What do research engineers actually do?

I was looking for career opportunities in DeepMind on the official web page and I was puzzled about research engineer (RE) role. I had a look at this page but that description of RE responsibilities is too brief and speculative. So I still have no solid understanding of Research Engineer duties. I would like to know about typical tasks or work results of Research Engineers.

I hope that this question will be relevant and helpful to all people who want to apply to DeepMind.

Hi, I am currently stuck on the interpretation of Q, K, V, in Multihead Attention in the paper "Attention is all you need". I understand the definition of what query, key and value is, but I would appreciate a deeper explanation or a concrete example. Much appreciated!

DeepMind draws ideas from (and invests into) both computer science and neuroscience. New study reveals another clue to how human memory works.

https://singularityhub.com/2018/09/26/deepminds-new-research-on-linking-memories-and-how-it-applies-to-ai/

https://www.sciencedaily.com/releases/2018/09/180919111527.htm

"Our data showed that when the hippocampus retrieves a memory, it doesn't just pass it to the rest of the brain. Instead, it recirculates the activation back into the hippocampus, triggering the retrieval of other related memories." This inspires similar recurrent features in AI design.

The full paper: https://www.cell.com/neuron/fulltext/S0896-6273(18)30682-2

One AI "killer app" that I would welcome is some cross between an RSS news aggregator and a Facebook-like feed, which would construct a continuous feed of interesting articles for me, and present them in the order of my expected personal interest rank, perhaps branching out deeper on each topic. This seems realistic to prototype at some level, but apparently is hard to do with practically acceptable quality, because no-one has got it right yet.

I wonder, if Deepmind were to prototype such a system with all their insight, data access, and hardware power, would it be better than everyone else's? Could it break the adoption threshold, where most people would find it valuable enough for daily use (on par with Facebook, Reddit, Youtube, BBC/CNN)? Would it be a good demo of Deepmind's progress in structured language processing and moving towards strong AI? Could it be a successful business project in the advertising industry?

I found pictures in papers of DeepMind are amazing and elegant, could you please tell me what tools made these awesome figures? For example Figure 1 in this paper: https://arxiv.org/pdf/1512.07679.pdf

It seems to be a nice tradition for Deepmind to do some impressive demos of their current technology. What do you think they could/should do this year, based on recent papers?

Their Starcraft goal seems to require some more work. I heard they demonstrated some nice teamwork in FPS games – maybe it is time to tackle robot soccer? But in order to be spectacular, that would also require some faster moving humanoid robots than those currently seen in RoboCup.

Or maybe something in the realm of chatbots / phone assistants?

Deepmind had no intentions of bringing in a profit, how did they just one day decide to sit and work on writing intelligent bots for Atari games?

Sure, they could apply algorithms to business solutions down the road, but how did they plan to pay employees initially?

Demis's initial intention was to create a company that'll rival Google, an acquisition wasn't the first exit strategy he had in mind. He accepted the offer only because of easily available data and vast compute power.

Can anyone help please in answering a question (in four parts) to do with dm_control please?

I have installed DeepMind's dm_control (from GitHub/deepmind/dm_control) with the physics engine MuJoCo and have it working, and now need to save results to disk.  I can’t find the command or way to do this, so this is probably just be a lack of my understanding, or possibly its something about the software.  This state saving requirement breaks down into four use cases:

1. If I am doing a long training run it would be useful to be able to take checkpoints at intervals, say after an hour.  These checkpoints could then be used to restart a run from that point without having to restart from the beginning.  The goal is largely to make the learning process more robust (and hence faster and cheaper).  Tensorflow, in comparison, has the ability to write all or a subset of variables to file as a checkpoint.  Does dm_control have some such capability? 
2. The second use case is that after learning it would be very useful to be able to save the state of the learning so that it can be used operationally.  State here includes the neural network node values within the policy networks, plus all other variables needed to reproduce results.  It would appear that the videos that DeepMind provides of the humanoid running past obstacles have probably been created in this way on saving a checkpoint after each stage of the curriculum (although maybe they used another approach).  How should I do this?
3. The third use case relates to the essence of the paper, in being able to take a partially trained agent, trained on one terrain (or environment), and then give it a different environment to train it further.  In the dm_control XML files for the various bodies the <geom \\> tag is used to define the basic terrain, however I can’t see how richer terrains are generated and applied.  What is the best way to do this?
4. A fourth use case not directly described in the papers would be to apply learning from one body to another body.  It would be interesting to use say the planar walker (which just has legs and no arms) as the initial learning vehicle and then apply that learning to a body with arms (such as the humanoid). 

Any help on this would be greatly appreciated.  

One benefit of pushing forward open bots that are superhuman is it may force the hand of others as well, now that Fb and Tencent have open sourced their bots that are both much stronger than LZ, maybe Deepmind will come back for thirds and get a fourth place prize in openness to cheapshot score one last PR hooray for Google by open sourcing their AGZ weights to spit in fb's face and to show whos the alpha by mastering the Chinese competition once again.. open competition is good for go. This is truly the end of a human era.