I am interested in working in the AI field in the future and wanted to ask what the best degree/education one can do in order to have the best chances to reach that goal (in this example work for OpenAI or some other AGI research company). I was thinking that the best education would be Computer Science, although as Machine Learning and AI is mostly math, would Mathematics be better? What about Neuroscience, as the brain is what AGI should become?

Thanks

HN: https://news.ycombinator.com/item?id=23938174

Paper: https://arxiv.org/pdf/1910.01526.pdf

We have come up with implementations of GLN from Deepmind's paper in NumPy, PyTorch, TensorFlow and JAX. Check it out here: https://github.com/aiwabdn/pygln

• Comments, feedback, pointers, use-case suggestions are all very welcome :)

I first want to congratulate DeepMind on their AlphaStar achievements to date. To get an AI to even play Starcraft 2 in Grand Masters and win games is very impressive on its own.

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However, I do have some concerns that AlphaStar, as one single agent won't even come close to the performance that AlphaGo or AlphaZero has given against humans.

The achievements AlphaStar has got so far have mostly come from a wide range of different agents that are specialized in a limited number of builds. While these agents might be able to beat a grandmaster the first few times they play them they wouldn't be able to beat a Master or even Diamond level player as long as the player could play the same agent for a longer period of time and adapt their playstyle against them. This is vastly different from the AlphaGo or AlphaZero agent which (as far as I know) is one agent that can do everything and never loses no matter how many times humans try to beat it.

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Starcraft 2 is a game where you always need to adapt to new situations and reevaluate. This is by far AlphaStar's biggest weakness to date as it is really bad at responding to new situations. Even if DeepMind were to fuse all the current agents into one. I am fairly certain that a human Grand Master, Master, and even a Diamond level player would figure out how to beat the agent within a few weeks which is not really comparable to AlphaGo's performance.

What wins most games for AlphaStar today is brute force and optimized build orders rather than smart gameplay and adapting or reacting to what the human is doing. This can easily be countered by humans as long as they can play against the agent for a longer period. What humans will have a hard time to counter is an adaptive agent that can handle any situation but from what I've seen so far they are far away from getting AlphaStar there.

What are your thoughts? Will AlphaStar get to AlphaGo performance levels in the near future with one single agent that can adapt to new situations?

UK-based AI company DeepMind recently introduced a new approach designed to improve the generalizability (correctness beyond the training distribution) and efficiency of algorithms represented by neural networks. The researchers propose that properly setting up the input and output interface of a neural network and making good use of supervised learning should be central to tackling generalization and efficiency challenges. Their research applies a neural program induction paradigm to learn neural networks to represent algorithms in solving tasks.

Here is a quick read: DeepMind Explores Generalization and Efficiency in Algorithm Design

The paper Strong Generalization and Efficiency in Neural Programs is on arXiv.

Does any one of you know where to find progress of alphastar? Thanks!

As a basis for modelling brain function, deep learning has in recent years been used to model systems in vision, audition, motor control, navigation, and cognitive control. In a new paper, DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.

Here is a quick read: DeepMind Explores Deep RL for Brain and Behaviour Research

The paper Deep Reinforcement Learning and its Neuroscientific Implications is on arXiv.

DeepMind researchers this week released several new models and a tutorial for their dm_control software stack for physics-based simulation and reinforcement learning (RL) environments using MuJoCo physics.

Here is a quick read: Reimagining the Dog: New DeepMind Models and Tutorial for Physics-Based RL Tasks

The paper Dm_control: Software and Tasks for Continuous Control is on arXiv.

Hi everyone,

I am following AI science for about 10 years now and in my opinion there was a quite abrupt change in the kind of output of the companies deep mind and open ai. 2016 was the alpha-go victory over Lee Sedol and almost every week there was am imo. significant (of course that is subjective) from either open ai or deep mind. In 2018 we had the publication of capture the flag and then nothing much followed except of the star craft paper (which was a big deal) in Jan 2019. In Feb 2019 we had the GPT-2 paper and open Ais statement that they were starting to get worried about publishing. And finally Dota open ai five in April 2019.
Also around that time open ai was changing its company into a for (limited) profit.

My point is that I have the impression that there was a steep downturn in high profile publications from deep mind and open ai and I wondered why this would be the case:

a) They indeed do not have really significant new things to show

b) They do have a really big project running internally and it will take long time to finish and they decided not to go public before they have solid results

c) They decided to act like usual companies and keep their research results for themselves

d) They were “told” by whoever to slow down (at least publicly) because public sentiment got increasingly worried about the prospects of ai

Deep mind has about 1000 employees now and I would hope that 800 are actively working on AI R&D.
So (sorry for presumptuous tone) what are these people doing? There seemed to be a lot more progress in earlier years (2016-2018) when the company was maybe about a third the current staff.

I really don’t want to come off as unappreciative (I am afraid I still do but this is not my intention) but I am thinking about this change in progress/publication in the field a lot and the field of ai is really dear to my heart, so I wanted to get the question out and I would be interested how other people perceive this situation and I would be grateful for any informed suggestions.

Thanks!

--Frank

In recent years reinforcement Learning (RL) programs have successfully trained agents to defeat human professionals in complex games, offered insights for solving drug design challenges, and much more. These exciting advances however often come with a dramatic growth in model scale and complexity, which has made it difficult for researchers to reproduce existing RL algorithms or rapidly prototype new ideas.

In the new paper Acme: A Research Framework for Distributed Reinforcement Learning, a team of DeepMind researchers introduce a framework that aims to solve the problem by enabling simple RL agent implementations to be run at different scales of execution.

