Data Science Insider: May 19th, 2023

In This Week’s SuperDataScience Newsletter: BT to Cut 55,000 Jobs and Embrace AI. Discovering Lesser-Known Python Data Science Tools. Drones Master Unseen Terrain with Liquid Neural Networks. Exploring Meshed Grids in NumPy: A Comprehensive Guide. Meta Unveils Custom AI Chip. 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. --------------------------------------------------------------- [BT to Cut 55,000 Jobs and Embrace AI]( brief: BT, the UK's largest telecoms provider, has announced plans to cut up to 55,000 jobs by the end of the decade, with a significant portion of the cuts in customer services due to the implementation of AI. The company aims to leverage AI tools, such as generative AI like ChatGPT, to improve services while ensuring that customers do not feel like they are dealing with robots. The reduction in staff is part of BT's cost-cutting measures and the adoption of more efficient technologies which will require fewer employees to serve customers. The move aligns with the broader trend in the telecoms industry, as Vodafone also recently announced plans to cut jobs in an effort to achieve efficiency savings. Why this is important: As we have seen repeatedly in these SuperDataScience newsletters, as AI continues to be integrated into various industries it brings both opportunities and challenges. By staying informed about AI advancements and their implications, data scientists can contribute to developing AI solutions that optimize processes, enhance customer experiences, and help organizations navigate the transition to an AI-driven future as well as play a pivotal role in addressing ethical considerations and social impact. [Click here to learn more!]( [Exploring Meshed Grids in NumPy: A Comprehensive Guide]( brief: This Medium article delves into the concept of meshed grids in NumPy (where a rectangular grid is created from an array of x and y values), providing an in-depth understanding of their significance and usage in data science. The author, known as Mathcube, begins by explaining the basics of NumPy arrays and their multidimensional nature. They then introduce the concept of meshgrids and demonstrate how to create them using the numpy.meshgrid() function. The article explores their various applications, such as plotting surfaces, evaluating functions over a grid, and generating coordinate matrices. Additionally, the author highlights the advantages of meshgrids in simplifying complex computations and enhancing visualization in scientific computing tasks. Why this is important: Through detailed explanations, code examples, and visual illustrations, this article serves as a comprehensive resource for data scientists seeking to leverage meshed grids effectively in NumPy for their analytical needs. [Click here to read on!]( [Drones Master Unseen Terrain with Liquid Neural Networks]( In brief: This news from MIT showcases a groundbreaking development in drone technology, where liquid neural networks (LNNs) are employed to enable autonomous navigation in previously unexplored or obscured environments. Researchers have devised a novel approach by utilizing a network of droplets, each representing a single neural unit, to perform computations and make decisions collectively. The droplets can communicate and exchange information through chemical signals, allowing the LNN to adapt and react to changing conditions in real time. The article discusses the experimental setup and highlights successful test scenarios where drones navigate through challenging terrains or regions with limited visibility. This innovative application of liquid neural networks opens up new possibilities for autonomous systems to operate in complex and unpredictable environments. Why this is important: By harnessing the power of LNNs, drones can overcome the limitations of traditional algorithms and navigate through environments that were previously inaccessible or hazardous. This advancement not only enhances the capabilities of drones for various applications such as search and rescue, mapping, and surveillance but also expands the repertoire of tools available to data scientists. [Click here to discover more!]( [Discovering Lesser-Known Python Data Science Tools]( In brief: Python's extensive collection of data science tools is renowned, but it can be challenging to identify the best ones amid the abundance of options. This article presents an overview of some newer and lesser-known data science projects that deserve attention from expert data scientists. The featured tools include ConnectorX, a fast data-loading library that facilitates seamless integration with popular Python data-wrangling tools. DuckDB is introduced as a lightweight and responsive in-process OLAP database engine optimized for analytical queries. Optimus offers a comprehensive toolset for data loading, exploration, cleansing, and writing. Polars emerges as a high-performance DataFrame library with a familiar Pandas-like syntax. Lastly, Snakemake is highlighted for automating and streamlining data science workflows, ensuring consistent and reproducible results. Why this is important: Libraries such as NumPy, Pandas, and scikit-learn are probably already a mainstay in your toolkit but having up-to-date knowledge of newly released and less popular tools will ensure that you stay at the cutting edge of the data science industry and can always select the one that most closely matches your needs. [Click here to see the full picture!]( [Meta Unveils Custom AI Chip]( In brief: Meta has introduced its first custom AI chip, signaling a significant advancement in hardware development for AI applications. The chip, called Meta A1, is specifically designed to accelerate ML workloads and enhance AI performance. It boasts impressive capabilities, including a high level of parallel processing power and energy efficiency. This Zdnet article discusses how Meta's chip aims to address the growing demand for AI computing power and the challenges posed by traditional hardware architectures. Additionally, it mentions Meta's collaboration with researchers and developers to optimize AI algorithms and unlock new possibilities in areas like computer vision and NLP. Custom AI chips, like Meta A1, offer the potential to significantly accelerate training and inference processes, enabling faster and more complex computations. Why this is important: Understanding the capabilities and architecture of these chips allows data scientists to leverage their full potential and optimize their AI workflows accordingly. By taking advantage of specialized hardware, data scientists can improve the scalability, speed, and energy efficiency of their AI applications, paving the way for more innovative and impactful data-driven solutions. [Click here to find out more!]( [Super Data Science podcast]( week's Super Data Science Podcast episode is critical listening for AI investors and generative AI creators. AI investor George Mathew talks with host Jon Krohn about the emerging generative AI stack, the critical elements of MLOps to ensure a scalable model, and the tools developers can use for a saleable product. [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