Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?

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A trio of researchers from Chonnam National University, Nanjing University of Information Science and Technology and the Chinese Academy of Sciences has found that a deep learning convolutional neural network was able to accurately predict El Niño events up to 18 months in advance. In their paper published in the journal Nature, Yoo-Geun Ham, Jeong-Hwan Kim and Jing-Jia Luo, describe their deep learning application, how it was trained and how well it worked in predicting El Niño events. El Niño-Southern Oscillation events are periods during which water warms above normal temperatures in tropical parts of the Pacific. When that warm water moves east, it leads to more rainfall and other weather events, such as hurricanes, in the Americas, and less rain in Australia and Indonesia. Current models can accurately predict such events using data from water temperature gauges spread across the globe up to a year in advance.

Having first appeared on the scene of machine learning in 1986 and artificial neural networks in 2000, the study of deep learning continues to explode with new research, advanced techniques, higher benchmarks, and broader applications. Keeping pace in such an active field with an average of 30 new deep learning papers uploaded to arXiv per day over the previous month is daunting, to say the least. While there are many key deep learning scientists and engineers active today, the following list of 12 researchers and innovators in the field are among the most important – and they so happen to actively share on social media, making their progress and insights much easier to keep up with. So, start paying attention to these 12 top deep learning individuals, and be prepared to expand your understanding and awareness of the incredible advancements deep learning is bringing to science, industry, and society. While it in no way correlates to everyone's contribution to the field, the list is sorted by the number of Twitter followers so you can see who appears to have the most reach today.

This code pattern is part of the Getting started with PowerAI Vision learning path. After a deep learning computer vision model is trained and deployed, it is often necessary to periodically (or continuously) evaluate the model with new test data. This developer code pattern provides a Jupyter Notebook that will take test images with known "ground-truth" categories and evaluate the inference results versus the truth. We will use a Jupyter Notebook to evaluate a PowerAI Vision image classification model. You can train a model using the provided example or test your own deployed model.

As the application of artificial intelligence (AI) in the field of drug development increases, market growth is greatly favored. Artificial intelligence (AI) is called engineering and science adopted to design intelligent machines, such as intelligent computer programs. A system that applies multiple human intelligence-based functions, such as learning, reasoning, and problem-solving skills in areas such as computer science, biology, linguistics, mathematics, and engineering. Artificial intelligence is regarded as the next boundary of medical innovation. Healthcare's AI is implemented to align structured and unstructured data.

A Google research team has recently created an artificial intelligence (AI) system that can detect 26 different skin diseases with the same accuracy as a licensed dermatologist. This deep learning technology evaluates images and metadata, such as self-reported symptoms and demographic information, to generate a ranked list of possible diagnoses just as a trained professional would. The Google team's findings were covered in a paper titled "A deep learning system for differential diagnosis of skin diseases" and in a blog post penned by, Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health. With nearly 2 billion patients having some form of skin condition globally and many areas lacking dermatologists, patients must often take such concerns up with their primary care physicians. Research has shown that while dermatologists diagnose these skin conditions with accuracies between 77-96%, the general practitioner does so with only 24-70% accuracy.

VBTansform 2019, was a largest AI conference (the AI event of the year) held at San Francisco, CA by VentureBeat Magazine. Accenture Chief Data Scientist, Dr. Ganapathi Pulipaka attended and joined 900 AI executives and practitioners (director-and-above C-Suite) from innovative brands with leading best practices and spoke along with other 120 speakers with disruptive emerging companies who presented more than 48 sessions. Several exhibitors from top tier brands like Accenture, Google, Verizon, IBM, Amazon, Cisco, Oracle, New York University, Microsoft, Uber, Data Robot, Intel, eBay, Johnson and Johnson, GE, Gap, Lyft, Etsy, Kohl's, New York Times, Amazon and many more brand speakers showcased their AI products and presented stories about real business results with their production strategies around the deployment with specialists in this area with practical lessons from their deployments and took the audience on a journey of disruptive AI technologies to keep an eye on. The sessions focused on six AI trends on natural language processing, smart speech, computer vision, Business AI integration, implementing AI across organization, IoT and AI at the Edge, intelligent RPA and automation. Reinforcement learning has been disruptive and the history of AI has showed that it took the gaming industry by storm.

People have been wondering whether machines may become intelligent since programmable computers were conceived. Today, Artificial intelligence is a hot field under active research with many applications in imaging, natural language processing (NLP), medical diagnosis, labor automation and even basic scientific research due to the success of machine learning (ML) especially the latest deep learning in such fields. The event will focus on the topic of ML in NLP. Dr. Hui Dai will talk about why we do NLP, the advances of ML in NLP and the future of MLNLP. Dr. Hui Dai got his PhD in physics from University of Chicago.

Researchers and entrepreneurs from the University of Toronto are set to showcase their innovative work to a global audience during Elevate 2019, Canada's largest technology and innovation festival. The week-long event, which kicks off Friday, features hundreds of speakers and is expected to draw tens of thousands of attendees. U of T will play a central role thanks to its contributions to Toronto's thriving technology ecosystem and research that underpins key advances in fields like artificial intelligence (AI) and precision medicine. At this year's festival, U of T experts will be featured prominently at Elevate AI, a day-long program on Sept. 25 devoted to conversations around AI research, applications and commercialization held at the MaRS Discovery District. The scheduled speakers include Brendan Frey, the founder and CEO of Deep Genomics, which is using AI to build life-saving genetic therapies.