Deep learning and advancements in contactless sensors have significantly enhanced our ability to understand complex human activities in healthcare settings. In particular, deep learning models utilizing computer vision have been developed to enable detailed analysis of human gesture recognition, especially repetitive gestures which are commonly observed behaviors in children with autism. This research work aims to identify repetitive behaviors indicative of autism by analyzing videos captured in natural settings as children engage in daily activities. The focus is on accurately categorizing real-time repetitive gestures such as spinning, head banging, and arm flapping. To this end, we utilize the publicly accessible Self-Stimulatory Behavior Dataset (SSBD) to classify these stereotypical movements. A key component of the proposed methodology is the use of \textbf{VideoMAE}, a model designed to improve both spatial and temporal analysis of video data through a masking and reconstruction mechanism. This model significantly outperformed traditional methods, achieving an accuracy of 97.7\%, a 14.7\% improvement over the previous state-of-the-art.

Multiple new programs and services are now advertising that AI can be used to write screenplays and automate the filmmaking process. For instance, the AI startup Deepmind recently announced the launch of a tool called Dramatron, what it calls a "co-writing" tool. According to its website, Dramatron is supposed to help screenwriters by using "hierarchical story generation for consistency across the generated text. Starting from a log line, Dramatron interactively generates character descriptions, plot points, location descriptions and dialogue..." This offends me on so many different levels that I can't really even begin to unpack them. I mean, why even write the script at all?

OpenAI today announced the launch of Spinning Up, a program designed to teach anyone deep reinforcement learning. OpenAI is well known for making funky-looking agents in virtual environments that learn how to walk on their own such as Humanoid v2 or POLO, a collaboration with University of Washington. Reinforcement learning involves providing reward signals to an agent in an environment incentivized to maximize its reward to meet a goal. RL has played a role in major AI breakthroughs such as Google DeepMind's AlphaGo and agents trained in environments like Dota 2. Spinning Up includes a collection of important reinforcement learning research papers, a glossary of terminology necessary to understand RL, and a collection of algorithms for running exercises. The program is being launched not just to help people learn how reinforcement learning works, but to make progress towards OpenAI's general goal of safely creating artificial general intelligence (AGI) by involving more people from fields beyond computer science. "Solving AI safety will require people with a wide range of expertise and perspectives, and many relevant professions have no connection to engineering or computer science at all.

This essay, originally published in eight short parts, aims to condense the current knowledge on Artificial Intelligence. It explores the state of AI development, overviews its challenges and dangers, features work by the most significant scientists, and describes the main predictions of possible AI outcomes. This project is an adaptation and major shortening of the two–part essay AI Revolution by Tim Urban of Wait But Why. I shortened it by a factor of 3, recreated all images, and tweaked it a bit. Read more on why/how I wrote it here.