Data Science Insider: June 21st, 2023

In This Week’s SuperDataScience Newsletter: Planned Olympic Surveillance Raises Civil Liberty Concern. Trino: Empowering Big Data Analysis. MIT's Breakthrough in ML Security. Economic Thinking in Next-Gen Data Science. James Cameron's AI Warning. 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. --------------------------------------------------------------- [Planned Olympic Surveillance Raises Civil Liberty Concern]( brief: The French government's plan to use real-time cameras with AI for security during the next summer's Olympics in Paris has sparked concerns over civil liberties. While the law explicitly prohibits facial recognition, the use of AI algorithms to detect anomalies, such as unattended bags and crowd rushes, has raised fears that the measures may become permanent and invasive. The technology is already being tested in some police stations, empowering human officers to respond to alerts raised by AI-monitored CCTV footage. Critics argue that even without facial recognition, AI video monitoring compromises anonymity and individual freedom, resembling the kind of surveillance practices seen in other countries like China- the controversy over which we’ve regularly covered in these newsletters. Why this is important: As developers increasingly integrate AI technologies into public safety systems, understanding the potential risks to civil liberties becomes crucial. Balancing security needs with privacy concerns requires a collaborative effort between data scientists, policymakers, and civil rights groups to ensure the transparent and ethical use of AI technologies in public spaces. [Click here to learn more!]( [Trino: Empowering Big Data Analysis]( brief: Trino is an open-source distributed SQL query engine that offers exceptional performance and scalability for handling large-scale datasets, executing complex SQL queries across distributed data sources and allowing data scientists to explore and analyse large datasets with ease and speed. It efficiently executes ad-hoc queries on various data sources, including Hadoop Distributed File System (HDFS), Amazon S3, and others, making it a powerful tool for data scientists dealing with big data challenges. This Medium article provides an in-depth analysis of Trino (formerly known as PrestoSQL), highlighting its significance in processing big data. The article explores Trino's architecture, features, and its ability to support real-time analytics, making it an invaluable asset for data professionals working with massive datasets. Why this is important: By leveraging Trino's performance and scalability, data scientists can overcome the challenges posed by big data and extract actionable intelligence from vast amounts of information, ultimately improving the effectiveness and efficiency of their data-driven projects. [Click here to read on!]( [MIT's Breakthrough in ML Security]( In brief: Researchers from MIT have achieved a groundbreaking advancement in privacy protection for ML models using Probably Approximately Correct (PAC) privacy. The PAC privacy framework ensures data privacy by preventing sensitive information leakage through trained models. By incorporating privacy guarantees during the model training process, the researchers have successfully struck a balance between data utility and privacy preservation. This breakthrough has significant implications for data scientists as it enables them to build more secure and privacy-respecting ML models without compromising performance. With PAC privacy, data scientists will be able to instil greater confidence in users and stakeholders regarding the safety and ethical use of sensitive data in various applications, fostering trust in ML technologies. Why this is important: The ability to safeguard sensitive information while maintaining model accuracy empowers data scientists to comply with data privacy regulations and maintain ethical practices in data handling. This breakthrough will allow data scientists to explore and experiment with confidential datasets securely, enabling the development of robust and privacy-respecting ML applications. [Click here to discover more!]( [Economic Thinking in Next-Gen Data Science]( In brief: Here at SuperDataScience we’re always looking to inspire the next generation of data scientists. This article explores the concept of thinking like an economist to enhance the capabilities of those who are looking to make a splash in the field and discusses the parallels between economic principles and data science methodologies, emphasizing the importance of considering trade-offs, incentives, and resource allocation in data-driven decision-making. By adopting an economic mindset, data scientists can optimize their strategies, prioritize projects based on potential returns, and effectively communicate insights to stakeholders. It advocates for the integration of economic thinking into the data science workflow, enabling professionals to derive more profound and actionable insights from data and contribute to more informed business decisions. Why this is important: Incorporating economic concepts helps data scientists evaluate the costs and benefits of different analytical approaches. Moreover, they can leverage economic reasoning to communicate insights persuasively to non-technical stakeholders, enabling better collaboration and decision-making across diverse teams. Embracing an economist's mindset empowers data scientists, like you, to become versatile problem solvers and deliver data-driven solutions that drive meaningful business outcomes. [Click here to see the full picture!]( [James Cameron's AI Warning]( In brief: Renowned filmmaker James Cameron has expressed his concern about AI's potential risks to humanity, drawing parallels to his Terminator movie, which was released in 1984 by saying “I warned you guys in 1984, and you didn't listen.” Speaking about the dangers of AI in an interview with CTV News, he highlighted the need for data scientists to be aware of the ethical implications surrounding its development and application. The Oscar-winning director questioned the motives of those who are developing the technology and queried whether it's for profit ("teaching greed") or for defence ("teaching paranoia"). He also went on to argue that AI could pose a threat to humanity as further advancements are made available. Why this is important: Understanding the ethical challenges and risks associated with AI is essential for data scientists to ensure the responsible and safe implementation of AI systems. By proactively addressing these concerns, we can contribute to building AI that respects human values and safeguards against potential harm, fostering a sustainable and beneficial AI-driven future. [Click here to find out more!]( [Super Data Science podcast]( this week's [Super Data Science Podcast]( episode, AI visionary and CEO of SingularityNET Dr. Ben Goertzel provides a deep dive into the possible realization of Artificial General Intelligence (AGI) within 3-7 years. Explore the intriguing connections between self-awareness, consciousness, and the future of Artificial Super Intelligence (ASI) and discover the transformative societal changes that could arise. [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