Generative Artificial Intelligence
A form of AI
system known as generative artificial intelligence (AI) or (GenAI) may produce
text, pictures, or other types of media in response to commands. In order to
produce data based on the training data set that was used to develop them,
generative AI systems require generative models, such as massive language
models.
Important
generative AI systems include Bard, a chatbot developed by Google using the
LaMDA model, and ChatGPT, a chatbot developed by Open AI utilising the GPT-3
and GPT-4 big language models. Artificial intelligence art systems like Stable
Diffusion, Midjourney, and DALL-E are examples of further generative AI models.
Numerous
sectors, including software development, marketing, and fashion, might benefit
from generative AI. Early in the 2020s, there was a significant increase in
investment in generative AI, with multiple smaller startups in addition to
well-known corporations like Microsoft, Google, and Baidu creating generative
AI models.
Modalities
A detailed
oil painting of figures in a futuristic opera scene
Théâtre
d'Opéra Spatial, an image generated by Midjourney
A generative
AI system is constructed by applying unsupervised or self-supervised machine
learning to a data set. The capabilities of a generative AI system depend on
the modality or type of the data set used.
Text:
Generative AI systems trained on words or word tokens include GPT-3, LaMDA,
LLaMA, BLOOM, GPT-4, and others (see List of large language models). They are
capable of natural language processing, machine translation, and natural
language generation and can be used as foundation models for other tasks. Data
sets include BookCorpus, Wikipedia, and others (see List of text corpora).
Code: In
addition to natural language text, large language models can be trained on programming
language text, allowing them to generate source code for new computer programs.
Examples include OpenAI Codex.
Images:
Generative AI systems trained on sets of images with text captions include such
as Imagen, DALL-E, Midjourney, Stable Diffusion and others (see Artificial
intelligence art, Generative art, Synthetic media). They are commonly used for
text-to-image generation and neural style transfer. Datasets include LAION-5B
and others (See Datasets in computer vision).
Molecules:
Generative AI systems can be trained on sequences of amino acids or molecular
representations such as SMILES representing DNA or proteins. These systems,
such as AlphaFold, are used for protein structure prediction and drug
discovery. Datasets include various biological datasets.
Music:
Generative AI systems such as MusicLM can be trained on the audio waveforms of
recorded music along with text annotations, in order to generate new musical
samples based on text descriptions such as "a calming violin melody backed
by a distorted guitar riff".
Video:
Generative AI trained on annotated video can generate temporally-coherent video
clips. Examples include Gen1 by RunwayML and Make-A-Video by Meta Platforms.
Multimodal:
A generative AI system can be built from multiple generative models, or one
model trained on multiple types of data. For example, one version of OpenAI's
GPT-4 accepts both text and image inputs.
Computational creativity
The
multidisciplinary field of computational creativity, also referred to as
artificial creativity, mechanical creativity, creative computing, or creative
computation, is at the nexus of artificial intelligence, cognitive psychology,
philosophy, and the arts (for example, computational art as part of
computational culture).
Computational
creativity aims to model, mimic, or duplicate creativity in order to accomplish
one or more goals. To develop a computer programme or system capable of
creativity at the level of a human. To develop a computational perspective on
human creativity and a better understanding of human creativity. Can create
software without having to be creative oneself that can improve creativity in
people. The study of creativity has both theoretical and practical problems,
which are addressed by the discipline of computational creativity. The
implementation of systems that demonstrate creativity is carried out
concurrently with theoretical study on the nature and appropriate definition of
creativity, with one strand of work influencing the other.
Media synthesis
is the term for computational creativity used in application.
The
problems of theory
The core of
creativity is a topic of theoretical perspectives. In particular, it should be
clarified under what conditions the model qualifies as "creative"
since true originality entails violating the rules or rejecting convention.
This is a variation of Ada Lovelace's argument against artificial intelligence,
which contemporary theorists like Teresa Amabile have rephrased. How can a
machine's behaviour ever be described as creative if it can only carry out the
tasks that were programmed into it?
The idea
that computers can only perform tasks that they have been designed to perform is
one that not all computer theorists would concur with, which is a crucial argument
in favour of computational creativity.
Because no
single perspective or definition seems to offer a complete picture of
creativity, the AI researchers Newell, Shaw and Simon developed the combination
of novelty and usefulness into the cornerstone of a multi-pronged view of
creativity, one that uses the following four criteria to categorize a given
answer or solution as creative:
The answer
demands that we reject ideas we had previously accepted
The answer
results from intense motivation and persistence
The answer
comes from clarifying a problem that was originally vague
Whereas the
above reflects a top-down approach to computational creativity, an alternative
thread has developed among bottom-up computational psychologists involved in
artificial neural network research. During the late 1980s and early 1990s, for
example, such generative neural systems were driven by genetic algorithms. Experiments
involving recurrent nets were successful in hybridizing simple musical melodies
and predicting listener expectations.
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