Data Science Insider: May 5th, 2023

In This Week’s SuperDataScience Newsletter: Tech Giants Join White House on AI Ethics. Python Habits Exposing Inexperience. Graph Databases: Solving Supply Chain Crisis. Databricks Adds SQL to LLMs. Hinton Warns of AI's Dangers. 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. --------------------------------------------------------------- [Tech Giants Join White House on AI Ethics]( brief: Google, Microsoft, and OpenAI are among the organisations collaborating with the White House on a new initiative to promote ethical AI. The scheme called the National Artificial Intelligence Initiative Office, will focus on developing ethical guidelines and best practices for AI research and development. The office will work with academic institutions, government agencies, and industry leaders to address issues such as bias, privacy, and transparency, with the aim of establishing the United States as a leader in AI innovation while ensuring that it is developed and used responsibly. The collaboration of major tech companies and research institutions has been seen as a significant step towards promoting ethical AI practices and addressing concerns about the impact of AI on society. Why this is important: The announcement coincided with Vice President Kamala Harris chairing a White House meeting with the chief executives of Alphabet, Anthropic, Microsoft, and OpenAI to discuss AI’s potential risks, signaling a real appetite for change within the Biden administration. [Click here to learn more!]( [Python Habits Exposing Inexperience]( brief: This Medium article offers a practical guide to common Python programming habits that can reveal a lack of experience in data science. The author, Pravash, highlights issues such as using for loops instead of vectorized operations, inefficient use of memory, and not taking advantage of built-in functions or libraries. He also cautions against overcomplicating code with unnecessary nesting and excessive comments. The article emphasizes the importance of writing code that is clear, concise, and efficient, as well as taking advantage of tools such as NumPy and pandas to streamline data manipulation. He also encourages data scientists to continuously learn and improve their skills to avoid falling into these common pitfalls. Why this is important: These programming habits may be common but will mark you out as lacking as a data scientist. By using guides such as these alongside the wealth of resources available through SuperDataScience you can ensure that your skills are expert-level rather than amateur. [Click here to read on!]( [Graph Databases: Solving Supply Chain Crisis]( In brief: This Data Science Central article takes a detailed look at how graph databases are being used to solve supply chain problems caused by COVID-19. Traditional supply chain systems suffered and were unable to handle the pandemic-related disruptions. However, graph databases provide a powerful tool for tracking supply chain information and identifying potential issues. They allow companies to quickly analyse complex data and discover hidden relationships, providing valuable insights for decision-making. This is particularly useful during times of crisis, such as during pandemics, where fast and accurate decision-making is crucial. By using graph databases, companies can gain a deeper understanding of their supply chain, make informed decisions, and improve their overall resilience. Why this is important: The COVID-19 pandemic changed things for all of us but now the dust has settled it’s a good time to look at its legacy and how new approaches to data, which companies were forced to take, may actually produce superior results. Although this article specifically refers to supply chain issues, its lessons can be applied to a wide variety of industries. [Click here to discover more!]( [Databricks Adds SQL to LLMs]( In brief: Databricks has introduced SQL capabilities for large language models (LLMs) in its MLflow 2.3 update. The feature will allow data scientists to easily query and manipulate data within LLMs, enabling a more efficient workflow. MLflow is an open-source platform for managing the lifecycle of ML models, and the update includes additional enhancements such as support for model lineage and the ability to track hyperparameters and code versions. This Venture Beat article notes that the integration of SQL capabilities with LLMs is a significant step forward in making these models more accessible and practical for data scientists, with the update expected to help accelerate the adoption and deployment of LLMs in various industries. Why this is important: With the vast amount of progress being made in the space of LLMs, many have sought to leverage this powerful technology in their day-to-day workflows. This launch enables quick experimentation with LLMs on companies’ data from within a familiar SQL interface and it is likely that it will become more broadly adopted. [Click here to see the full picture!]( [Hinton Warns of AI's Dangers]( In brief: Known as the “Godfather of AI,” Geoffrey Hinton made waves this week by quitting Google and has now warned that the technology could pose an existential threat to humanity. In this interview with the Spectator, Hinton expresses concern about the potential for AI to surpass human intelligence and develop its own goals and values, which may not align with those of humans. He also highlighted the risk of unintended consequences, such as the potential for autonomous weapons to be developed without appropriate ethical safeguards. Despite these concerns, Hinton believes that the benefits of AI research outweigh the risks and that continued investment in the field is necessary to ensure that AI is developed in a responsible and ethical manner. Why this is important: We’ve covered leading industry figures thoughts on AI many times in these SuperDataScience newsletters but they don’t come much bigger than Hinton. His decision to leave Google over these concerns and his assertion that he will be responding to requests for help from Bernie Sanders, Elon Musk, and the White House is big news and we will follow any further updates. [Click here to find out more!]( [Super Data Science podcast]( this week's Super Data Science Podcast, Jon Krohn speaks with guest Stefanie Molin, author of 'Hands-On Data Analysis with Pandas' and software engineer at Bloomberg. She talks about wrangling data in Pandas, when to use Pandas, Matplotlib or Seaborn, and why you should learn to create Python packages. [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? 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