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Deep CEE: AI Learning Model Helping Astronomers Identify Galaxy Clusters Quickly

"Data mining techniques such as deep learning will help us to analyse the enormous outputs of modern telescopes" said John Stott from Lancaster University

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galaxy clusters, artificial intelligence
Galaxy clusters represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy. Wikimedia Commons

Researchers have developed an Artificial Intelligence (AI)-powered tool that has been trained to “look” at colour images and identify galaxy clusters quickly.

The “Deep-CEE” – Deep Learning for Galaxy Cluster Extraction and Evaluation – model is based on neural networks, which are designed to mimic the way a human brain learns to recognise objects by activating specific neurons when visualising distinctive patterns and colours.

Matthew Chan, a PhD student at Lancaster University in Britain trained the AI by repeatedly showing it examples of known, labelled objects in images until the algorithm is able to learn to associate objects on its own. Then the researchers ran a pilot study to test the algorithm’s ability to identify and classify galaxy clusters in images that contain many other astronomical objects.

galaxy clusters, artificial intelligence
Chandra Six Galaxy Cluster (X-rays). Wikimedia Commons

“Data mining techniques such as deep learning will help us to analyse the enormous outputs of modern telescopes” said John Stott from Lancaster University. “We expect our method to find thousands of clusters never seen before by science,” Stott said.

Galaxy clusters represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy. New state-of-the-art telescopes have enabled astronomers to observe wider and deeper than ever before, such as studying the large-scale structure of the universe and mapping its vast undiscovered content.

By automating the discovery process, scientists can quickly scan sets of images, and return precise predictions with minimal human interaction.

Artificial intelligence, galaxy clusters
Matthew Chan, a PhD student at Lancaster University in Britain trained the AI by repeatedly showing it examples of known, labelled objects in images until the algorithm is able to learn to associate objects on its own. Pixabay

This will be essential for analysing data in future. The upcoming Large Synoptic Survey telescope (LSST) sky survey (due to come online in 2021) will image the skies of the entire southern hemisphere, generating an estimated 15 TB of data every night. “We have successfully applied Deep-CEE to the Sloan Digital Sky Survey,” said Chan.

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“Ultimately, we will run our model on revolutionary surveys such as the LSST that will probe wider and deeper into regions of the Universe never before explored,” Chan added. The study was presented at the Royal Astronomical Society’s National Astronomy meeting at Lancaster University. (IANS)

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Is Oracle Digital Assistant Smarter Than Amazon Alexa? Find Out Here!

Here's Why Oracle's digital assistant better than Amazon's Alexa

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Amazon Alexa may lag behind Oracel Digital Assistant. Pixabay

Alexa may be your perfect living room assistant, but when it comes to specific queries with particular vocabulary from enterprises, it lags behind in rich capabilities that Oracle Digital Assistant (ODA) has to offer, a top company executive has stressed.

Oracle Digital Assistant can become your intelligent front-end — your smart router that’s able to send all your specific questions to relevant bots, according to Suhas Uliyar, VP-Product Management, Oracle Digital Assistant and Integration Cloud.

“It knows how to handle conflicts, manage security and so on and so forth. It has got Artificial Intelligence (AI) and is AI-trained so we call the routing of your questions to be relevant, and call it a skill now. Instead of bot, it’s a skill,” Uliyar told IANS during an interaction.

According to him, there are a couple of differences compared to Alexa as it works on the same model.

“Alexa is very implicit, where you have to say — Alexa, ask this skill to do something. While Oracle Digital Assistant is both explicit and implicit and you don’t need to sort of say ‘go ask the HCM (Human Capital Management) bot,’ for instance. It’ll just figure out that the question is for HCM bot and will answer accordingly,” Uliyar elaborated.

The other thing is to use the word ‘assistant’ and, according to him, we are overloading the term ‘assistant’ because if you have an ‘assistant’, she or he is smart enough to understand who you are, what your preferences are, know how you work.

“Most of the chatbots respond to a simple question and answer. Next time, it will probably remember who you are. So, the whole context is memory, and also the process side of things,” the Oracle executive added.

Oracle Digital Assistant provides the platform and tools to easily build AI-powered assistants that connect to your backend applications.

The digital assistant uses AI for natural language processing and understanding, to automate engagements with conversational interfaces that respond instantly, improve user satisfaction, and increase business efficiencies.

Most of those voice-enabled application programming interfaces (APIs) are being trained using what’s called Open Common Domain Models, which means that it understands our normal speaking style and content.

“What if an enterprise has a specific vocabulary? For example, a very common thing in Enterprise Resource Planning (ERP) is what’s EBITDA for a company. You try saying EBITDA to Alexa or any other such assistant in the market today, and you’ll most likely draw a blank,” Uliyar told IANS.

Oracle AI
Oracle digital assistant uses AI for natural language processing and understanding. Pixabay

Earnings before interest, tax, depreciation and amortization (EBITDA) is a measure of a company’s operating performance.

According to him, to serve customers with delightful experiences every single time, there has to be a lot of innovation happening — whether it’s mobile, chatbots, Blockchain, AI or AR/VR.

“With all these new realities, what enterprises really need is a platform that can pull that ‘holistic experience’ altogether. That’s sort of the topmost challenge that enterprise customers want to solve,” he noted.

Oracle Digital Assistant is very sophisticated. It has got deep learning and is based on a technology called Sequence-to-Sequence vectoring and creates what we call as logical forms of the statement.

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“We call it a deep semantic parsing. It’s the underlying technology and is very different given the advancements of deep learning. We can do a much better job instead of understanding the linguistic constructs, versus in the past. This is quite a bit of advancement. We’re definitely very excited about pushing the boundaries,” said Uliyar. (IANS)