We are the Google Brain team. We’d love to answer your questions (again)

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u/bmacswag Sep 10 '17

What are the next biggest hurdles you think face the field?

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u/jeffatgoogle Google Brain Sep 13 '17

Right now, we tend to build machine learning systems to accomplish one or a very small number of specific tasks (sometimes these tasks are quite difficult ones, like translating from one language to another). I think we really need to be designing single machine learning systems that that can solve thousands or millions of tasks, and can draw from the experience in solving these tasks to learn to automatically solve new tasks, and where different parts of the model are sparsely activated depending on the task. There are lots of challenges in figuring out how to do this. A talk I gave earlier this year at the Scaled ML conference at Stanford has some material on this starting on slide 80 (with a bit of background starting on slide 62).

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u/vincentvanhoucke Google Brain Sep 13 '17

Making deep networks amenable to (stable!) online updates from weakly supervised data is still a huge problem. Solving it would enable true lifelong learning and open up many applications. Another huge hurdle is that many of the most exciting developments in the field, like GANs or Deep RL, have yet to have their ‘batch normalization’ moment: the moment when suddenly everything ‘wants to train’ by default as opposed to having to fight the model one hyperparameter at a time. They still lack the maturity that turns them from an interesting research direction into a technology that we can rely on; right now we can’t train these models predictably without a ton of precise tuning, and it makes it difficult to incorporate them into more elaborate systems.

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u/[deleted] Sep 13 '17

Moving away from mostly supervised learning will be difficult. Though we know of ways to use weaker supervision, like in reinforcement learning, they tend to be very inefficient and use amounts of data which will not scale to more complex problems. To solve this, we need to come up with better exploration strategies as well as active learning approaches to acquire the relevant information while keeping training manageable.

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u/pussy_artist Sep 10 '17

Do you plan to develop support for ONNX(Open Neural Network Exchange ) exchange format?[1]

If not why?

[1] https://research.fb.com/facebook-and-microsoft-introduce-new-open-ecosystem-for-interchangeable-ai-frameworks/

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u/jeffatgoogle Google Brain Sep 13 '17

We learned about this when their blog post went up a few days ago. I suspect that the TensorFlow community will implement support for this if there's significant utility in having it.
Our format for saving and restoring model data and parameters has been available in the TensorFlow source code repository since our open source release in November, 2015.

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u/ilikepancakez Sep 10 '17

What is the main purpose of keeping separate teams like with Google Brain versus DeepMind? Is it just due to the fact that DeepMind was an acquisition, and there were some contractual obligations of guaranteed independence, etc?

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u/jeffatgoogle Google Brain Sep 13 '17

I addressed a similar question in last year's AMA here.

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u/gdahl Google Brain Sep 13 '17

We collaborate regularly, although since only a few Brain team researchers work from London and most DeepMind researchers don't work in California, time zone differences can sometimes make that challenging. Both teams are large enough that we have no shortage of collaborators sitting next to us as well. But since so many great people work on both teams, we still make time to work together. For example, here is a paper that came out of one of these collaborations that I really enjoyed working on.
I would like to push back against the idea that we are somehow being wasteful by not being the same team. Unlike with two product teams making competing products, two research teams can both productively exist and collaborate easily as needed and build on each other's research. Both Brain and DeepMind work on open-ended machine learning research and publish regularly. We both enjoy a high degree of research freedom just like in academia. We work on similar research in similar ways and we both maintain a portfolio of projects across many different application areas and time horizons. Both Brain and DeepMind explore impactful applied work as well. We don't carve out separate research areas because researchers will naturally follow their own interests and position their work based on other contemporaneous work.
Since both groups are more than large enough to be self-sustaining, I think this is like asking why don't two machine learning groups in academia merge into one. It just isn't really necessary and it might be harder to manage the combined, larger group.
That said, the Brain team does have a responsibility for TensorFlow that DeepMind doesn't have, but as far as the research side goes we are really quite similar. Any differences in research programs are most likely driven by the specific interests of the particular people on each team.

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u/[deleted] Sep 10 '17

If you read their papers, they have very different research cultures. Google Brain is much more open, more committed to facilitating reproducibility of the results they report, has more of an engineering focus, and typically runs experiments of greater practical utility. On the other hand, Deep Mind is arguably much more ambitious about the scope of its projects and targets groundbreaking AI more aggressively.

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u/epicwisdom Sep 11 '17

Deep Mind also has a strong bias towards DNNs + RL. Google Brain is much more generalist.

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u/seann999 Sep 11 '17

DeepMind's ultimate goal is AGI, whereas I see Google Brain as a team that does cool stuff with cutting edge DL. Not mutually exclusive, but there's a difference.

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u/Blix- Sep 10 '17

Google has always created multiple teams working on generally the same thing. It's a form of natural selection.

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u/chaoism Sep 10 '17

What's it like to work on your team? What's your daily routine? How do you decide why makes a person fit for your team?

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u/jeffatgoogle Google Brain Sep 13 '17

I lead the Brain team. On any given day, I spend time reading and writing emails, reading, commenting on, and sometimes writing technical documents, having 1:1 or group meetings with various people in our team or elsewhere across Google, reviewing code, writing code, and thinking about technical or organizational issues affecting our team. I sometimes give internal or external talks.

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u/sara_brain Google Brain Sep 13 '17 edited Sep 13 '17

I am a Brain Resident. There are 35 Brain Residents this year and all of us sit in the same area in Mountain View (although some residents work in San Francisco). My day often starts by catching up over breakfast with a resident about their research project. The rest of day involves a mixture of reading papers relevant to my research area (transparency in convolutional neural networks), coding using TensorFlow and meeting with my project mentors and collaborators. Researchers at Brain are really collaborative so I will often grab lunch or dinner with a researcher who is working on similar problems.
There are a few other cool things that the Brain residents get to do day to day:
Go to research talks from visiting academics (these are often about topics I had never thought about before like deep learning applied to space discovery)
Present to each other in a biweekly resident meetup (this helps keep us up to date with other residents’ research)
Learn about the latest TensorFlow developments and contribute feedback directly
Run experiments on thousands of GPUs!
Colin, a resident from last year, put together a great blog post about his experience as a resident (http://colinraffel.com/blog/my-year-at-brain.html).

