- From: Paola Di Maio <paoladimaio10@gmail.com>
- Date: Sun, 7 Feb 2021 11:00:16 +0800
- To: Dave Raggett <dsr@w3.org>
- Cc: W3C AIKR CG <public-aikr@w3.org>, public-cogai <public-cogai@w3.org>
- Message-ID: <CAMXe=SoYKB-kKGJvqbaJw5WkJ=yy+CxE0Jh4qbRTtHOKZTkdVg@mail.gmail.com>
Thank you Dave I ll try to jot some of the concepts you mention on that slide Human intelligence/cognition is such a vast topic, its useful to try to narrow down the epistemic perspective when designing AI however, transposing the cognitive model into explicit KR is necessary, with some expectation perhaps of some types of genetic algorithms they call them ML/NN these days and which are not there yet, such as than a future model of the baby AI capable of self learning I d like to note a couple of points before the brain shuts down for the day and resets onto other tasks I work as a teacher, mostly for adults but increasingly enjoy teaching kids, its a totally different experience which has started feeding materials into the research angle for cognitive systems It must be noted that human learning/intelligence is very plastic and context dependent Physical conditions, such as nutrition, sleep social conditions, such as a stressful social environment such as family tensions environmental conditions such as air and noise pollution, as well as the availabilty of clothes, suitable desk and chair, equipment, stationary, they all influence learning. One of my students used to sleep in my class, and failed most subjects consistently, btu she was nice, not a lazy person at all, Only after recommending a blood test it turned out she had sugar and iron deficiency, some supplements and a change of diet fixed and the next year she got B+ without trying much So measuring human intelligence based on exams results is a gross simplification some subjects like maths or physics can be taught relatively easy, even calcultators or a program can be written to give the right answer , not much thinking required, just apply the logic in the right sequence and the result is correct by default however some subjects like essay writing or rethoric..... has anyone seen an AI capable of rethoric? Now we have codified the solution for a rubik cube, but before such solutions existed, solving a rubik cube took considerable more time and effort, now even a robot can solve the rubik cube https://www.theverge.com/2019/10/15/20914575/openai-dactyl-robotic-hand-rubiks-cube-one-handed-solve-dexterity-ai#:~:text=In%202016%2C%20semiconductor%20maker%20Infineon,in%20less%20than%200.4%20seconds . Big topic I ll expand on the slide when I can p but On Sat, Feb 6, 2021 at 10:09 PM Dave Raggett <dsr@w3.org> wrote: > See inline ... > > On 5 Feb 2021, at 21:08, Paola Di Maio <paoladimaio10@gmail.com> wrote: > > Hi Dave > I am cc ing the lists, because this exchange is part of the discourse in > our respective CGs and relates > to a post to the lists > > well, I do study the child mind, but not with CogAI > > Because it is not yet clear what CogAI does in relation to other approaches > (the slide aims to help clarify) > > so, is the method of COGAI (as your emails suggest) mimicking? is there a > reference for that? > > > The idea is to match or improve upon human performance through executable > software implementations of functional models inspired by observations and > theories of human behaviour, especially that of children. > > > you write > > * If we can successfully reproduce how the best people reason,....* > > how does COGAI defin best people ? > > > That would depend on what you’re looking for. One metric is how good > people are at passing exams. > > Human-like AI is perhaps a better term than Cognitive AI as it makes it > immediately clear that the focus is on mimicking human abilities. > > > >> an afterthought >> >> in respect to mimicking how humans reason and communicate well, >> each human is different, we can generalize up to a point >> >> and mimicking may result in some kind of parrot engineering .... >> useful to start with but nowhere near intelligence at its best >> >> >> You’re missing the big picture. If we can successfully reproduce how the >> best people reason, we will be in a strong position to improve on that by >> going beyond the limits of the human brain. The more we understand, the >> further and faster we can go. This is an evolutionary path that will go >> very much faster than biological evolution. At the same time we can make AI >> safe by ensuring that it is transparent, collaborative and embodies the >> best of human values. >> >> Human-like AI will succeed where logic based approaches have struggled. >> 500 million years of evolution is not to be dismissed so easily. >> >> I remember the enthusiastic claims around “5th generation computer >> systems” and logic programming at the start of the 1980’s, and had plenty >> of fun with the prolog language. However, the promise of logic programming >> fizzled out. Today, 40 years on, much of the focus of work on knowledge >> representation is still closely coupled to the mathematical model of logic, >> and this is holding us all back. We need to step away and exploit the >> progress in the cognitive sciences. >> >> I am especially impressed by how young children effortlessly learn >> language, given the complexity of language, and the difficulties that adult >> learners face when learning second languages. Another amazing opportunity >> is to understand how some children are so much better than others when it >> comes to demanding subjects like science and mathematics. Moreover, warm >> empathic AI will depend on understanding how children acquire social skills. >> >> Let’s lift up our eyes to the big picture for human-like AI. >> >> Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett >> W3C Data Activity Lead & W3C champion for the Web of things >> >> >> >> >> > Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett > W3C Data Activity Lead & W3C champion for the Web of things > > > > >
Received on Sunday, 7 February 2021 03:01:11 UTC