Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software. But before machine learning models can perform classification tasks, they need to be trained on a lot of annotated examples. Data annotation is a slow and manual process that requires humans to review training examples one by one and giving them their right labels. In fact, data annotation is such a vital part of machine learning that the growing popularity of the technology has given rise to a huge market for labeled data. From Amazon’s Mechanical Turk… This story continues at The Next Web
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(NYU Tandon School of Engineering) The paper "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification," has won the 2020 Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society (SPS) Signal Processing Letters Best Paper Award. The article is by Justin Salamon, formerly a research scientist at NYU Tandon's Center for Urban Science and Progress (CUSP) and Juan Pablo Bello, the director of CUSP.
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Interest in machine learning has grown steadily over recent years. Specifically, enterprises now use machine learning for image recognition in a wide variety of use cases.  There are applications in the automotive industry, healthcare, security, retail, automated product tracking in warehouses, farming and agriculture, food recognition and even real-time translation by pointing your phone’s camera.  Thanks to machine learning and visual recognition, machines can detect cancer and COVID-19 in MRIs and CT scans. To read this article in full, please click here
Illustration by Alex Castro / The Verge Just last month, a stunning report showed how Amazon fulfillment centers across the country saw rising injury rates between 2016 and 2019, sourced from internal company data. And now, proposed legislation in Washington state would mean Amazon could pay a higher workers’ compensation premium than other warehouse-owning companies next year. To make this happen, the state wants to put warehouses that function like Amazon’s into a separate risk classification. While the proposed classification for “fulfillment centers” doesn’t say the word Amazon anywhere, how they are defined seems to fit the description of the company: Have an online marketplace to sell their own merchandise and third-party sellers’ merchandise; Sell their own name brand... Continue reading…
(University of Texas at Arlington) Won Hwa Kim, an assistant professor of computer science at The University of Texas at Arlington, is using a two-year, $175,000 grant from the National Science Foundation to use machine learning for earlier detection of Alzheimer's disease.
The United States Air Force has introduced a new classification for hardware that it says refers to its ‘digital future.’ Called the ‘eSeries,’ this classification refers to aircraft that is digitally engineered and virtually tested well before the first physical prototype is created, a ‘paradigm shift’ that is particularly important during the pandemic. The classification will apply to the very … Continue reading
Machines are just better at finding alien worlds than us humans A machine-learning algorithm has sniffed out 50 highly likely exoplanets previously hidden in data collected by NASA’s now-defunct Kepler space telescope.…
(Human Brain Project) A new "mathematical language" to classify seizures in epilepsy could lead to more effective clinical practice, researchers from Europe, the US, Australia and Japan propose in a new publication in eLife. An epilepsy model developed by the Human Brain Project provides the basis for the novel framework, which could also push forward basic understanding of the disease.
Box Shield can now scan files in real-time and automatically classify them based on their contents.
What’s a data scientist to do if they lack sufficient data to train a machine learning model?One potential avenue is synthetic data generation, which researchers at IBM Research advocate in a newly published preprint paper.They used a pretrained machine learning model to artificially synthesize new labeled data for text classification tasks.“Depending upon the problem at hand, getting a good fit for a classifier model may require abundant labeled data.However, in many cases, and especially when developing AI systems for specific applications, labeled data is scarce and costly to obtain,” wrote the paper’s coauthors.However, in many cases, and especially when developing AI systems for specific applications, labeled data is scarce and costly to obtain.”
Quantum-based communication and computation technologies promise unprecedented applications, such as unconditionally secure communications, ultra-precise sensors, and quantum computers capable of solving specific problems with a level of efficiency impossible to reach by classical computers.In recent times, quantum computers are also envisioned as nodes in a network of quantum devices, where connections are established via quantum channels and data are quantum systems that flow through the network, thus setting the bases for a future "quantum internet".With the design of these quantum information networks come new theoretical challenges, given that it is necessary to establish optimised automated information treatment protocols to work with quantum data, in the same way as current communcation networks automatically manage information.UAB researchers have had to deal with one of these challenges for the first time: the problem with sorting data from a quantum systems network according to the state in which they were prepared.The researchers have devised an optimal procedure that can identify clusters of identically prepared quantum systems.The protocol developed by researchers at the UAB shows a natural connection to an archetypical use case of classical machine learning: clustering data samples according to whether they share a common underlying probability distribution.
