In the first set of experiments, we adopt the model from Section 4.2 where agents For simplicity, we refer to the first setting as the "constrained agent" case, and the To begin, we verify our theoretical findings from Section 4.2. First we let the decision-maker lead and the agents follow, and then we switch the roles. In this section, we verify our findings on a model where the decision-maker's problem is the same In particular, the agents' risk R takes the form: R ( µ,θ) = λ 2 null µnull We remark that though this setup is conceptually very similar to that in Section 4.2 (increasing In our experiments we once again let the decision-maker lead and the agents follow, and then we switch the roles. In Figure 4 we empirically observe that there is gap between the decision-maker's risk at their Stackelberg equilibrium and at the agents', and that the decision-maker consistently achieves a lower Further, we note that agents consistently prefer leading, meaning that both the decision-maker and agents prefer if the order-of-play is flipped. Our empirical results suggest that the agents' equilibrium is a strictly better equilibrium in terms of the social cost (defined classically in game theory as the sum of the agents' and In Figure 5 we again observe that the proposed dynamics converge to the Stackelberg equilibria.

In the field of autonomous driving, two important features of autonomous driving car systems are the explainability of decision logic and the accuracy of environmental perception. This paper introduces DME-Driver, a new autonomous driving system that enhances the performance and reliability of autonomous driving system. DME-Driver utilizes a powerful vision language model as the decision-maker and a planning-oriented perception model as the control signal generator. To ensure explainable and reliable driving decisions, the logical decision-maker is constructed based on a large vision language model. This model follows the logic employed by experienced human drivers and makes decisions in a similar manner. On the other hand, the generation of accurate control signals relies on precise and detailed environmental perception, which is where 3D scene perception models excel. Therefore, a planning oriented perception model is employed as the signal generator. It translates the logical decisions made by the decision-maker into accurate control signals for the self-driving cars. To effectively train the proposed model, a new dataset for autonomous driving was created. This dataset encompasses a diverse range of human driver behaviors and their underlying motivations. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.

The megatrend of Artificial Intelligence is transforming the algorithms of business in exciting ways. This reference, aimed at the business decision-maker, will help you make the most of AI in your organization. It provides clear articulations of fundamental concepts, succinct examples of highly impactful use cases, and tips you can put in place to ensure your AI projects stay on track to deliver value. We keep this online reference updated so you will always have access to the best of our thoughts. This reference is part of a series.

Many AI techniques are being used in organizations, and AI will continue to get sophisticated in the third wave of AI. In the Fourth Industrial Age, human creativity, AI at scale, and Emotion AI will be dominant forces that will shape the digital organizations of the future. What is the Third Wave of AI? In the first wave, artificial intelligence (AI) systems followed clear rules and were directed at individual applications of the algorithms to cover every eventuality. The second wave was the introduction of deep learning and reinforcement learning systems that mapped inputs to outputs to solve a certain type of problem.

Gary M. Shiffman, Ph.D. is the Founder and CEO of Giant Oak and Co-Founder and CEO of Consilient. He is the creator of GOST and Dozer. According to my company's research, a full 25% of PPP fraud cases brought by the Department of Justice could have been easily prevented. The fraud is so obviously clumsy that it is embarrassing to whomever approved the loans. Decision-makers consume a lot of data.

Are you being asked to provide GPUs to your application developers and data scientists for machine learning or high performance computing? Are users asking for more than one GPU to be usable for their application? Are you interested in cost-effective ways to share GPUs across the entire data science team? If any of these types of questions apply to you, then this new E-Book from VMware on the key decisions to take about GPU use on vSphere will be a great read for you. GPUs provide the computing power needed to run machine learning programs efficiently, reliably and quickly.