With Interest: The Week in Business: A Facial Recognition Ban, and Trade War Blues

NYT > Economy

Here's what you need to know in business news. The city's Board of Supervisors voted on Tuesday to prohibit the use of facial recognition technology within city limits. It's a somewhat symbolic move: The police there don't currently use the stuff, and the places where it is in use -- seaports and airports -- are under federal jurisdiction and therefore unaffected by the new regulation. The major television networks tried to sell their fall advertising slots in an annual pageant known as the upfronts. In a week of star-studded presentations, skits and boozy mingling, representatives of major advertisers flocked to New York to see what the networks have in store.


Deep Singularity: Does the World Need a new AI-based Operating System to ignite the AI Winter?

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There has been tumultuous excitement in the Computer Science field surrounding the potential of applying machine learning algorithms to the commercial problems at enterprise scale over the last few decades. Solving complex conundrums and HPC at web scale will need a new enterprise-grade operating system and hardware data flow computing with ASIC or FPGA chips to minimize the data movements and maximize the shorter communication paths to reboot the fourth industrial revolution and execute more parallel instructions on a single semiconductor chip die. The data lifecycle management of IoT requires unlimited number of threads to spawn on an operating system at the same time simultaneously with guaranteed QoS. All the IoT operating systems such as Android Things, Arm Mbed OS, Embedded Apple iOS and macOS, Google Brillo, Green Hills Integrity, Nucleus RTOS, RIOT OS, RTOS, Windows, WindRiver VxWorks, or Linux targeting Raspberry Pi or BeagleBoards, Intel Edison IoT Boards, and Arduino platforms can provide the performance based on the number of cores in the machine without unlimited threads. However, regardless of an exponential increase in the hardware resources to deliver the multitasking capabilities and memory management, corporations have hit the wall on Moore's Law plagued with communication delays requiring precision programming in C and C through Open MPI and heavy parallelprogramming which does not seem to be the norm for developing regular applications.


Fiverr Relies on SageMaker to Streamline and Simplify Machine Learning Models Amazon Web Services

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When it came time for Fiverr to analyze the data around user behavior on its platform, the freelance services website did what many modern businesses do in terms of recruiting the right tool for the task: they engaged Amazon Web Services's SageMaker. Amazon SageMaker is an API that enables developers and data scientists to readily build, train, and deploy machine learning models to serve any need and at any scale. "This was a very important milestone for us because, moving forward, we want this to be the standard in the data science deployment and all our predictive modeling," says Eran Abikhzer-Agam, head of data at Fiverr. "We want our data scientists to be completely independent as much as possible, and SageMaker allowed us to do that." Like any online business, Fiverr was interested in streamlining the user experience on its platform, tracking user behavior, and predicting when a customer might need a bit of help.


DeepFool -- A simple and accurate method to fool deep Neural Networks.

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Let's go over the Algorithm: 1. The algorithm takes an input x and a classifier f . And the loop variable to 1. 4. Start and continue loop while the true label and the label of the adversarially perturbed image is the same. 5. Calculate the projection of the input onto the closest hyperplane. With multiclass classifiers, let's say the input is x and for each class there is a hyperplane (straight plane that divides one class from the others) and based on the place in the space where x lies it is classified into a class. Now, all this algorithm does is, it finds the closest hyperplane, and then projects x onto that hyperplane and pushes it a bit beyond, thus misclassifying it with the minimal perturbation possible.


How to Decide Between Amazon SageMaker and Microsoft Azure Machine Learning Studio

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But there are other tools that also claim to make machine learning easier and speed model development. I am wondering how they compare? So, this week, I am taking a look at Amazon SageMaker (SageMaker) and how it compares to Studio. What I found when I looked at SageMaker in comparison to Studio is a significantly different approach to model building. The vendors of each tool would both claim to offer a fully managed service that covers the entire machine learning workflow to build, train, and deploy machine learning models quickly.


How to Decide Between Amazon SageMaker and Microsoft Azure Machine Learning Studio

#artificialintelligence

But there are other tools that also claim to make machine learning easier and speed model development. I am wondering how they compare? So, this week, I am taking a look at Amazon SageMaker (SageMaker) and how it compares to Studio. What I found when I looked at SageMaker in comparison to Studio is a significantly different approach to model building. The vendors of each tool would both claim to offer a fully managed service that covers the entire machine learning workflow to build, train, and deploy machine learning models quickly.


TensorFlow Model Optimization Toolkit -- Pruning API

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Since we introduced the Model Optimization Toolkit -- a suite of techniques that developers, both novice and advanced, can use to optimize machine learning models -- we have been busy working on our roadmap to add several new approaches and tools. Today, we are happy to share the new weight pruning API. Optimizing machine learning programs can take very different forms. Fortunately, neural networks have proven resilient to different transformations aimed at this goal. One such family of optimizations aims to reduce the number of parameters and operations involved in the computation by removing connections, and thus parameters, in between neural network layers.


Enterprise AI: Diving into Machine Learning

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Data in the real world, of course, isn't as simple as it is in the previous example. There are always complexities and nuances to data. To stick with our housing market example, the value of houses might also be influenced by dwelling type, lot size, recent upgrades, proximity to a neighborhood park and intangible variables like curbside appeal. And, in the real world, houses wouldn't all be in the same neighborhood, so your machine learning model must also consider the ZIP code for the property. To consider this wider range of variables, we need to dig deeper into the data scientist's toolbox and pull out some more sophisticated machine learning methods, including random forests and gradient boosting.


achaiah/pywick

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Pywick is a high-level Pytorch training framework that aims to get you up and running quickly with state of the art neural networks. Does the world need another Pytorch framework? But we started this project when no good frameworks were available and it just kept growing. Pywick tries to stay on the bleeding edge of research into neural networks. If you just wish to run a vanilla CNN, this is probably going to be overkill.


A new era: artificial intelligence and machine learning in prostate cancer

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The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to'big data' enables the'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.