Ayad Akhtar's brilliant new play, McNeal, currently at the Lincoln Center Theater, is transfixing in part because it tracks without flinching the disintegration of a celebrated writer, and in part because Akhtar goes to a place that few writers have visited so effectively--the very near future, in which large language models threaten to undo our self-satisfied understanding of creativity, plagiarism, and originality. And also because Robert Downey Jr., performing onstage for the first time in more than 40 years, perfectly embodies the genius and brokenness of the title character. Check out more from this issue and find your next story to read. I've been in conversation for quite some time with Akhtar, whose play Disgraced won the Pulitzer Prize in 2013, about artificial generative intelligence and its impact on cognition and creation. He's one of the few writers I know whose position on AI can't be reduced to the (understandable) plea For God's sake, stop threatening my existence! In McNeal, he not only suggests that LLMs might be nondestructive utilities for human writers, but also deployed LLMs as he wrote (he's used many of them, ChatGPT, Claude, and Gemini included). To my chagrin and astonishment, they seem to have helped him make an even better play. As you will see in our conversation, he doesn't believe that this should be controversial. In early September, Akhtar, Downey, Bartlett Sher--the Tony Award winner who directed McNeal--and I met at Downey's home in New York for what turned out to be an amusing, occasionally frenetic, and sometimes even borderline profound discussion of the play, its origins, the flummoxing issues it raises, and, yes, Avengers: Age of Ultron. We were joined intermittently by Susan Downey, Robert's wife (and producing partner), and the person who believed that Akhtar's play would tempt her husband to return to the stage. The conversation that follows is a condensed and edited version of our sprawling discussion, but I think it captures something about art and AI, and it certainly captures the exceptional qualities of three people, writer, director, and actor, who are operating at the pinnacle of their trade, without fear--perhaps without enough fear--of what is inescapably coming.

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning based approaches mainly focus on training a single robot-structure-specific policy through the centralized learning mechanism, lacking adaptability to changes like robot malfunction. To solve this limitation, this work treats each finger as an individual agent and trains multiple agents to control their assigned fingers to complete the in-hand manipulation task cooperatively. We propose the Multi-Agent Global-Observation Critic and Local-Observation Actor (MAGCLA) method, where the critic can observe all agents' actions globally, and the actor only locally observes its neighbors' actions. Besides, conventional individual experience replay may cause unstable cooperation due to the asynchronous performance increment of each agent, which is critical for in-hand manipulation tasks. To solve this issue, we propose the Synchronized Hindsight Experience Replay (SHER) method to synchronize and efficiently reuse the replayed experience across all agents. The methods are evaluated in two in-hand manipulation tasks on the Shadow dexterous hand. The results show that SHER helps MAGCLA achieve comparable learning efficiency to a single policy, and the MAGCLA approach is more generalizable in different tasks. The trained policies have higher adaptability in the robot malfunction test compared to the baseline multi-agent and single-agent approaches.

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we're thrilled to share our conversation with Dr. Varshita Sher. Dr. Sher is currently working as a data scientist at the Alan Turing Institute's Applied Research Centre, leveraging deep-learning technology to solve problems in the NLP and Computer Vision domains. She has a Master's degree in Computer Science from the University of Oxford and a Ph.D. in Learning Analytics from Simon Fraser University. Her work in the last eight years has focused on the intersection of research and implementation of AI/ML algorithms in myriad sectors, including Edtech, Fintech, and Healthcare.

We really know of only a single intelligence abstraction approach that truly does work, the one based on the interconnection of spatio-temporal signal integrators in a vast graph: Neural Network. We also know of only one method that was able to generate such abstracted intelligence: Evolution. The proof that this abstraction and this generative method works is us, you and I, the result of billions of years of trial and error. There is nothing mystical about the human brain, it is but a vast graph of signal integrators, carved out in flesh through billions of years of evolution. In this paper we discuss: intelligence abstraction based on neural networks, complex-valued artificial neurons and their computational potential to be equivalent to biological ones, the approaches that could result in the generation of such intelligent graphs of interconnected complex-valued neurons, an architecture of infomorphs whose brains are complex-valued neural substrates, and why an ALife approach on high enough granularity level is our best chance of evolving organisms that are truly intelligent.

Thanks for coming over my blog and read my articles. This post is brought to you by the loving donations and purchases to sherwork.com On going with the talk about Artificial Intelligence as we see the developments in our current days it is something inevitable and I am impartial because I believe everybody has a point. I agree that it should be a regularization on the creation of AI beginning to be an open source so everybody in the world can see what everybody else is doing, the legislation under the creation of IA whatever function may have their main program should be to help humans, love humans, and be compassionate towards life, to develop a consciousness of kindness that can be implemented and functional. Now, the possibility of somebody stealing the AI is present so it should have an operation system that in the event of trying to modify the positive program into a harming humans it should have a self destructive mechanics by law. That way neither the IA nor the human trying to alter the machine can try it.

Efficient learning in the environment with sparse rewards is one of the most important challenges in Deep Reinforcement Learning (DRL). In continuous DRL environments such as robotic arms control, Hindsight Experience Replay (HER) has been shown an effective solution. However, due to the brittleness of deterministic methods, HER and its variants typically suffer from a major challenge for stability and convergence, which significantly affects the final performance. This challenge severely limits the applicability of such methods to complex real-world domains. To tackle this challenge, in this paper, we propose Soft Hindsight Experience Replay (SHER), a novel approach based on HER and Maximum Entropy Reinforcement Learning (MERL), combining the failed experiences reuse and maximum entropy probabilistic inference model. We evaluate SHER on Open AI Robotic manipulation tasks with sparse rewards. Experimental results show that, in contrast to HER and its variants, our proposed SHER achieves state-of-the-art performance, especially in the difficult HandManipulation tasks. Furthermore, our SHER method is more stable, achieving very similar performance across different random seeds.

It has been years since video games surpassed blockbuster movies as the biggest releases in media, but that's never stopped games makers wanting to get a slice of the action on the big screen. Now Call of Duty's makers Activision Blizzard are planning an assault to rival Disney's Marvel Universe. It plans to use the multi-layered, interconnected approach that has made Marvel's superheroes a dominant force in cinema to turn the first-person shooter into an all-conquering film franchise of its own. Two people are tasked with pulling that off, Stacey Sher and Nick van Dyk, the co-presidents of Activision Blizzard Studios, an in-house production division that hopes to succeed where almost everyone else has failed by turning games into commercially and critically successful film and TV. According to the pair, work on the Call of Duty films has already generated multiple scripts, and involved extensive research with military experts and retired soldiers.

This is not a controversial statement. A quick trip through recent cinematic history reveals a universally dismal track record. This is a genre whose high point is the Resident Evil series, whose touchstones include Mortal Kombat, Lara Croft: Tomb Raider, and Prince of Persia. This summer's Angry Birds and World of Warcraft may have produced legitimate commercial successes, but that's only thanks to the Chinese market. In the US they both tanked, commercially and critically.