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Using TensorFlow and Azure to Add Image Classification to Your Android Apps

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TensorFlow is a well established, open source machine learning and deep learning framework that can be used to create and run a wide range of different models, usually using powerful machines in the cloud. In addition, TensorFlow also supports running models on mobile devices through the TensorFlow.Mobile library, taking advantage of the hardware acceleration available on modern phones to run models incredibly fast on low powered mobile devices. In this post, we'll discuss how to build an image classifier to identify different fruits using the Azure Custom Vision service, and create a simple Android app to use this model to classify images. Creating ML models can be time consuming and require large data sets. To make it easier to create image classification models, Microsoft has created the Custom Vision Service, which uses a technique called transfer learning to allow you to train an image classifier using only a small number of images, instead of the thousands that traditionally would be required to train such a model.


The Next Generation Of Artificial Intelligence

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It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.


The Next Generation Of Artificial Intelligence

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AI legend Yann LeCun, one of the godfathers of deep learning, sees self-supervised learning as the ... [ ] key to AI's future. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.


The Next Generation Of Artificial Intelligence

#artificialintelligence

AI legend Yann LeCun, one of the godfathers of deep learning, sees self-supervised learning as the ... [ ] key to AI's future. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.


Voice Recognition Biometrics Market to Increase by $2.6 Billion

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With increasing innovations and adoption across industries, the Voice ID biometrics market is forecast to grow by $2.6 billion in the next 4 years according to analyst firm Technavio. About 32% of the market's growth will originate from APAC during the period. The rise in adoption of biometric voice recognition in the healthcare sector is one of the major factors that will have a positive impact on the growth of this market in the coming years. The rising usage of smartphones and personal digital assistants in the healthcare sector has led to apps for electronic prescriptions, diagnosis and treatment, coding and billing. To prevent unauthorized access to confidential data, these apps could be integrated with voice biometrics leading to greater adoption of voice ID biometrics in the coming years.


Convergence Of Financial Institutions' Risk Management Aided By Artificial Intelligence

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The attention on technology and risk in financial institutions has often been laser focused on transactions. That makes sense because of the large volume of trades and other transactions that are at risk from fraud, hacking and other issues. However, financial institutions also have other risks. Organizations have been slowly increasing the use of artificial intelligence (AI) in other areas, and there are signs that AI is supporting a convergence of risk management tools in financial institutions. Financial transactions, trading in particular, have long been a focus of technology.


Adobe's 'Liquid Mode' uses AI to automatically redesign PDFs for mobile devices – TechCrunch

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We've probably all been there: You've been poking around your phone for an hour, deep in some sort of Google research rabbit hole. You finally find a link that almost certainly has the info you've been looking for. Now you get to pinch and zoom your way through a document that's clearly not meant for a screen that fits in your hand. Given that the file format is approaching its 30th birthday, it makes sense that PDFs aren't exactly built for modern mobile devices. But neither PDFs nor smartphones are going away anytime soon, so Adobe has been working on a way to make them play nicely together.


Adobe's Liquid Mode Leverages AI to Reformat PDFs for Mobile Devices

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Emergent Insight: On August 13, 2020 Emergent Enterprise posted an article about the incredibly poor UX of the PDF file. Perhaps Adobe has provided some relief with a new AI-fueled tool that reformats PDF files for better readability on mobile devices and presumably other formats such as AR & VR. Kyle Wiggers explains Liquid Mode from Adobe in this post at VentureBeat and how it might make life easier for all of us who swipe, scroll, pinch and zoom PDF files. Perhaps the most striking statistic here is that 65% find PDF files frustrating. And yet we throw them at our audiences with regularity.


Golden Era of Digital Healthcare

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Many experts have already started contemplating that the damage due to COVID19 that is apparently visible today; may its economy, social, political or other aspects of our lives, is just the tip of the iceberg, and the gigantic submerged part is yet to be realized by the world and will be visible gradually post-COVID. I am praying that this prediction comes out wrong, otherwise it's not going to be any less than a horrible nightmare for all of us. On the contrary, probably the biggest positive aspect that COVID has attributed and initiated so far is that, it acted as a wakeup alarm for the entire world to focus on the healthcare systems. It brutally proved that healthcare and medicine today is far behind to fight against a pandemic or any biological warfare, the possibility of which cannot be ignored in the future. COVID has proven that in this highly connected world we should be concerned about everything that is happening even in the remotest corner of the world, because the same can come and knock at your door the very next day.


Where is AI Going in 2021?

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Where is AI going to in 2021? I was asked this question by my friends including Tripp Braden, Danielle Guzman and April Rudin, as well as others. For a definition of AI, Machine Learning and Deep Learning see the article that I authored entitled "An Introduction to AI". Edge Computing refers to on the device closer to where the data is generated at the edge of the network. We'll see AI increasingly inferencing on the devices around us including mobile devices, sensors and smart cameras with Graphical Processing Units (GPUs, or specialised AI chips for sake of simplicity) embedded in the device.