fhuszar

Some of the deeper - and wider - convolutional neural network-based SR methods do this, implicitly. We have a few papers, like SRGAN (https://arxiv.org/abs/1609.04802) or AffGAN (https://arxiv.org/abs/1610.04490) that generate pretty interesting context-aware textures. Sophisticated hallucination of missing details comes at a cost: As one would expect, in some cases the algorithm guesses incorrectly. See for example Figure 5 of AffGAN paper, column 3: when superresolving an image of a snake, the algorithm mistakenly thought it's a body of water, and produces textures accordingly. Overall we found that most people find the results of GAN-based approaches superior to other ML-based techniques.

jq,http://stedolan.github.io/jq/,best tool to handle JSON files in command line #The tool that immediately comes to my mind is jq, a tool to transform and process JSON objects. It's one of those powerful tools that is super easy to learn and once I started using it I just couldn't live without. The only negative thing I have to say is that it does not have good native support to transform between JSON and CSV.

Yes, startups try to game VCs already. But if the 'features' that your business needs to achieve for VC are 1) hard to achieve and 2) generally drive you towards success then what's the issue with that? We want more startups to succeed and VC investment is an important feedback mechanism that ought to drive more startups towards success or early discovery of failure.

I mean let's say startups try to game the VC game and they learn they need to demonstrate product market fit before seeking investment. Is this a bad thing? We'll try to be as transparent about our insights as possible so startups know what we think drives/indicates their success the best.

I think you are right in that final decision is certainly not going to be reduced to a supervised ML algorithm classifying investment opportunities. But I don't think this fundamentally has to do anything with antagonistic and non-antagonistic - you just need different types of methods

The problem with this is that people's skills combine non-linearly. Just like a cluster of 100 machines is not 100 times faster than a single machine. After all, why do we need kite surfers, just tie a surfer and a kite jumper together.

OK, so let's say I'm the scientist working alongside a couple of computer science people. Now, every time I have to remove a comma from the files they exported for me, should I ask them to please write a magic line into the shell for me. Every time I have to parse data in json or whatever else I don't know anything about I wait for them to do it for me. Every time data has to be loaded from a database, I explain what data I want, wait for the computer science person to write and execute a query for me? Just doesn't work this way.

When working with data you have to be able to experiment, and to experiment you have to have an idea about what's possible and what's not. If, as a scientist you do not understand what these practical tools can do for you, your experimentation will be severely limited. You have to be able to pair up the most promising mathematical approach with the simplest techniques that get you there. This is very hard to do, unless the same person knows about both maths and computer tools.

But more fundamentally, machine learning (or data science) does not equal statistics + computer science. The skillset you need to be a powerful part of the team is not simply a union of various computer science and statistics skills. It requires a different mindset.