AI Trends on TED
Source: tai_blog

Introduction: The AI Zeitgeist Through the TED LensIf you are like me  you turn to TED videos to satisfy your curiosity or to be educated on innovative ideas. In recent years  I have regularly watched their Artificial Intelligence videos to learn more about AIs capabilities  potential  and risks. Almost every week  I notice a new TED AI video on my YouTube homepage  which inspired me to do some digging. There are over 550 AI-themed TED videos dating back to 2007!   The dataset  interactive app and YouTube playlist at the end of the article.   As I started to explore TEDs rich library of content  it dawned on me that these conversations are a window into how AI technology and adoption is evolving. With this in mind I started my most extensive data analysis project to date  to give some structure and track the TED trends. I used the YouTube videos as my source  a little LLM  and a lot of Python to build a knowledge graph and started analyzing. This article is about the analysis  not the graph  but follow me for a future article about the build.   The Evolution of AI on TEDMy first step in the analysis was to better understand the video publishing trends over time. Using this dataset as a foundation I started to investigate what was the story behind these trends.   Early Days: Visionaries and Pioneers (20072015)In the mid-2000s  when AI was still a niche topic for most  TED was already featuring talks by visionaries like Ray Kurzweil  known for his work on The Singularity  and Jeff Hawkins of Palm Computing fame. IBMs Jeopardy playing Watson  launching in 2011  was the biggest AI story of the time. During this period  AI discussions were sporadic  appearing occasionally but not consistently every year.   It is also notable that TEDx events were already in the thousands by 2012 (source: Forbes)  so either the content was not focused on AI  or these videos were not published on YouTube (or are now archived)   The Tipping Point (20162017)Based on the dataset  a shift began in 20162017  marked by an increase in AI coverage. DeepMinds AlphaGo was mastering Go  not by memorization  but by creating its own new strategies that beat the Go masters  such as its victory over world champion Lee Sedol in 2016.   At the same time TEDx events were spreading (with over 100 000 talks by 2017  source: TED Blog) and the topic of AI intrigued many of the new community-based presenters.   The Deep Learning Boom (20172019)The increase in AI-related TED talks during 20172019 resulted from several factors converging at once. This period saw advances in deep learning and neural networks research and at the same time  companies/venture capitalists increased their investments in AI startups. Data Science became a popular career choice  and Big Data was a hot topic. AI technologies also reached the top of Gartners Hype Cycle for Emerging Technologies  reflecting high public interest and high expectations.   These factors  tech progress  more funding  growing expertise  and public excitement  led to more AI discussions at TED talks. People were seeing how AI would impact different aspects of society and industry. TED became a forum for exploring this AI shift as it happened.   The Pandemic Interlude (20202021)During 20202021  much of the focus on TEDs main channel shifted to healthcare  remote work  and the social impacts of the COVID-19 pandemic. AI was not the main topic but was an undercurrent in the discussions about technological solutions to pandemic-related challenges.   The ChatGPT Era (Late 2022-Present)ChatGPT-3s release in late 2022 sparked renewed interest in AI  especially in Large Language Models (LLMs). Throughout 2023 and 2024  AI and LLMs have taken center stage at TED. Presenters have covered a wide range of topics  from the technologys capabilities and opportunities to its societal impacts and potential risks.   And to no ones surprise  TED is not alone. A snapshot from Google Trends shows the impact of AI on search is even more dramatic. Interest in AI experienced a parabolic shift and is only now stabilizing at levels 10x what they were before ChatGPT.   The volume and publishing cadence of the videos tell part of the story  now lets see what we can extrapolate from the videos themselves.   What Can We Learn from the Video DataNext we will dig into the content of this collection of YouTube transcripts and metadata. My analysis involved extracting key concepts (topics  people  organizations  etc.) as well as categorizing the videos to build a knowledge graph. With this we can learn about the categories  people and organizations that dominate the TED AI video and also provide insights into the general zeitgeist of Artificial Intelligence.   Key CategoriesAI is a general-purpose technology  much like electricity or the Internet  with the potential to achieve significant results across a wide range of applications. This diversity is reflected in the categories and topics in the video dataset. AI has the potential to impact various areas of life  including business  society  healthcare  education  work  art  entertainment  and more. Alongside these emerging applications  we also see videos addressing a broad set of concerns  including ethics  governance  safety  and security.   In terms of distribution  the TED catalog is actually very balanced across these two extremes. Applying AI to business and industry is a major focus of the TED catalog  with 126 videos dedicated to this category. However  this focus is balanced by a significant number of videos addressing societal impacts (113) and AI ethics and governance (99). The pattern continues with substantial categories focused on healthcare (63) and education (55)  balanced by concerns about the future of work (36). As we move into the smaller categories  this pattern of balance persists. Overall  about 55% of the videos primarily focus on opportunity topics  and 45% focus on more risk-related topics.   