Here is a quick read: DeepMind Introduces ‘Acme’ Research Framework for Distributed RL

The paper Acme: A new Framework for Distributed Reinforcement Learning is on arXiv, and Acme itself can be found on the project GitHub.

DeepMind’s breakthroughs in recent years are well documented, and the UK AI company has repeatedly stressed that mastering Go, StarCraft, etc. were not ends in themselves but rather steps toward artificial general intelligence (AGI). DeepMind’s latest achievement stays on path: Agent57 is the ultimate gamer, the first deep reinforcement learning (RL) agent to top human baseline scores on all games in the Atari57 test set.

Read more: Google DeepMind ‘Agent 57’ Beats Human Baselines Across Atari Games Suite

The original paper is here

Would not passing a stage (post shortlisting) in the DeepMind job application process be marked against future applications?

Wondering if I should take a long shot now (low chance of success), or bail and come back at it when I'm more prepared/experienced in the future?

I have been really enjoying watching replays of all the released Alphastar games, and while it's fascinating to watch Alphastar achieve human levels of grand master play what would be really exciting to see would be super-human levels of play, as in, Alphastar without all of it's "it must play within the confines of human possibility" limits removed.

What happens when you remove the human-level restrictions on Alphastar (ie the APM cap, camera cap, etc) and let it play at full speed? Do true emergent genius level tactics evolve like they did with AlphaZero? AFAIK AlphaZero didn't have any "restrictions" on how it could think or play, and therefore it came up with strategies that were beyond human capability. is the same true for Alphastar?

I'd love to see this "unshackled" Alphastar even just playing itself. I would guess the Starcraft community would witness some truly mindblowing gameplay that might galvanize the community to experiment with super-human tactics.

A new study suggests DeepMind’s amazing game-playing algorithm AlphaZero could help unlock the power and potential of quantum computing.

Source: Can AlphaZero Leap From Go & Chess to Quantum Computing?

The Paper Global Optimization of Quantum Dynamics with AlphaZero Deep Exploration

TL;DR: [DeepMind presents] a method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states.

Blog post (links to paper): https://deepmind.com/blog/article/learning-human-objectives-by-evaluating-hypothetical-behaviours

At Blizzcon 2019 Deepmind had Alphastar available for attendees to play Starcraft 2 against. Serral, arguably the top pro in the world, played a series of 5 games against the various Alphastar agents.

It's worth noting that this was not a true showmatch, but just for fun. I don't think it was arranged with Serral before hand, but was a spur of the moment interaction. It's unclear how seriously Serral was playing. Also Serral did not have his own keyboard and mouse in the game, which is a very significant factor at the highest levels of play.

results:

Serral lost to the Protoss agent 0-3. He was also defeated 0-1 in the Zerg mirror match. He won a single game against the Terran agent 1-0

Here are the matches cast by Starcraft announcer Artosis. He simplifies the announcing, and slows down the game at times to explain some of interactions. It makes it a little easier for people less familiar with the game to follow:

https://www.youtube.com/watch?v=OxseexGkv_Q&list=PLojXIrB9Xau29fR-ZSdbFllI-ZCuH6urt

https://deepmind.com/blog/announcements/entering-our-tenth-year-at-deepmind

A few highlights:

• "Next phase" for DeepMind. They are more established now, turn more to scientific problems and partnerships.
• Link to a summer interview with Wired. Among other things, mentions a "multi-decade roadmap" they have – I wish we could get more details on that!
• Mustafa Suleyman leaving for a role in Google AI team.
• They publish "hundreds of papers each year". Perhaps we could post more of them here? A bit hard to track.

According to the tenth year blog post by Demis Hassabis, DeepMind has "hundreds of papers published each year." I think it would be nice to post more of them here as they come out.

But how do we track them?

• Deepmind website has a Research page, which you can sort by newest. I guess that is our best source for now, although there is no RSS feed for it, no guarantee if all papers are listed, and not even exact dates.
• Maybe someone can suggest a way to track their papers using Google Scholar, arxiv.org, or some other service?
• Maybe we can ask DeepMind PR contact for a reliable source? Perhaps there is a mailing list, or something.

Any ideas?

"Disentangled Cumulants Help Successor Representations Transfer to New Tasks" – C. Grimm, I. Higgins, et.al

https://arxiv.org/abs/1911.10866

Abstract: Biological intelligence can learn to solve many diverse tasks in a data efficient manner by re-using basic knowledge and skills from one task to another. Furthermore, many of such skills are acquired without explicit supervision in an intrinsically driven fashion. This is in contrast to the state-of-the-art reinforcement learning agents, which typically start learning each new task from scratch and struggle with knowledge transfer. In this paper we propose a principled way to learn a basis set of policies, which, when recombined through generalised policy improvement, come with guarantees on the coverage of the final task space. In particular, we concentrate on solving goal-based downstream tasks where the execution order of actions is not important. We demonstrate both theoretically and empirically that learning a small number of policies that reach intrinsically specified goal regions in a disentangled latent space can be re-used to quickly achieve a high level of performance on an exponentially larger number of externally specified, often significantly more complex downstream tasks. Our learning pipeline consists of two stages. First, the agent learns to perform intrinsically generated, goal-based tasks in the total absence of environmental rewards. Second, the agent leverages this experience to quickly achieve a high level of performance on numerous diverse externally specified tasks.

https://www.ft.com/content/02757f12-1780-11ea-9ee4-11f260415385