Today's tutorial is on saliency detection, the process of applying image processing and computer vision algorithms to automatically locate the most "salient" regions of an image. In essence, saliency is what "stands out" in a photo or scene, enabling your eye-brain connection to quickly (and essentially unconsciously) focus on the most important regions. For example -- consider the figure at the top of this blog post where you see a soccer field with players on it. When looking at the photo, your eyes automatically focus on the players themselves as they are the most important areas of the photo. This automatic process of locating the important parts of an image or scene is called saliency detection.

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.

The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner.

Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.

Donald Trump has responded to the EU's decision to charge Google with a record-breaking fine. "I told you so!" the president tweeted. "The European Union just slapped a Five Billion Dollar fine on one of our great companies, Google. They truly have taken advantage of the US, but not for long!" The post is just the latest mark of deteriorating relations between the EU and the US.

Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to where your data lives. This tutorial will show you an example of how you can send your python code from Juptyter notebooks to execute within SQL Server.

Earlier this year at the QCon.ai I also presented an interactive workshop, using R and the Microsoft Cognitive Services APIs, to automatically generate captions for images, and to create a tool to recognize images of hotdogs. Video from both the presentation, and the workshop (which starts at the 10:00 mark), is now available to view the QCon.ai You can find the slides for the presentation here. The R code for the "Not Hotdog" workshop is available as an Azure Notebook which you can clone and use to follow along with the workshop.

Microsoft is set to launch "all-new Xbox hardware", the company has announced. It has revealed little more other than the fact that the announcements will made at Gamescom, the major industry event next month. It is unlikely that the announcement refers to a new console: the Xbox One X was only released in November, and the next full new version of the Xbox is not expected until 2020. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

A long-standing problem in model free reinforcement learning (RL) is that it requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to increase the sample efficiency of RL when we have access to demonstrations. Our approach, which we call Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. We perform experiments in a competitive four player game (Pommerman) and a path-finding maze game. We find that this weak form of guidance provides significant gains in sample complexity with a stark advantage in sparse reward environments. In some cases, standard RL did not yield any improvement while Backplay reached success rates greater than 50% and generalized to unseen initial conditions in the same amount of training time. Additionally, we see that agents trained via Backplay can learn policies superior to those of the original demonstration.

About IBM: IBM received a record-breaking 9,043 U.S patents in 2017, including 1,400 artificial intelligence and 1,900 cloud technology patents. Business focus: cloud computing, AI, software and services. Valuation is "dirt cheap," and the dividend yield is at a 24-year high.--SeekingAlpha.com IBM is the preferred IT provider for: 92 of the world's largest 100 banks 10 out of 10 of the world's largest insurers 8 out of 10 of the world's largest airlines 9 of the world's largest retailers Principal domain: IBM.com. Date/time: 18 July 2018, 5pm EDT Link to IBM's 2Q18 Earnings webcast Materials for download (to be updated after market close on July 18).