The fact that opportunities and risks weigh evenly in TED presentations mirrors the dilemma we face as a society  what will it cost us to embrace the potential of AI?   Influential PeopleNow lets move on to what can be learned about AI by examining the individuals mentioned in these TED videos. Key individuals frequently mentioned in the videos fall into three categories:   Technical Thought Leaders: Known for their pioneering contributions and thought leadership in AI (e.g.  Alan Turing  Stephen Hawking  Ray Kurzweil  Marvin Minsky).Business Leaders: Visionaries in the business world who have significantly influenced the adoption and application of AI/Technology (e.g.  Elon Musk  Bill Gates  Mark Zuckerberg  Steve Jobs).Expert Reference Points: Masters in their fields who have been profoundly impacted by AI advancements (e.g.  Garry Kasparov in chess  Lee Sedol in Go  Michelangelo in art).While many of these names are well-known  there were a few that I had to research  with the larger list feeling almost like a whos who in AI quiz.   More so than the abstract trends and concepts  understanding the individuals in AI helps to give a broader context to what we see in the video library. This AI moment is historical  and these individuals will be an important part of that history.   Leading OrganizationsOrganizations also play an important role  and while I dont think the list of most referenced organizations will surprise anyone  it does highlight key shifts over the 17 years of TED videos.   Google is mentioned almost twice as often as the next organization  even considering their DeepMind acquisition as a separate entity.OpenAI has rapidly gained prominence  despite being a relative newcomer.MIT and Stanford are the leading academic institutions for AI research and development.IBM  Amazon  and Meta have been minimally referenced in this latest LLM wave  and over 80% of their mentions happened before 2022.Organizations have much more inertia than individuals  and I think we will continue to see Google  Microsoft  MIT  Amazon  etc.  for many more years. That is not to say there will not be upstarts like OpenAI   but it is far more likely their star will fade or they get consumed (e.g. DeepMinds acquisition by Google). For this trend  our 17 year window might not be enough.   ConclusionThese TED Talks serve as a window into the AI revolution  reflecting its journey from a niche subject to a transformative force in our society. This analysis leverages video content to provide insight into AI technology trends  societal opportunities and risks  and the individuals and companies driving its emergence.   As AI continues to evolve  TED videos will remain valuable resources for understanding its potential  challenges  and the critical conversations surrounding its development and implementation. While these individual presenters and videos are incredibly powerful on their own  analyzing them in aggregate reveals trends that enhance our broader understanding.   The story of AI is still in its early chapters  and it will be fascinating to see how these trends evolve and what new topics emerge in this dynamic field.   Resources to Explore FurtherPlaylist of YouTube VideosVideo DatasetApp to Search and Explore VideosData Call-OutsThe goal of the dataset is to be directional and insightful. I believe it achieves this  but alas it is not perfect.   The video playlist contains videos published through May 2024. As a result many of the charts have full year data for other years and partial for 2024.This playlist was manually generated  by myself. I may have made errors or applied judgment on what to include inconsistently I did my best.There are other TED Videos published that are not on the YouTube channel  so this playlist and dataset is incomplete.The playlist includes all of the TED channels in this analysis. By including all of these channels  we can get a broader cross-section of what people are interested in sharing and discussing. The main TED channel features videos from the official TED conference and TEDx videos that have been promoted to the main channel. TEDx actually has many more videos  as it comes from numerous community-organized events. There is also a TED-Ed channel  which focuses on educational content. Lastly a seemingly inactive TED Institute channel that was more corporate-focused.The extractions and category assignments were done with OpenAI ChatGPT-4o. There can be inconsistencies and errors.While not a focus of this analysis  the YouTube stats (Views  Likes  Comment  etc) were updated at the beginning of July 2024. There is an inconsistency with YouTube metrics in that a video published months or years before another video has had more time to accumulate  the Views  Likes  and Comments. Since the last video was added at the end of May  there was at least a one-month period for the video statistics to accumulateMethodologyBelow is the general methodology I used in conducting this analysis. I plan to do a separate article on the process and techniques in the future (follow me to learn more).   Identified YouTube and established a playlist of relevant videos thru ~June 1  2024Used APIs and Python to gather both YouTube metadata and transcripts.Processed the data in a Python notebook  including transcript summarization  concept extraction  and categorization. This was done with the OpenAI API (i.e. LLMs).The results were stored in a knowledge graph comprising over 3 500 nodes and 11 000 relationships.Manually reviewed the captured nodes and relationships to remove issues/errors  and merge similar concepts (Stanford vs Stanford University  etc).Created datasets useful for analysis (e.g.  video count by year/channel  video count by person  etc) then created visualizations.As a side effort I loaded this knowledge graph data into a JSON file for the web app.