Data science is diligently being used in the field of astronomy and here’s how-

Use of data science in astronomy can be largely found in the concept of data-driven astronomy (DDA). It is the production of astronomical knowledge based on archival data sets. DDA is similar to industrial data science in a way that the data sets are not taken with the experiment in mind, but rather are a byproduct of other processes or investigations. Successful DDA needs to be quite close to the data and often involves working closely with instrument and survey specialists.

To further understand the impact of data science on astronomy, one must go through the famous Galaxy Zoo project from 2007. Kevin Schawinski, an astrophysicist at Oxford University, had the task to classify 900,000 images of galaxies gathered by the Sloan Digital Sky Survey for a period of 7 years. He had to look at every single image and note whether the galaxy was elliptical or spiral and if it was spinning. The task seems like a pretty trivial one. However, the huge amount of data made it almost impossible. Because, according to the estimations, one person had to work 24/7 for 3-5 years in order to complete it. So, Schawinski and his colleague Chris Lintott came up with a better idea. This is where they decided to apply the technique of data science to analyze those images.

Currently, researchers are involved with specific projects including large empirical and simulation data sets: spacecraft imaging data from solar system missions, spacecraft survey data for exoplanets, sky surveys at radio, infrared, and optical wavelengths, data sets from gravitational wave detectors, and cosmological simulations of large-scale structures in the universe. These data sets will provide attractive contexts for learning and applying modern analysis methods.

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What’s new in the world of artificial intelligence?

Artificial intelligence is changing the course of our lives with its constant developments. Before the pandemic and now in the new normal, AI remains to be a key trend in the tech industry. It is reaching wider audiences as years pass and scientists, engineers, and entrepreneurs who involve themselves with modern technologies are reaping the benefits of AI and its branches, IoT and machine learning.

Organizations that overlooked digital transformation and the power of artificial intelligence are picking the pace of AI adoption. When COVID-19 was creating chaos across industries, it became evident that disruptive technologies and the automation that comes with it are more than crucial.

While 2020 was a great year for artificial intelligence working with its true potential, here are the latest advancements in the field of AI that are promising exciting times for the future of this technology.

1. An AI algorithm that can predict virus spread from animals to humans

Researchers from the University of Gothenburg have found an artificial intelligence model to predict what kind of virus can possibly spread from animals to humans. Using artificial intelligence, the algorithm studies the role of carbohydrates to understand the infection path. In scientific terms, carbohydrates are called glycans and they play a significant role in the way our bodies function. Almost all viruses first affect the glycans in our bodies, so did the coronavirus. Led by Daniel Bojar, assistant professor at the University of Gothenburg, the new AI model can analyze glycans with improved accuracy. The model analyses the infection process by predicting new virus-to-glycan interactions to better understand zoonotic diseases.

2. Another AI algorithm that can outsmart hackers with fake documents

The world is evolving with disruptive technologies and that includes hackers and cyber attackers. Cyberattacks have become more common amidst the remote working culture, where sensitive files and documents have become the prime targets. To deal with this pressing concern, V.S.Subrahmanian, a cybersecurity researcher at  Dartmouth College, created an algorithm called Word Embedding-based Fake Online Repository Generation Engine (WE-FORGE) that generates fake patents that are under development. This makes it difficult for hackers to find what they are looking for. The system generates convincing fakes based on the keywords of a given document. For each keyword it identifies, it analyses a list of related topics and replaces the original keyword with one randomly chosen related word.

3. DataRobot to launch a new enterprise AI platform to leverage business and customer’s value

DataRobot announced its second major platform launch, DataRobot version 7.1 with new MLOps management agents, time series model enhancements, and automated AI reports. With an aim to provide lifecycle management for remote AI and machine learning models, DataRobot’s new launch will offer feature discovery push-down integration for Snowflake and time series Eureqa model improvements. Through this, Snowflake users can use automatic discovery and computation of individual independent variables in the Snowflake data cloud. Apart from these additions, DataRobot also provides a no-code app builder that has the ability to convert deployed models into AI apps with no coding.

4. Exscientia is leveraging AI-powered drug discovery with Allcyte’s deep learning platform

Exscientia’s US$60M acquisition of Allcyte will boost AI-drug discovery. Allcyte is an Austrian company that is developing an artificial intelligence platform to study how cancer treatments work on different individuals. Post the acquisition, this technology will work with Exscientia’s native software that uses AI to identify potential drug targets, build the right drugs, and send them for trials. Exscientia will now be able to work with a precision medicine approach to design drug molecules, ensuring improved efficiency.

5. As a relief for asset managers, Redi2 and IBM Private Cloud Services pioneering a new solution

The creator of the SaaS delivery model for financial services billing solutions, Redi2 Technologies, has announced a collaboration with IBM Private Cloud Services to improve flexibility. The combination of these technologies will add strong value for Redi2 Revenue Manager clients. Top asset managers throughout the world can take advantage of the improvements like fast reaction options for clients who need quick responses to changes, move data from one country to another or expand their infrastructure requirements.

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