最近有关于人工智能变得自我意识的讨论。让我们从工程学的角度谈论它：您需要知道什么以及这对您意味着什么。

主张和技术

趋势背后的技术是变压器模型：一种对许多单词和句子进行训练的神经网络体系结构，并将在句子中预测下一个单词。变形金刚的世界已经变得非常流行：这些模型可以理解文本和其他顺序数据的关系和趋势。最终应用程序可以是从情感分析到图像字幕到对象识别的任何内容。

新闻中的架构之一是Google的对话应用程序语言模型（LAMDA），根据Google的博客可以“以一种看似无数的主题来以自由流动的方式进行。”这是因为用于训练模型的输入数据是基于对话的，并且该模型以“明智和具体”的方式进行培训。

You can explore and implement transformer models in MATLAB here:https://github.com/matlab-deep-learning/transformer-modelswith models such as BERT, and GPT-2.

It's important to keep in mind that a small number of researchers are focused on this aspect of AI, while a much larger community is focused on using transformers and other AI architectures to improve the systems we use every day. While transformers are a powerful architecture, they are one of many model architectures that can provide real results for a variety of applications in AI.

对“新闻”的反应

很抱歉成为一个沮丧的人，但是我们周围不存在Sentient AI。

Here's one post I found that used GPT-3 to have a conversation with very… unique characters:https://www.aiweirdness.com/interview-with-a-squirrel/

此外，还有一些研究人员宁愿让人们专注于现实，现实生活中的AI的好与坏。

My recommendation, when faced with technology in the news, is to approach everything with a healthy sense of skepticism and focus not on the outcome, but how that work could relate to, or improve, the work you are already doing.

Why you can still be excited about AI

We don't need to sensationalize AI for the technology to be useful. True, AI might not walk among us, but it is solving real problems. Remove the hype from AI by being mindful of statements such as, "AI can exceed human accuracy". Is this true? Maybe not. Regardless, it distracts from the reason you should consider using deep learning and machine learning techniques in your work.

这对工程师意味着什么？

与往常一样，让我们​​将其带回工程师，以及我们可以从这个故事中夺走的三件事。

1. Focus on the tasks in which AI can (actually) help。这里有两个例子的人工智能用于真实,practical applications:

使用AI模拟进行计算流体动态求解器：link to story		使用神经网络在医学成像中诊断：link to story

2. Focus on AI results in addition to accuracy.Keep in mind fairness and偏见：越来越多的工程师和科学家正在关注解释性技术，以帮助解释他们的工作。解释性和可解释性都是帮助确保AI创建的概念，而不会对数据的特定功能进行隐式和明确的偏见。
   Also, track your experiments to replicate results: I've mentionedExperiment Managerbefore, but being able to replicate and prove your results is essential to AI project success.
   

   在此处观看有关MATLAB的可视化的快速视频：https://www.youtube.com/watch?v=qmbywijvea

3. critical
   • Be wary of "super-human" results. AI that "exceeds human level accuracy" may not be an accurate statement, and if you are looking to use AI to simply reach super-human levels, you may be disappointed in the results. Be mindful of who is making these claims and bring it back to the problem at hand: What are you trying to accomplish, and how will AI help you?
   • 小心未来派的承诺。诸如“我们不在那里的陈述然而“承诺将最终到达的未来世界。我们应该避免对应该为科幻小说保存的未来自然机器人的漫长而持续的辩论。未来的AI承诺分散我们今天生活的世界，AI可以帮助您的帮助在许多不同的应用中解决当前问题。