Dr. Laber gave a presentation on analyzing data and statistics to show us where it was used in the real world, such as the prime location to open a restaurant (and what factors to consider) and what data can tell us about where more armor should be placed on planes.
One the big examples he showed us was using machine learning to determine which emojis are the most used in sex trafficking cases. Since sex traffickers hide their advertisements through cryptic wording and emojis, it’s become increasingly hard to track them down, while still making their services accessible to their clients. Using machine learning, the program could identify whether someone was being trafficked/a minor or whether they were doing it out of their own will, as law enforcement prioritizes the former. We also talked about machine learning in precision medicine that determines the amount of dosage a patient should get by comparing the patient’s characteristics to those of former patients.
