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NovaSignal's AI-Guided Robotic Platform Aims To Change The Diagnosis Of Stroke

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NovaSignal's AI-driven automated cerebral doppler ultrasound system. Los Angeles based NovaSignal Inc. recently launched a second version of their artificial intelligence (AI)-guided robotic platform for assessing cerebral blood flow in order to guide real-time diagnosis. The platform uses ultrasound to autonomously capture blood flow data, which then gets sent to their HIPAA-compliant cloud system so that clinicians can access the exam data from anywhere on their personal devices. Founded in 2013, the company states they have raised over $25 million in federal research funding and hold 18 patents. They also have over 130 peer-reviewed citations to their work.


Team builds first living robots--that can reproduce

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Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses. Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction--and applied their discovery to create the first-ever, self-replicating living robots. The same team that built the first living robots ("Xenobots," assembled from frog cells--reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble "baby" Xenobots inside their Pac-Man-shaped "mouth"--that, a few days later, become new Xenobots that look and move just like themselves. And then these new Xenobots can go out, find cells, and build copies of themselves. "With the right design--they will spontaneously self-replicate," says Joshua Bongard, Ph.D., a computer scientist and robotics expert at the University of Vermont who co-led the new research.


Difference between distributed learning versus federated learning algorithms - KDnuggets

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Distributed machine learning algorithm is a multi-nodal system that builds training models by independent training on different nodes. Having a distributed training system accelerates training on huge amounts of data. When working with big data, training time exponentially increases which makes scalability and online re-training. For example, let's say we want to build a recommendation model, and based on the user interaction everyday, we wish to re-train the models. We could see the user-interaction as high as hundreds of clicks per user and millions of users.


Unlimited Creativity

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AI art is the newest way to create beautiful, intriguing, and breathtaking artwork. There are no limitations on what you can do with AI art in terms of creativity or imagery. The only limit is your imagination! The revolution in AI art begins today, so get started creating your masterpiece today!


How the public clouds are innovating on AI

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The three big cloud providers, specifically Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), want developers and data scientists to develop, test, and deploy machine learning models on their clouds. It's a lucrative endeavor for them because testing models often need a burst of infrastructure, and models in production often require high availability. These are lucrative services for the cloud providers and offer benefits to their customers, but they don't want to compete for your business only on infrastructure, service levels, and pricing. They focus on versatile on-ramps to make it easier for customers to use their machine learning capabilities. Each public cloud offers multiple data storage options, including serverless databases, data warehouses, data lakes, and NoSQL datastores, making it likely that you will develop models in proximity to where your data resides.


Building a Chess Engine: Part 2

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Hi everyone, this will be the second instalment in my tutorial series for building a chess engine. This lesson will focus on building an AI agent that we can play. This lesson is going to be more technical than part 1, so please bear with me. I try to supply both equations and diagrams to help make things a little easier. Now that we have finished building our chess game, we can begin designing an AI that plays it.


Landing AI: Unlocking The Power Of Data-Centric Artificial Intelligence

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Artificial intelligence (AI) has been hugely transformative in industries with access to huge datasets and trained algorithms to analyze and interpret them. Probably the most obvious examples of this success can be found in consumer-facing internet businesses like Google, Amazon, Netflix, or Facebook. Over the last two decades, companies such as these have grown into some of the world's largest and most powerful corporations. In many ways, their growth can be put down to their exposure to the ever-growing volumes of data being churned out by our increasingly digitized society. But if AI is going to unlock the truly world-changing value that many believe it will – rather than simply making some very smart people in Silicon Valley very rich – then businesses in other industries have to consider different approaches.


AI Trends 2021–2025

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One of the trends of 2021 that will continue at least for the next few years is the rise in popularity of the PyTorch framework. This can be seen from graphical data that the use of PyTorch has been steadily growing over the past few years, and though the popularity of the two frameworks has shown some correlation, their trends are different. At the same time, dynamism and flexibility are on the side of PyTorch. PyTorch contrasts the Tensor board with its own tool, Visdom. It doesn't have many features, but it is easier to use.


World's first living robots can now reproduce, scientists say

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Formed from the stem cells of the African clawed frog (Xenopus laevis) from which it takes its name, xenobots are less than a millimeter (0.04 inches) wide. The tiny blobs were first unveiled in 2020 after experiments showed that they could move, work together in groups and self-heal. Now the scientists that developed them at the University of Vermont, Tufts University and Harvard University's Wyss Institute for Biologically Inspired Engineering said they have discovered an entirely new form of biological reproduction different from any animal or plant known to science. "I was astounded by it," said Michael Levin, a professor of biology and director of the Allen Discovery Center at Tufts University who was co-lead author of the new research. "Frogs have a way of reproducing that they normally use but when you ... liberate (the cells) from the rest of the embryo and you give them a chance to figure out how to be in a new environment, not only do they figure out a new way to move, but they also figure out apparently a new way to reproduce."


Supervised, Semi-Supervised, Unsupervised, and Self-Supervised Learning

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The exponential number of research and publications have introduced many terms and concepts in the domain of machine learning, yet many have degenerated to merely buzzwords without many people fully understanding their differences. The most common, and perhaps THE type that we refer to when talking about machine learning is supervised learning. In simple words, supervised learning provides a set of input-output pairs such that we can learn an intermediate system that maps inputs to correct outputs. A naive example of supervised learning is determining the class (i.e., dogs/cats, etc) of an image based on a dataset of images and their corresponding classes, which we will refer to as their labels. With the given input-label pair, the current popular approach will be to directly train a deep neural network (i.e., a convolutional neural network) to output a label prediction from the given image, compute a differentiable loss between the prediction and the actual correct answers, and backpropagate through the network to update weights to optimise the predictions.