Pygmalion
James williams in his book, stand out of our light (2018) argues that a ‘next-generation threat to human freedom has emerged in the systems of intelligent persuasion that increasingly direct our thoughts and actions. For too long, we’ve minimized the resulting harms as “distractions” or minor annoyances. Ultimately, however, they undermine the integrity of the human will at both individual and collective levels. Liberating human attention from the forces of intelligent persuasion may therefore be the defining moral and political task of the information age’. The main use of pygmalion ai is for research and content creation while kobold ai pays more head to story creation and playing text-based adventure games.
Moreover, the presence of such knowledge in an agent is expressed by the ability of the agent to execute certain tasks. He argues that a category of ‘implicit’ knowledge, intermediate between explicit and tacit, arises, and says its articulation to a substantial extent can be conveyed verbally. Although implicit may lack the precision or crispness of the explicit but by contrast, it holds even greater sway on the agent empowered to interpret it than the explicit due to deep values it carries.
It talks about behavioral patterns- high expectations lead to better performance and vice versa. Despite having such low vram requirements, the performance of pygmalion is very efficient, as much as that of other large models. Pygmalion ai offers different models, such as pygmalion ai pygmalion-350m, pygmalion-1.3b, pygmalion-2.7b, pygmalion-6b, and the recently introduced pygmalion-7b. These models vary in the number of trainable parameters, with pygmalion-7b being similar in performance to pygmalion-6b.
If one of central ethos of equivalence is mutual trust, then it is difficult to visualize how an ai system can offer itself as a trusted companion in an emotionally laid situation where we feel personal and deep grief and pain of the loss a loved one. The idea that the computer can console us by following rules embedded in its system, ignores the very essence of what human emotion is, tacit and personal that cannot be totally explicated in the forms of rules. It can be felt but and cannot be learned even in the form of rules of familiarity. The story of pygmalion in greek mythology parallels the pygmalion effect in machine learning. Just as pygmalion shaped his reality through his expectations, the biases and expectations of the creators or trainers of a machine learning algorithm can influence its outcomes. Awareness of these biases is crucial for unbiased decision-making and creation.
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