Data Science Insider: September 1st, 2023

In This Week’s SuperDataScience Newsletter: Virtualitics Takes Data Viz Tech from Stars to Wall Street. Open-Source Tool Speeds Up Programming in Python. Baidu’s AI Chatbot Ernie Bot Publicly Available. Next-Gen Solution for Medical Claims Data Exploration. Machine Learning Algorithm can Distinguish Tic from Non-tic Movements Cheers, - The SuperDataScience Team P.S. Have friends and colleagues who could benefit from these weekly updates? Send them to [this link]( to subscribe to the Data Science Insider. --------------------------------------------------------------- [Virtualitics Takes Data Viz Tech from Stars to Wall Street]( brief: Virtualitics, a company that specialises in data exploration technology, was initially founded by the CEO Michael Amori in 2016 for the purposes of analyzing and understanding astronomical data. In the past, the company was successful in securing contracts with the US military, which remains one of its most loyal customers today. However, in 2023, the organisation has taken a different course, expanding into the commercial sector and onboarding its new client Citi, a financial services provider recognised worldwide. The iViz technology is incredibly versatile and can be applied within other fields and disciplines besides astronomy. In particular, the software allows users to identify similar patterns in data as well as generate 3-D data visualisations, leveraging the data exploration capabilities to their fullest potential. Furthermore, the Virtualitics software allows data scientists to discover various correlations across the tabular data set while refraining from over-relying on the initial hypothesis test. Why this is important: The software accentuates the potential of intelligent data exploration, underpinned by potent AI capabilities. Moreover, its reach into the commercial space may signal the transformation of data operations within the financial services industry, as global giants like Citi shift their focus toward machine learning and data-driven optimizations. [Click here to learn more!]( [Open-Source Tool Speeds Up Programming in Python]( brief: While in the past few years, Python has become a staple among the other universal programming languages, it is renowned for its notorious inefficiency in terms of speed. A team of AI experts from the University of Massachusetts Amherst is about to change that with their new revolutionising profiler called “Scalene.” Data scientists and programmers usually use profilers when working with Python code to identify any issues. It’s the first profiler that can tease out the code’s errors with exact precision and suggest troubleshooting solutions. The software targets the aspects of the Python code that often cause its slow speed: the CPU, GPU, and memory usage. Then, once the problem has been detected, utilizing the same technology that acted as a foundation for ChatGPT, Scalene provides programmers with ideas to optimize the lines of code that aren’t performing well. Scalene has been already downloaded by the general public over 750,000 times. Why this is important: According to Emery Berger, the leader behind the project, computers are not becoming faster anymore, and computer scientists should focus on improving their programming and coding instead. This makes Scalene a paramount development, as it would allow data scientists to boost Python's speed to a much greater degree. Further popularization of the profiler tool may completely transform the world of coding, making it more accessible and user-friendly and removing the frustration over the code's shortcomings. [Click here to read on!]( [Baidu’s AI Chatbot Ernie Bot Publicly Available]( In brief: On Thursday, 31st August, the world watched from the sidelines as China stepped in to unveil its own version of the ChatGPT language model called “Ernie Bot”. Baidu, a Chinese research and artificial intelligence firm, is behind this invention. Once the company has made the product available to the public, they could instantly see the software dominating the charts among app downloads on the Chinese Apple IOS store within the first day. Additionally, the company’s stock rose by over 3%. Just like ChatGPT, Ernie Bot can respond to questions and requests by producing text and images, following instructions provided by the user. While this development is guaranteed to bring a lot of value to many users, the Baidu CEO, Robin Li, also expects to receive and incorporate feedback to improve the app model further. Despite the fact that there are specific regulations around AI in China, the CEO considers those as being “more pro-innovation than regulation”. Why this is important: As several Chinese companies release their AI Chat Bot software that is on par with the US's ChatGPT, it marks the official entering of China into the AI race. On the one hand, there is a sense of excitement about such advancements in the realm of AI and deep learning, and these aspects are becoming more and more integrated into the fabric of the current world order. However, these new technologies beg the question of the future impact of the AI regulations that are currently being implemented, as well as the roles and responsibilities the data science community will have to take on. [Click here to discover more!]( [Next-Gen Solution for Medical Claims Data Exploration]( In brief: Offering a groundbreaking data solution for the struggling healthcare sector, PurpleLab Healthcare Analytics has announced the launch of CLEAR, a data-driven software that focuses on a more systemic approach to healthcare data analysis. C.L.E.A.R. stands for Comprehensive Layout for Exploration, Analysis, & Research; it aims to remove the barriers that healthcare organizations face by providing patient-level data for exploration and offering streamlined data export options in an easily accessible format. To achieve that, the platform works together with Purple Lab's HealthNexus, a no-code analytics platform that enables access to real-world data. "We’re excited to be developing the tools that help healthcare providers and organizations (HCPs and HCOs) get better claims insights to make this happen," Mark Brosso, founder and CEO of PurpleLab explains. "CLEAR streamlines data by identifying duplicates, highlighting primary claims and their statuses, and pinpointing specific patient visits. It is expected to become the ultimate method of obtaining faster and clearer insights into the patient's journey, the details of their claims, and the outcomes. Why this is important: Not only does the tool facilitate a more in-depth understanding of patients' data on an individual level, but also the data-centered approach towards claims and records can enable medical professionals to help patients in a more efficient manner. Additionally, it may reduce the burden of organisations' cost and time resources, which remains to be one of the most complex challenges for the healthcare sector globally. [Click here to see the full picture!]( [ML Algorithm can Distinguish Tic from Non-tic Movements]( In brief: According to the latest findings that were put forward during the 2023 International Congress of Parkinson’s Disease and Movement Disorders, machine learning can now be used for distinguishing tics from extra movements in patients with tic disorders. Specifically, the Random Forest Classifier algorithm had an 83% success rate of accurately detecting the differences in movements between patients and healthy controls who voluntarily participated in the study. Relying on the predictions, the data scientists computed a set of variables, including the number of tics per minute, the maximum duration of a tic segment, changes from tic to non-tic segments, tic and non-tic clusters, etc. The aim was to enable the algorithm to differentiate tics from non-tic extra movements, simultaneously measuring the relevant parameters that would together produce a single tic detection score. Moreover, even though the clinical trial was done only for facial tics, the same algorithm may be used to study other body regions, improving the detection of the tic disorder in its different forms. Although the score was quite high, researchers believe that the classification accuracy still requires more work. Why this is important: Detecting the frequency and clustering of tics from the participants' videos using the machine learning method is the first step in obtaining highly reliable and precise measurements of the disorder manifestations variables. Consequently, integrating machine learning tools into clinical practice can be useful for medical professionals to diagnose and assess the disorder in a faster and more accurate manner. [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast]( episode, Meta's Dr. Laurens van der Maaten takes you on a journey to the forefront of AI. Explore cutting-edge projects in privacy-preserving ML, protein synthesis, climate change solutions, and more on this week's episode, hosted by Jon Krohn! [Click here to find out more!]( --------------------------------------------------------------- What is the Data Science Insider? This email is a briefing of the week's most disruptive, interesting, and useful resources curated by the SuperDataScience team for Data Scientists who want to take their careers to the next level. Want to take your data science skills to the next level? Check out the [SuperDataScience platform]( and sign up for membership today! Know someone who would benefit from getting The Data Science Insider? Send them [this link to sign up.]( # # If you wish to stop receiving our emails or change your subscription options, please [Manage Your Subscription]( SuperDataScience Pty Ltd (ABN 91 617 928 131), 15 Macleay Crescent, Pacific Paradise, QLD 4564, Australia