Prof. Bengio is perhaps one of the key (if not the only key voice) who so clearly articulates in great detail what is lacking in DL to date and what could be one path forward ( and is kind enough to give links to all relevant references). Few exhibit the intellectual honesty and earnestness in helping the rest of us understand what to expect in the future. Wish I had teachers like him when I went to school.
It took me a month to fully understand everything he discussed in this presentation (at a high level). I think this is the future. Would love to hang out and discuss if anyone is in Toronto.
Isn't causality just a special case of correlation across time? At least that's how it seems to works for human intuition of causal effect, I think if so, I don't see the fact that modern neural nets are only capable of learning correlations as an impediment for them to also learn causal relations.
I am not sure about that, but I want to pose another question: if you invert time what happens? Does this thought experiment help in looking for clarifications? The correlation of x at time t with x' at time t' should not be affected by that change. But a causal relation should be affected, as far as I can see. I am now at the conference, if I manage to meet Bengio I may ask directly to him and report his answer here.
@Shikhar Srivastava @Shikhar Srivastava Shikhar Srivastava "Say we're in a given state. If events A & B are simply correlated, and if B occurs, we can consequently say there's a probability of A occuring. Now if event B is caused by event A, and if B occurs, then A has already occured, and so the probability of the event A occuring forward in time is independent of event B - as in we don't expect A to occur simply because B has occurred. However, if A has occurred, then B must occur with the known P(B|A) probability. Hence the directionality of the relationship." Let me see if I understand...basically, the problem is that simultaneous correlation between A and B, say in a bayesian net, is not able to grasp the fact that a future occurrence of A becomes conditionally ind. from a past occ. of B, whereas a future occ. of B is still conditionally dependent from a past occ. of A, hence the asymmetry problem. Then why not treat A in time t=0 and A in time t>0 as different events, so it wouldn't make any sense to compute P(At>0 | Bt=0), since there wouldn't be any connection in the graph of relations? Doesn't it solve the problem of dependence/independence asymmetry, since the A that preceded B would still be dependent on B, but the A that comes after would just be another variable? I guess the problem is a limitation of representation of sequential relations in a Bayesian Net, which is unfeasible, but this is not a difficult for a neural net such as RNNs to model, which are able to grasp those sequential relationships.
@@ans1975 Just inverting time does not mean anything causal world; Causal world talks about antecedent follows precedent, because precedent causes antedent
@UCGTnKVtLrM0sI8QZbeEFo7Q Even though almost all causal relations are encoded in data, it seems like without a causal model it is some what impossible to infer those causal relations from data ( even with RNNs)
I am a horrible sister I just went to somome IS in the room and I just wanted to make sure He doesn't get into too much trouble what is like snnsnsjjsjsn Always make doubble an tripple sure that the absuive persons know a meeting has been argreed multiple Times and so that they can't deny it and schools so good for that too because it's so good that it can't be rejected socialy without going to tue relm of negelect- Like saying a sister May not teach her brother how to do things. Need to keep maybe book of Interactions w Jackob so I have a better case?
I like that Yoshua approaches the theory of neural networks in the language of probability at its core.
Prof. Bengio is perhaps one of the key (if not the only key voice) who so clearly articulates in great detail what is lacking in DL to date and what could be one path forward ( and is kind enough to give links to all relevant references). Few exhibit the intellectual honesty and earnestness in helping the rest of us understand what to expect in the future.
Wish I had teachers like him when I went to school.
It took me a month to fully understand everything he discussed in this presentation (at a high level). I think this is the future. Would love to hang out and discuss if anyone is in Toronto.
Yoshua: "Conscience is the next big thing"
Next job offering: AI Conscience Engineer
Following job: Conscient AI
XD te pasas
From computer science to comscience.
Like a TH-cam video, the AI will be able to convince you of anything and its opposite.
A big chunk of knowledge maybe pre verbal. Look at our cats, dogs, and other mammals.
"In our community, the C-word (consciousness) ..." =D
Transformers. Deep Learning. Training. Hard Problem. Fixed-Size set. Prune.....I could keep going...
Isn't causality just a special case of correlation across time? At least that's how it seems to works for human intuition of causal effect, I think
if so, I don't see the fact that modern neural nets are only capable of learning correlations as an impediment for them to also learn causal relations.
I suggest to read "The Book Of Why by Judea Pearl", especially the first two chapters
I am not sure about that, but I want to pose another question: if you invert time what happens?
Does this thought experiment help in looking for clarifications?
The correlation of x at time t with x' at time t' should not be affected by that change.
But a causal relation should be affected, as far as I can see.
I am now at the conference, if I manage to meet Bengio I may ask directly to him and
report his answer here.
@Shikhar Srivastava @Shikhar Srivastava Shikhar Srivastava
"Say we're in a given state. If events A & B are simply correlated, and if B occurs, we can consequently say there's a probability of A occuring.
Now if event B is caused by event A, and if B occurs, then A has already occured, and so the probability of the event A occuring forward in time is independent of event B - as in we don't expect A to occur simply because B has occurred. However, if A has occurred, then B must occur with the known P(B|A) probability. Hence the directionality of the relationship."
Let me see if I understand...basically, the problem is that simultaneous correlation between A and B, say in a bayesian net, is not able to grasp the fact that a future occurrence of A becomes conditionally ind. from a past occ. of B, whereas a future occ. of B is still conditionally dependent from a past occ. of A, hence the asymmetry problem.
Then why not treat A in time t=0 and A in time t>0 as different events, so it wouldn't make any sense to compute P(At>0 | Bt=0), since there wouldn't be any connection in the graph of relations? Doesn't it solve the problem of dependence/independence asymmetry, since the A that preceded B would still be dependent on B, but the A that comes after would just be another variable? I guess the problem is a limitation of representation of sequential relations in a Bayesian Net, which is unfeasible, but this is not a difficult for a neural net such as RNNs to model, which are able to grasp those sequential relationships.
@@ans1975 Just inverting time does not mean anything causal world; Causal world talks about antecedent follows precedent, because precedent causes antedent
@UCGTnKVtLrM0sI8QZbeEFo7Q Even though almost all causal relations are encoded in data, it seems like without a causal model it is some what impossible to infer those causal relations from data ( even with RNNs)

Hello Yoshua.
Who's the speeker who introduced Mr YB? Is she a researcher too ?
why?
Leon Bottu.
Yes he's one of the great researcher
The link for the slides don't work! Please update them!
I am a horrible sister I just went to somome IS in the room and I just wanted to make sure He doesn't get into too much trouble what is like snnsnsjjsjsn Always make doubble an tripple sure that the absuive persons know a meeting has been argreed multiple Times and so that they can't deny it and schools so good for that too because it's so good that it can't be rejected socialy without going to tue relm of negelect- Like saying a sister May not teach her brother how to do things. Need to keep maybe book of Interactions w Jackob so I have a better case?
大牛挖坑