209 karma · joined June 1, 2015
[1] German only: https://de.wikipedia.org/wiki/Park_am_Gleisdreieck
Also, if we think about it in terms of decision manifolds, it seems the distance between queen and king is too large for the simple - man + woman to have an effect. Why not scale that substraction, so it leads to a change in predicted class without removing king? But of course finding a justifiable weight would be hard..
What makes Kreuzberg attractive for startups and a Google campus is that it's central and perfectly connected infrastructurewise. Most other regions like Schöneberg, parts of Friedrichshain, Prenzlauer Berg and maybe Moabit, are all harder to reach from some other regions, even though more start-up employees live there. You can see on the maps of rental e-scooters like Coup how during the day there is a lot of activity towards Kreuzberg whereas after work the district is basically empty of their scooters. Imho kreuzberg is too dirty for most startupers. I guess they don't want to see the heroin junkies of Kotti when they do their grocery shopping.
In general I liked the sentiment of the activists against placing a Campus in Kreuzberg. Nevertheless I didn't like much of their public attitude ("bullets for google") and some arguments seemed superficial ("other Google campuses have increased rents" idk about the causality and factor here). I wouldve liked a Google campus in Schöneberg for example, just as I liked the Google campus in Madrid. In Madrid it offered a nice environment for work, some interesting talks and I didn't feel like it was in an artsy district that suddenly gentrified and turned hip. This could've added something to Berlin, but meddling with the activist scene in Kreuzberg was a poor choice.
Anyone else having the same issue?
Interested in doing a PhD in machine learning for healthcare? We are offering a PhD position at Charité Berlin.
German is not required! ______________________________________________________________ Deep Learning in clinical neuroimaging
PhD scholarship (starting October/November 2018, initially for 2 years; Promotionsstipendium II at Charité)
At the Berlin Center for Advanced Neuroimaging and Bernstein Center for Computational Neuroscience (Charité), we are looking for a motivated and highly talented PhD student for various research questions within the interdisciplinary field of deep learning and clinical neuroimaging. In particular, we employ convolutional neural networks for finding new representations from neuroimaging data in order to predict disease conversion and future clinical disability in neurological as well as psychiatric diseases. Whereas previous disease decoding approaches mostly relied on expert-based extraction of features in combination with standard classification algorithms and thus strongly depend on the choice of data representation, convolutional networks are capable of learning hierarchical information directly from raw imaging data. By this, they have a great potential for finding unexpected and latent data characteristics and might perform as a real “second reader”. A major focus will be on visualization techniques to make the learned content of convolutional neural networks visible.
Requirements for the PhD student: - Very good degree in computer science, mathematics, physics, psychology, computational neuroscience or related subject. - Very good programming skills (e.g. Python) - Experience in machine learning - Good writing and communication skills (in English)
Please send your application (motivation+CV) in one pdf-file (in English or German) to:
Dr. Kerstin Ritter Berlin Center for Advanced Neuroimaging, Bernstein-Zentrum für Computational Neuroscience Charité - Universitätsmedizin Berlin Sauerbruchweg 4, Charitéplatz 1, 10117 Berlin Email: kerstin.ritter@bccn-berlin.de
On the other hand the risks are incredible, FR in combination with AR systems could completely eradicate privacy. Autocratic governments could use it for a new levels of surveillance. Military usage could perfect the use of autonomous weapons etc.
It's just not worth it and should be internationally outlawed. Just like A-bombs and chemical warfare.
Using a fail safe network is hard because adversarial examples usually have a high accuracy at a false class. So using an accuracy threshold in the main network wouldn't work. Using a network as described in the paper and then a different kind of classifier might be worth trying. But it has also been shown that adversarial examples can transfer to different kind of models (don't know if random forests have been tried as well).
If you want to run it you should read through it first, it says above every function what it is going to disable. I would go with a simple rule: If you don't understand it, don't disable it. Otherwise it contains code for enabling all features again in case you start to miss something.
Edit: especially UI part seems to disable a lot of things by default that you might not want to disable!
Of course some good writers are still there but they are lost in all this really annoying noise that stopped me from reading medium.
I guess those things would be fine to share without revealing the inner workings.
I am currently enrolled in the EIT Digital master school. Funded by the EU, with scholarships for both EU and non EU citizens there was a huge amount of applicants from outside the EU, mainly "poorer" countries. Turns out, of those very few remainend and mostly those that got a full scholarship.
Applicants != graduates;