Litbit helps customers turn their "Internet of Things" into conscious personas that can learn, think and do helpful things. Customers can train their AI personas using sight, sound, touch and other sensors to recognize specific situations. Since different customers may be training different AI personas at different times, the training load tends to be bursty and unpredictable. Some of these training jobs (e.g., Spark ML) make heavy use of CPUs, while others (e.g., TensorFlow) make heavy use of GPUs. In the latter case, some jobs retrain a single layer of the neural net and finish very quickly, while others need to train an entire new neural net and can take several hours or even days.

DeepManta, a flexible algorithm, can demonstrate visual object recognition for smart cities, such as identifying vehicles, their type and position and counting them. Different objects, such as miniature cars, move into a camera's field of view, where the AI will selectively recognize them. When a car is recognized, the algorithm generates a visual annotation, labeling the car with the logo of the brand and model information, and encompassing it with 2D and 3D boxes to locate it spatially in the video in real time. In addition to automotive applications, the algorithm's automated perception capacity opens up new services with significant social and business impacts. "DeepManta delivers one of the promises of AI: providing assistance to users by automatizing and parallelizing tasks that normally would require their full attention," says List's Stéphane David,"it excels at each individual task, but requires much less overall memory and processing power than parallel architectures that use one algorithm per task."

Natural language processing (NLP) is a technology spawned from the need for machines to understand and communicate with humans in human language, not formal computer languages. The concept behind NLP is simple: if and when machines can understand and communicate with humans in natural (human) language, it democratizes data science, enabling humans to access, analyze, and leverage data more intelligently and become more efficient as they offload redundant, data-heavy tasks to machines. NLP is most commonly understood as a user interface (UI) technology, enabling two-way communications with computers via speech or text. However, NLP is also a critical technology for extracting insights and analysis from a vast amount of previously unindexed and unstructured data; mining video and audio files, emails, scanned documents, and more. NLP adoption is accelerating, but not because of the creation of new NLP algorithms, as the data science in that regard is mature.

Just as an art buyer parting with almost half a billion dollars must be confident that the work they buy is genuine, and not from a disciple, banks need to be sure that a credit application has been made by a real person and not a fraudster. Credit application fraud is a big problem for banks, and is only getting bigger with the advent of digital loan origination channels. To try and detect fraud, banks give scores to credit applications in order to estimate the likelihood of it being fraudulent. How much better could those estimations be if banks adopted "deep learning"? The answer is "a lot better".

Abstract--Long Short-T erm Memory networks trained with gradient descent and back-propagation have received great success in various applications. However, point estimation of the weights of the networks is prone to over-fitting problems and lacks important uncertainty information associated with the estimation. However, exact Bayesian neural network methods are intractable and non-applicable for real-world applications. In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights. T o assess the proposed algorithm, we apply it to outlier detection in five real-world events retrieved from the Twitter platform. I NTRODUCTION The recent resurgence of neural network trained with back-propagation has established state-of-art results in a wide range of domains. However, backpropagation-based neural networks (NN) are associated with many disadvantages, including but not limited to, the lack of uncertainty estimation, tendency of overfitting small data, and tuning of many hyper-parameters.

One of the greatest benefits of artificial intelligence (AI) to humankind is its influence on the medical field. "Powered by some of the most sophisticated technology, AI is assisting in improving medical diagnosis," says Anton Jacobs, managing director at African value-added technology distributor, Networks Unlimited. From an AI doctor and chatbot to AI's powerful applications, machine learning and deep learning, a world that used to be all about coding, is transitioning into using computer programming to assist in life changing health issues such as early cancer detection. A massive advantage is that AI has the power to pool knowledge from the best specialists worldwide and provide it to patients anywhere geographically. "Imagine what this could mean to patients living in rural areas. They'd finally have the same access to knowledge as patients in top medical facilities," adds Jacobsz.

Christina Cardoza, formerly known as Christina Mulligan, is the Online & Social Media Editor of SD Times. She covers agile, DevOps, AI, machine learning, mixed reality and software security. Follow her on Twitter at @chriscatdoza!

The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn't just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain. That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain. The key words here are "inspired by."

Need help installing packages with pip? see the pip install tutorial The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.

Replacing the doctor with an intelligent medical robot is a recurring theme in science fiction, but the idea of individualised medical advice from digital assistants like Alexa or Siri, supported by self-surveillance smartphone data, no longer seems implausible. A scenario in which medical information, gathered at the point of care, is analysed using sophisticated machine algorithms to provide real-time actionable analytics seems to be within touching distance. Medical practice has so far been largely unchanged by the digital revolution that has disrupted so many other industries, but perhaps artificial intelligence (AI) will provide the improvements in medical care and research promised for so long. At its inception in the 1950s, the central goal of AI research was to produce a system with general intelligence capable of passing the so-called Turing test, the display of intelligent behaviour indistinguishable from that of a human being. Through the past 60 years, the field has experienced several cycles of excitement and disillusionment with seemingly little progress, but since 2010 substantial success has been made in deep learning, producing systems able to learn without having to be explicitly programmed, by building a model from sample inputs.