Inmultiagent domains, coping withnon-stationary agents thatchange behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.

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Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization. However, AD systems have been restricted to the subset of programs that have a continuous dependence on parameters. Programs that have discrete stochastic behaviors governed by distribution parameters, such as flipping a coin with probability $p$ of being heads, pose a challenge to these systems because the connection between the result (heads vs tails) and the parameters ($p$) is fundamentally discrete. In this paper we develop a new reparameterization-based methodology that allows for generating programs whose expectation is the derivative of the expectation of the original program. We showcase how this method gives an unbiased and low-variance estimator which is as automated as traditional AD mechanisms. We demonstrate unbiased forward-mode AD of discrete-time Markov chains, agent-based models such as Conway's Game of Life, and unbiased reverse-mode AD of a particle filter.

Program verification is vital for ensuring software reliability, especially in the context of increasingly complex systems. Loop invariants, remaining true before and after each iteration of loops, are crucial for this verification process. Traditional provers and machine learning based methods for generating loop invariants often require expert intervention or extensive labeled data, and typically only handle numerical property verification. This paper introduces a new benchmark named LIG-MM, specifically for programs with complex data structures and memory manipulations. We collect 312 programs from various sources, including daily programs from college homework, the international competition (SV-COMP), benchmarks from previous papers (SLING), and programs from real-world software systems (Linux Kernel, GlibC, LiteOS, and Zephyr).

Recently, after an update that was supposed to make ChatGPT "better at guiding conversations toward productive outcomes," according to release notes from OpenAI, the bot couldn't stop telling users how brilliant their bad ideas were. ChatGPT reportedly told one person that their plan to sell literal "shit on a stick" was "not just smart--it's genius." Many more examples cropped up, and OpenAI rolled back the product in response, explaining in a blog post that "the update we removed was overly flattering or agreeable--often described as sycophantic." The company added that the chatbot's system would be refined and new guardrails would be put into place to avoid "uncomfortable, unsettling" interactions. But this was not just a ChatGPT problem. Sycophancy is a common feature of chatbots: A 2023 paper by researchers from Anthropic found that it was a "general behavior of state-of-the-art AI assistants," and that large language models sometimes sacrifice "truthfulness" to align with a user's views.

It appears everywhere you go; artificial intelligence (AI) seems to be the only two words on everyone's lips. From the rise in AI-powered chatbots to the new era of computer-generated art, it's hard to turn a blind eye to – what could be – the future of technology. However, according to a new report by Slate, AI still has a long way to go before it is considered an adequate extension of human intelligence. Slate journalist, Heather Tal Murphy, investigated AI's inability to create hands and found something even more disturbing. Long-standing rumors that AI will replace designers – ultimately making them obsolete – came to a halt after social media discovered the program's inability to create realistic hands.

An international team of researchers is applying artificial intelligence techniques to design energy-efficient district heat pump systems that better serve human needs and behaviors while reducing the carbon footprint of buildings. The $1.5-million project is funded by the National Science Foundation's (NSF) Partnerships for International Research and Education (PIRE) program and led by Zheng O'Neill of the J. Mike Walker '66 Department of Mechanical Engineering at Texas A&M University. The PIRE program funds only an estimated 10-15 projects nationwide at a time, according to NSF. The research is also supported by the Texas A&M Engineering Experiment Station's Energy Systems Laboratory, of which O'Neill is an associate director. The project focuses on the technology of district heat pump systems, which distribute energy to buildings through a system of heat pumps and insulated networked pipes.

The Minor in Artificial Intelligence is designed for non-Computer Science majors to have significant exposure in fundamentals of computer science and the modern technical area of artificial intelligence, which can provide valuable knowledge and skill in the development of students' majors and for the job market.

Pop artist Taryn Southern, who appeared on American Idol in 2004, created the lyrics and melodies for "I AM AI" but left most of the other work to software programs. The album's first song "Break Free," which was released on Monday, was developed with the help of startup Amper Music. Amper is one of several AI music services Southern is working with on the album, which will debut later this year. Southern has only basic piano skills, so she turned to the program to deliver the instrumental part of the song. The AI developed the harmonies, chords and sequences.

Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.