Pharmaceutical companies are set to benefit from intelligence on asthma as Aerobit, provider of a smart respiratory device, teams with artificial intelligence experts to unlock the potential of big data. To more effectively manage asthma care, Aerobit has introduced a smart inhaler which tracks medication use and records each time an inhaler is taken by an asthma patient. Patients receive reminders and alerts for their prescribed medical regime. Doctors are able to track treatment progress and compliance. In recognising the value of big data, Aerobit has teamed with artificial intelligence experts Eularis, to enable pharmaceutical companies to gain a deeper understanding of patient needs, drug efficacy and adherence, and real world data to drive accelerated growth for asthma drugs.

Deep learning is not as complex a concept that non-science people often happen to decipher. Scientific evolution over the years have reached a stage where a lot of explorations and defined research work needs the assistance of artificial intelligence. Since machines are usually fed with a particular set of algorithms to understand and react to various tasks within a matter of seconds, working with them broadens the scope of scientific breakthroughs resulting in the invention of techniques and procedures that make human life simpler and enriching. However, in order to work with machines, it is important for them to understand and recognize things just the way the human brain does. For example, we may recognize an apple through its shape and colour. For a robot to go through the same cognitive process, it must be fed with programming structures to recognize the same.

This screencast shows how to use Skymind's DataVec to ingest Comma Separated Values from a text file, convert the fields to numeric using a DataVec Transform Process in Spark, and save the modified data. Transforming non-numeric data to numeric data is a key preliminary step to using a Neural Network to analyze the data.

This screencast shows how to use Skymind's DataVec to build an image pipeline to prepare data for processing in a Neural Network using DeepLearning4j. Topics covered include, ParentPathLabelGenerator and Image Scaling.

Tom provides an overview of how to build a simple neural net in this introductory tutorial. This screencast shows how to build a Linear Classifier using Deeplearning4j.

If you've read anything about technology in the news recently you might be inclined to think that Artificial Intelligence (AI) is poised to take over the world, leaving us mere mortals to find new ways to earn a crust. Indeed, the plethora of new services, even turning AI into its own service (AIaaS – AI as a Service), will start to redefine the way we communicate, work, and experience the world. Google's CEO Sundar Pichai has been bold enough to say "In the long run, we're evolving in computing from a'mobile-first' to an'AI-first' world".1The There's a lot that needs to happen before AI can automate entire professions, and we're seeing research that shows that jobs requiring human interaction (thankfully HR is one of these) will be the hardest to replace.2 As with any broad scale changes that affect people's livelihoods and the economies that we rely on for stability, views about the future impacts are many and varied.

Artificial intelligence is expected to transform a wide range of industries, as simple tasks are automated and carried out by machines. The IT sector is no different, with machine learning algorithms increasingly being targeted at automating and improving data centre operations. A notable example has been Google, which recently revealed that it is using its own DeepMind technology to manage power consumption at its huge server farms, reducing the amount of electricity needed by 40 percent. There is also potential for AI technology to automate functions carried out by IT operations teams. Machine learning offers a way to manage infrastructure and react quickly to faults without human intervention.

DeepMind Health, the division of the Google-owned AI company that's focused on building links to healthcare providers to drive the application of machine learning algorithms for preventative medicine, has inked a fresh data-sharing agreement with the NHS Royal Free Hospital Trust in London. It's the second agreement signed between the pair -- and it supersedes their original agreement inked last year, which ran into controversy after a freedom of information request by New Scientist revealed the volume of patient identifiable medical data (PID) flowing from the Royal Free to DeepMind, and raised questions about whether NHS information governance principles were being correctly followed. The data in question was being used to power an app called Streams, built by DeepMind but using an NHS algorithm to generate alerts on patients at risk of Acute Kidney Injury (AKI). At the time the collaboration was made public, last February, no details were provided about how much PID was being shared between DeepMind and the NHS -- leading to huge consternation when the scope of the arrangement emerged. The U.K.'s data watchdog, the ICO, began investigating complaints about the data-sharing agreement. The Streams app also ran into trouble when it was revealed DeepMind and the Royal Free had not registered it as a medical device with the oversight body, the MHRA, despite piloting the app in the Royal Free's hospitals.

On behalf of all at Intel who are focused everyday on supporting you to build, optimize and innovate on Intel architecture, I am proud to announce the launch of Intel Nervana AI Academy. We're at a great moment in the evolution of artificial intelligence (AI) that will open up incredible new experiences fueling the next wave of business opportunity, scientific discovery, and societal improvement. After decades of collectively advancing Intel architecture, we have reached the point that Moore's Law is creating new opportunities for the data science community. We're now supporting artificial intelligence as an enablement technology, through optimized machine and deep learning, providing developers new ways to add value to solutions and applications. Intel is investing heavily in AI and you.

Artificial Intelligence has emerged from the realm of science fiction and become a part of the millennial way of life. The factory of the future is taking shape in Japan and it is being built on an AI platform. Here, robots will learn from each other and every robot will have an embedded GPU to perform real time AI. In 2016, AlphaGo, a computer programme from Google's Deep Mind, had a historic win over the world's most celebrated Go champion, Lee Sedol of South Korea. The same year, Microsoft achieved human parity in speech recognition.