Deep learning models are beginning to gather steam in the medical community. While they've been hampered in the past, researchers are learning how to properly train them to handle vast amounts of data at once. Equipped with a store of knowledge, these new computer models may soon be able to help people around the world with faster and more accurate diagnosis.

Modern machine learning is arguably the intermediate step between heuristics and artificial intelligence (AI). While AI will be a form of machine learning, the current generation of ideas we use will probably no longer be relevant when a breakthrough happens which allows for true artificial intelligence. Machine learning transcends traditional algorithms and rules when done right because it adapts to patterns which are harder to quantify and qualify. You don't just map out rules, you show the system what you want, have, or let it experience the system and it seemingly learns from it. Modern day machine learning isn't magic (despite what SaaS companies may say), and ironically it isn't even all that intelligent.

The introduction of unexpected system disturbances and new system dynamics does not allow initially selected static system and controller parameters to guarantee continued system stability and performance. In this research we present a novel approach for detecting early failure indicators of non-linear highly chaotic system and accordingly predict the best parameter calibrations to offset such instability using deep machine learning regression model. The approach proposed continuously monitors the system and controller signals. The Re-calibration of the system and controller parameters is triggered according to a set of conditions designed to maintain system stability without compromise to the system speed, intended outcome or required processing power. The deep neural model predicts the parameter values that would best counteract the expected system in-stability. To demonstrate the effectiveness of the proposed approach, it is applied to the non-linear complex combination of Duffing Van der pol oscillators. The approach is also tested under different scenarios the system and controller parameters are initially chosen incorrectly or the system parameters are changed while running or new system dynamics are introduced while running to measure effectiveness and reaction time.

The term'Artificial Intelligence' (AI) is currently trending as a revolutionary new technology that will change the face of business forever. The reality though is that AI is actually not new, and the phrase is being misused to cover a broad spectrum of different concepts. One of those is deep machine learning, which actually is new and fairly revolutionary. It's already being applied by organisations like Amazon and Google to perform complex analytics that previously was impossible. The trouble is, for the majority of businesses, it is just too expensive and too complicated to be of much use.

"Artificial intelligence has firmly entered our daily lives. AI used by adults, kids, teenagers, students in many parts of everyday tasks. It makes our lives easier and it's a fact." For decades, technology has been the centerpiece of invention and innovation, and would probably remain so even in the unforeseeable future. The fact that we can access information anywhere, and at any time, is not something to be taken for granted.

Technology company Continental is opening of a Deep Machine Learning Competence Centre in Budapest in May 2018 and with that adding 100 new jobs in the region. "Artificial intelligence is a core competency in the development of automated driving. We are expanding our expertise in the area of Deep Machine Learning to enable automated driving and to support our Vision Zero – a future without accidents," Karl Haupt, head of Continental's Advanced Driver Assistance Systems business unit, said in a press release. The Budapest Competence Centre for Deep Machine Learning will be integrated in an existing Global Software Factory network with other development locations inside the Advanced Driver Assistance Systems business unit. "More and more technological and development processes are added to the well-prosperous automotive operation at Continental's domestic units. Our high-tech developments create a demand for highly qualified labour, which can be one of the guarantees for the future success and sustainability" – added Daniel Rabai, Head of Focus County at Continental in Hungary.

The linguist refers to Machine Learning and its fixed structure. That means, Deep learning takes out the EXPERT to improve overall performance. Deep Learning provides for an inherently fascinating subject, and one that I just love to geek out upon. In my last LinkedIn post I spoke of Deep Learning in and as it relates to Web Development (you can read that particular blog post here Deep learning and Web development) today however I want to take a look at deep machine learning. Deep Learning has provided for amazing achievements within realms that span from speech perception to object recognition.

Bart Peintner has been closely involved with important developments in artificial intelligence through its recent resurgence. At SRI, one of the world's hotbeds of AI research, he pursued work that pressed the limits of natural language processing and user-behavior modeling. Now, as CTO and co-founder of startup Loop AI Labs, he is furthering the cause of unsupervised machine intelligence -- also known as deep machine learning -- for applications. It's important because teaching machines to do human's work can be labor intensive. When did you start Loop AI Labs, and what was the underlying goal?