Data Science Insider: April 7th, 2023

In This Week’s SuperDataScience Newsletter: Renewable Energy Struggles to Hire Data Scientists. Italy Bans ChatGPT Over Privacy Concerns. Python Deep Learning for Audio Classification. Optimising Neural Networks for Specific Tasks. Visualization Creates Stunning Population Density Maps. 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. --------------------------------------------------------------- [Renewable Energy Struggles to Hire Data Scientists]( brief: The renewable energy sector is struggling to recruit data scientists, according to a report by Hyperion Executive Search. The report notes that while demand for data scientists in the industry has increased significantly, there is a limited pool of candidates with the required skills and experience. The shortage of talent could potentially hinder the sector's growth, as data scientists play a vital role in developing models to optimise renewable energy generation and storage. The report recommends that the industry needs to offer competitive salaries and benefits, as well as opportunities for career development and training, to attract and retain talent. The industry also needs to work with universities to develop relevant degree programs and offer apprenticeships and internships to students. Why this is important: As data scientists, we are aware of the massive benefits that we can bring to an immense range of industries. The renewable energy sector is growing at a vast rate and by using resources, such as those offered by SuperDataScience, we can ensure that our skills are up to date and make us good candidates to fulfill a gap in the employment market. [Click here to learn more!]( [Italy Bans ChatGPT Over Privacy Concerns]( brief: Italy's data protection authority has banned OpenAI's advanced chatbot ChatGPT over privacy concerns. It also announced an investigation into OpenAI and its compliance with the General Data Protection Regulation. The watchdog said the model had been used by millions since its launch in November 2022 and warned about the potential risks of AI, including job losses and the spreading of misinformation and bias. As we saw in last week’s newsletter, several tech leaders including Elon Musk have called for a suspension of AI systems like ChatGPT, citing fears about AI's lack of regulation. The watchdog gave OpenAI 20 days to address its concerns, failing which it could be fined up to €20m ($21.7m) or 4% of its annual revenues. Why this is important: It seems like a week doesn’t pass without there being some controversy surrounding ChatGPT. Italy’s move could influence other regulators to follow suit and curtail its reach. We’ll be sure to continue to follow all of the latest developments here at SuperDataScience. [Click here to read on!]( [Python Deep Learning for Audio Classification]( In brief: This article from Towards Data Science explores the use of deep learning to classify audio files. It provides a step-by-step guide on building a deep learning model using Python libraries such as TensorFlow and Keras. The model is trained on a dataset of audio files that have been preprocessed to extract features such as mel frequency cepstral coefficients (MFCCs). The article also covers techniques for optimising the model's performance, such as using dropout regularization and early stopping. The final model is able to achieve an accuracy of over 90% on the classification task. The article also offers practical advice by discussing potential applications of audio classification in fields such as music genre recognition, speech recognition, and security. Why this is important: This guide originates from a Kaggle competition’s winning solution. This particular approach won the BirdCLEF 2022 competition for being able to identify bird species by sound- advancing the science of bioacoustics and supporting ongoing research to protect endangered Hawaiian birds. [Click here to discover more!]( [Optimising Neural Networks for Specific Tasks]( In brief: Researchers at MIT have developed a new method to design neural networks that are tailored to specific tasks. The method, called "Neural Architecture Search with Explanations (NASE)", allows designers to create networks optimised for tasks such as image classification or natural language processing by generating multiple design options and explaining why each option works best for the given task. The researchers say the method has the potential to reduce the time and cost of designing and deploying neural networks, as well as improving their performance. The team tested NASE on several standard benchmark datasets and found that it consistently produced networks with better performance than those designed by human experts. Why this is important: These optimal building blocks, called activation functions, have been shown to work even as neural networks grow very large. This combined with the improved function means that this method would have a wide array of real-world applications. [Click here to see the full picture!]( [Visualization Creates Stunning Population Density Maps]( In brief: Spencer Schien, a data visualization designer, has created a series of population density maps that show the staggering differences between different areas of the world. Using data from the Global Human Settlement Layer, Schien's maps reveal how some parts of the world are so crowded that they appear as solid masses of colour. Other areas, meanwhile, have such low populations that individual buildings can be seen. Schien's project was motivated by a desire to help people visualize the vast differences in population density around the world. By highlighting these disparities, he hopes to encourage people to think more deeply about how populations are distributed and how this can affect our lives. Why this is important: These images are fun and interesting to look at but also serve as a great reminder of the power that visualization has in data projects. [Click here to find out more!]( [Super Data Science podcast]( this week's Super Data Science Podcast, Vin Vashishta speaks to host Jon Krohn about how to leverage GPT-4 and outperform your competitors in both speed and value. Learn how GPT-4 has outmatched its predecessors – and many skilled workers – in this latest iteration of large language models. [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