I LIKE MOVIES (2022), written and directed by Chandler Levack, is a wonderfully realistic depiction of the growing pains of a young person.
I saw it in a Tokyo theater.
The protagonist of the film, Lawrence Kweller, a movie geek in a rural town in Canada, is convincingly played by Isaiah Lehtinen. The uniquely moving aspect of this piece is that there is nothing particularly unique about the Lawrence character, except that he loves movies too much. As a result of his passion, Lawrence ruins relationships, and his greatest dream of going to NYU (spoiler alert!) does not materialize, as should be in a true-to-heart depiction of the tumultuous period of the typical adolescence.
Lawrences's efforts to make it in life as a film director reflects the experience of Levack herself. The gender swap was well-thought and clever, resulting in an answer song to Lady Bird (2017). New York, or the United States in general, symbolically seen from the locality of Canada in which this masterpiece is situated would find resonance in many people's heart. The closing of the film is in a sense a self-assertion of the "True North" (a self-referential phrase in the Canadian national anthem), well fitting for a nation that produced similarly humanistic delights such as the sitcom Schitt's Creek in recent years. The paying of respect to the first nation people at the end of the credit roll was fitting and truly like Canada.
AI Snake Oil by Arvind Narayanan and Sayash Kapoor was a very interesting read. The authors make a salient point that compared to generative AIs, predictive AIs perform very badly. From gun shootings to the prospect of a person's academic performance, or from the next hit song to the breakout of civil wars, AI technologies categorically cannot predict the future. For sure, humans are equally bad at predicting, but the point is that AIs cannot be expected to do any better. Any illusion on this would lead to snail oil.
In addition to the disaster of predictive AI, another related, and significant deficiency of AI is content moderation. The authors argue how and why filtering out potentially harmful posts on the social media are hard. Some of the difficulties come from the incredible ingenuity of humans to bypass any perceived restrictions or algorithmic structuring. A side effect of this is that a lot of people need to be employed to label bad contents manually, a task with negative effects on mental health.
Topics such as top-N accuracy, the Gartner hype cycle, and the reproducibility crisis in AI research are very effectively analyzed and streamlined. I recommend anyone interested in AI's status quo and beyond to read this wonderfully written book.