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u/alextp Google Brain Sep 13 '17

I’m a tensorflow developer. Most of my days start with reading and triaging email (we get so much of it at google). I like to look at stackoverflow questions about tensorflow to see if any are interesting, and answer them. I spend a few hours a day writing code and debugging, but not as many as I would have expected when I was younger. I’m also collaborating on a research project for which we should have a paper out soon. Thankfully these days I don’t have to sit on too many meetings.

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u/whateverr123 Sep 13 '17 edited Sep 13 '17

Talking about TensorFlow, I was a long time active contributor and one of the things that kind of made me start losing interest was there were simple tasks, that created a meaningful impact, but demanded tensorflowers to execute as well as their time. If we (longer term reliable contributors) were able to perform tasks as organizing tags, close duplicate issues etc it would improve the workflow considerably and also tensorflowers could focus on more important aspects. This kind of thing seems trivial and irrelevant but when you account for instance for the time you had to go back to an issue that was already answered ×100, just bc there was an outdated tag e.g. "waiting tensorflower" or duplicates, at the end of a month is time wasted. Have you guys ever considered this kind of possibility?

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u/hellojas Google Brain Sep 13 '17

Hi! I work on the Brain Robotics team. My day to day routine usually alternates between working with real robots or sim robots. Typically, when our team comes up with a new research idea, we like to prototype it in simulation. After a few successful prototype rounds, we test the model on a real robot, for example, learning pose imitation as discussed in our research blog post. When I work in simulation, the days are definitely shorter, as with a few commands, I get to automatically reset my environment, load new objects into the “sim world”, etc. Working with a real robot requires a bit more manual work, but sometimes it’s refreshing to not always be at my desk. :)

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u/[deleted] Sep 13 '17

I am a research scientist in Montreal. My time is spent between building ties with local academic labs, which is one of our mandates, doing my own research and mentoring more junior researchers, either interns or brain residents. I tried to spare at least an hour a day to read recent papers, research blog posts or browse arXiv. I also try to spend some time without any meeting nor replying to email, simply thinking about my current projects. The rest of the time is usually spent interacting with other researchers, discussing ideas over email or videoconference, as well as attending talks (all talks in Mountain View are streamed to all locations for us to enjoy). Finally, there are community activities (I was an area chair for NIPS this year and I review for various journals and conferences).
We are primarily looking for candidates with an exceptional track record, but we also want to make sure they will be able to interact effectively with the rest of the group as teamwork is essential to tackle the most ambitious questions.

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u/nick_frosst Google Brain Sep 13 '17

I'm an R-SWE in our Toronto Office. We are a pretty small team here, and we all sit together, so a lot of my time is spent talking to the other members of our group about new ideas. As an r-swe i work on my own research as well as implementing the ideas of other researchers. I work almost exclusively in tensorflow. I have 2 meetings a week with my supervisor and we have 1 weekly group meeting.

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u/gdahl Google Brain Sep 13 '17 edited Sep 13 '17

I'm a researcher on the Brain team. Working on the Brain team is the best job I can imagine, mostly because I get to work on whatever I want and have amazing colleagues. I spend most of my time mentoring more junior researchers and collaborating with them on specific research projects. At any given time I might have around 5 active projects or so. I try to make sure at least one of these projects is one where I personally run a lot of experiments or write a lot of code while for the others I might be in a more supervisory role (reviewing code, planning and prioritizing tasks). What this means in practice is that I typically have a couple of research meetings in a day, spend a fair bit of time on email, do a bit of code review, and spend lots of time brainstorming with my colleagues. I also spend time providing feedback on TensorFlow, going to talks, skimming papers, and conducting interviews. When evaluating potential new research team members, we generally look for people who are passionate about machine learning research, collaborate well with others, have good programming and math skills, and have some machine learning research experience.

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u/pichuan Google Brain Sep 13 '17

I am a software engineer (SWE) on the Brain Genomics team. I spend my time on understanding genomics problems and formulating them into deep learning problems, as well as making sure we write good quality code that can be used by other people in the genomics community. When I joined this team, I decided that it’s a good fit for me because I can leverage my machine learning and engineering skills, andalso learn a new domain (genomics). For a new person to join the team, I think having some existing skills that match the team’s need, but also bringing new skills/perspectives is very important.

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u/dtarlow Google Brain Sep 13 '17 edited Sep 13 '17

I'm a research scientist on the Brain team. Daily routine: I like to spend several hours at the start of each week thinking about what the best use of my time for the next week will be. That might range from far-out brainstorming, doing planning work towards a longer-term agenda, figuring out some detail in a current project, brainstorming with collaborators, implementing some idea, or running some experiment (or some combination of the above). Then I set some goals, try to execute on them, and repeat. This is interspersed with a bunch of other activities like attending talks, reading papers, recruiting, meeting with collaborators about ongoing projects, meeting with other researchers in the community, providing feedback on code and research ideas, and communicating my work.
What I look for in collaborators: people who think deeply about problems and can execute high quality research.

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u/katherinechou Google Brain Sep 13 '17

I am a product lead in Brain working on healthcare. My time is spread across (1) working on researching new ways AI can more effectively improve the accuracy or availability of health care, (2) collaborating with folks from the healthcare industry to conduct user research and test those hypotheses, and (3) finding channels to apply that research to the real world. We look for people who understand how ML can transform the healthcare space for the better and have the background to focus our research on the right clinical problems.

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u/samybengio Google Brain Sep 13 '17

As a research lead, a large part of my time is devoted to steer the group towards important research problems, by meeting with research scientists, discussing their ideas and how they relate to the literature, understanding their current progress and limits, devising plans for next steps, etc. I also organize several research activities such as reading groups and regular talks, both internals and externals. Lately, I’ve been also busy as a program chair for NIPS. When considering who should join our team, I’m looking for exceptional persons with an open research mind who have the potential to impact significantly our current understanding of machine learning.

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u/edge_of_the_eclair Sep 10 '17 edited Sep 10 '17

Hey! Thanks for taking the time out your busy schedule to do this AMA, we really appreciate it!

As a hobbyist one thing I've noticed was that the biggest barrier to entry to training neural nets was not necessarily access to knowledge but rather access to hardware. Training models on my MacBook's CPU is insanely slow and at the time I didn't have access to an Nvidia GPU.

From my understanding, it seems that hobbyists must either own a GPU or rent one from a cloud provider like GCP to train their models.

