Data Science Insider: December 8th, 2023

In This Week’s SuperDataScience Newsletter: Google Unveils Gemini. Unveiling Data Modelling Secrets with Snowflake. Demystifying Transformers. Considerations for Aspiring Data Scientists. McDonald's Partners with Google for AI Innovation. 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. --------------------------------------------------------------- [Google Unveils Gemini]( brief: Google's recent release of Gemini has been touted as a groundbreaking AI model and signals a new phase in the ongoing AI boom, challenging OpenAI's ChatGPT. Unlike traditional large language models (LLMs) primarily anchored to text, Gemini is hailed as a "natively multimodal" model, capable of learning from diverse data sources, including audio, video, and images. This development extends beyond the text-centric limitations of LLMs, presenting a potential shift in AI product capabilities. The executive behind Gemini is DeepMind co-founder Demis Hassabis, who emphasizes the need to combine LLMs with other AI techniques to enhance understanding. This unveiling intensifies the competition between Google and OpenAI, both seeking radical approaches for future AI advancements. Why this is important: The emphasis on combining LLMs with other AI techniques underscores the necessity of holistic approaches in AI development. As Google and OpenAI compete on groundbreaking ideas, data scientists must stay abreast of evolving AI paradigms, acknowledging the dynamic landscape and embracing innovative methodologies for future advancements in the field. [Click here to learn more!]( [Unveiling Data Modelling Secrets with Snowflake]( brief: In this Medium article, Serge Gershkovich reveals hidden features in Snowflake, the data platform that harnesses the power of the cloud for efficient data modelling. He introduces virtual table columns, akin to Oracle's, allowing dynamic calculations without physical storage. Gershkovich also addresses the loss of conceptual nuances in physical models, proposing creative solutions using comments and naming conventions to convey relationship names and cardinality. He emphasizes the importance of understanding Snowflake's metadata operations, particularly the over 30 DESCRIBE functions. While Snowflake's documentation is thorough, Gershkovich recommends tapping into the community's wealth of knowledge for continuous learning and staying abreast of platform secrets, allowing data scientists to uncover and master Snowflake's hidden data modelling features and expand their toolkit. Why this is important: Understanding virtual columns, relationship nuances, and leveraging metadata efficiently enhances the development of robust and cost-effective data models. [Click here to read on!]( [Demystifying Transformers]( In brief: Transformers, introduced in 2017, stand as a cornerstone in ML, gaining further prominence with models like ChatGPT and GPT-4. Their impact extends across Natural Language Processing (NLP), excelling in handling long-range dependencies. The architectural components involve tokenization, embeddings, and a central attention mechanism. Additional elements like multi-head attention, Feedforward Neural Networks, Layer Normalization, and Skip connections complete the structure. For data scientists, a comprehensive understanding of these components is imperative but often sadly lacking. Proficiency in tokenization, embeddings, and attention mechanisms empowers data scientists to effectively utilize transformers in tasks such as machine translation. Their expertise contributes significantly to advancements in NLP and the diverse applications of transformers and this Towards Data Science article helps demystify them. Why this is important: Data scientists' expertise plays a pivotal role in advancing machine translation, sentiment analysis, and text generation, contributing substantially to the evolving landscape of ML. [Click here to discover more!]( [Considerations for Aspiring Data Scientists]( In brief: As a reader of the SuperDataScience weekly newsletter, it may be safe to assume that you are a keen data scientist. If it is not your primary profession you will find this article - which explores seven reasons why aspiring individuals should reconsider pursuing a career in data science - illuminating. It emphasizes challenges such as the need for effective collaboration, the dynamic nature of projects, role ambiguity, the importance of aligning with business objectives, engaging in "boring" yet essential tasks like data cleaning, the perpetual need for learning and upskilling, and embracing the continuous challenges inherent in the field. The article underscores that a successful data science career requires resilience, adaptability, and a willingness to navigate an evolving landscape. Why this is important: Understanding these challenges provides potential data scientists with insights into the multifaceted nature of the profession, allowing them to make informed career decisions and ensuring long-term professional fulfilment. [Click here to see the full picture!]( [McDonald's Partners with Google for AI Innovation]( In brief: Fast food giant McDonald's has announced a partnership with Google to leverage generative AI for global expansion and enhanced customer experience. The collaboration aims to employ AI technologies through cloud computing, focusing on innovation in equipment, supply-chain processes, and simplifying operations for restaurant crews. McDonald's anticipates improved product quality, with the integration of AI contributing to hotter and fresher food globally. The move aligns with McDonald's broader strategy to add 10,000 restaurants and increase loyalty program membership to 250 million by 2027, targeting an additional $45 billion in sales. The implementation of a universal software system aims to ensure consistency across all McDonald's outlets and digital platforms but currently, the specific AI applications remain undisclosed. Why this is important: McDonald's has emphasized improved operational efficiency as the driver of this innovation, yet questions linger regarding potential implications for human workers and the overall impact of AI-driven automation in the fast-food industry. [Click here to see the full picture!]( [Super Data Science podcast]( In this week's [SuperDataScience]( Podcast episode, scikit-learn co-founder Gaël Varoquaux and Jon Krohn discuss the evolution of scikit-learn. From its origins as a memory-efficient Python implementation of support vector machines to its present-day status as a pivotal resource in machine learning, Gaël paints a vivid picture of its remarkable growth. Join us for a glimpse into scikit-learn's evolution, the realm of open-source collaboration, and the transformative power of data-driven insights in today's dynamic data landscape. [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? 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