If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
TensorFlow has a large ecosystem of libraries and extensions. If you're a developer, you can easily add them into your ML work without having to build new functions. In this article, we will explore some of the TensorFlow extensions that you can start using right away. To start, let's check out domain-specific pre-trained models from TensorFlow Hub. TensorFlow Hub is a repository with hundreds of trained and ready-to-use models.
The traditional method of training AI models involves setting up servers where models are trained on data, often through the use of a cloud-based computing platform. However, over the past few years an alternative form of model creation has arisen, called federated learning. Federated learning brings machine learning models to the data source, rather than bringing the data to the model. Federated learning links together multiple computational devices into a decentralized system that allows the individual devices that collect data to assist in training the model. In a federated learning system, the various devices that are part of the learning network each have a copy of the model on the device.
Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones. So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we'll seek to answer in this look at a technique known as federated learning. The traditional process for training a machine learning model involves uploading data to a server and using that to train models.
TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. By eliminating the need to collect data at a central location, yet still enabling each participant to benefit from the collective knowledge of everything in the network, FL lets you build intelligent applications that leverage insights from data that might be too costly, sensitive, or impractical to collect. In this session, we explain the key concepts behind FL and TFF, how to set up a FL experiment and run it in a simulator, what the code looks like and how to extend it, and we briefly discuss options for future deployment to real devices.
A new computing tool developed by Google will let developers build AI-powered apps that respect your privacy. Google on Wednesday released TensorFlow Federated, open-source software that incorporates federated learning, an AI training system. It works by using data that's spread out across a lot of devices, such as smartphones and tablets, to teach itself new tricks. But rather than send the data back to a central server for study, it learns on your phone or tablet itself and sends only the lesson back to the app maker. Federated learning runs "part of the machine learning algorithm right next to where the data is on the device," Alex Ingerman, a product manager at Google Research, said in an interview.
TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. For example, FL has been used to train prediction models for mobile keyboards without uploading sensitive typing data to servers. TFF enables developers to use the included federated learning algorithms with their models and data, as well as to experiment with novel algorithms. The building blocks provided by TFF can also be used to implement non-learning computations, such as aggregated analytics over decentralized data.