This paper attempts to explore human identity by utilizing neural networks in an indirect manner. For this exploration, we adopt diffusion models, state-of-the-art AI generative models trained to create human face images. By relating the generated human face to human identity, we establish a correspondence between the face image generation process of the diffusion model and the process of human identity formation. Through experiments with the diffusion model, we observe that changes in its external input result in significant changes in the generated face image. Based on the correspondence, we indirectly confirm the dependence of human identity on external factors in the process of human identity formation. Furthermore, we introduce Fluidity of Human Identity, a video artwork that expresses the fluid nature of human identity affected by varying external factors. The video is available at https://www.behance.net/gallery/

A study that used artificial intelligence–generated content to "participate" in online discussions and test whether AI was more successful at changing people's minds than human-generated content has caused an uproar because of ethical concerns about the work. This week some of the unwitting research participants publicly asked the University of Zürich (UZH), where the researchers behind the experiment hold positions, to investigate and apologize. "I think people have a reasonable expectation to not be in scientific experiments without their consent," says Casey Fiesler, an expert on internet research ethics at the University of Colorado Boulder. A university statement emailed to Science says the researchers--who remain anonymous--have decided not to publish their results. The university will investigate the incident, the statement says.

The modern enterprise is facing an unprecedented surge in digital identities, with machine identities now significantly outnumbering human identities. This paper examines the cybersecurity risks emerging from what we define as the "human-machine identity blur" - the point at which human and machine identities intersect, delegate authority, and create new attack surfaces. Drawing from industry data, expert insights, and real-world incident analysis, we identify key governance gaps in current identity management models that treat human and machine entities as separate domains. To address these challenges, we propose a Unified Identity Governance Framework based on four core principles: treating identity as a continuum rather than a binary distinction, applying consistent risk evaluation across all identity types, implementing continuous verification guided by zero trust principles, and maintaining governance throughout the entire identity lifecycle. Our research shows that organizations adopting this unified approach experience a 47 percent reduction in identity-related security incidents and a 62 percent improvement in incident response time. We conclude by offering a practical implementation roadmap and outlining future research directions as AI-driven systems become increasingly autonomous.

Recent pose-transfer methods aim to generate temporally consistent and fully controllable videos of human action where the motion from a reference video is reenacted by a new identity. We evaluate three state-of-the-art pose-transfer methods -- AnimateAnyone, MagicAnimate, and ExAvatar -- by generating videos with actions and identities outside the training distribution and conducting a participant study about the quality of these videos. In a controlled environment of 20 distinct human actions, we find that participants, presented with the pose-transferred videos, correctly identify the desired action only 42.92% of the time. Moreover, the participants find the actions in the generated videos consistent with the reference (source) videos only 36.46% of the time. These results vary by method: participants find the splatting-based ExAvatar more consistent and photorealistic than the diffusion-based AnimateAnyone and MagicAnimate.

Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection of portrait images. However, existing solutions are vulnerable in producing truthful details, and usually suffer from several defects such as (i) The generated face exhibit its own unique characteristics, \ie facial shape and facial feature positioning may not resemble key characteristics of the input, and (ii) The synthesized face may contain warped, blurred or corrupted regions. In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input. Concretely, we inject several SOTA face models into the generation procedure, achieving a more efficient label-tagging, data-processing, and model post-processing compared to previous solutions, such as DreamBooth ~\cite{ruiz2023dreambooth} , InstantBooth ~\cite{shi2023instantbooth} , or other LoRA-only approaches ~\cite{hu2021lora} . Besides, based on FaceChain, we further develop several applications to build a broader playground for better showing its value, including virtual try on and 2D talking head. We hope it can grow to serve the burgeoning needs from the communities. Note that this is an ongoing work that will be consistently refined and improved upon. FaceChain is open-sourced under Apache-2.0 license at \url{https://github.com/modelscope/facechain}.

During the early years of the Cold War, an array of underwater microphones monitoring for sounds of Russian submarines captured something otherworldly in the depths of the North Atlantic. The haunting sounds came not from enemy craft, nor aliens, but humpback whales, a species that, at the time, humans had hunted almost to the brink of extinction. Years later, when environmentalist Roger Payne obtained the recordings from U.S. Navy storage and listened to them, he was deeply moved. The whale songs seemed to reveal majestic creatures that could communicate with one another in complex ways. If only the world could hear these sounds, Payne reasoned, the humpback whale might just be saved from extinction. When Payne released the recordings in 1970 as the album Songs of the Humpback Whale, he was proved right. It was played at the U.N. general assembly, and it inspired Congress to pass the 1973 endangered species act. By 1986, commercial whaling was banned under international law.

With this level of interaction, a new identity problem is emerging as machines operate on behalf of humans. Collaboration between humans and machines is a working reality today. Along with this comes the need for secure communication as machines operate increasingly on behalf of humans. While people need usernames and passwords to identify themselves, machines also need to identify themselves to one another. But instead of usernames and passwords, machines use keys and certificates that serve as machine identities so they can connect and communicate securely.

I recently read The Age of AI: And Our Human Future by Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher. The book describes itself as "an essential roadmap to our present and our future." We certainly need more business-, government-, and philosophical-centric books on artificial intelligence rather than hype and fantasy. Despite high hopes, in terms of its promise as a roadmap, the book is wanting. Some of the reviews on Amazon focused on the lack of examples of artificial intelligence and the fact that the few provided, like Halicin and AlphaZero, are banal and repeatedly filled up the pages.

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era? While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the "general intelligence" that humans have, he said in a recent interview with AI for Business (AIB), a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI. According to De Cremer, AI will never have "a soul" and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. "The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see," he noted. An edited transcript of the interview appears below. AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm?

Looking to extend its industry leading security capabilities, Blue Prism announced a partnership with SailPoint, a market leader in enterprise identity management. This partnership gives enterprises added visibility and transparency into governing digital workers, resulting in improved compliance reporting, full automation lifecycle management and better security. The integration of Blue Prism's connected-RPA platform with SailPoint helps organizations maintain and control credentials of digital workers, including those that meet defined Separation of Duties (SoD) policies. By maintaining these credentials and granting access through SailPoint, digital workers can execute systems-based tasks, just as their human counterparts do, securely and at scale. This ability to disable or delete credentials quickly and accurately, while monitoring and auditing access, gives enterprises improved compliance reporting and full lifecycle management and security.