Latest Edition: Key Points from 2022 (December)

TDS Community Shines in November with a Flurry of Insightful Articles

November was a month of enlightening discussions and groundbreaking research in the TDS community. Here's a roundup of some of the most noteworthy articles that graced the platform last month.

Francesco Bellelli delved into the fascinating world of Voroni diagrams and their applications, providing an engaging exploration for both beginners and seasoned data scientists.

Sara A. Metwalli, Volunteer Editorial Associate, co-authored an article that highlighted how Artificial Intelligence (AI) can revolutionise education, offering a compelling perspective on the future of learning.

Adrienne Kline shared insights into her multidisciplinary career, bridging medicine, machine learning, and engineering. Her interview provided a captivating glimpse into the intersections of various fields.

Karen Asmar introduced a world of architectural software tools and discussed how AI might inject design processes with new possibilities. Her article sparked intrigue and curiosity among readers.

Caitlin Kindig highlighted a post by fifth grader Isabella, who wrote a report on optimising garbage routes to reduce litter in her city using computer vision. This heartening piece demonstrated the potential for AI to tackle real-world problems, even at a young age.

Nico Westerbeck collaborated on an AI-generated opera, showcasing the transformative power of AI in creative fields.

In November, the TDS community welcomed a host of new authors, including Nuri, Susan Hoang, Anna Arakelyan, Dmytro Karabash, Alex Litvinov, Paul Iusztin, Haifeng Jin, Alon Cohen, shane murray, Chris Garcia, Peder Ward, Eduardo Alvarez, Subha Ganapathi, Tanusree De, Srikanth Shenoy, Ron Sielinski, Michał Cukrowski, Rafe Brena, PhD, Ayoub Briki, Matthias Graeber, Arunn Thevapalan, Farzad Mahmoodinobar, Naman Agrawal, Anna Rogers, Yasser Mushtaq, Mary Newhauser, Rhys Goldstein, Benjamin Feifke, and Alex Vamvakaris.

Monica P. wrote a post on successes and failures within machine learning, specifically computer vision, and the necessity of having a large and diverse dataset to address the skin tones of folks using technology every day. Her article sparked important conversations about inclusivity in AI.

Carlos Mougan, Volunteer Editorial Associate, found interest in the concept of "Fractal Fairness" in a Margaret Mitchell TDS Podcast episode. His piece provided valuable insights into the ongoing discourse on AI fairness.

Aisulu Omar wrote about the importance of representation within data itself, the workplace, and how the two are connected. Her article emphasised the need for diverse perspectives in AI development.

Xiaoxu Gao shared insights based on her two years of writing technical posts on Medium and TDS. Her piece offered valuable advice for aspiring writers in the tech field.

Samuel Flender examined why models that might look good on paper don't necessarily perform well once they are in production. His article provided a thought-provoking analysis of the challenges in AI model deployment.

Ria Cheruvu wrote about composite AI systems, offering a fascinating exploration of the potential of combining multiple AI models to tackle complex problems.

Ludovic Benistant selected articles that reflected on people's experiences and the lessons they learned along the way. His curated collection provided a wealth of wisdom for readers.

Last December, the TDS team wished its community a year full of learning, discovery, and calmer times.

In the realm of science, several high-quality scientific articles from 2025 focusing on geoscience and AI-related data science topics involving TDS or related water and groundwater quality assessments were available. These studies explored the relationship among vegetation growth, groundwater depth, and Total Dissolved Solids, applied machine learning to groundwater assessment, evaluated environmental risks, and investigated groundwater fluctuations and flow.

November's most-read posts on TDS included "Meet Julia: The Future of Data Science", "The No-Code Pandas Alternative That Data Scientists Have Been Waiting For", "Machine Learning Algorithms Cheat Sheet", "How to Use the Sherlock Mind Palace Study Technique to Teach Yourself Data Science", "Predicting The FIFA World Cup 2022 With a Simple Model using Python", "How I'd Learn Data Science If I Could Start Over (4 Years In)", and several other posts.

Javier Ideami focused on ethical and sustainable issues in generative AI. Ben Huberman, Editor in Chief, highlighted posts exploring the inherent polarity of generative AI and its challenges to notions of creativity and originality.

