The Most Meaningful and Life-changing Presentation I Have Ever Seen was at the Nvidia GTC 2019 Conference in Washington, DC
Ah yes, the Nvidia GPU Technology Conference (GTC) – my favorite times of the year! Some of the greatest minds in AI come together to show off the work they are doing on Nvidia GPUs or the AI research they are conducting. For an AI fanatic, it’s a brain overload!
In November, I attend the GTC conference in Washington, DC. Though this conference is significantly smaller than its sister conference in San Jose, it was still packed full of fantastic presentations. The main terms I heard over and over were BERT and PyTorch. BERT is taking the natural language processing (NLP) world by storm and PyTorch is quickly gaining momentum and market share among AI/ML/DL frameworks. In this post, I’m going to highlight one of the sessions that stood out to me. In fact, it was possibly the most meaningful and even life-changing presentation I have ever seen at a conference.
Robert Pless (@rbpless), Department Chair and Patrick & Donna Martin Professor of Computer Science at George Washington University, presented a talk entitled “Explainable Deep Learning to Fight Sex Trafficking.” You can watch the video of the presentation here. In the talk, he discussed a project he is conducting with the FBI and that is currently in use by the National Center for Missing and Exploited Children. The project is very simple and elegant, and is having a major impact in helping to stop or prevent crimes against children.
The goal of the project is to be able to identify, down to the specific hotel room, locations where sex trafficking is occurring. His team has developed an app called “TraffickCam.” With the app, you can take 4 pictures of a hotel room. The pictures are uploaded and stored in an image database. These images can then be compared to images that law enforcement officials gather from the Internet or other sources in their trafficking investigations. By identifying distinctive regions in the images you uploaded and the images in the database, they can identify, down to the specific room, where the photograph was taken. From this information, law enforcement can target their investigation much more specifically. As of the presentation time, there were ~356,000 images from ~32,000 hotels in the database and this number is growing. However, this data set is only able to identify the correct hotel room in 8-35% of the searches. How can this number increase -- more data! I have already added images from 3 hotel rooms since I heard the talk. You can help increase the number, too. Update: As of June 25, 2020, almost 2.9 images from over 255,000 distinct hotels, have been uploaded by users. There are 102,000 iPhone users and 42,000 Android users! What an impact. I have personally uploaded picture from almost 2 dozen hotels.
The image below is taken from Dr. Plesse's presentation. On the left, you see the picture of the hotel room TraffickCam is trying to identify. On the right, you see 2 heat maps, or similarity maps. These maps give a visual representation of the part(s) of the image that the network has found that made it decide the images were similar, and possibly the same. Notice that in this picture, it is the headboard. In other pictures in the presentation, you can see it sometimes recognizes framed photos on the wall or other distinguishing features.
While this topic is quite disturbing, I am incredibly excited to see AI being used in such a helpful and life-changing manner. AI sometimes gets a bad rap, particularly about privacy, but this project is proof that it can change the world for the better. If you haven’t already, please download the app from the App Store or Google Play. It takes less than one minute to take the photos of your hotel room. That one minute can change someone’s life forever. For more information on the project, visit www.traffickcam.com or visit their Facebook page. Be sure to watch the YouTube video for more info on how the project started https://www.youtube.com/watch?v=zhfHOR6yc98 and read the paper at https://arxiv.org/abs/1901.11397.
If you have questions and want to connect, you can message me on LinkedIn or Twitter. Also, follow me on Twitter @pacejohn and LinkedIn https://www.linkedin.com/in/john-pace-phd-20b87070/
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