Democratizing machine learning will transform IoT
即使是几年前，运行机器学习在电池供电，无线设备的无线设备对于嵌入式工程师来说是极其困难的。主要问题之一是机器学习需要博士计算机和数据科学背景。
今天，这些限制不再存在，由于无线物联网筹码和缩小版本的机器学习，称为微小机器学习或Tinyml的缩小版本。
虽然无线物联网设备通常被描述为过去限制资源，但是今天不能在行业的前沿真正应用相同的标签。例如，在蓝牙行业中，嵌入式双核处理器可以在大量闪存和RAM的高达128MHz上运行。RAM对于TinyM1应用特别有用。
In machine learning, optimized neural networks can be shrunk down to run on the small computing resources. Reducing a high-level, computationally intensive AI ormachine learning training modeldown into a TinyML version makes it possible for organizations to apply it to real-world IoT sensor-based applications. IoT applications focus on three key sensing areas: pattern and anomaly recognition in voice and audio, vibration, and vision in the form of digital images and video.
简单地说：机器学习已经成为可访问to all in wireless IoT because of abstracting the machine learning complexity away from the end user, such as through graphical data representations.
Why is this such a big deal?
凭借Tinyml，制造商可以介绍本地智能和决策几乎any product or application。Doing any kind of AI or machine learning before this technology meant sending vast amounts of data up to the cloud for analysis. The power required for data analysis in the cloud tended to rule out battery operation, and the cloud was also very costly because it involved powerful web servers.
然而，在边缘的机器学习，只发送必要的数据，例如警报，直到云。即使对于连续监测应用，应用程序也可以在深度睡眠模式下，大部分时间保存电池并延长电池操作。
无数产品和applications can be made 10 to 100 times smarter以前没有考虑或以商业可行的方式更有用。
尽可能多地在边缘进行数据处理也意味着可以实现更大的安全性和隐私
The smart home
智能家庭提供了一个应用程序的示例，其受益于Tinyml。
The smart home is a mess。竞争系统太多与彼此不相容，智能家居设备太难以设置。几乎所有的设备也要求您下载应用程序，这是一个无限制的应用程序所提供的功能。应用程序只是凌乱地混乱了用户的智能手机。
The smart home industry has recognized this problem in the form of the连接在IP上(CHIP) initiative, an open standard that will allow full compatibility between various smart home products.
However, the use machine learning is how we will finally see the smart home fulfilling on its potential. Machine learning will make setting up and using smart home devices not only simple and indispensable in their usefulness.
智能锁将超越基于应用的手动控制，基于家庭的实际生活模式几乎自动自动控制。如果您忘记锁定前门时通常会锁定它，则锁将锁定自身。它将在您每天带狗散步时解锁。它甚至可以在通过打开外部门而是锁定内部以防止进入家庭时暂时访问临时访问包裹。
最重要的是，智能家庭必须自己智能，而不会将数据发送到云以获取决策。
Machine learning for the masses
With machine learning, IoT will become an indispensable part of everyday lives, both private and professional.
最终用户不一定关心机器学习细节，但它们将关心毫不费力，直观的交互，无缝智能和有用操作方面的结果。许多设置和维护任务will be eliminated， 也。
Thousands ofTinyML-powered applicationsand products will emerge over the coming years. And these will all work together to make the world a bit safer, more comfortable, more efficient, less prone to breakdown and delays, more economical in its consumption of scarce resources and less wasteful.
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