And—given the right circumstances—being different is a superpower.People with Asperger’s applaud the way she reframed a “disorder,” as it used to be called in the Diagnostic and Statistical Manual of Mental Disorders, into an asset.But Thunberg’s comments also fuel a lingering debate about whether Asperger’s even exists as a distinct condition—and if it doesn’t, why people are still so attached to the designation.Asperger syndrome, first coined in 1981, describes people who have problems with social interaction, repetitive behaviors, and an intense focus on singular interests.Sheldon Cooper, the theoretical physicist on the long-running TV show “The Big Bang Theory,” became an exaggerated prototype, a brilliant person who missed social cues and couldn’t grasp irony.It became a diagnosis in 1994, distinct from autistic disorder, but the lines were blurry even then.
Primates’ retinal ganglion cells receive visual info from photoreceptors that they then transmit from the eye to the brain.But not all cells are created equal — an estimated 80% operate at low frequency and recognize fine details, while about 20% respond to swift changes.This biological dichotomy inspired scientists at Facebook AI Research to pursue what they call SlowFast.An implementation in Facebook’s PyTorch framework — PySlowFast — is available on GitHub, along with trained models.As the research team points out in a preprint paper, slow motions occur statistically more often than fast motions, and the recognition of semantics like colors, textures, and lighting can be refreshed slowly without compromising accuracy.On the other hand, it’s beneficial to analyze performed motions — like clapping, waving, shaking, walking, or jumping — at a high temporal resolution (i.e., using a greater number of frames), because they evolve faster than their subject identities.
the first aim of advertising is to sell a thought, an honest or service whereas the last word goal in Advertising Market Analysis is to live advertising impact or influence on sales of that concept, sensible or service.The scope and coverage of Advertising Market Analysis embody analysis in Advertising Market Objectives and products appeals, media choice, advertising effectiveness, and Advertising Market Research budget/expenditure.This deals with info regarding already developed advertising ways, enhancements in Advertising Market Strategy, markets, etc.The advertising objectives may be framed in terms of awareness, ever-changing attitudes, ever-changing predisposition to shop for or some combination of the 3.(ii) Selection of Message to be Advertised:This side of analysis conducts tests in relation to the entire advertisements, its elements, and also the dominant theme.
This course introduces R programming environment as a way to have hands-on experience with Data Science.It starts with a few basic examples in R before moving onto doing statistical processing.The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming.The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programsWriting simple R programs to do basic mathematical and logical operations
This course introduces Python programming as a way to have hands-on experience with Data Science.It starts with a few basic examples in Python before moving onto doing statistical processing.The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming.The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programsWriting simple Python scripts to do basic mathematical and logical operations
It’s often assumed that as the complexity of an AI system increases, it becomes invariably less interpretable.But researchers have begun to challenge that notion with libraries like Facebook’s Captum, which explains decisions made by neural networks with the deep learning framework PyTorch, as well as IBM’s AI Explainability 360 toolkit and Microsoft’s InterpretML.In a bid to render AI’s decision-making even more transparent, a team hailing from Google and Stanford recently explored a machine learning model — Automated Concept-based Explanation (ACE) — that automatically extracts the “human-meaningful” visual concepts informing a model’s predictions.As the researchers explain in a paper detailing their work, most machine learning explanation methods alter individual features (e.g., pixels, super-pixels, word-vectors) to approximate the importance of each to the target model.This is an imperfect approach, because it’s vulnerable to even the smallest shifts in the input.By contrast, ACE identifies higher-level concepts by taking a trained classifier and a set of images within a class as input before extracting the concepts and sussing out each’s importance.
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Luk thung, a popular subgenre of Thai folk music that emerged shortly after World War II, consists of poetic lyrics often sung with a distinctive vibrato and accompanied by traditional instruments like the khene (mouth organ), phin (lute), and saw sam sai (fiddle).Its aesthetic is distinct in the musical world, and it predictably trips up music classification algorithms trained on Western genres.That’s why researchers at Chulalongkorn University in Thailand investigated a system capable of identifying specific types of luk thung songs from lyrics and audio alone.“Luk thung … is one of the most prominent genres and has a large listener base from farmers and urban working-class people,” wrote the coauthors.“For the purpose of personalized music recommendation in the Thai music industry, identifying Luk thung songs in hundreds of thousands of songs can reduce the chance of mistakenly recommending them to non-Luk thung listeners.”The researchers’ system comprised two models — one that classified lyrics and another that classified audio — that fed into a final classifier that aggregated intermediate features learned from both individual models.
Not every regression or classification problem needs to be solved with deep learning.For that matter, not every regression or classification problem needs to be solved with machine learning.After all, many data sets can be modeled analytically or with simple statistical procedures.On the other hand, there are cases where deep learning or deep transfer learning can help you train a model that is more accurate than you could create any other way.For these cases, PyTorch and TensorFlow can be quite effective, especially if there is already a trained model similar to what you need in the framework’s model library.[ Make sense of machine learning: AI, machine learning, and deep learning: Everything you need to know.
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