Welcome

"A Maximalist Hypothesis: ChatGPT and other large language models, respectively, contain and understand all true humane knowledge, which is averaged out of their training data, as conceptual abstractions, concistent causal systems, and models of the world. This wealth of knowledge, however, materializes only when prompting. I'm not interested in the simplest problems a language model fails, but the most complex ones it succeeds or fails."

Janne P. Hukkinen

Robot-AGI Researcher, independent

Working on [random notes]

2024 prospecting PhD studies

research question, tentative: how slow explicit system 2 reasoning and problem solving [in LLM space] can be automatized to fast system 1 'scaffoldable' capacities?
research motto and orienting big question: how AGI robot could be implemented?
for a possible explanatory goal, what causal pattern offers explanation – explanatory pluralism FTW
- [x] how to reason and solve problems in system 2 using LLM's and a set of ML modules?
- [x] how to implement cognitive scaffolding?
- [x] what kind of representations constitute whats-it-like reality?
- [ ] goal: consciousness. explanation: aligment of biological/human and artificial consciousness
- [ ] ...
[pass] cognitive architectures
[x] OODA / Sense Think Act Loops
[ ] sensory, motor, and cognitive capacities
...
big & interesting questions:
- what are the first principles of cognition?
- labor-division and orchestration problems of cognitive architectures: for a particular cognitive tasks/work..
  - what kind of set of cognitive modules are needed: amount, individual roles, and capacities?
  - how the processing of modules should be organized in order to fulfil a set of desired high-level goals/tasks/work/end states?
interesting questions:
- grounding mechanisms in science and philosophy

2023 AGI (artificial general intelligence)

systemic underpinnigns, constraints, assumptions, and cognitive design tools
how much world knowledge can be pumped out of large language models?
how to orchestrate a modular cognitive agent / system?
world knowledge & understanding: representations in latent embedding space, how far we can get with tera byte language data, and respective Transformer/GPT-3 language models? When is embodied, enacted, and situated grounding needed, if ever?
do we need cognitive theory any more? how can it inform us?

theory, cognitive architecture, design & evaluation framework
what is known and unknown?
curriculum learning / critical developmental periods

Hands on

python, pytorch: practical machine learning applications and semi-automatic pipelines
(audiovisual dubbing and interpretation: image classification, speech synthesis; video colorization)
infection chain tracking web app: jä.tk: django, postgres, bootstrap
latex, linux, python, bash, esphome, freebsd, zfs, nginx, postfix, ...

Publications

Hukkinen, J. P. (2022). Towards a Checklist of AGI Implementation – Can a Critic Become a Solutionist?
Poster.html, A4 PDF, A1 .PDF, .svg to to Mind and Matter 2022 conference. Helsinki.
Oulasvirta, A., Hukkinen, J. P., & Schwartz, B. (2009). When more is less: The paradox of choice in search engine use. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 516–523. https://doi.org/10.1145/1571941.1572030

Education

MA (cognitive science. minors: computer science)
BA

Profiles

Twitter @Hukkinen. ActivityPub @HuK@mas.to. Youtube. Linkedin