Hi /Mar(e?)k/,
Thanks for the link. This is a hard problem, which is why they resort to
getting Mechanical Turk people to label the phrases. That's effectively
programming the system, so it's yet another apparent step forward, which is
nothing of the kind.
The "emotional content" of text is all about how the current input echoes other
contexts in which the words were used, and a memory of how we felt then. This
is something which no AI system can truly learn, since it has no genuine
intrinsic emotional "sense".
Jeff has no illusions about this. He's said many times that the goal of
creating an emotional AI is flawed. It's a hubristic notion rooted in our wish
to dominate: why create an emotionally sentient intelligence only to have to
choose between granting it rights or enslaving it?
By all means the systems we will build should have an awareness of our
emotions, but this will be treated just like any variable in their thinking,
rather than being an intrinsic governor of their minds, as we have.
Regards,
Fergal Byrne
—
Sent from Mailbox for iPhone
On Sun, Dec 8, 2013 at 6:03 PM, Marek Otahal <[email protected]> wrote:
> Hi all,
> I just stumbled upon an article about natural language processing and
> sentiment analysis, a topic I'm interested in.
> http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/
> The idea they used seems to work for them well and is simple in its nature.
> When I've seen it, I immediately thought CLA (for its sequence learning
> ability) and CEPT (for the "ontological matrix" view on words). This
> combined should give interesting results and has a good (commercial)
> usecase.
> Tricky part would be training the classifier, getting some people to label
> the phrases (or some smart ways around this).
> Regards, Mark
> --
> Marek Otahal :o)
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