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 a while, it is only recently that Deep Learning architectures,
such as deep neural networks, are within reach of specialist app developers. Deep Learning architectures allow 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 has made 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 enterprise apps we develop for UK businesses.
Recognising and distinguishing between different voices within an audio stream when more than one person is talking is very difficult for artificial intelligence (AI).
However, Google has developed a new technique of "Speaking Diarization" which is able to achieve circa 92 per cent precision.
At the end of October 2018 the basic algorithms were made publicly available in open source form so that they can also be used by third-party developers.
We of course took advantage of Google’s largesse and have downloaded what is called their “Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN)” algorithm and
are currently experimenting with it. The potential of this technology is enormous, e.g. the AI being able to follow a doctor-patient conversation.
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.
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.
Some Innovative Uses for Machine Learning & Artificial Intelligence
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?
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.
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. Learn More
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. Learn More
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.
Visualizing memorization in RNNs
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned and what concepts it typically represents.
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.
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.
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