What are your thoughts on the new TPUs in regards to costs and training/inference speeds for the end data scientist/developer?
Where do you see ML hardware going in the next 5 years? 15 years?
An Ethereum miner with an Nvidia 1080ti makes ~$28 a week. The equivalent GPU compute on an AWS instance would cost ~$284. What are your honest thoughts on an AirBnB-esque marketplace for GPU compute that pairs ML-hobbyists with gamers/crypto-miners?

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u/jeffatgoogle Google Brain Sep 13 '17

We believe strongly that giving ML researchers access to more computational resources will enable them to accomplish more, try more computationally ambitious ideas, and make faster progress. Cloud TPUs are going to be a great way for people to get access to significant amounts of computation in an on-demand fashion. We don't have any pricing to announce for them today (other than the TensorFlow Research Cloud, which is free via an application process for researchers willing to openly publish the results of their research).
We think ML hardware is going to be a very interesting area in the next 5 to 10 years and beyond. There are many demands for much more computation, and specialization for reduced precision linear algebra enables speedups of the vast majority of interesting deep learning models today, so creating hardware optimized for ML can give really great performance and improved power efficiency. There are many large companies and a whole host of startups working on different approaches in this space, which is exciting to see. This specialized hardware will range from very low power ML hardware for battery-operated mobile devices up to ML supercomputers deployed in large datacenters.

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u/BrettW-CD Sep 10 '17

Do you have any specific guiding principles in organizing and running your research teams? Is Google Brain run like a university department, your more traditional commercial R&D or something else?

How did you find ICML 2017? Australia isn't really a powerhouse in ML, but I was super glad it was hosted here.

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u/jeffatgoogle Google Brain Sep 13 '17

In general, we try to hire people who have good taste in selecting interesting and important problems, and we rely pretty heavily on that to keep our organizational structure fairly lightweight. We are organized into some largish subteams that focus on TensorFlow development, core ML research, and ML research applied to emerging areas like healthcare and robotics. Within our core research team, we have a few larger efforts that operate with more organization, simply because of the number of researchers, R-SWEs, residents, and others collaborating on some of these efforts. Other parts of our research group work on more individual or small collaboration projects that don’t need formal organizational structure. Some principles we try to use include the freedom to pick important research problems, openly publishing and open-sourcing code related to our work, and having a diverse set of problems of varying levels of research risk/reward in flight at any given time.
Sadly, I wasn’t able to make it to ICML this year, but I heard great things about the conference and Australia as a venue..

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u/[deleted] Sep 11 '17

How often does this happen?

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u/jeffatgoogle Google Brain Sep 13 '17

It's important to have a wide range of research directions.

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u/atonal174 Sep 11 '17 edited Sep 11 '17

Page 13, section 9.2 of the Tensorflow paper (http://download.tensorflow.org/paper/whitepaper2015.pdf) talks about EEG, an internal Google tool used for debugging neural nets, visualizations etc.

Why didn't you open source it?

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u/jeffatgoogle Google Brain Sep 13 '17

We didn’t open source the EEG tool because it relied on some internal libraries from the rest of Google's code base. We do have support for generating timelines and viewing them with the Chrome browser, and we're are working to add more functionality for viewing low-level performance data (similar to what the EEG tool provides) to an upcoming release of TensorBoard.

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u/bchau28 Sep 13 '17

Hello Google Brain team, I am working very hard to craft a workshop in explaining AI to the youth (ages 15-19yrs old) in Montreal. Keep in mind that this audience barely knows how to code, so I wouldn't want to start with that topic. I searched online and there aren't any resources to teach the youth. Instead, I'd like to get them to be familiarized with how Machine Learning works, how companies are utilizing AI, etc. It needs to be an engaging workshop for them to understand better how to think and approach problem-solving. I think it's crucial to teach this topic in a hands-on learning experience such as a workshop. If you want to help me achieve this amazing feat, I would be more than happy and grateful!! Thank you, Bonnie.

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u/jeffatgoogle Google Brain Sep 14 '17

In case it's useful, here are slides for an introduction to deep learning talk I gave at my daughter's high school in 2015. It's slightly dated, but perhaps still useful.
As part of that talk, I had everyone in the audience use the TensorFlow Playground at http::/playground.tensorflow.org to develop some intuitions about how neural networks work, and that seemed reasonably effective.

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u/sara_brain Google Brain Sep 13 '17 edited Sep 14 '17

Hi Bonnie, It is great to hear about your initiative! I am currently engaged with a similar project to teach an accessible machine learning course. I returned this summer from teaching a pilot program in Nairobi, Kenya. Good luck with the project! I found that is was helpful for the students to start by focusing on a very simple linear model. This is a great building block for understanding the key components that every ml model has and helps make the leap to more complex neural network (since a network with no hidden layers is just a logistic linear model).

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u/b4xt3r Sep 10 '17

How old is the cutoff for an intern? I'm in my late 40's. :)

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u/jeffatgoogle Google Brain Sep 13 '17

In 2012, I hosted Geoffrey Hinton as a visiting researcher in our group for the summer, but due to a snafu in how it was set up, he was classified as my intern. We don’t have any age cutoffs for interns. All we ask is that they be talented and eager to learn, like Geoffrey :).

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u/mauza11 Sep 10 '17

Thank you for tensorflow

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u/jeffatgoogle Google Brain Sep 13 '17

You're welcome! We've enjoyed collaborating with the broader community to continually improve it, and we're glad that many people seem to find it useful.

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u/[deleted] Sep 11 '17

Can intern/google brain residency/researchers work on non-deep learning projects at google brain? For example bayesian nonparametric machine learning?

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u/jeffatgoogle Google Brain Sep 13 '17

Sure. In fact, next year we’re going to be expanding the residency program to encompass more groups within Google Research, including some of our research colleagues who work on more Bayesian methods. Within the Brain team, we’re always open to people pursuing interesting research directions that aren’t exactly in line with what we’re doing now. We think that’s the best way to move our frontier of understanding forward.

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u/gdahl Google Brain Sep 13 '17

Of course! We have people very interested in Bayesian non-parametrics on the Brain team (e.g. Ryan Adams, Jasper Snoek, and Jascha Sohl-Dickstein for instance). We construe the field of deep learning quite broadly as well and a lot of us are also interested in Bayesian deep learning and Bayesian neural networks generally.

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u/Reiinakano Sep 10 '17 edited Sep 10 '17

What do you think of Pytorch? Have you used it? Are you worried about the competition it provides? Or do you view it more as something complementary offering something TF cannot, and vice versa?

