Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models

Large Language Models (LLMs) are evolving to integrate multiple modalities, such as text, image, and audio into a unified linguistic space. We envision a future direction based on this framework where conceptual entities defined in sequences of text can also be imagined as modalities. Such a formulation has the potential to overcome the cognitive and computational limitations of current models. Several illustrative examples of such potential implicit modalities are given. Along with vast promises of the hypothesized structure, expected challenges are discussed as well.Comment: 9 pages, 5 figure

Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL’s potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research

LookBook: pioneering Inclusive beauty with artificial intelligence and machine learning algorithms

Technology's imperfections and biases inherited from historical norms are crucial to acknowledge. Rapid perpetuation and amplification of these biases necessitate transparency and proactive measures to mitigate their impact. The online visual culture reinforces Eurocentric beauty ideals through prioritized algorithms and augmented reality filters, distorting reality and perpetuating unrealistic standards of beauty. Narrow beauty standards in technology pose a significant challenge to overcome. Algorithms personalize content, creating "filter bubbles" that reinforce these ideals and limit exposure to diverse representations of beauty. This cycle compels individuals to conform, hindering the embrace of their unique features and alternative definitions of beauty. LookBook counters prevalent narrow beauty standards in technology. It promotes inclusivity and representation through self-expression, community engagement, and diverse visibility. LookBook comprises three core sections: Dash, Books, and Community. In Dash, users curate their experience through personalization algorithms. Books allow users to collect curated content for inspiration and creativity, while Community fosters connections with like-minded individuals. Through LookBook, users create a reality aligned with their unique vision. They control consumed content, nurturing individualism through preferences and creativity. This personalization empowers individuals to break free from narrow beauty standards and embrace their distinctiveness. LookBook stands out with its algorithmic training and data representation. It offers transparency on how personalization algorithms operate and ensures a balanced and diverse representation of physicalities and ethnicities. By addressing biases and embracing a wide range of identities, LookBook sparks a conversation for a technology landscape that amplifies all voices, fostering an environment celebrating diversity and prioritizing inclusivity