New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
In this free issue: current machine learning deep learning trends, news, resources, sneak preview of paid subscriber content. Having a searchable blog that requires authentication allows us to show every one what kind of resources are available. Free signups get previews and paid subscribers can quickly access and search for relevant resources. We also link to our Medium blog networks this way we have all the information in one place, organized by topics and keywords. Current easter eggs We routinely send easter eggs to paid subscribers.
Transformers and pre-trained models can be considered one of the most important developments in the recent years of deep learning. Beyond the research breakthroughts, Transformers have redefined the natural language understanding(NLU) space sparking a race between lead AI vendors to build bigger and more efficient neural networks. The Transformer architecture has been behind famous models such as Google's BERT, Facebook's RoBERTa or OpenAI's GPT-3. Is not surprising that many people believe that only big companies have the resources to tackle the implementation of Transformer models. Earlier this year, the deep learning community was astonished when Microsoft Research unveiled the Turing Natural Language Generation (T-NLG) model which, at the time, was considered the largest natural language processing(NLP) model in the history of artificial intelligence(AI) with 17 billion parameters.
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Training with artificial images is becoming increasingly important to address the lack of real data sets in various niche areas. Yet, many today's approaches write 2D/3D simulations from scratch. To improve this situation and make better use of existing pipelines, we've been working towards an integration between Blender, an open-source real-time physics enabled animation software, and PyTorch. Today we announce blendtorch, an open-source Python library that seamlessly integrates distributed Blender renderings into PyTorch data pipelines at 60FPS (640x480 RGBA). Batch visualization from 4 Blender instances running a physics enabled falling cubes scene.
Whether you've noticed it or not, Deep Learning (DL) plays an important part in all our lives. From the voice assistants and auto-correct services on your smartphone to the automation of large industries, deep learning is the underlying concept behind these meteoric rises in human progress. A major concept that we implement in deep learning is that of neural networks. A neural network is a computing algorithm of an interconnected system of mathematical formulae used to make predictions by "training" the algorithm on data relevant to the prediction to be made. This is partly inspired by the way neurons are connected in biological brains.
Deep Learning based Network Detection and Response technology leader included in "America's Most Promising Artificial Intelligence Companies"Blue Hexagon, deep learning innovator of Cyber AI You Can Trust was recognized in the 2020 Forbes AI 50 list. As one of America's most promising artificial intelligence (AI) companies, Blue Hexagon is the only real time deep learning cybersecurity company to instantly stop zero-day malware and threats before infiltration, detect and block active adversaries and reduce SOC alert overload."Traditional We are able to achieve 99.8% threat detection accuracy and sub-second verdict speed with our deep learning technology to revolutionize security operations," said Nayeem Islam, CEO of Blue Hexagon. "Forbes included us for using artificial intelligence in meaningful business-oriented ways. We're proud to be included in their list, and believe AI will fundamentally change the way we protect against cyber threats."In
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Background/aims Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. Methods Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. Results Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. Conclusion The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.
Originally published at LinkedIn Pulse. Early last month, I presented a half-day tutorial on at this year's virtual CVPR 2020. This is a very unique experience, and I would like to share some of the highlights of the tutorial. The tutorial focused on a critical problem that arises as AI moves from experimentation to production; that is, how to seamlessly scale AI to distributed Big Data. Today, AI researchers and data scientists need to go through a mountain of pains to apply AI models to production dataset that is stored in distributed Big Data cluster.
Deep learning opens a new level of capabilities within the artificial intelligence realm, but its use has been limited to data scientists. Nowadays, finally, it may be ripe for "democratization," meaning it is poised to become an accessible set of technologies available to all who need it -- with numerous business applications. Deep learning, which attempts to mimic the logic of the human brain for analyzing patterns, is starting to see widespread adoption within enterprise AI initiatives. A majority of companies with AI implementations, 53%, plans to incorporate deep learning into their workplaces within the next 24 months, a recent survey of 154 IT and business professionals conducted and published by ITPro Today, InformationWeek and Interop finds. Deep learning is now driving rapid innovations in AI and influencing massive disruptions across all markets, a new report published by Databricks asserts.