"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay.
Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge. Part of the technology trepidation stems from uncertainty around the terms and definitions of'AI' and'machine learning.' Organizations are also unsure how to deploy new AI capabilities alongside existing infrastructure and processes. This is especially true in inspection systems, where there are significant investments in cameras, specialized sensors, and analysis software with well-established processes for end-users. The cost and complexity of algorithm training is also a concern for businesses evaluating AI.
Digital Transformation is an ongoing process for utilities today. However, to be successful they must focus on technologies that deliver the services customers want. Machine Learning offers enormous potential for utilities to discover more about their customers and for solving the common issues utilities face every day. Today, it is undisputed that Digital Transformation is essential for utilities. However, organizations often find the results of their Digital Transformation efforts disappointing.
Support Vector Machine(SVM) is a powerful classifier that works with both linear and non-linear data. If you have a n-dimensional space, then the dimension of the hyperplane will be (n-1). The goal of SVM is to find an optimal hyperplane that best separates our data so that distance from the nearest points in space to itself is maximized. To keep it simple, consider a road, which separates the left, right-side cars, buildings, pedestrians and makes the widest lane as possible. And those cars, buildings, really close to the street are the support vectors.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Consider the animal in the following image. If you recognize it, a quick series of neuron activations in your brain will link its image to its name and other information you know about it (habitat, size, diet, lifespan, etc…). But if like me, you've never seen this animal before, your mind is now racing through your repertoire of animal species, comparing tails, ears, paws, noses, snouts, and everything else to determine which bucket this odd creature belongs to. Your biological neural network is reprocessing your past experience to deal with a novel situation. Our brains, honed through millions of years of evolution, are very efficient processing machines, sorting out the ton of information we receive through our sensory inputs, associating known items with their respective categories.
With changing technology landscape, software Engineering has come a long way, thanks to the evolving intelligent systems. Machine Learning and Deep Learning Technologies have created avenues to execute tasks efficiently and more intelligently. In this entire transformations, Machine learning and Deep Learning frameworks have played a huge role allowing innovation to take the centre stage. So much is said about Machine Learning and the multifaceted benefits it offers. But it is actually difficult to comprehend the advantages unless the fundamentals are laid out clearly.
Big Data Intelligence: A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer's ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.
You probably remember the neural network that generates photos of people who don't actually exist. You might even remember the one that spits out photos of nonexistent cats, or the one that whips up fake résumés, or the one that dreams up listings for imaginary rental properties. Now, a programmer named Aldo Cortesi has created an even stranger algorithm -- one that draws silhouettes for nonexistent animals, some of which look plausible and others which look like nothing you've ever seen before. In a post about the project, Cortesi wrote that he was indeed inspired by algorithms that generate human likenesses. "Looking at these images, it seems like the neural net would have to learn a vast number of things to be able to do what these networks were doing. Some of this seems relatively simple and factual -- say, that eye colours should match," he wrote.
New Delhi, Jul 02: Bio-derived fuels are gaining widespread attention among the scientific community across the world. The work on biofuels is in response to the global call for reducing carbon emissions associated with the use of fossil fuels. In India too, biofuels have caught the imagination of researchers. For instance, researchers of the Indian Institute of Technology (IIT) Hyderabad have started using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.
What seizes your attention at first glance might change with a closer look. That elephant dressed in red wallpaper might initially grab your eye until your gaze moves to the woman on the living room couch and the surprising realization that the pair appear to be sharing a quiet moment together. In a study being presented at the virtual Computer Vision and Pattern Recognition conference this week, researchers show that our attention moves in distinctive ways the longer we stare at an image, and that these viewing patterns can be replicated by artificial intelligence models. The work suggests immediate ways of improving how visual content is teased and eventually displayed online. For example, an automated cropping tool might zoom in on the elephant for a thumbnail preview or zoom out to include the intriguing details that become visible once a reader clicks on the story.