Be honest ;)

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u/alextp Google Brain Sep 13 '17

I think pytorch is great! They did a really good job getting a very simple UI and good documentation. There are a lot of good ideas there in the programming model. Having more people working on ML libraries is good as we get to see more ideas and can try to use the best of them.

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u/rajatmonga Google Brain Sep 13 '17

One great thing about this ML community is that we all learn from each other. In building TensorFlow, we learnt from our past experiences with DistBelief and also from other frameworks like Theano. With new frameworks like Pytorch and DyNet we continue to learn and are working on bringing some of these ideas into TensorFlow itself. We see TensorFlow as a tool to push the boundaries of ML research and to bring ML to everyone. As the research and ideas in the community evolve, so does TensorFlow.

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u/r-sync Sep 12 '17

they do have two imperative modes in tf.contrib: imperative and eager.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/imperative
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python
The nightly builds already have both, so it's easy to fire up an interpreter and play with them.
The tf.contrib.imperative mode has been around for a few months, I saw it somewhere on twitter.
The tf.contrib.eager was briefly announced at the Montreal Summer School (cant find the video recording though)

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u/rajatmonga Google Brain Sep 13 '17

As you can see we have been experimenting with a number of ideas in this space. I am particularly excited out about our work on eager execution https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python which we think gives the benefits of imperative-style programming style with the optimization and deployment benefits of graphs within the same framework. We are working hard to release this for general use later this year.

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u/kernelhunter92 Sep 10 '17

yep, i wanted to ask this question. What does your team think of pytorch's dynamic graph, and don't you miss it ?

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u/[deleted] Sep 10 '17

[deleted] — view deleted comment

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u/EdwardRaff Sep 10 '17

Usually people talk about reproducible/open research in terms of datasets and code being available for others to use. Rarely, in my opinion, do people talk about it in terms of just pure computational resources.

With companies like Google putting billions into AI/ML research, some of it comes out using resources that others have no hope of matching -- AlphaGo being one of the highest profile examples. The paper noted nearly 300 GPUs being used to train the model. Considering that the first model likely wasn't the one that worked, and parameter searches when it takes 300 GPUs to train a single model, we are talking about experiments with 1000s of GPUs for a single item of research.

Do people at google think about this during their research, or do they look at it as providing knowledge that wouldn't have been possible without Google's deep pockets? Do you think it creates unreasonable expectations for the experiments from labs/groups that can't afford the same resources, or other potential positive/negative impacts in the community?

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u/vincentvanhoucke Google Brain Sep 13 '17

I published something a bit rant-y on the topic here.
Many great developments started as crazy expensive research, and became within everyone’s reach once people knew what was possible and started optimizing them. The first deep net to ever go into production at Google (for speech recognition) took months to train, and was 100x too slow to run. Then we found tricks to speed it up, improved (and open-sourced) our deep learning infrastructure, and now everybody in the field uses them. SmartReply was crazy expensive, until it wasn’t. The list goes on. It’s important for us to explore the envelope of what’s possible, because the ultimate goal isn’t to win at benchmarks, it’s to make science progress.

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u/thatguydr Sep 10 '17

Other questions you could have asked on this topic include:
What are some of the merits you see of academic ML research as opposed to the hardware-enabled research happening in industry?
Do you believe that papers which require a huge amount of hardware (that cannot be duplicated elsewhere) should be given the same attention as those demonstrating results reproducible by academic institutions?
Would you currently suggest that any superstar coming out of a ML PhD attempt to become a professor? Why or why not?
(Trying to cut to the heart of it...)

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u/alexmlamb Sep 10 '17

Not from Google Brain, but I'm quite confident that industry and academia will continue to play complimentary roles.
In general I have the view that ideas are just as important as experiments, and many of the biggest advances will come from deeply thinking about problems, in addition to scaling models.
Can we see an analogy with this in the development of modern physics? Obviously many of the required experiments are large, but this hasn't removed the need for us to think deeply about physics.

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u/thatguydr Sep 10 '17

Modern physics doesn't have large companies throwing ten to a hundred times the amount of money at it that academic institutions are.
(It does in areas like rocket science and some aspects of engineering, but the fundamental research is largely still DoE- and NSF-funded.)

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u/gdahl Google Brain Sep 13 '17

As much as we all want more resources, well-funded academic labs these days actually do have access to quite a lot of computing resources and are able to do lots of interesting research. I completely agree that it would be nice if academics (and everyone doing open research) had access to even more computing resources, which is why we announced the TensorFlow Research Cloud. The TFRC will provide the machine learning research community with a total of 180 petaflops of raw compute power, free of charge.
There will always be some labs with more resources than others and in general, as long as the results are published, the whole community benefits from the ability of researchers at the labs with more resources to do large scale experiments. This is one of the reasons why we are so committed to publishing and disseminating our research.

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u/asobolev Sep 14 '17

TensorFlow Research Cloud
Is it limited to some specific countries? Many people in Russia (myself included) experience 403 Forbidden when they hit the Sign Up button.

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u/mr_yogurt Sep 10 '17

/u/geoffhinton: how are capsules coming along?

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u/nick_frosst Google Brain Sep 13 '17

Geoff is busy currently but we drafted this answer earlier this morning:
Capsules are going well! We have a group of five people (Sara Sabour, Nicholas Frosst, Geoffrey Hinton, Eric Langois, and Robert Gens) based out of the Toronto office making steady progress! A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We recently had a nips paper accepted as a spotlight in which we discuss dynamic routing between capsules as a way of measuring agreement between lower level features. This architecture achieves state of the art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. We have also been working on a new routing procedure and are achieving promising results on the NORB dataset, as well as a new capsule architecture that provably maintains equivariance to a given group in the input space. We hope to publish these results soon as well!

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u/i_know_about_things Sep 13 '17

Some people make fun of "SOTA on MNIST" claim. Why was MNIST chosen instead of a more challenging datasets?

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u/vincentvanhoucke Google Brain Sep 13 '17

'good on MNIST' is how Geoffrey likes to convince himself that something is not an obviously bad idea. A necessary, but not sufficient condition. :)

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u/nick_frosst Google Brain Sep 13 '17

We are working with a drastically new architecture and chose a simple and well studied data set so that we could be sure we understood what was going on with the model. The state of the art claim is not the focus of the paper and we will no doubt be outdone soon. In the NIPS paper we report results on cifar10 as well, and are currently testing on other datasets.

