Data Science Insider: April 28th, 2023

In This Week’s SuperDataScience Newsletter: Academics Urge AI Developers to Study Consciousness. The Evolution of Data Science Unpacked. Synthetic Data Improves AI Privacy. Build Interactive Sentiment Analysis Reports. Data Workshops in Ukraine: Learn and Support. 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. --------------------------------------------------------------- [Academics Urge AI Developers to Study Consciousness]( brief: Several academics from around the world have signed an open letter urging AI developers to research and understand consciousness as AI systems become more advanced. The letter calls for a greater scientific understanding of how consciousness can apply to AI and how society can exist alongside it. While most experts agree AI that is nowhere near possessing feelings or human-level consciousness, it is rapidly evolving. As we have previously covered in these newsletters, Elon Musk and other experts have called for AI developments to be halted until safety measures are designed and implemented. The Association for Mathematical Consciousness Science (AMCS), which compiled the open letter, said that it did not have a view on whether AI development should be paused. Why this is important: Here at SuperDataScience we’ve frequently covered the debates that run rampant in the AI community about the sentient nature of AI and the ethics surrounding it. This latest development shows how seriously the academic community is taking it and we will continue to cover any further developments. [Click here to learn more!]( [The Evolution of Data Science Unpacked]( brief: Data science has rapidly evolved in recent years, expanding beyond its origins in statistics and computer science. This shift has led to new challenges and opportunities in the field, including the need for interdisciplinary collaboration and the development of ethical frameworks for data analysis. This excerpt from How Data Happened: A History from the Age of Reason to the Age of Algorithms suggests that a key approach to navigating these challenges is "unpacking" data science, or breaking it down into its component parts and examining the relationships between them. This process can help researchers and practitioners identify the assumptions, biases, and values that underpin data science, and develop more nuanced and effective approaches to data analysis and interpretation. Why this is important: As data scientists we may be well aware of our job role, but it is important that we understand the range of subjects that it encompasses. Definitions are often tied up in control and capital but by unpacking the discipline we can have a clearer idea of where our industry originated and how it currently stands. [Click here to read on!]( [Synthetic Data Improves AI Privacy]( In brief: According to data scientists from MIT’s Laboratory for Information and Decision Systems, researchers are turning to synthetic data to overcome the issues of privacy and representation in AI. The synthetic data is created by ML algorithms and neural networks. It analyses a real data set and learns the statistical relationships within it, creating new data sets containing different data points but retaining the same relationships. Synthetic data can help to tackle issues beyond privacy such as cost, lack of real-world data available, and biases. ML algorithms must consume vast amounts of information to learn whereas synthetic data can be created much faster and cheaper than gathering it from the real world. Why this is important: This work is fascinating as it shows us how researchers believe that synthetic data can help create better data. However, in order to do this effectively there needs to be a balance found between precision and fakery. [Click here to discover more!]( [Build Interactive Sentiment Analysis Reports]( In brief: This article from Towards Data Science discusses the process of building an interactive sentiment analysis report using the Natural Language Toolkit (NLTK) and Altair visualization library in Python. In it, a Product Manager at Datapane - John Micah Reid- outlines the steps involved in text pre-processing, including tokenization, stop words removal, and stemming, before applying the NLTK's Vader sentiment analysis tool to generate sentiment scores for each piece of text. The sentiment scores are then visualized using Altair, allowing users to interactively explore the data and drill down into specific text samples. The author also shares their approach to evaluating the accuracy of the sentiment analysis by comparing the results against human-labeled data. Why this is important: This article provides a useful guide and practical guide for data scientists, like us, who are interested in creating interactive sentiment analysis reports. Why not put its lessons into practice today?! [Click here to see the full picture!]( [Data Workshops in Ukraine: Learn and Support]( In brief: In this article, InfoWorld discusses how Data Workshops for Ukraine (DWU) is using technology education to support Ukraine's IT industry and economy. DWU aims to bridge the gap between technical skills and opportunities in Ukraine by providing free, high-quality training to individuals looking to upskill or change careers. The training is conducted by experienced professionals from Ukraine's leading IT companies and covers topics such as data science, web development, and cybersecurity. Participants are not only able to learn valuable skills but also support Ukraine's economy by contributing to the growth of its IT sector. DWU's efforts highlight the power of technology education to create a positive social impact and transform lives. Why this is important: Here at SuperDataScience we’re constantly giving our readers encouragement to upskill and seek personal development opportunities. When you’re next taking our advice on board and looking to learn something new maybe you will consider doing it in a way that can make a difference with DWU’s two-hour workshops offering training in data visualization and analysis with R, ChatGPT in R,Python, and SQL costing just $20 or €20. [Click here to find out more!]( [Super Data Science podcast]( this week's Super Data Science Podcast, Vincent Gosselin, CEO and co-founder of Taipy, an open-source Python library, joins Jon Krohn to discuss how to accelerate productivity in Python and build scalable, reusable, and maintainable data pipelines. [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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