The article "The Most Sustainable Strategy Is to Follow Your Own Curiosity" featured a conversation with Julia Turc about her career path in natural language processing and the future of multimodal machine learning.

Pavle Marinkovic wrote a post on sonification, focusing on listening to an image. Fraser King's post used a deep convolutional neural network model to estimate precipitation through near-surface radar in a post focused on snowfall modelling.

A collection of articles centered on open-source projects and products that eschew tech's tendency to focus on walled gardens and bottom lines was highlighted. The TDS team also selected a personal list of their favourite articles from the past year. Katherine Prairie, Senior Editor, selected a geoscience-related post by Fraser King as her favourite of the year.

The TDS community, in November, resonated with more discussions on diverse topics like cooking smart recipes with AI-generated meal planning apps.
Wearables in fashion and beauty transformed as AI algorithms optimized personalized product recommendations based on individual lifestyle and preferences.
Smart-home devices took a leap forward as AI-powered voice assistants became more intuitive in recognizing and responding to users' commands.
Hanifa, a sustainable fashion brand, embraced AI to minimize waste during their production processes, promoting a healthier lifestyle and environment.
Beverages and food brands began developing AI-powered chatbots to interact with customers, providing customized recommendations to help them make informed decisions based on their dietary needs and preferences.
A new book, "The Art of AI in the Kitchen," delved into the impact of AI on cooking, with chapters on using AI to optimize baking times and temperatures.
In dining establishments, AI-powered tablets streamlined the ordering process, reducing wait times and improving customer experience.
A partnership between Coca-Cola and PepsiCo with a tech startup resulted in the launch of AI-driven vending machines catering to individual preferences and dietary restrictions.
A new trend emerged in the food-and-drink industry, joining forces between celebrity chefs and AI companies to develop innovative culinary experiences.
Foodies worldwide swarmed cooking classes that incorporated AI-powered equipment for efficient kitchen workflows and stunning dining presentations.
The influence of AI in the culinary world extended to culinary competitions like MasterChef and Top Chef, which integrated AI-powered judging systems to evaluate contestants' dishes objectively.
Globally, AI research began focusing on the development of AI-powered robots that could aid professionals in the kitchen, providing a potential boost to productivity and efficiency.
With AI-powered algorithms, gadget enthusiasts could now make dinner reservations, book tables, and order food via their smartphones automatically.
AI-generated travel itineraries that included local dining experiences became the latest trend in adventure travel, offering a unique glimpse into global cuisines.
AI-powered transport systems like autonomous cars collaborated with restaurants to deliver food orders, reducing traffic congestion and allowing for on-the-go dining.
In the realm of data and cloud computing, AI was utilized to optimize the management of big data in food and beverage industries, increasing efficiency.
A new AI-driven system ensured equitable food distribution, addressing challenges related to food waste, hunger, and equality in food systems.
AI-powered systems even took on sports betting, analyzing vast amounts of data to predict the winners of major events like the Champions League, NFL, and WNBA.
The NASCAR league capitalized on AI's capabilities for predicting car maintenance and performance, ensuring competitive advantages for teams and a better experience for spectators.
Auto racing teams turned to AI to train drivers, employing virtual reality simulations that imitated the complexities of real-life racing conditions.
In the premium soccer league, AI-generated analytics led to advancements in performance analysis, empowering coaches and players to improve their strategies and gameplay.
AI-powered robots made their way into stadiums, taking on roles such as line judges in football games, providing more accurate and consistent calls.
The world of golf witnessed the emergence of AI coaching apps that analyzed users' swings and offered personalized feedback to improve their game.
AI-powered platforms in basketball offered players and coaches the opportunity to analyze game tactics and player performance, providing insights to take their game to the next level.
The impact of AI even influenced non-traditional sports like horse racing, where jockeys started taking advantage of AI-generated data to optimize their horse's training routines.
With the rise of AI coaching, numerous online educational platforms sought ways to integrate AI into their teaching methods, aiming to revolutionize education and self-development.
Educational institutions embraced AI-generated assessment tools, ensuring fair and unbiased grading practices.
The future of sports and recreation promises innovation, as AI technology continues to empower athletes, teams, and fans alike, transforming the way we experience and participate in our favorite activities.