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Deep Learning Boosts Call Center Speech Recognition During the COVID-19 Crisis – IAM Network

#artificialintelligence

A business operation hard hit by COVID-19 is the call center. Industries ranging from airlines to retailers to financial institutions have been bombarded with calls--forcing them to put customers on hold for hours at a time or send them straight to voicemail. A recent study from Tethr of roughly 1 million customer service calls showed that in just two weeks, companies saw the percentage of calls scored as "difficult" double from 10 percent to more than 20 percent. Issues stemming from COVID-19--such as travel cancellations and gym membership disputes--have also raised customer anxiety, making call center representatives' jobs that much more challenging. Companies thinking about investing in speech recognition should consider a deep learning-based approach, and what to take into consideration before implementing it.


Deep Learning A-Z : Hands-On Artificial Neural Networks

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Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.


Feature Visualization on Convolutional Neural Networks (Keras) DataStuff

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According to Wikipedia, apophenia is "the tendency to mistakenly perceive connections and meaning between unrelated things" . It is also used as "the human propensity to seek patterns in random information". Whether it's a scientist doing research in a lab, or a conspiracy theorist warning us about how "it's all connected", I guess people need to feel like we understand what's going on, even in the face of clearly random information. Deep Neural Networks are usually treated like "black boxes" due to their inscrutability compared to more transparent models, like XGboost or Explainable Boosted Machines. However, there is a way to interpret what each individual filter is doing in a Convolutional Neural Network, and which kinds of images it is learning to detect.


Convolutional Neural Networks (CNN or ConvNet) by Sunny Solanki

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The convolutional neural network is a type of artificial neural network which has proven giving very good results for visual imagery over the last few years. Over the years many version of convolutional neural network has been designed to solve many tasks as well as to win image net competitions. Any artificial neural network which uses the convolution layer in its architecture can be considered as ConvNet. ConvNets typically start with recognizing smaller patterns/objects in data and later on combines these patterns/objects further using more convolution layers to predict the whole object. Yann Lecun developed the first successful ConvNet by applying backpropagation to it during the 1990s called LeNet.


AI could help scientists fact-check covid claims amid a deluge of research

MIT Technology Review

An experimental tool helps researchers wade through the overwhelming amount of coronavirus literature to check whether emerging studies follow scientific consensus. Why it matters: Since the start of the coronavirus pandemic, there has been a flood of relevant preprints and papers, produced by people with varying degrees of expertise and vetted through varying degrees of peer review. This has made it challenging for researchers trying to advance their understanding of the virus to sort scientific fact from fiction. How it works: The SciFact tool, developed by the Seattle-based research nonprofit Allen Institute for Artificial Intelligence (AI2), is designed to help with this process. Type a scientific claim into its search bar--say, "hypertension is a comorbidity for covid" (translation: hypertension can cause complications for covid patients)--and it will populate a feed with relevant papers, labeled as either supporting or refuting the assertion.


A deep learning framework for analysis of astronomical images

AIHub

Researchers have developed a model for generating pixel-level morphological classifications of astronomical sources. Morpheus can analyze astronomical image data pixel-by-pixel to identify and classify all of the galaxies and stars in large data sets from astronomy surveys. Morphology represents the structural end state of the galaxy formation process, and astronomers have long connected the morphological character of galaxies to the physics of their formation. Therefore, being able to measure such morphologies is a very important task in observational astronomy. There are a number of models that have addressed many of these requirements in complimentary ways.


A Layman's Guide to Deep Convolutional Neural Networks

#artificialintelligence

This post is a part of a medium based'A Layman's guide to Deep Learning' series that I plan to publish in an incremental fashion. The target audience is beginners with basic programming skills; preferably Python. This post assumes you have a basic understanding of Deep Neural Networks a.k.a. A detailed post covering this has been published in the previous post -- A Layman's guide to Deep Neural Networks. Reading the previous post is highly recommended for a better understanding of this post. 'Computer Vision' as a field has evolved to new heights with the advent of deep learning.


IIT-Ropar and TSW Launch a PG Programme in Artificial Intelligence

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IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.


Artificial intelligence can make personality judgments based on photographs

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Russian researchers from HSE University and Open University for the Humanities and Economics have demonstrated that artificial intelligence is able to infer people's personality from'selfie' photographs better than human raters do. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces. The technology can be used to find the'best matches' in customer service, dating or online tutoring. The article, "Assessing the Big Five personality traits using real-life static facial images," will be published on May 22 in Scientific Reports.


How AI and ML can help solve the climate change problem? - Edurific Education

#artificialintelligence

Climate change is the biggest problem that the life on this planet is facing today. It will need every possible situation including technologies like Machine Learning and Artificial Intelligence. Here are 5 ways machine learning can help combat global climate change. Carbon Tracker is an independent financial think-tank working toward the UN goal of preventing new coal plants from being built by 2020. By monitoring coal plant emissions with satellite imagery, Carbon Tracker can use the information it gathers to convince the finance industry that carbon plants aren't profitable.