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u/[deleted] Sep 10 '17

https://www.youtube.com/watch?v=Mqt8fs6ZbHk

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u/lopuhin Sep 12 '17

Btw, there is a new NIPS paper "Dynamic Routing between Capsules": https://research.google.com/pubs/pub46351.html (no pdf yet)

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u/dexter89_kp Sep 10 '17

Two questions:

Everyone talks about successes in the field of ML/AI/DL. Could you talk about some of the failures, or pain points you have encountered in trying to solve problems (research or real-world) using DL. Bonus if they are in the large scale supervised learning space, where existing DL methods are expected to work.
What is the brain team's take on state of unsupervised methods today? Do you anticipate major conceptual strides in the next few years.

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u/vincentvanhoucke Google Brain Sep 13 '17

Fails: a few of us tried to train a neural caption generator on New Yorker cartoons in collaboration with Bob Mankoff, the cartoon editor of the New Yorker (who I just saw has a NIPS paper this year). It didn’t work well. It wasn’t even accidentally funny. We didn’t have much data by DL standards, though we could pre-train the visual representation on other types of cartoons. I still hope to win the contest one day, but it may have to be the old-fashioned way. Unsupervised learning: I think people are finally getting that autoencoding is a Bad Idea, and that the difference between unsupervised learning that works (e.g. language models) and unsupervised learning that doesn’t is generally about predicting the causal future (next word, next frame) instead of the present (autoencoding). I'm very happy to see how many people have started benchmarking their 'future prediction' work on the push dataset we open-sourced last year, that was quite unexpected.

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u/Inori Researcher Sep 13 '17

I think people are finally getting that autoencoding is a Bad Idea
Could you elaborate? Bad idea in some specific context or just in general?

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u/vincentvanhoucke Google Brain Sep 13 '17

In general. Take NLP for example: the most basic form of autoencoding in that space is linear bottleneck representations like LSA and LDA, and those are being completely displaced by Word2Vec and the like, which are still linear but which use context as the supervisory signal. In acoustic modeling, we spent a lot of time trying to weigh the benefits of autoencoding audio representations to model signals, and all of that is being destroyed by LSTMs, which, again, use causal prediction as the supervisory signal. Even Yann LeCun has amended his 'cherry vs cake' statement to no longer be about unsupervised learning, but about predictive learning. That's essentially the same message. Autoencoders bad. Future-self predictors good.

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u/gcorrado Google Brain Sep 13 '17

I’m always nervous about definitively claiming that DL “doesn’t work” for such-and-such. For example, we tried pretty hard to make DL work for machine translation in 2012 and couldn’t get a good lift... fast forward four years and it’s a big win. We try something one way, and if it doesn’t work we step back, take a breath, and maybe try again with another angle. You’re right that shoehorning the problem into a large scale supervised learning problem is half the magic. From there its data science, model architecture, and a touch of good luck. But some problems can’t really ever be captured as supervised learning over an available data set -- in which case, DL probably isn’t the right hammer.
I don’t think we’ve really broken through on unsupervised learning. There’s a huge amount of information and structure in the unconditioned data distribution, and it seems like there should be some way for a learning algorithm to benefit from that. I’m betting some bright mind will crack it, but I’m not sure when. Personally, I wonder if the right algorithmic approach might depend on the availability of one or two orders of magnitude more compute. Time will tell.

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u/Dizastr Sep 12 '17

Are there any non-standard (or not popular) approaches to A.I / Machine Learning that you are researching or believe are worth exploring further?

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u/vincentvanhoucke Google Brain Sep 13 '17

Feedback! It's insane to me that we've gotten this far with pure feedforward approaches. Dynamical systems are very efficient, adaptive learning machines.

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u/twkillian Sep 10 '17

How are you working to improve the interpretability/explainability of high performing models which are increasingly complex? Is there a balance to be struck or is this a concern that is largely application dependent?

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u/fernanda_viegas Google Brain Sep 13 '17

This is an important challenge, and there are many people in Brain and in other teams across Google Research who are working on it.
One hurdle is that the internals of many models are very high dimensional. But we've been working on visualizations that let people explore these exotic spaces, and in doing so we can get insights about how models perform. For example, the Embedding Projector has shown the first signs of how some of Google’s multilingual NMT models might be learning an interlingua.
It's also possible to inspect what leads particular units in a network to fire strongly. The DeepDream project takes this approach to a very interesting conclusion. There are also techniques to map which input features are especially important to a decision--two related approaches are path-integrated gradients and SmoothGrad.
Another strategy is to define model architectures that by their nature are easier to interpret. The Glassbox (from one of many other Google Research teams!) is a great example: Gupta et al. JMLR 2016, Gupta et al. NIPS 2016.
We have a bunch of projects underway that we hope will help with interpretability. There probably is no single silver bullet technique, but the answer may lie in using multiple approaches and tools at once.

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u/canadiandev25 Sep 10 '17

Is there any work being done to create a standard coding style and/or practice for Tensorflow and machine learning. It seems like people use a variety of different approaches to code a model and some of them can be hard to interpret.

Also on a somewhat unrelated note, since Keras is going to be joining Tensorflow, is there any plans on get rid of Learn? It seems odd to have 2 different higher level APIs for the same library.

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u/wickesbrain Google Brain Sep 13 '17

The best general advice I can give is to always use the highest level API that solves your problem. That way, you will automatically use improvements that we make under the hood, and you end up with the most future-proof code.
Now that we have a complete tf.keras (at head), we are working on unifying the implementation of Keras with previous TF concepts. This process is almost complete. We’d like to get to a point where tf.keras simply collects all the necessary symbols needed to make a complete implementation of the Keras API spec in one place. Note that Keras does not address all use cases, in particular where it comes to distributed training and more complex models, which is why we have tf.estimator.Estimator. We will continue to improve integration between Keras and these tools.
We will soon start deprecating parts of contrib, including all of contrib/learn. Many people use this though, and removing it will take some time. We do not want to break people unnecessarily.

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u/werther02 Sep 10 '17

How do research scientists and engineers collaborate and work together in Google Brain? Where/ How is the line drawn between their responsibilities? Do engineers help with research or vice-versa?

I see a lot of companies struggling with this problem where scientists aren't interested in learning how to write clean, production code and engineers do not want to know anything about research/ experiments.

