Artificial Intelligence and Machine Learning

We can provide cutting-edge Artificial Intelligence & Machine Learning capabilities to bespoke Android and web apps!


Whilst Artificial Intelligence and Machine Learning have been around for many years it is only in the last year or so that Deep Learning architectures, such as deep neural networks, are within reach of specialist enterprise app developers. Deep Learning architectures allow mobile app developers to integrate Machine Learning (i.e. allowing apps to use data and then learn for themselves) into low-cost bespoke apps. Think of Machine Learning as teaching applications to think like humans whilst being far better than humans with regard to speed of computation and eliminating human error.

We have been testing the use of artificial intelligence and machine learning for our bespoke apps and we are simply blown away by what we can achieve in the sphere of image recognition by training deep convolutional neural networks with machine learning algorithms. Google’s Artificial Intelligence unit (Google Brain) has hade available powerful Machine Learning frameworks like TensorFlow (an open source software library for numerical computation) which are causing a major paradigm shift in the way in which we can give "intelligence" to the bespoke Android enterprise apps we develop for UK businesses. A couple of high profile uses of Machine Learning and Tensorflow can be seen below.

Machine Learning algorithm learns chess in 4 hours!
An algorithm developed by Google’s Artificial Intelligence unit, DeepMind, was able to beat world-leading specialist chess software Stockfish 8 just four hours after being given the rules of chess and being told to learn by playing simulations against itself. "Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case." Read the paper published by Cornell University on 5 December 2017 for more information.
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Machine Learning with NASA's Kepler data finds solar system similar to ours!
NASA’s Kepler Space Telescope observed hundreds of thousands of stars between 2009 and 2013 with the aim of identifying planets of a similar size to Earth which were orbiting sun-like stars. Such planets could possibly sustain life and therefore be populated by alien species similar to our own. Engineers at Google Brain trained a deep convolutional neural network using data from the Autovetter Planet Candidate Catalog at NASA's Exoplanet Archive. They then fed the Kepler Space Telescope non-transiting planets data into TensorFlow (an open source software library for numerical computation for machine learning). This then identified two new planets with a high degree of confidence. One planet adding to the five already known planets orbiting Kepler-80 and the other planet adding to the seven already known planets orbiting Kepler-90 making Kepler-90 similar to our solar system (i.e. a sun orbited by 8 planets). The combination of the Harvard College Observatory (HCO) and the Smithsonian Astrophysical Observatory (SAO) is known as the Harvard-Smithsonian Center for Astrophysics (CfA) and they published a very interesting paper on this discovery in December 2017. We have included a link to the actual paper on Harvard-Smithsonian's server.
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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

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

Some Innovative Uses for Machine Learning & Artificial Intelligence

Assisting Pathologists in Detecting Cancer with Deep Learning
Investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow.

Understanding Medical Conversations
Could the voice recognition technologies already available in Google Assistant, Google Home, and Google Translate be used to document patient-doctor conversations and help doctors and scribes summarize notes more quickly?

DeepVariant: Highly Accurate Genomes With Deep Neural Networks
The open source release of DeepVariant, a deep learning technology to reconstruct the true genome sequence from HTS sequencer data with significantly greater accuracy than previous classical methods.

Closing the Simulation-to-Reality Gap for Deep Robotic Learning
Simulating many years of robotic interaction is quite feasible with modern parallel computing, physics simulation, and rendering technology. Moreover, the resulting data comes with automatically-generated annotations, which is particularly important for tasks where success is hard to infer automatically.


Machine Learning News

Please find below links to recent articles published by Google's "Distill: Supporting Clarity in Machine Learning" initiative which is a new open science journal and ecosystem supporting human understanding of machine learning.

The Building Blocks of Interpretability
Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them -- and the rich structure of this combinatorial space.

Using Artificial Intelligence to Augment Human Intelligence
By creating user interfaces which let us work with the representations inside machine learning models, we can give people new tools for reasoning.

Sequence Modeling with CTC
A visual guide to Connectionist Temporal Classification, an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems.

Feature Visualization
How neural networks build up their understanding of images.

Why Momentum Really Works
We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.

Research Debt
Science is a human activity. When we fail to distill and explain research, we accumulate a kind of debt...

Experiments in Handwriting with a Neural Network
Several interactive visualizations of a generative model of handwriting. Some are fun, some are serious.

Deconvolution and Checkerboard Artifacts
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts.

How to Use t-SNE Effectively
Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading.

Attention and Augmented Recurrent Neural Networks
A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it.


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