们的训练集。

Sure, here's the translated text in simplified Chinese, while maintaining the HTML structure: ```html 他们使用基于 LLaVa 模型进行图像/文本嵌入和代码生成。 ```

他们使用 VerMCTS 确保代码能够编译。

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html

🤔 — 所以我们能做更多吗?

```

Sure, here's the translation of "These authors also produced AutomaTikz" in simplified Chinese while keeping the HTML structure: ```html 这些作者也制作了 AutomaTikz。 ```

Sure, here's the translated text in simplified Chinese: ```html

想法是创建一个提示,可以构建一个图表模板,以便以后可以更新。
```

Sure, here is the translated text in simplified Chinese within an HTML structure: ```html

在下面的示例中,您可以想象一下,如果您想要手动执行这个简单的感知器图,您将需要多少。

```
Prompt: Visual representation of a multi-layer perceptron: an interconnected network of nodes, showcasing the structure of input, hidden, and output layers that facilitate complex pattern recognition.

Sure, here is the translated text in simplified Chinese: ```html 我试图在提示中输入一个模糊的概念,看看模型会输出什么。还没有完全准备好。 ```

A test on a customer support scenario. This model is not really usable yet.

Sure, here's the translation in simplified Chinese, maintaining the HTML structure: ```html

🤑 — 我们可以用这个想法变得富有

```

```html

不幸的是,你并不是第一个有这个想法的人。有许多许多人工智能图表创业公司。

```

Sure, here is the translation in simplified Chinese while keeping the HTML structure: ```html 让我们引用其中几个: ```

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html

文本到图表:

```
  • Sure, here is the translated text in simplified Chinese, while keeping the HTML structure: ```html EdrawMax:提示到图表模板 ```
  • Sure, here's the translated text in simplified Chinese, keeping the HTML structure: ```html ChatUML:更专注于聊天体验以便于模板迭代 ```
  • LLM chat way of building complex diagram templates

Sure, here's the translation of your text into simplified Chinese while keeping the HTML structure: ```html

但是其中一些初创公司试图通过他们的图表工具为商业问题提供更多的工具和解决方案:

```
  • Sure, here's the translation of the text into simplified Chinese, while keeping the HTML structure: ```html 数据库关系设计师:Softbuilder:您编写用户故事,您会得到技术规范文档 ```
  • Softbuilder offer : user need to tech specs
  • Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html
    决策树构建基于上下文:Flowcharts.ai。我会将其描述为高级的GoogleForm。
    ```

最有可能的情况是,过于接近工具定位是一种危险的情况。一个开源模型可能会挑战你的市场地位。

Certainly! Here's the translation in simplified Chinese, while keeping the HTML structure: ```html 其他应用工具更复杂,挑战更大。通过文本到图表的例子,您可以看到在提示中解释用例时生成的限制有多大。 ```

Sure, here's the translated text in simplified Chinese, within an HTML structure: ```html

🔮 — 预测时间:这一切将如何演变

```

对于基于LLM的个人助理的生产力方面,这种图示方向看起来很有前景。

以下是简体中文翻译,保留HTML结构: ```html

许多公司雇用人员将数据编译成图表或使用精美的决策树来表示流程。因此,立即完成这项工作非常有价值。

```

Sure, here's the translated text in simplified Chinese within an HTML structure: ```html

考虑到LLM在总结信息方面的优势以及图表中信息的压缩程度,将两者结合起来将会是一个颠覆性的游戏规则。

```

这是我的赌注:对于谷歌的 Gemini、微软的 Copilot 和 OpenAI 的 GPT4,生成图表将是一个重大的增值。

The final conclusion of this article

2024-06-11 04:32:49 AI中文站翻译自原文