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scenes, making it one of the more challenging
areas of AI generation.
As with any AI model, the quality of outputs
from these generation models heavily depends on
the training data used. The ethical implications,
especially in voice and video generation, are
profound. Issues like consent, misrepresentation,
and the potential for creating misleading content
must be carefully considered to ensure responsible
use of these powerful technologies.
3. MULTI-MODAL MODELS
In the realm of AI, multi-modal models are
groundbreaking, combining various forms of data
processing such as text and images within a single
framework. These models mark a significant step
towards AI systems that interact with the world in
a manner more akin to human cognition.
synthesizing voice outputs that are increasingly
indistinguishable from actual human speech. GPT-4V is an exemplary multi-modal model that
extends the capabilities of text-based AI, like
AI in music generation has progressed to the point GPT-3, by incorporating visual understanding. It
where it can compose complete musical pieces in can analyze and respond to both textual and visual
various genres. These models learn from a wide inputs, enhancing its applicability in complex
range of musical compositions and can create scenarios. For image-generation tasks, GPT-4V
music that resonates with human emotions and collaborates with a separate model, DALLE-3,
cultural contexts. They are used in background to produce images based on textual descriptions,
score generation, aiding composers, and demonstrating a combined effort of different
sometimes even in standalone musical creations. specialized AI systems.
Video generation models combine aspects of
AI IN MUSIC GENERATION
image and motion generation to create new video HAS PROGRESSED TO THE
content. This includes everything from creating POINT WHERE IT CAN COMPOSE
short clips based on textual descriptions to altering COMPLETE MUSICAL PIECES
existing videos for purposes like movie special THAT RESONATES WITH HUMAN
effects or deepfakes. The complexity of video EMOTIONS AND CULTURAL
generation lies in understanding and replicating CONTEXTS.
the dynamics of movement and change in visual
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