Susan Horton cuts open ice pops for her daughters at home in Cotati, California, in July 2024. Pregnant with her fifth child, Susan Horton had a lot of confidence in her parenting abilities. Then she ate a salad from Costco: an "everything" chopped salad kit with poppy seeds. When she went to the hospital to give birth the next day, she tested positive for opiates. Horton told doctors that it must have been the poppy seeds, but she couldn't convince them it was true.

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.

The rat sat still in the middle of her cage, moving only in response to my touch, and even then only as if in slow-motion. My subject, GRat66, was a few months old, and except for her long bare tail, fit neatly into my palm a few minutes earlier, when I injected a few drops of a potent opiate under her skin, near the belly. Now, her beady black eyes bulged as she faded into an opiate stupor. I was preparing to implant minuscule electrodes into the rat's brain. The opiate would serve as an analgesic before, during, and after the surgery. It was the fall of 2018 and I was hoping the results of the surgery would help answer some questions that had been tormenting me as I embarked on the sixth year of my Ph.D. in neuroscience. How do the parts of the brain controlling movement interact with those responsible for visual sensation? Why do neurons in the visual areas jolt to action when an animal moves, even in the dark?

Obviously, Netflix, Facebook, mobile games, and virtually every content platform out there (including this one) will continue to leverage AI to get people to use their products. The impetus for protection will either land on the individual (although self-control becomes harder and harder as engagement mechanics improve) or on the burgeoning market of'Unplug' apps. Products like Offtime, an app that helps users unplug by blocking Facebook and games may become increasingly popular as people see their time being eaten up by binge-worthy TV shows.