Artificial Intelligence (AI) and Machine Learning (ML)

We are about to enter very exciting times when combining Artificial Intelligence and Machine Learning capabilities into the custom-made Android apps we develop for clients.

Android Neural Networks API
Released in November 2017, the Android Neural Networks API (NNAPI) is used to perform demanding, in-depth, data-processing activities for Machine Learning on smartphones and tablets and contributes a foundation for powerful Machine Learning frameworks like TensorFlow Lite which instruct and teach neural networks. The API is available on smartphones and tablets running Android 8.1 (API level 27) or higher.

NNAPI aids "inferencing" (i.e. deduction, the steps needed to move from assumptions to conclusions) by applying data from Android devices to previously worked out models such as categorising images, forecasting user behavior and providing pertinent responses to a search query.
Android Neural Networks API

TensorFlow Lite API
TensorFlow™ is an open source software library for high-end computation developed following many years of work by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organisation and is the AI technology which powers Google Photos to Google Cloud Speech. Released on 14 November 2017 as a "developer release" (i.e. bleeding-edge but work in progress), the TensorFlow Lite API allows Android apps on smartphones to accomplish Machine Learning.

Machine Learning will change the computing landscape radically over the coming months and years, especially as users become accustomed to voice operated intelligent personal assistants such as Amazon Alexa/Echo and Google Home where thay have human-like interactions with their devices via the camera and voice interaction.
TensorFlow Lite API

Google Cloud Machine Learning Engine
Google Cloud Machine Learning Engine allows us to create machine learning models that work on all types of data with no size restrictions. It can take any TensorFlow model and perform large scale training (i.e. the software learns from the data) and support thousands of users and many terabytes of data (a terabyte = 1,024 gigabytes).

The service is integrated with Google Cloud Dataflow for pre-processing, allowing us to access data from Google Cloud Storage and Google BigQuery should we wish to. Basically it allows us to harness the power of Google's global data centres and networks to perform high-end Machine Learning which was previously only available to the likes of massive research organisations such as CERN (European Organization for Nuclear Research) when analysing data from the Large Hadron Collider to confirm the existence of the Higgs boson.
Google Cloud Machine Learning Engine

Democratising Deep Learning
A big step towards making deep learning accessible to everyone as deep machine learning transitions from an area of research to mainstream software engineering.

For example, a video is simply many individual frames stuck together and by passing say a video from a smartphone app to TensorFlow Lite, this can break out each video frame as a single image and from each image it can break out each element as numeric data points and then compare those in real-time to millions of stored data points in pre-compiled models to tell what each frame of the video contains and what the video is about. The opportunities are limitless for enterprise apps (i.e. create an Android app to take frequent photos or video of your factory production line and any products not meeting quality control guidelines could be flagged and an action taken instantly).

TensorFlow without a PhD

How to construct and train a neural network to recognise handwritten digits.

TensorFlow on Android

Implement a machine learning model that is tailored to a custom dataset.

TensorFlow Models

The latest open source TensorFlow models for image classification.



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