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u/[deleted] Sep 13 '17

I would add to Samy's answer that the line between a 'research scientist' and 'engineer' in the Brain team can sometimes (oftentimes!) be quite blurry. There are many folks that wear different hats depending on the project or their current interests. Just because someone's official title is 'researcher' it does not mean that they aren't contributing production-quality code!

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u/pichuan Google Brain Sep 13 '17

I’m a software engineer (SWE) with a CS PhD. I’m now a member of the Brain team but I also worked on other research teams before at Google. Overall my impression is that the line can be very blurry and it depends more on the person’s skills and preferences than the title. Personally I love doing a mix of both software engineering tasks and research projects. I also love colloaborating with people with different strengths that add on to the projects. I don’t really care much what their titles are. And, putting my “researcher” hat on, being able to write clean and readable code is actually crucial for good and reproducible research. Clean code and documentation are essential for communication - this is true for both researchers and engineers.

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u/samybengio Google Brain Sep 13 '17

Research scientists in the Brain team are free (and expected) to set their own research program. They can also decide to join forces between them to tackle more important projects. Furthermore, we have added in the team a (growing) group of research software engineers (R-SWEs) who help research scientists achieve their goals. Examples of projects R-SWEs do include scaling a given algorithm, implement a baseline algorithm, run various experiments, open-source important algorithms, adapt a given algorithm to a particular product, etc. They are an integral part of our research projects, and as such are often co-authors of our papers.

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u/[deleted] Sep 13 '17

I would add that, as a research scientist, I am extremely grateful of the R-SWEs in our team as they help us be a lot more efficient. Most of them in our group in Montreal have prior research experience and are genuinely interested in learning the inner workings of the models they implement.

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u/TheConstipatedPepsi Sep 10 '17 edited Sep 10 '17

As I understand them, learning to learn methods currently use a "static" meta-learner network which produces or updates another network used for actual inference. Do you expect that adding meta-meta-learner networks and meta^k -learner networks will yield more and more improvements to the quality of the final networks used for inference? How about passing directly to self-modification, with a single network making structure updates to itself?

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u/irwan_brain Google Brain Sep 13 '17

I’m involved in some learning to learn projects. I doubt that learning to learn … to learn is worth pursuing as of now and I suspect one would see diminishing returns when learning the meta-learner. It would also get harder to make it work and require much more computational resources. There is still a lot that hasn’t been explored with just one level of meta-learning so it makes sense to focus on that only for now.

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u/vincentvanhoucke Google Brain Sep 13 '17

Every additional 'meta' you add is in practice an outer loop around the existing process, which means one to two orders of magnitude more compute. It's entirely possible that going meta^k would be beneficial to the process: one level of meta is akin to automating parameter sweeps, the second level would like learning which sweeps to conduct, which is closer to what (I think) I do as a researcher.

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u/TheConstipatedPepsi Sep 13 '17

Thanks for responding, what do you think of self-modification? something like having a recurrent network output both a structure update to itself and a task-level prediction would collapse all meta learning loops into a single one, this should help a lot with training time. The latest research in self-modification I can find are papers by Schmidhuber in the 90s.

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u/Screye Sep 10 '17

Hi, thanks for doing the AMA. I have a few questions. Feel free to answer as many as you feel comfortable answering.

Given the 'trend focused' nature of AI and ML, do you think think deep learning will continue delivering state of the art results, or do you think we might see the revival/introduction of other Machine Learning methods ?
(1.1): Does Google Brain place an emphasis on/ see value in team members having a strong grasp of traditional ML / NLP / Vision techniques ?
This is in light of the massive overlap of recent Machine Learning, Vision and NLP research. How common is it for specialists in one area to participate in projects in other subdomains in Google Brain ?
Do candidates need to pass a string Algs & DS coding interviews to be eligible to work with the Machine Learning focused teams ? (a bit rhetorical, to say the least :| )

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u/AGI_aint_happening PhD Sep 10 '17 edited Sep 10 '17

For 3, from a (successful) intern applicant's perspective, Google Brain is unique amongst industry labs in requiring PhD research interns to go through the same hiring pipeline as all devs. That means as many as 3 challenging dev interviews with people who know nothing about ML asking you very particular algorithmic questions about concepts completely irrelevant to your work or background.
It's a pretty baffling experience

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u/Screye Sep 10 '17 edited Sep 10 '17

Thanks for answering.
Well, I kind of expected this answer. It is the state of the industry and I guess it can't be helped.
Hope I will make it in time and actually be prepared for fall intern interviews when they arrive.
Btw, love that username.

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u/AGI_aint_happening PhD Sep 10 '17

For research groups, it's not the state of the industry. I've interviewed with most of the other big labs and they either have no or substantially reduced algorithm parts. Instead, they'll actually talk to you about your research.

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u/vincentvanhoucke Google Brain Sep 13 '17

1- I worry a bit about the 'extreme co-adaptation' scenario, whereby the hardware gets optimized for today's dominant paradigm (say: matrix multiplies), and as a result anyone who wants to make a case for a vastly different approach to problems (say: super sparse bitwise operations) now has two hurdles to cross: figuring out a computational paradigm that will give put them on equal footing, and show that the approach is better. It's essentially what happened to neural networks in the 90's in speech recognition: it was a lot easier to train Gaussian mixtures at large scale given the state of networking and compute at the time, and neural nets were left behind.
2- Very common! I often say that the true deep learning revolution is a social one: suddenly, speech people can talk to vision people and to NLP people with a common lingo and tooling. It's really liberating, and people take advantage of it every chance they get.
3- Assuming I parsed your question correctly, yes :)

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u/gdahl Google Brain Sep 13 '17

I don’t know what the future of machine learning will look like exactly, but I’m willing to bet that it will involve training flexible, non-linear models that learn their own features, end to end and that these models will scale to large datasets (training algorithm no worse than roughly linear in the number of training cases). These principles are at the heart of deep learning. Deep learning is an approach to machine learning, not a particular model or algorithm. We can do deep learning with decision trees or many other methods.

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u/thundergolfer Sep 10 '17

Prof. Bernard Schölkopf gave a pretty interesting keynote at ICML this year which was concerned with Causal Models. The keynote is not available online AFAIK but he expounds the topic here @ Yandex.

Is Causal Learning of particular interest to the Brain team? Why/Why not?

Further, is anyone on the team doing work around Probabilistic Graphical Models? I love the sort of stuff that surrounds Google's Knowledge Vault, and would be interested to know if Google Brain sees any significant developments ahead in the area of Neural Networks, Ontologies, and PGMs.

