Artificial intelligence (AI) can substantially improve our understanding of the climate and the Earth system, according to a study. Complex dynamic processes such as hurricanes, fire propagation, and vegetation dynamics can be better described with the help of AI, said researchers from the Friedrich Schiller University Jena (FSU) in Germany. As a result, climate and Earth system models will be improved, with new models combining AI and physical modelling, according to the study published in the journal Nature. In the past decades mainly static attributes have been investigated using machine learning approaches, such as the distribution of soil properties from the local to the global scale. For some time now, it has been possible to tackle more dynamic processes by using more sophisticated deep learning techniques.

Deep learning may need a new programming language that's more flexible and easier to work with than Python, Facebook AI Research director Yann LeCun said today. It's not yet clear if such a language is necessary, but the possibility runs against very entrenched desires from researchers and engineers, he said. LeCun has worked with neural networks since the 1980s. "There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it's not clear at all that the community will follow, because people just want to use Python," LeCun said in a phone call with VentureBeat. "The question now is, is that a valid approach?"

The revolution that is artificial intelligence (AI) is happening, but it is perhaps a bigger learning curve than most would imagine. We're still talking about a learning handicap that requires the input of unbelievably huge volumes of data, the tricky subject of contextual thought and hackable security issues. Still, it's a revolution in the making that will touch every single industry in the world, and potentially change nearly every aspect of human life--eventually. In mid-December, IBM released what it calls its "sneak-peek" into the future of AI, and its work in advancing, trusting and scaling artificial intelligence. If there's going to be a revolution, we have to start looking at new frontiers for AI.

One of the most exciting areas of deep learning is computer vision. Through recent advances in convolutional neural nets we have been able to create self driving cars, facial detection systems and automated medical imagery analysis that out performs specialists just to name a few. In this article I will show you the fundamentals of convolutional neural nets and how you can create one yourself to classify hand written digits. Unlike many fields of deep learning which are hyped to the public to seem like they are replications of biological functions in the human brain, convolutional neural nets come very close. Back in 1959, David Hubel and Torsten Wiesel conducted expirements on cats and monkeys which gave important revelations of how the visual cortex functions. What they found was that many neurons have a small local receptive which only react to small finite areas of the total visual field.

Amazon on Wednesday announced the Amazon ML Solutions Lab, a new program that connects Amazon Web Services (AWS) customers with Amazon's own machine learning experts. Through boot camps, workshops, advisory professional services and other forms of programming, the program is designed to help Amazon's cloud customers figure out how to leverage machine learning. Competing cloud providers, from Microsoftto Oracle, have for some time touted their capabilities in AI and machine learning as a key value proposition as they try to make a dent in Amazon's market dominance. Amazon's response has been to point to its own internal use of machine learning. "Amazon has been investing in machine learning for more than 20 years, innovating in areas such as fulfilment and logistics, personalization and recommendations, forecasting, fraud prevention, and supply chain optimization," Vinayak Agarwal, a senior product manager for AWS Deep Learning, wrote in a blog post about the new service.

As a Research Scientist at OpenAI, you'll be responsible for analyzing the AI policy landscape; developing and defining OpenAI's policy positions; representing those policy positions in public and private forums; and engaging with relevant stakeholders and information sources to further your own technical understanding of artificial intelligence. This role offers candidates the chance to define and shape OpenAI's policy activities and affords them considerable latitude in how they approach the role. There will also be ample opportunities to collaborate with OpenAI's other teams, including the AI Safety team, to craft technically-informed policy. OpenAI has a range of policy interests relating to AI which include (but are not limited to): forecasting the rate of progress of AI technologies, analyzing how malicious actors may re-purpose AI, understanding how AI might change the geopolitical landscape, and exploring how AI will alter the makeup of the economies it is deployed into.

VivaLNK, a connected healthcare technology vendor, has introduced its Internet of Things-enabled medical wearable Sensor Platform, which comes with a range of sensors, edge computing technologies and an "Internet of Health Things" data cloud. This platform captures human vitals and biometrics, and delivers data from the patient to edge computing devices, as well as to the cloud, for application integration and analysis. Available through the VivaLNK Developer Program, the Sensor Platform enables IoT technology partners to capture streams of patient data such as heart and respiratory rates, temperature, ECG rhythms, activity and more. Partners such as Vitalic Medical, a digital health vendor specialising in the early detection of patient health deterioration and potential falls, is developing a bedside monitoring system using the platform. "Our growing aging patient population, rising complex health conditions and increasing staff workloads make it challenging for medical professionals to detect early signs of patient deterioration and prevent falls," Sue Dafnias, CEO of Vitalic Medical, said.

Reach multiple minimas to create a powerful ensemble or just to find the best one using Cyclical Learning Rates with Decay. Ideally decay milestones should intersect with cyclical milestones for smooth transition as shown below. Can be used with any optimizer such as Adam. Sample - (follow similarly for CyclicLinearLR) milestones specifies when learning rate should shoot back up and decay_milestones when learning rate should be decayed. It can be called even in every batch.

A quick search will reveal the intensity of this clash of frameworks. Here is one great article by Kirill Dubovikov. At its core, the duel is fuelled by the similarity of the two frameworks. Taking all of this into account, we can say that almost anything created in one of the frameworks can be replicated in the other at a similar cost. At /Data, we are constantly surveying the developer community to track the trends and predict the future of different technology sectors.

Configuring neural network models is often referred to as a "dark art." This is because there are no hard and fast rules for configuring a network for a given problem. We cannot analytically calculate the optimal model type or model configuration for a given dataset. Fortunately, there are techniques that are known to address specific issues when configuring and training a neural network that are available in modern deep learning libraries such as Keras. In this crash course, you will discover how you can confidently get better performance from your deep learning models in seven days.