Advancements in AI have led to agents in networked environments increasingly mirroring human behavior, thereby blurring the boundary between artificial and human actors in specific contexts. This shift brings about significant challenges in trust, responsibility, ethics, security and etc. The difficulty in supervising of agent behaviors may lead to issues such as data contamination and unclear accountability. To address these challenges, this paper proposes the "Network Behavior Lifecycle" model, which divides network behavior into 6 stages and systematically analyzes the behavioral differences between humans and agents at each stage. Based on these insights, the paper further introduces the "Agent for Agent (A4A)" paradigm and the "Human-Agent Behavioral Disparity (HABD)" model, which examine the fundamental distinctions between human and agent behaviors across 5 dimensions: decision mechanism, execution efficiency, intention-behavior consistency, behavioral inertia, and irrational patterns. The effectiveness of the model is verified through real-world cases such as red team penetration and blue team defense. Finally, the paper discusses future research directions in dynamic cognitive governance architecture, behavioral disparity quantification, and meta-governance protocol stacks, aiming to provide a theoretical foundation and technical roadmap for secure and trustworthy human-agent collaboration.

Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of the first open foundation models designed for human behavior modeling. Built upon open-source large language models and fine-tuned on a diverse range of behavioral data, Be.FM can be used to understand and predict human decision-making. We construct a comprehensive set of benchmark tasks for testing the capabilities of behavioral foundation models. Our results demonstrate that Be.FM can predict behaviors, infer characteristics of individuals and populations, generate insights about contexts, and apply behavioral science knowledge.

Healthcare provider Kaiser Permanente reached a $200-million settlement in October with the state of California over long waits experienced by patients needing behavioral health services. Greg Adams, Kaiser's chair and chief executive, cited a shortage of qualified care providers as a major reason for delays in treatment. Such shortages are prevalent statewide: In one survey, only 27% of Californians said their community has enough mental health professionals to serve the needs of local residents. Among adults in the state with any psychiatric illness, 63% said they received no mental health services in the past year. Earlier this year, I found myself among the millions of Californians with mental health needs.

For many years, researchers knew that rodents' squeaks tell a lot about how the animals are feeling. Much like a wagging tail on a dog, certain vocalizations indicate the rodents are happy. Conversely, other vocalizations indicate the rodents are stressed, or even depressed. But why were they interested in the rodents' moods? These researchers wanted to understand the rodents' responses to various stimuli.

I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Understanding the behavior of artificial intelligence(AI) agents is one of the pivotal challenges of the next decade of AI. Interpretability or explainability are some of the terms often used to describe methods that provide insights about the behavior of AI programs.

Recent examples of detrimental uses of AI technology -- in particular, around opaque behavior or opinion manipulations -- have rightfully raised concerns around the use of machine learning technology in our daily life. Many people and companies are now wary when it comes to sharing or leveraging personal data. Those concerns are legitimate and call for stronger enforcement of ethical values and privacy. In this article, I would like to put forth that this debate shouldn't hinder the potential benefits for individuals in using their personal data. In particular, it can help people reshape personally relevant behaviors in a transparent and controllable manner.

Recent examples of detrimental uses of AI technology -- in particular, around opaque behavior or opinion manipulations -- have rightfully raised concerns around the use of machine learning technology in our daily life. Many people and companies are now wary when it comes to sharing or leveraging personal data. Those concerns are legitimate and call for stronger enforcement of ethical values and privacy. In this article, I would like to put forth that this debate shouldn't hinder the potential benefits for individuals in using their personal data. In particular, it can help people reshape personally relevant behaviors in a transparent and controllable manner.

Lawrenceville, NJ, USA--May 19, 2020--ISPOR--The Professional Society for Health Economics and Outcomes Research (HEOR) held its second Virtual ISPOR 2020 plenary session this afternoon, "HEOR and Clinical Decision Making--Advancing Meaningful Progress." Virtual ISPOR 2020 is the Society's first completely virtual conference that was redesigned as an online event when the COVID-19 pandemic required a necessary cancelation of the in-person conference. The rise of big data and artificial intelligence bring wide-ranging opportunities for HEOR to become a relevant part of clinical decision making. In this plenary, panelists explored data-driven, collaborative approaches to clinical decision making and ways that HEOR can help strengthen health service delivery and enhance the patient experience. Nigam Shah, MBBS, PhD was the first to provide introductory remarks. He outlined a patient journey that has the potential to generate a wide variety of disparate data sources--including claims, ICD codes, medications, procedures, lab tests, clinical notes, and more--that can be used to inform artificial intelligence (AI).

The last decade saw the emergence of systematic large-scale replication projects in the social and behavioral sciences, (Camerer et al., 2016, 2018; Ebersole et al., 2016; Klein et al., 2014, 2018; Collaboration, 2015). These projects were driven by theoretical and conceptual concerns about a high fraction of "false positives" in the scientific publications (Ioannidis, 2005) (and a high prevalence of "questionable research practices" (Simmons, Nelson, and Simonsohn, 2011). Concerns about the credibility of research findings are not unique to the behavioral and social sciences; within Computer Science, Artificial Intelligence (AI) and Machine Learning (ML) are areas of particular concern (Lucic et al., 2018; Freire, Bonnet, and Shasha, 2012; Gundersen and Kjensmo, 2018; Henderson et al., 2018). Given the pioneering role of the behavioral and social sciences in the promotion of novel methodologies to improve the credibility of research, it is a promising approach to analyze the lessons learned from this field and adjust strategies for Computer Science, AI and ML In this paper, we review approaches used in the behavioral and social sciences and in the DARPA SCORE project. We particularly focus on the role of human forecasting of replication outcomes, and how forecasting can leverage the information gained from relatively labor and resource-intensive replications. We will discuss opportunities and challenges of using these approaches to monitor and improve the credibility of research areas in Computer Science, AI, and ML.

There've been years of hype about the invincible power of Artificial Intelligence (AI), but a substantial gap between what AI promises and its verity for business transformation still exists. Tech-companies have pitched AI's capabilities for a long-time however, for the majority of the organizations, the benefits of AI continue to be volatile. Artificial intelligence projects are indeed precedence for most of the companies though, there are several possible drawbacks for the unwary. It's not easy to gauge the proportion of businesses that leverage artificial intelligence today. Recent reports demonstrate that adoption rates fall somewhere between 20% and 30% – with adoption generally interpreted as'implementing AI in some form'. KPMG's survey among 30 of the global 500 companies showed that while 30% of respondents used AI for a particular range of purposes, only 17% of the companies implemented the technology at a large scale within the enterprise.