From virtual reality police training programmes to gun-toting drones and iris scanners, a public security expo in China showed the range of increasingly high-tech tools available to the country's police. The exhibition, which ran Tuesday to Friday in Beijing, emphasised surveillance and monitoring technology just as the Communist government's domestic security spending has skyrocketed. Facial-recognition screens analysing candid shots of conference attendees were scattered around the exhibition hall, while other vendors packed their booths with security cameras. From virtual reality police training programmes to gun-toting drones and iris scanners, a public security expo in China showed the range of increasingly high-tech tools available to the country's police. More innocuous applications, like smart locks for homes and big data applications to reduce traffic congestion, also occupied large swathes of the conference.

Lifelong learning can be viewed as a continuous transfer learning procedure over consecutive tasks, where learning a given task depends on accumulated knowledge -- the so-called knowledge base. Most published work on lifelong learning makes a batch processing of each task, implying that a data collection step is required beforehand. We are proposing a new framework, lifelong online learning, in which the learning procedure for each task is interactive. This is done through a computationally efficient algorithm where the predicted result for a given task is made by combining two intermediate predictions: by using only the information from the current task and by relying on the accumulated knowledge. In this work, two challenges are tackled: making no assumption on the task generation distribution, and processing with a possibly unknown number of instances for each task. We are providing a theoretical analysis of this algorithm, with a cumulative error upper bound for each task. We find that under some mild conditions, the algorithm can still benefit from a small cumulative error even when facing few interactions. Moreover, we provide experimental results on both synthetic and real datasets that validate the correct behaviour and practical usefulness of the proposed algorithm.

As the industry continues to shift into value-based care, many organizations are leveraging new technology to support care delivery. But new technology requires a change in how care is provided, which should begin in medical school and continue throughout a clinician's career. "Outcomes and staff retention are driven, in part, by providing access to lifelong learning to advance skills and knowledge," said Cathy Wolfe, Wolters Kluwer health learning, research and practice CEO and president. "Advanced technologies like machine learning, artificial intelligence and virtual simulation are transforming adaptive learning models in ways that optimize learning and improve knowledge retention," she added. As a result, many healthcare organizations are investing in staff development to support evidence-based care, which can improve outcomes, reduce care variability and help with high reimbursements, Wolfe explained.

The fundamentals issues in Deep Learning are access to data, processing power and data scientists (i.e. But there are four more fundamental things that set companies apart. In this 10min presentation you will learn what the main challenges in Deep Learning are. For the next part of the tutorial, see a live example of running a TensorFlow MNIST example on Valohai: https://www.youtube.com/watch?v L5CcJ...

Algorithmic bias, or the notion that human biases can be magnified when consciously or subconsciously programmed into algorithms, has been a hot topic in machine learning. How do we create "fair" algorithms that behave in as unbiased a manner as possible? Google has released a free 60-minute online course on fairness as part of its popular Machine Learning Crash Course. This includes a short video lecture, materials on types of bias and how to identify and evaluate for bias, and a programming exercise to put your learning into action. Even if you are not a software engineer, you are a consumer of the fruits of their work and it behoves all of us to educate ourselves on how they function (and malfunction).

Recent developments such as Virtual and Augmented Reality as well as the introduction of gamification in corporate learning are changing the face of training. The challenge still remains to engage and entertain as well as teach in an environment that is harder to control by L&D professionals. With corporate education becoming almost entirely learner-centric, the solution I was advocating in one of my previous articles is personalized adaptive learning. It makes sense in the context of things and studies already support its benefits. However, there is some effort to be put in development and implementation so companies may not jump at the idea.

Google loves machine learning (ML). Now, it's launched a new course module that aims to help you, a human, recognize your own bias before training ML models. Named'Fairness,' the course is 70 minutes on how humans are compromising machine learning models. As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected by a model's predictions) in the forefront of their minds. Additionally, they should proactively develop strategies to identify and ameliorate the effects of algorithmic bias.

Hospitals are on the radar of hackers as "soft" and valuable targets. The modern medical facility is connected to the internet in a multitude of ways. These connections include email clients, multi-location data integration systems, medical devices, and off-premise vendor support; all which leave hospitals and clinical networks extremely vulnerable to attack.

During the first phase of this program, students take Udacity's "Introduction to Deep Learning with PyTorch" course. The duration of this course is two months. Program participants will receive support from community managers throughout their learning experience in this course, and will be part of a dynamic student community and network of scholars. The top 300 students from the first phase of the program will earn a full scholarship to Udacity's Deep Learning Nanodegree program, where they'll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. Students will use PyTorch, and have access to GPUs to train models faster, as they learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask.

Cloud OnBoard Big Data and Machine Learning is a free, full-day training event that will provide you with a detailed view of Google Cloud Platform's data processing and machine learning capabilities. Through a combination of instructor-led presentations and demos, you will learn how to leverage the ease, flexibility, and power of data and machine learning tools like Cloud Dataproc, Dataflow, Machine Learning APIs, Tensorflow and BigQuery. Who should attend this event? The topics that will be discussed at Cloud OnBoard Big Data and Machine Learning are best-suited for data analysts, data scientists and business analysts. This event will provide powerful insights if you're responsible for: