In his session at 21st Cloud Expo, James Henry, Co-CEO/CTO of Calgary Scientific Inc., introduced you to the challenges, solutions and benefits of training AI systems to solve visual problems with an emphasis on improving AIs with continuous training in the field. He explored applications in several industries and discuss technologies that allow the deployment of advanced visualization solutions to the cloud. Speaker Bio James Henry is Co-CEO/CTO of Calgary Scientific Inc., a company specializing in bringing real time interactive software to cloud and mobile platforms. He has 25 years of experience leading software teams in many industries including the oil and gas, healthcare, telecommunication, geolocation, construction and simulation industries. His current interest is in enabling people, data and AIs to interact in real time to solve complex problems.

A new report from the Washington-based Center for a New American Security raised the alert level of the US defence community over China's rise as an artificial intelligence (AI) superpower, one that could effectively destroy the American military by 2030. The meticulous report no doubt will send a chill through the halls of the Pentagon. Kania, as co-founder of the China Cyber and Intelligence Studies Institute, is well suited to write the investigative report using available Chinese-language open-source materials that reveal China's military thinking and progress on AI. Kania reported that China's military is pursuing advances in'impact and disruptive military applications of AI' and given it'high-level priority within China's national agenda for military-civil fusion'. The goal is to become the world's'premier innovation centre' in AI by 2030.

Machine learning projects are favorably accepted, as they were either the pioneers to providing specific niche services, or they have provided a large range of required services to users. Despite there being many projects, what will work best for you depends upon your machine learning goals - and also on the ecosystem you work in. It is possible the projects you are considering may differ, but they all have the same feature of providing services to a massive number of users. Besides the big machine learning projects, there are several smaller projects that are quite popular, as they provide both flexible and niche services for a smaller number of users. Machine learning is quite expensive. Even if you have all you need, like the toolkit, skills, hardware and data, there is still a process involved in fabricating and fine tuning the machine tooling model.

In the business intelligence (BI) world, more and more companies are talking about machine learning (ML) being leveraged in their software. However, try to talk to them about it and there is silence. Infrastructure vendors, IBM, NVIDIA, Intel, Oracle (remember, that's where Sun went), Qualcomm and more are talking up their chips for ML. Again, try to talk with them about a real business case study, a customer who has implemented a system, and, if you get back anything, you get back anonymous companies described in a paragraph or even just a sentence. On the other hand, the success of Apple Siri and Amazon Echo, the continued growth of Microsoft Cortana Echo, and the entrance of Google Now show that voice recognition is rapidly becoming mainstream in the consumer world.

DeepMind, the artificial intelligence (AI) unit of Google owner Alphabet, is trying to find out whether AIs can learn how to cheat. The research is potentially important because of the fears of many--such as Elon Musk and Stephen Hawking--that AIs could eventually end up turning on us, taking over the world and/or killing us when they get smart enough. Of course, there are plenty of people who think such fears are overblown, or who dismiss the very idea of an "intelligence explosion," but DeepMind clearly thinks the problem is at least worth addressing. According to Bloomberg, it is doing so through a test that involves running AI algorithms in simple, two-dimensional, grid-based games. What we call AI these days is really based on a concept called machine learning, where algorithms can learn how to do things without being shown how to do them--they effectively teach themselves how to evolve, in order to achieve a goal set by their creator.

In an effort to facilitate improvements in recognition accuracy, NEC has developed automatic optimization technology for deep learning. In a statement, the company explains that if deep learning systems become excessively familiar with data, they become unable to accurately recognize data that they have not learned. This "overtraining" results in degradation of recognition accuracy when dealing with data that was not used in the learning process. To prevent overtraining, "regularization" technology is commonly used, which regulates the extent of learning to prevent it from reaching an excessive degree. "This technology predicts the progress of learning at every layer based on the structure of an artificial neural network, and enables regularization to be automatically configured accordingly," said Akio Yamada, General Manager of NEC's Data Science Research Laboratories.

This presents a challenge for its adoption in healthcare. To address this problem, Tokyo, Japan-based Hitachi developed a technology for risk prediction with analyzing the results presented by deep learning and extracting the several dozens of actionable factors for each patient from the vast amount of data collected from heart failure patients. These are elements familiar to clinicians and can support medical decision-making in clinical practice. Through a standard statistical approach based on this risk prediction model, the extracted factors were used to calculate the risk of hospital readmission, and the relevance of the factors was calculated. Thus, this explainable AI technology can enhance prediction accuracy and the quality of medical decision-making.

Since AlexNet took the research world by storm at the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), deep learning has become the go-to method for image recognition tasks, far surpassing more traditional computer vision methods used in the literature. In the field of computer vision, convolution neural networks excel at image classification, which consists of categorising images, given a set of classes (e.g. Nowadays, deep learning networks are better at image classification than humans, which shows just how powerful this technique is. However, we as humans do far more than just classify images when observing and interacting with the world. We also localize and classify each element within our field of view.

Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. "Machine Learning" is just the most recent case in point. It's had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Let's make up our minds. As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives.

The data science tech market is buzzing with new and interesting Machine Learning libraries and tools almost everyday. In an increasingly growing market, it becomes difficult to choose the right tool or set of tools. More importantly, Artificial Intelligence and Deep Learning based projects require a different approach than traditional programming which makes things tricky to zero-in on one library or a framework. The choice of a framework is largely based upon the type of problem, one is expecting to solve. But there are other considerations too.