ergodic
Joined 15 years ago
Karma
36
Submissions
20
Member since
Oct 27
Recent submissions
Comment · 8 years ago
Comment · 8 years ago
IMO the economical argument is the most robust. I see it like this:
There is a big margin between
1) the amount of information that you are legally obligated to provide to public and private sectors
2) the amount information that can be mined about you through facebook and other services (+smart data-crunching)
Safeguarding that information that falls between 2 and 1 is not a matter of moral standpoints or having something to hide. It has only to do with a properly informed rational decision about your personal economy and your well being.
That information that you are willfully leaking will weaken your position in any future negotiation including, financial credit, insurance and employment among others. After Cambridge Analytica it is more clear than ever that it will also weaken your ability to protect yourself against well armed psychological manipulation.
and that is all assuming that public and private institutions do not make mistakes, that legal boundaries and procedures are flawless and that you leave in a well executed democracy.
Comment · 10 years ago
I have not followed the news but was this really "considered the country's own answer to the iPhone"?. By whom exactly?
One thing is to say "A Spanish company's iPhone killer is a scam" an another "Spain's iPhone killer is a scam". I hope racism/cliches did not play a role here.
Comment · 11 years ago
>And I'm not sure that's a bad thing.
You surely can find other cases in which you end up paying more than the average. In the end, what matters is information asymmetry in any negotiation. In those terms, the consumer seems to be in an increasingly weaker position with the recent technological changes, social media, etc.
As a consumer I don't think that allowing market micro-segmentation is a sensible strategy. It's more something that I don't know very well how to stop, and it worries me.
Comment · 12 years ago
Context: I lived in Lisbon four years and know one of the founders of Unbabel. I do not know you, neither your story.
Regarding your comments:
> That's one thing I really hated about Portugal, they just don't offer any information in English
>Thanks, but from the experience I had with both my employer and my landlord in Portugal I'd rather not do business with any Portuguese people ever again. Sorry.
>it does seem like fucking people over is business as usual in Portugal
>Have you had any experience with the Portuguese state or legal system? I'd probably still be standing in a queue somewhere.
Blaming the whole country for a 90 day experience does not seem very proportional to me.
Again, I do not know your story and can not speak for the veracity of your facts. What I do know is Lisbon (and I have lived many years in Spain and Germany to compare with). From that I know that you are just being, at best, exaggerated and I wonder if you have been exaggerating other stuff too.
Comment · 12 years ago
nice!, does it have OCR?
Comment · 12 years ago
Cool, you should try to sell this to community managers working on twitter.
Comment · 13 years ago
no RSS?
Comment · 13 years ago
It would be interesting to see if this is really so. The post just cites circumstantial evidence and does not reason why this would happen.
I personally hope the days of amateur, low frequency, niche blogging are not over. It seems to me as one of the fundamental goods that internet has brought and, as the post indicates, a solid reason to have RSS readers.
Comment · 13 years ago
At the risk of being a bit annoying... regarding speech recognition, as said here
https://news.ycombinator.com/item?id=5376319
they use DNNs not DBNs (DBNs only used for pre-training, sometimes). Also if you read Microsoft's paper Table2, 7 hidden layer networks, which clearly qualifies as deep, work just fine with Back-propagation. Just a bigfat-MLP, no preprocessing! but 17.4 Word Error Rate (WER) vs 17.0 WER for DBN pre-training.
>they're not much like the old ANNs. DBNs are generative probabilistic graphical models using Bayesian inference.
MLPs (DNNs) can also be interpreted probabilistically. Just a directed model where inference is attained by marginalization of the hidden binary nodes in a layer-wise manner and by using a naive mean field approximation. All that to say the classic "forward-pass" ;).
Also could you indicate me a source confirming that Siri (Nuance) also switched to DNNs?. I am interested in that.
>Conceptually, going deeper (LOL) allows the networks to learn higher level concepts.
That is the really interesting part!. Now, I have not seen a proof for that. Wondering at individual neurons modeling individual features of e.g. a face or so is also a trend of the 90s and does not count as proof. I said this because it is what I usually hear.
Until now the justifications I saw for multiple layers of perceptrons being suitable for modeling arbitrary high level abstractions are reduced to
1) MLPs are universal approximators. This in my opinion is a superficial argument. GMMs also allow modeling "any" distribution and Taylor series any linear function, but in reality there are physical limitations to this argument. Maybe is true if you had a billion layer net, but will you get there?. If you had that computing power maybe a more realistic modeling of the brain might work better
2) They resemble how brain architecture works and similar arguments. Which I am fairly sure is not true. There are more human-brain based approaches to AI like e.g. cortical learning algorithms and those just seem to model that stuff to a certain extent.
Comment · 13 years ago
I did not find that on the paper, are you referring to randomly switching off neurons?. I would be surprised if this would not be a technique of the original neural networks wave.
Comment · 13 years ago
>DNNs can be thought of a stacked Restricted Boltzmann Machines
Agree, as explained in Hinton et al 2006.
http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf
But this is just for pre-training, as I said. If you look at Seides paper, they pre-train treating the MLP as a DBN and then they train it as a classic MLP with BP. Also using layer-wise BP pre-training does bring performance close to DBN pre-training, with no use of DBNs paradigms at all.
>Their structure and training is very different to traditional MLPs
I insist if we are talking of the same DNNs explained in Microsofts paper, this is not true. If we were to be talking about different DNNs please elaborate I would love to hear about that (seriously, no irony here).
Comment · 13 years ago
Again, the use of senones instead of monophones or diphones is just changing the output targets is not a novelty per sé.
Comment · 13 years ago
rather than being so aggressive and waiting for downvotes you could choose to be constructive ;) and answer a concise technical question to a sub-thread that you yourself started
Comment · 13 years ago
I read it in diagonal but the paper seems to use the same DNN architecture as before. They seem to tweak the pretraining with layer-wise back-propagation (instead of full MLP-as-DBN pre-training). This does not imply anything new with respect to what I commented and the cited paper.
The only reference to differences I found is about differences between a DNN and a MaxEnt models, which is again not an argument for differences between DNNs and MLPs.
Could you point me to a concrete paragraph?, I would be happy to be mistaken in this regard.
It is very complete. Similar to pytorch (which borrowed some ideas from Dynet) and backed up by top academia (not big tech, hence its lower profile). It has a python wrapper as well, as everything nowadays.
If you are into deep learning for sequence to sequence (machine translation) Marian is also C++ centered. Fastest MT around. Complete (transformer and the like), extendable. Not that reading friendly from what I have heard.
Disclaimer: Python type of guy here.