Data Science Insider: October 13th, 2023

In This Week’s SuperDataScience Newsletter: Creating Lifelike AI with Matrix Neurons. ChatGPT: A Year On. Five Free Platforms for Data Science Portfolios. Non-Coding Data Science Roles. AI Creates Robots in 30 Seconds. 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. --------------------------------------------------------------- [3D-Printed Neural Networks Mimic Real Brains]( brief: Modern AI is limited in its ability to emulate the behaviour of living beings. Traditional neural networks replicate the structure of biological neural networks but focus on functionality rather than the intricacies of the biological processes. This article delves into the historical development of mathematical neurons and the importance of hidden associative layers in AI. It explains that AI's inability to replicate the dynamic individuality of living neurons hinders the creation of truly lifelike AI. The proposed solution involves a new mathematical neuron with a variable dynamic position function, creating a three-dimensional preference matrix, allowing AI to form its own character and learn from sensory information. This innovation opens doors to AI that behaves and acts like living organisms. Why this is important: Understanding the fundamental limitations of existing AI models and the potential for AI to mimic living systems offers insights into the future of AI development and the role of dynamic mathematical neurons in creating more sophisticated and adaptable AI systems. This knowledge empowers data scientists to contribute to the evolution of AI technology, pushing the boundaries of what AI can achieve in imitating the complexity of biological organisms and individuality in decision-making. [Click here to learn more!]( [ChatGPT: A Year On]( brief: After a year of using ChatGPT, users have uncovered the tool's impressive capabilities and limitations. Initially embraced for its playful applications, such as playing 20 questions or generating songs, ChatGPT now aids in various practical tasks. It acts as a jargon demystifier, translating complex professional or medical terminology, serves as a critical eye for copywriting, and even helps users be kinder in their work emails. Additionally, it assists users in creating recipes from available ingredients, with recent image-recognition features simplifying the process. However, the tool's occasional inaccuracies and limitations, particularly in fact-checking, serve as a reminder of its imperfections. Data scientists must understand these limitations when integrating ChatGPT into work processes, ensuring accurate and reliable outcomes. Why this is important: In the year since ChatGPT launched, we’ve covered many stories about its impact in these SuperDataScience newsletters. Now that we’ve reached its anniversary and the fanfare has died down, it’s a good time to reflect on how it's being used in the real world. [Click here to read on!]( [Five Free Platforms for Data Science Portfolios]( In brief: In today's data-driven world, a robust data science portfolio is crucial for career success. It serves as a powerful tool for showcasing skills, experience, and project work. This article highlights five free platforms that facilitate the creation and sharing of data science portfolios. Kaggle, a top-notch platform for data science and ML, allows participation in competitions and project publication. DagsHub offers a user-friendly space for hosting data science projects and seamless ML model deployment. On LinkedIn, with its extensive user base, data scientists can amplify their profiles by sharing technical blog posts and project summaries. Medium provides a blogging platform to communicate project details and industry knowledge, while DataSciencePortfol.io offers a straightforward, data science-focused platform for creating a professional portfolio. Why this is important: These platforms enable data scientists to demonstrate real-world expertise, stand out in a competitive field, and connect with potential employers and clients. Building and updating an impressive portfolio early in one's career is vital for making the most of these opportunities. [Click here to discover more!]( [Non-Coding Data Science Roles]( In brief: This Analytics Insight article delves into seven data science roles that don't hinge on coding skills. Data Analysts, using tools like Excel and Tableau, harness data for informed decision-making without extensive coding. Business Intelligence Analysts leverage user-friendly BI tools to create interactive reports, minimizing the need for coding expertise. Data Consultants, skilled in problem-solving and communication, translate data insights into strategic recommendations without coding. Market Research Analysts use specialized software, reducing the need for extensive coding. Data Visualization Specialists craft compelling visuals with tools like Tableau and Power BI, benefiting stakeholders without coding.Data-driven Strategists and Data Product Managers rely on strong data comprehension and strategic acumen, making coding skills secondary. Why this is important: These roles underscore the growing importance of data-driven decision-making, rendering coding skills optional in a burgeoning job market. [Click here to see the full picture!]( [AI Creates Robots in 30 Seconds]( In brief: AI is now capable of designing autonomous robots in just 30 seconds, a significant development that has the potential to democratize robot design. In a recent experiment, a team of researchers from Northwestern University, MIT, and the University of Vermont asked an AI to design a walking robot, resulting in an unconventional, squishy creation that moves by spasming when filled with air. These robots look like no other creature and are generated using simulated evolution. They exhibit forward locomotion and consistently develop legs for movement. While the robots are currently simple and perform basic tasks, this advancement holds promise for more advanced robot design by combining gradient descent methods used in artificial neural networks with evolvable bodies. Why this is important: The ability of AI to design products efficiently can address various complex issues, such as climate change, the development of antibiotics, and more. This development is essential for data scientists as it represents a significant advancement in AI capabilities and highlights the potential for broader applications in product design and innovation. [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast]( episode, Dr. Amira Abbas, Quantum Computing Researcher at the University of Amsterdam, explores the captivating world of Quantum Machine Learning. Learn about the distinct characteristics of qubits and the vital processes of Quantum ML. For those keen on exploring further, Amira offers noteworthy ML tools suggestions to kickstart your journey in Quantum Computing. [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