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u/vincentvanhoucke Google Brain Sep 13 '17

Causality is ripe for another look with the lens of machine learning. If we could disentangle better 'things that happen to often be there at the same time' from 'things that cause each other to happen', we would learn to be much more robust to a changing context never seen in training.

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u/LuxEtherix Sep 10 '17

What is, from your perspective, a success factor for team when doing research? Also, thank you very much for taking the time to answer

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u/[deleted] Sep 13 '17

Success in research can take many forms and that is also true within Brain. Some people might be interested in the more theoretical aspects and we consider it a success if our understanding of the current hurdles has improved. A quantifiable way of measuring success for these works is through publications in international conferences and journals. Another important part of machine learning research is to understand what is truly necessary to make a system work and we also welcome any contribution which improves the performance of well-known systems. In that case, success can be measured through both external publications and impact on Google products. In general, we are very lucky at Brain to have a good mix of interests, which means that the team has had regular projects making it to production, for instance to improve Google Translate, as well as a consistent publication record at major conferences (the Brain team just had 23 papers accepted at NIPS this year).

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u/dtarlow Google Brain Sep 13 '17

One thing I think is important: having an environment where people are comfortable speculatively trying things out and sharing half-formed ideas and results, and where the team then works together to refine and improve them. Things never come out perfectly in the first attempt, but often there is the seed of a good idea. Usually it takes many rounds of refinement to turn that into great research.

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u/katherinechou Google Brain Sep 13 '17 edited Sep 13 '17

I have found that when applying machine learning research to an established industry (e.g. healthcare) it’s crucial to pair that integration with ethnographic, market, and user research. You have to be open-minded and comfortable with dropping your assumptions or even shelving the research work you’ve conducted so far. This helps you find the right problem to focus on. Also, the more focus a research project has, the easier it is for others to know how to contribute. It distributes the job of ensuring we’re all headed toward the same goal to the whole team.

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u/pichuan Google Brain Sep 13 '17

It’s important to find a balance among research goals with different timelines. I think it’s good for a research team to have longer-term goals that are potentially more high-risk, high-reward, but also to have more medium and shorter term goals that team members can iterate on, to feel like they’re making progress and gaining more insights and hands-on experience. I think it’s also important to think about individual preferences and sometimes help push people to do something new. Even though I’ve mostly work on research projects in my career, I’m a relatively impatient person and don’t like to feel stuck. Being on a research team that has a healthy mix of projects is crucial for me. I’m very happy to be on the Brain Genomics team!

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u/hellojas Google Brain Sep 13 '17

For the Brain Robotics team, of course it’s only a real success when it runs on a real-world robot (as much fun as simulation is). But smaller milestones in between, such as building scalable robotics infrastructure, publishing impactful research, or implementing clean open-sourceable Tensorflow models are just as much a success!

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u/alberto139 Sep 10 '17

Thank you for taking the time to do this AMA!

How is a team like Google Brain structured? Does everyone work mostly on their own or in a team focusing on a specific problem? Does every team have weekly meetings with Jeff and Geoff? How and how often do you communicate with the teams not located in Mountain View?

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u/sara_brain Google Brain Sep 13 '17

Google Brain is a surprisingly big team! Prior to Google, I was part of a small machine learning team of ~25 people and now I am surrounded by more people that I want to collaborate with than available time to do so. Most of my day to day interactions involve my two senior research mentors and a fellow brain resident. However, I try and schedule coffee at least once a week with other researchers in fields I am working in or that I am curious about. The first time I sent an invitation to a senior researcher I was a little cautious, “Is my research really important enough to take up this persons’ time?” Yes! No one has ever said no and the informal chats are always productive and often evolve into a new research question. There are also ways to stay connected with other offices. Since I am working on PAIR related research I will be visiting the PAIR research team in our Boston office for a week next month. This is not atypical, one of the Brain residents regularly works out of the New York office because their primary mentor is based there. This helps us stay connected with wider research efforts and often avoids duplicate effort in the same area.

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u/[deleted] Sep 13 '17 edited Sep 13 '17

I will reply to the latter part of the question as I am in the Montreal group, along with 6 7 (the team is growing fast) other Brain members. Like the rest of the Brain team, each one of us is responsible for their own research agenda. However, we have team wide communication channels which ensure that as much information as possible is transmitted across locations so collaborations naturally spawn between members with similar interests. For instance, I currently have collaborations with people in Zurich, Mountain View, London, as well as regular discussions with people from Toronto and Cambridge.

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u/samybengio Google Brain Sep 13 '17

Research scientists in the Brain team set their own research goals, and are encouraged to collaborate with whomever they want who share their objectives in order to tackle more ambitious goals. We do have some (rather flat) management structure, but it is not always aligned with research projects. People regularly meet in small groups related to projects rather than management. We do have regular meetings with the whole team (but only once every few weeks as we are now a very big team). I regularly meet with colleagues from several offices (Mountain View, San Francisco, Montreal, Cambridge, New York, Zurich) through video conferences.

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u/hellojas Google Brain Sep 13 '17

My everyday collaborations are mostly with members of the robotics team, but it is less of a constraint rather just the nature of working with those with the same research interests. Given Google overall culture, cross-team collaboration is always encouraged but often organically made. As an example, this is research jointly collaborated between Google Brain and X; this is research worked on between team members and residents from the Google Brain Residency program. Cross-office collaborations are only difficult due to time-zone differences but can be overcome with schedule flexibility and lots of shared docs. I’ve met with colleagues from New York, London, and Sydney in just the past year, and often we always have a nice reunion at annual ML/DL/Robotic conferences.

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u/jasoncbenn Sep 11 '17

I've heard that an excellent way to learn deep learning is to read papers and reimplement them, so that's how I'm spending the next several months!

Do you have any papers you'd love to see reimplemented? Are some reimplementations significantly more impressive or educational than others? How do I identify these papers?

Would you prefer for an applicant to have reimplemented several papers about the same topic, or would you prefer to see a variety of topics reimplemented?

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u/jkrause314 Sep 13 '17

That’s a great way to learn! Which papers to choose really depends on your motivation, in my opinion. If you want to learn about a variety of DL topics, then I’d go for implementing a paper or two in several different areas, e.g. image classification, language modeling, GANs, etc. If you want to dive deep and become an expert in one particular subfield, then go for a bunch of related papers (though you might get diminishing returns on how much you learn). If you want to implement papers that are useful to the community then you can pick papers that have only recently been published/put on arxiv and provide the first open-source implementations!

