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Tapestry, Explore, Leverage, Delve, Testament, Dynamic, Captivate, Landscape…

What do these words have in common?

For those of us who have used ChatGPT as a text generator, these words may sound quite familiar. They are part of the advanced, and sometimes repetitive, vocabulary ChatGPT often use when generating an answer to a prompt (unless prompted to specific language requirements). While it may enhance the perception of a text, it makes it quite easy to detect if it is AI-generated.

Generate a CV, email, or ask ChatGPT to enhance your writing, I’m sure one of these words will appear at least once!

For those non-native English speakers like me, this may cause quite the dilemma. Personally, I use ChatGPT in my everyday life, I use it to generate emails, holiday itineraries, meal ideas, career guidance, etc. Therefore, I see and read AI generated text almost every day.

I often find myself repeating new English words I encounter – consciously or subconsciously – , as it is how I learned English in the first place! So, the first time I saw the word “tapestry” in an AI-generated response, I thought it was brilliant! I looked up its general meaning, its synonyms, and antonyms. Thereafter, “tapestry” was part of my vocabulary.  The same process occurs when I read books, articles or any media – if I pick up new words from those sources, there are no consequences. But if GenAI vocabulary is used in your own writing, will it be detected as AI-generated?

GenAI detection tools attempt to find the author’s personality in a text, by examining stylometric features – such as vocabulary richness. Although your academic ideas are well-researched, and presented in your own writing, the text might include GenAI vocabulary without you even realising.

Will this become an issue in terms of GenAI detection tools being introduced around universities? Let me know what you think!

References:

Browne, S. (2024, April 30). These are the words ChatGPT loves to use. Retrieved from LinkedIn: https://www.linkedin.com/pulse/words-chatgpt-loves-use-sarah-browne-7msfe/

AI detection tools: the challenge of today’s digital age. (n.d.). Retrieved from Contadu: https://contadu.com/ai-detection-tools-the-challenge-of-todays-digital-age/#:~:text=AI%20detection%20tools%20rely%20on,than%20text%20written%20by%20humans

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In recent years, the world of sports has seen a rise in technologies aimed at improving athlete performance, organizational efficiency, and fan engagement. Research by Qi et al. (2024) identifies five emerging sport technologies – Augmented Reality (AR), athlete trackers, predictive analytics, exoskeletons and AI assistance. Chen & Dai (2024) identifies two emerging technologies that stand out in shaping sport safety: Artificial Intelligence (AI) and the Internet of Things (IoT). The interplay of AI and IoT has transformed the approach to safety precautions. AI’s ability to process large sets of data, and IoT’s ability of capturing real-time monitoring, allows for early risk identification and mitigation – welcoming a new era of safety in sports.

American Football and the National Football League (NFL), has for a long time been under fire for safety concerns of their players. The sport involves serious collisions, which can lead to long-term brain damage. To tackle this issue (pun intended), the NFL has started taking safety initiatives – among them the introduction of ‘Digital Athlete’ with Amazon Web Services (AWS) (Langton, 2024). The ‘Digital Athlete’, uses AI and Machine Learning (ML) techniques to analyze big data from multiple sources, including AWS-powered Next Gen Stats, video footage and embedded player sensors. It runs simulations based on factors like player movement, weather and equipment, giving a complete view of player experiences. This technology enables injury risk assessments, personalized recovery programs, and real-time decision-making to optimize player performance and safety (Langton, 2024). The initiative is still in the beginning stages, it was first introduced in the 2023 season, across 32 NFL clubs. The NFL and AWS are set to further improve ‘Digital Athlete’ by incorporating 3D Pose Estimation, to assess player movement leading to injury, and developing a first computer vision model, to gain a better understanding of the forces causing concussions or other injuries (Langton, 2024). 

The ‘Digital Athlete’ is a testimony for the rise of emerging technology in sports, and how it can change the future landscape of athlete safety. As the sports industry embraces these technologies – highlighted by the convergence of AI, IoT, and other emerging technologies identified by Qi et al. (2024) – we are moving toward a future where proactive measures can significantly reduce the risk of injury while enhancing performance, creating a safer environment for athletes. 

However, can technology completely prevent injuries in a context where human interaction is involved?

References:

Chen, Z. and Dai, X. (2024) ‘Utilizing AI and IOT technologies for identifying risk factors in sports’, Heliyon, 10(11). doi:10.1016/j.heliyon.2024.e32477.

Langton, J. (2024) Building a digital athlete: Using AI to rewrite the playbook on NFL Player Safety, NFL.com. Available at: https://www.nfl.com/playerhealthandsafety/equipment-and-innovation/aws-partnership/building-a-digital-athlete-using-ai-to-rewrite-the-playbook-on-nfl-player-safety (Accessed: 20 September 2024). 

Qi, Y. et al. (2024) ‘Digital Technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and Competitive Integrity in the digital era’, Technology in Society, 77, p. 102496. doi:10.1016/j.techsoc.2024.102496.