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Supervised vs Unsupervised & Discriminative vs Generative

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Highlights: GANs and classical Deep Learning methods (classification, object detection) are similar, but they are also fundamentally different in nature. Reviewing their properties will be the topic of this post. Therefore, before we proceed further with the GANs series, it will be useful to refresh and recap what is supervised and unsupervised learning. In addition, we will explain the difference between discriminative and generative models. Finally, we will introduce latent variables, since they are an important concept in GANs.


Black Sun on Steam

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Text freely in natural language with your AI assistant Hopper: let her fly the ship, get her help during combat or ask for a market analysis to maximise your trading profit. She knows a lot - including terrible space jokes. A large 2D open-world universe containing numerous solar systems, space stations and ships to discover. Large means astronomical and impossible-to-fly-through. Be grateful if the jump engine works properly. Find your kidnapped brother while making interstellar friends and enemies.


Will evolving regulations stymie AI innovations?

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"A model is as good as the underlying data," said Jayachandran Ramachandran, SVP of Artificial Intelligence Labs at Course5 Intelligence during his MLDS talk "Will evolving regulations stymie AI innovations? He discussed how industries and governments recognise this problem and develop regulations and recommendations. He also touched on the recommendations and implications crelated to European Union's AI regulations draft. Today, most countries have an AI policy and strategies in place. The EU is at the forefront of AI regulations and drafts. "The EU draft in 2021 is acting as a benchmark for other countries," Ramachandran noted. The draft seeks to ensure the AI policy is human-centric, sustainable, secure, inclusive and trustworthy. Additionally, the draft focuses on a seamless transition of AI from the lab to the market. Any system deployed for the users based in the EU will be under the scope of this AI regulation. If the consumers are based outside the EU, they will not be held ...


Rise of the machines: Robot umpires moving up to Triple-A baseball for 2022

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Robot umpires have been given a promotion and will be just one step from the major leagues this season. Major League Baseball is expanding its automated strike zone experiment to Triple-A, the highest level of the minor leagues. MLB's website posted a hiring notice seeking seasonal employees to operate the Automated Ball and Strike system. MLB said it is recruiting employees to operate the system for the Albuquerque Isotopes, Charlotte Knights, El Paso Chihuahuas, Las Vegas Aviators, Oklahoma City Dodgers, Reno Aces, Round Rock Express, Sacramento River Cats, Salt Lake Bees, Sugar Land Skeeters and Tacoma Rainiers. The independent Atlantic League became the first American professional league to let a computer call balls and strikes at its All-Star Game in July 2019 and experimented with ABS during the second half of that season. It also was used in the Arizona Fall League for top prospects in 2019, drawing complaints of its calls on breaking balls.


Harnessing Noise In Optical Computing For AI - AI Summary

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In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. A research team led by the University of Washington has developed new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. Optical computing noise essentially comes from stray light particles, or photons, that originate from the operation of lasers within the device and background thermal radiation. Of course the optical computer didn't have a human hand for writing, so its form of "handwriting" was to generate digital images that had a style similar to the samples it had studied, but were not identical to them.


Deepmind Researchers Propose 'ReLICv2': Pushing The Limits of Self-Supervised ResNets

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The supervised learning architectures generally require a massive amount of labeled data. Acquiring this vast amount of high-quality labeled data can turn out to be a very costly and time-consuming task. The main idea behind self-supervised methods in deep learning is to learn the patterns from a given set of unlabelled data and fine-tune the model with few labeled data. Self-supervised learning using residual networks has recently progressed, but they still underperform by a large margin corresponding to supervised residual network models on ImageNet classification benchmarks. This poor performance has rendered the use of self-supervised models in performance-critical scenarios till this point.


Real-time Analytics News for Week Ending January 22 – RTInsights

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… an AI/Machine Learning (ML) platform designed to address the needs of … gap between the quantum computing and machine learning communities.


Machine-learned, light-field camera detects 3D facial expressions – News Medical

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The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically …


how-artificial-intelligence-will-power-the-next-wave-of-healthcare-innovation-in-future

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Data is certain to revolutionize healthcare in the same way it transformed other industries. But it will need help. Today, healthcare providers are collecting exabytes of patient data from hospitals, clinics, imaging and pathology labs, and more. These data provide a wealth of information about human health but are difficult to understand due to their lack of structure and sheer volume. Fortunately, sophisticated AI and machine learning solutions can carry the torch of innovation.


COVID-19 detection in CT and CXR images using deep learning models

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Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc.