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AI continues to flourish in business despite the pandemic and a turbulent economy

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Nearly three-quarters of businesses now consider artificial intelligence (AI) critical to their success, and AI continues to grow in importance across companies of various sizes and industries, according to a new report. And despite turbulent times, more than two-thirds of respondents to Appen Limited's 2020 State of AI Report do not expect any negative impact from the COVID-19 pandemic on their AI strategies. Nearly half of companies have accelerated their AI strategies, 20% doing so "significantly," betting their AI projects will have a positive impact on their organization's resiliency, efficiency, and innovation, according to the annual report. SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium) Yet almost half (49%) of respondents feel their company is behind in their AI journey, suggesting a critical gap exists between the strategic need and the ability to execute among business leaders and technologists, Appen said. Surprisingly, respondents are not that leery of AI: The report also found that only 25% of companies said unbiased AI is mission-critical.


PyTorch for Beginners - Building Neural Networks

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Deep learning and neural networks are big buzzwords of the decade. Neural Networks are based on the elements of the biological nervous system and they try to imitate its behavior. They are composed of small processing units – neurons and weighted connections between them. The weight of the connection simulates a number of neurotransmitters transferred among neurons. Mathematically, we can define Neural Network as a sorted triple (N, C, w), where N is set of neurons, C is set {(i, j) i, j N} whose elements are connections between neurons i and j, and w(i, j) is the weight of the connection between neurons i and j.


Most Learning Is Slow In The Field Of Machine Learning

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The first day of The Rising 2020 started with an informal session with Sara Hooker, a researcher at Google Brain where she shared some of her personal reflections on how to navigate in the field of machine learning and why we need to celebrate failures as well as success. Sara started her session with a simple story where she shared her childhood dream of being featured in the magazine, The Economist. In fact, she mentioned that "one of my goals was to eventually be an economist." However, when that happened in 2016, it wasn't a pleasing feeling for Sara; instead, it was a feeling of "unease" and seemed problematic. A lot of this could be attributed to the article that The Economist did, which profiled the efforts of fast.ai, a course that's run by Jeremy Howard and Rachel Thomas, and utilised Sara as an example of their success.


NVIDIA AI Lets You See What Your Pet Would Look Like If It Were A Meerkat

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One of NVIDIA's many different artificial intelligence projects (and by far the best one to date) lets you envision what your pet might look like it it were a meerkat. In case you didn't know, NVIDIA has its own research group dedicated solely to research into AI, and that includes developing new AI systems and agents which can do some pretty neat things. As the researchers say, although they take AI research very seriously, there's still no excuse not to have some fun with the products of their labors. It's the name given to an AI system they developed around a year ago which can generate a selection of images that are sorts of translations of your own pet's face into what said pet might look like if they were other types of animals. "With GANimal, you can bring your pet's alter ego to life by projecting their expression and pose onto other animals," explain the developers.


The 5 Components Towards Building Production-Ready Machine Learning System

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The biggest issue facing machine learning is how to put the system into production. To conceptualize this framework, there is a significant paper from Google called ML Test Score -- A Rubric for Production Readiness and Technical Debt Reduction -- which is an exhaustive framework/checklist from practitioners at Google. It is a follow-up to previous work from Google, such as (1) Hidden Technical Debt in ML Systems, (2) ML: The High-Interest Credit Card of Technical Debt, and (3) Rules of ML: Best Practices for ML Engineering. As seen in Figure 1 from the paper above, ML system testing is more complex a challenge than testing manually coded systems, since ML system behavior depends strongly on data and models that cannot be sharply specified a priori. One way to see this is to consider ML training as analogous to the compilation, where the source is both code and training data.


MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners

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To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation.


Regularization -- Part 2

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These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!


Transfer Learning : the time savior

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The whole backdrop of Artificial intelligence and deep learning is to imitate the human brain, and one of the most notable feature of our brain is it's inherent ability to transfer knowledge across tasks. Which in simple terms means using what you have learnt in kindergarten, adding 2 numbers, to solving matrix addition in high school mathematics. The field of machine learning also makes use of such a concept where a well trained model trained with lots and lots of data can add to the accuracy of our model. Here is my code for the transfer learning project I have implemented. I have made use of open cv to capture real time images of the face and use them as training and test datasets.


Federal Government Inching Toward Enterprise Cloud Foundation - AI Trends

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The federal government continues its halting effort to field an enterprise cloud strategy, with Lt. Gen. Jack Shanahan, who leads the Defense Department's Joint AI Center (JAIC), commenting recently that not having an enterprise cloud platform has made the government's efforts to pursue AI more challenging. "The lack of an enterprise solution has slowed us down," stated Shanahan during an AFCEA DC virtual event held on May 21, according to an account in FCW. However, "the gears are in motion" with the JAIC using an "alternate platform" for example to host a newer anti-COVID effort. This platform is called Project Salus, and is a data aggregation that is able to employ predictive modeling to help supply equipment needed by front-line workers. The Salus platform was used for the ill-fated Project Maven, a DOD effort that was to employ AI image recognition to improve drone strike accuracy.


How AI can empower communities and strengthen democracy

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Each Fourth of July for the past five years I've written about AI with the potential to positively impact democratic societies. I return to this question with the hope of shining a light on technology that can strengthen communities, protect privacy and freedoms, or otherwise support the public good. This series is grounded in the principle that artificial intelligence can is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes.