Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the high dimensional LASA, multi-link pendulum, and Rosenbrock datasets.

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the $2$D, $4$D, and $8$D LASA datasets.

Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding state-dependent transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all fully observed Markov games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we complement our game-theoretic analysis by showing that state-of-the-art RL algorithms learn socially optimal policies given our augmentation. Our experiments include classic static dilemmas like Stag Hunt, Prisoner's Dilemma and a public goods game, as well as dynamic interactions that simulate traffic, pollution management and common pool resource management.

"Modern economies are held together by innumerable contracts," which underpin high-performing and trusted trading relationships.a While data scientists, with contracting professionals, are busy developing artificial intelligence (AI) tools for contract management, not enough attention is paid to resolving an important issue, namely new challenges posed when contracting for the use of AI tools and data. This column argues such consideration is essential for enhancing the competitive advantage of providers and users of AI tools and would contribute to public good. I begin by discussing efforts made to apply AI and machine learning (ML) for contract management and then shift attention to challenges of contracting for AI by addressing a question central to this column: What characteristics of AI/ML make it distinctively difficult to contract? And what solutions exist to deal with these challenges?

Individuals should be treated as individuals and not on the basis of their membership in racial groups, especially by our government. Unfortunately, a new executive order encourages federal agencies to focus on racial group identity rather than the character and qualifications of employees and contractors. It will result in racial quotas in hiring, procuring, and even using artificial intelligence throughout the government. The executive order's stated goal is advancing racial equity throughout the federal government. The word "equity" appears 21 times.

Evisort, the no-code contract intelligence platform beloved by legal, procurement and sales operation teams worldwide, is entering a new era with the launch of the industry's first generative artificial intelligence (AI) capabilities. Evisort's reimagined AI technology provides transparent, understandable contract recommendations to drive decision-making and contract execution. Evisort's generative AI builds upon its existing AI capabilities by creating entirely new content. The Evisort AI Labs' innovation empowers legal and contracting professionals to use Large Language Models to draft, redline and negotiate contracts automatically -- freeing up time for strategic counseling. Evisort AI Labs' technology can also suggest edits that speed re-negotiations on existing complicated contracts.

Malbek commissioned benchmark study from the World Commerce & Contracting to deliver deep industry insights about AI's evolving role in Contract Lifecycle Management Malbek, today's most cutting-edge, AI-fueled Contract Lifecycle Management (CLM) platform, today announced the availability of its commissioned study from the World Commerce & Contracting (WorldCC) about the barriers and benefits of AI-enabled contracting across organizations. The first annual report found more than a quarter (26%) of an organization's workforce is in some way involved in contract management, and contract-related data in the typical large organization sits in 24 different systems. The combination of multiple touchpoints and systems demonstrates a clear risk for lost time and productivity, while increasing potential for errors. Report findings show the need for AI to help mitigate risk in CLM while also indicating that there is a sense of enthusiasm around the use of technology in contract management. Given the pressures of today's modern workforce working in disparate locations and many companies adopting remote workforce practices, companies turn to AI in CLM because of a growing need for speed and efficiency in virtual, collaborative environments.