Current Trends in AI

作者Johanna Pingel，，，，April 15, 2022

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这篇文章来自客座博客Paul Pilotte，产品营销，AI和数据科学以及David Willingham，产品营销，深度学习

自从我们追踪AI Trends for 2021 a year ago。While some industries have cut back on AI investment in the near term, others have increased their investment above what they had forecasted, offsetting the loss. Many are using this time to invest in upskilling through remote learning, with AI-themed courses among the most sought after by the engineering and scientific community. The goal is to make sure they are primed and ready to take on more AI projects in 2022.

这是AI内部的4个当前趋势。

1	Collaboration between frameworks

New trend materializing for 2022

TensorFlow，Pytorch和Matlab是三个不同的深度学习框架，您可以用来开发AI模型。当开发包括AI在内的较大系统时，可以面对一个问题：使用哪个框架？选择一个变得复杂，尤其是如果没有单个框架无法满足所开发的系统的所有要求：工程团队可能希望在MATLAB中使用数据预处理应用程序，并重用在TensorFlow中创建的预处理的AI模型。满足这些要求的解决方案是使框架相互互操作。

在2021年之前，互操作非常复杂，并且经常涉及一定程度的时间密集型手动重新编码。但是，2021年看到了互操作性进步的成熟，例如直接TensorFlow Converter for MATLAB，，，，which has reduced the need for manual recoding,opening up more interoperability workflows。在2022年，我们通过理解，采用和扩大对互操作性功能的支持，预测Tensorflow，Pytorch和Matlab之间的更多协作。万博1manbetx

2	AI aligns Engineering and IT

2021预测，持续在2022年及以后

需要通过使AI模型进入生产是一个主要主题，这是2021年的主要主题。这不仅证明了AI模型的潜力，而且还意识到AI模型的价值是生产作为工程系统的一部分。这个空间中的几乎每位分析师都回应了这一势在必行。2021年还标志着将DEVOPS纳入AI模型的增长趋势的一年，以便IT和OT团队可以将AI模型在操作系统中的部署，监视和生命周期的所有权。我们希望这一趋势在2022年继续加速，这是许多公司数字化转型计划的推动力。如果您尚未参与开发AI模型和负责运营系统的生产团队之间的研发团队之间的密切合作，那么您可能会在2022年。

请参阅此趋势的例子：Korea Institute of Energy Research Develops AI-Based Predictive Maintenance Models for Offshore Wind Power

3	无代码 /低代码

A trend that materialized in 2021; we expect this trend to continue in 2022 and beyond

As the benefits of applying AI is becoming increasingly realized, many of the common techniques and workflows are being commoditized by vendors through no-code, low-code and auto-code workflows for AI modeling. The benefits of this are vast, first it broadens the community of AI users from a few data science and coding experts to a larger audience of people who have the domain knowledge to know where to apply AI, but not necessarily the coding skill set. Secondly it increases efficiency and scale through automated workflows.

Like the data-centric AI movement, no-code/low-code/auto-code is not a new trend. For example, for over 20 years engineers have been using MATLAB and Simulink to auto-generate or auto-code, production quality code for embedded systems.

Here are highlights of the use of no-code/low-code, auto-code by engineers using MATLAB and Simulink:

无代码-三井化学品创建和部署了无代码应用程序用于自动检查其制造生产线上的板形产品。s manbetx 845现在，多个工程师正在使用该应用程序，将视觉检查时间减少多达80％。

自动代码 -NXP Eindhoven自动生成神经网络的HDL代码进行电力研究在ASIC上运行模型时。和Elektrobit用于部署A的自动化C/C ++代码驾驶员疲劳监测系统使用对象检测。

与无代码/低编码在更广泛的编程社区中普及 we expect this trend to only grow for AI in 2022.

4	Even more AI在工程和科学中

2021年预测，持续在2022年

We predicted that in 2021 AI would be one of the first tools engineers and scientists look to for innovative solutions in solving problems and building applications.

有许多例子是AI被用来完全替换或增强数学，物理和工程学科（例如控制和信号处理）方面的传统技术。

引擎已经变得更加成熟,number of systems and parts to be analyzed has increased significantly, Daihatsu turned to using AI to judge the level of knocking sound, which was previously only done by skilled workers. They needed a tool that could combine deep learning and acoustic analysis. Using machine learning and feature extraction, Daihatsu engineers easily created classification models, making it possible to examine feature values multiple times in a short period. As a result, Daihatsu developed AI that can judge the knocking sound with the same accuracy as skilled workers.

For more on this trend, read the story: 日本的Daihatsu Motor Co使用AI对发动机声音进行了分类。