Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors. Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API. In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences. To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents \textit{memorize} their characters and experiences. Experimental results show interesting observations that help build future simulacra of humankind.

Beauty is now in the AI of the beholder. The dating app is testing an artificial intelligence tool that selects users' best-looking photos for their profiles, in the hope it will enhance the chances someone will swipe right. The tool will look at a user's photo album and select the five images that best represent them. Bernard Kim, the chief executive of Tinder's owner, Match Group, said AI could answer people's concerns about which picture best represents them and take the stress away from selection. "I really think AI can help our users build better profiles in a more efficient way that really do showcase their personalities," Kim said in a call with investors and analysts.

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We construct a complexity-based morphospace to study systems-level properties of conscious & intelligent systems. The axes of this space label 3 complexity types: autonomous, cognitive & social. Given recent proposals to synthesize consciousness, a generic complexity-based conceptualization provides a useful framework for identifying defining features of conscious & synthetic systems. Based on current clinical scales of consciousness that measure cognitive awareness and wakefulness, we take a perspective on how contemporary artificially intelligent machines & synthetically engineered life forms measure on these scales. It turns out that awareness & wakefulness can be associated to computational & autonomous complexity respectively. Subsequently, building on insights from cognitive robotics, we examine the function that consciousness serves, & argue the role of consciousness as an evolutionary game-theoretic strategy. This makes the case for a third type of complexity for describing consciousness: social complexity. Having identified these complexity types, allows for a representation of both, biological & synthetic systems in a common morphospace. A consequence of this classification is a taxonomy of possible conscious machines. We identify four types of consciousness, based on embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii) group consciousness (resulting from group interactions), & (iv) simulated consciousness (embodied by virtual agents within a simulated reality). This taxonomy helps in the investigation of comparative signatures of consciousness across domains, in order to highlight design principles necessary to engineer conscious machines. This is particularly relevant in the light of recent developments at the crossroads of cognitive neuroscience, biomedical engineering, artificial intelligence & biomimetics.

Is the effort that AI/machine learning requires worthwhile? Usually when a new technology starts getting noticed, the subsequent hype in the industry is inevitable. The hype is partly created by the vendor and consulting community looking for new business, partly created by professionals in the industry wanting to keep up and comment on the latest trends, partly created by companies with the ambition to be seen moving with the times and becoming early adopters. Some hypes even have their lifecycle traced by Gartner, which coined the term "hype cycle". As all this energy and enthusiasm sometimes ignores "the gap from an academic paper to reality and the application to an engineered product."

It's the ultimate dating dream: a place filled with top-notch single people who don't behave like assholes. One app thinks they've cracked the code by insisting on good manners, but is it really that easy? Inner Circle wants to be your go-to place for finding decent human beings who you actually want to date. The app, which started in Amsterdam in 2012, promises a selective group of "smart, hot, ambitious professionals." After spreading throughout Europe, they debuted in New York at the end of last year.