hello everyone this is giovanni lion from the hong kong polytechnic university
and the topic of my research is concept formation in computational creativity
i will start by addressing the fundamental assumption about my research,
that is, evaluation of creativity is fundamentally subject to a human bias,
we will be the one to judge whether something is creative or not
therefore when we talk about non-human creativity is irrelevant whether the
artifact has been produced by a human or not as long as we regard it as creative
another fundamental assumption in computational creativity, at least up until a decade ago,
was that parts of the human creative process would need to be simulated
well, the last decade of achievements in deep learning, and generative deep learning
in particular, has shown that, perhaps, this simulation is not needed or we can get results
even without trying to simulate the human,
because of course neural networks aren't really a simulation of the human brain
so what are we talking about? generative deep learning tools such as autoencoders,
generative adversarial networks and transformers
so what exactly do these neural networks learn, because of course, they learn from
datasets and they would naturally learn the bias of these datasets, we don't know exactly how this
information is stored, there is no way for this network to do any symbolic reasoning and it's
hard to transfer knowledge from one network to another and so this questions brought me to dig
deeper into what concepts actually are and so I went into a literature review looking for concept
ontologies, that is theories of concepts that are able to explain phenomena related to concepts
turns out the last sixty years of research in this field has produced up to five big categories
of theories, the main one being classical theory and a big opponent of that in the
70's was prototype theory, which stems from experimental psychology research
so classical theory of concepts rests on definitions
but definitions have a problem, which was identified actually dates back to Plato,
that is as simple as, we cannot really find clear definitions for high-level concepts
such as KNOWLEDGE, JUSTICE and GOODNESS and even more mundane
concepts such as BALD are very hard to really strictly define
so creativity and design I believe belong to this category, definitions for those
will always be hard to find if we're trying trying to find a very strict and clear one
another aspect of concepts that is key is compositionality, that is for example,
in this sentence you understand the meaning of this sentence because of the parts,
so because of the words and what they mean, but also because of how they're
connected with each other and the order in which the words appear in the sentence and
that is a fundamental aspect that still hasn't been explained
so in my research I am addressing the following research questions,
first of all I try to understand which concept ontologies
fit the current GDL trend and the second is about compositionality,
so whether this aspect of compositionality can be implemented and is relevant or is missing
as an output I would like to create a tool for
generating creative artifacts in multiple domains that is music, language and images
so for my studies I will be addressing each domain separately,
currently I am involved in the music domain with a local composer, her name is Vicky Fung,
she has composed over 40 songs in the canto-pop scene here in Hong Kong
and we're looking at using transformers and VAEs to generate more music like hers
the second study will be on language and I will be involved with Hanson
Robotics and Singularity.NET working on the robot Grace.
the third study will be on images but is yet to be defined
thank you so much for your attention and looking forward to your questions and your comments
and thank you very much