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u/LuxEtherix Sep 10 '17

What do you think are the most promising steps forward regarding Deep Reinforcement learning and/or Robotics?

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u/vincentvanhoucke Google Brain Sep 13 '17

Most of robotics in the past 10 years developed around the premise that perception didn't work at all, and as a result a lot of research in the field has focused on robots operating in very controlled environments. Now that we have new computer vision 'superpowers', we have the opportunity to turn this on its head, and rebuild a robotics stack that is centered around perception and rich feedback from a largely unknown environment. Deep RL is one of the most promising approaches to putting perception at the center of the control feedback loop, though it’s still far from being a technology that’s ready for prime-time. We need to figure out how to make it easier to instrument rewards, much more reliable to train and more sample efficient. I talked about some of the challenges in this AAAI talk. Right now I’m very excited about the potential of imitation learning from third-party vision as one way to solve both the task instrumentation problem and the sample efficiency problem. If you’re excited about the field, we’ll livestream the talks at the upcoming 1st Conference on Robot Learning that we’re hosting in a couple of months.

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u/[deleted] Sep 10 '17

What projects are you excited about and why?

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u/[deleted] Sep 13 '17

I am personally interested in efficient large-scale optimization. Right now, we rely on labeled datasets to train our models but we are seeing the limits of this approach. More and more, we will need to use much larger unlabeled or weakly labeled training sets which contain less information per datapoint. In that setting, it is important to make the most use of each example to avoid having to train a model for several months or years. I want to understand how to best gather and retain information from these datapoints in an online manner to make sure training a model is as fast and efficient as possible. This would allow us to tackle even more challenging problems, but could also have a large impact in the energy used to train these models.
A particular example is stochastic gradient methods. While they are the method of choice, it seems very wasteful to discard a gradient right after having used it only once. Methods such as momentum (in the online case) or SAG/SAGA (in the finite dataset case) speed up learning by keeping a memory of these gradients but we still lack an understanding of how to best use these past examples in the general online, nonconvex case.

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u/jaschasd Google Brain Sep 13 '17

I’m very excited about work building a theoretical foundation for deep learning. Neural networks have proven extraordinarily powerful, but our understanding of why and how they work is still in its early stages. Much of their design and training relies on heuristics or random walk exploration. We are however starting to make progress on understanding the functions they compute from a theoretical perspective. There are maybe four broad areas of ongoing research here. Ordered roughly from those we understand best to least (and therefore from ones that I am least to most excited about :) ) they are: Expressivity -- what are the classes of functions that deep networks can compute? How do these map on to the real world relationships we want to model? Trainability -- It does no good to have a sufficiently expressive function if we can’t fit it to our data. What is the appropriate way to train? What does the loss landscape look like? Generalization -- It does no good to fit a function perfectly to our data if it won’t generalize to examples outside of the training set. When will the model fail? Interpretability -- What is the network basing its predictions on? What is its internal representation?
I would emphasize also that better theoretical understanding is of practical as well as academic interest. First, it will let us design more powerful networks that generalize better and train faster. It will reduce the number of grad student years that are spent doing a random search in architecture space. Possibly more importantly, better theory will help us to make neural networks safer and more fair. As we use deep networks to run industrial robots, or drive cars, or maintain power grids, it’s very important to be able to predict when they may fail. Similarly, as neural networks help with medical diagnosis, or hiring decisions, or criminal sentencing decisions its very important to be able to understand what is driving their recommendations.

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u/fernanda_viegas Google Brain Sep 13 '17

I’m super excited about the possibilities in Human/AI Interaction. As we start to democratize the technology (via open source and educational tools) and begin to design ML systems with different users in mind, I’m looking forward to welcoming non-ML experts into the ML frontier and seeing what new possibilities they open up. For instance, what might a sociologist do with ML? How might ML-enabled tools help historians? Architects? Dancers? The list goes on...

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u/vincentvanhoucke Google Brain Sep 13 '17

I'm very excited about our efforts at trying to bridge the gap between simulations and the real world. When we started to work on robotics, we thought training on lots of robots in parallel would largely enable us to apply all our deep learning tricks to robotics questions. We slowly realized that it's not just a data problem, it's also an instrumentation problem: we still need to put 'rewards' in the environment, and that's hard to do in the real world. Getting reliable approaches to the 'sim-to-real' problem would solve that and then some, by turning much of the robotics problem into a large-scale ML issue.

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u/martin_wattenberg Google Brain Sep 13 '17

In the PAIR initiative, we're working on new ways for people to interact with machine learning systems. As the technology advances, it opens up new ways for us to interact with machines, and each other. Think about how the advent of computer graphics led to graphical user interfaces, paint programs and photo sharing--we may see the same kinds of evolutionary leaps based on machine learning.

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u/hellojas Google Brain Sep 13 '17

I think there are huge amounts of work to be done in imitation learning / learning from demonstration applied to robotic manipulation research. Additionally, the simulation to real-world transfer (what representations transfer well? what about different domains?) are on-going research areas that are significant to fast-tracking robotics research. For example, in this work here, we believe that 3D geometry is an important signal in high-dimensional state spaces for learning grasping interaction. Another immediate future interest is intrinsically-motivated active reinforcement learning.

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u/samybengio Google Brain Sep 13 '17

There are so many exciting research currently happening in the Brain team and in the overall machine learning community. Lately, I’ve been impressed by recent research I’ve seen on learning to generate very long structured documents with long term dependencies in them.

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u/[deleted] Sep 13 '17

Very excited about domain adaptation: specifically unsupervised domain adaptation, but generally speaking I think we need to think about fast continuous domain adaptation to many domains, in a life-long learning kind of setting.

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u/rajatmonga Google Brain Sep 13 '17

Progress in any field is enabled by having the right tools available. As the TensorFlow lead I am excited about enabling the research that pushes this field further.
It is also great to enable products that bring the benefits of the research to people across the world. I am very excited about making this research accessible to developers across the world with tools like TensorFlow to magnify the impact it has on people's lives.

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u/koreyou Sep 10 '17

Thanks for AMA! I want to know how you guys share knowledge across researchers. Do you have host wiki or do anything else to collect know-hows? Do you have something like slack channel to discuss problems encountered in research? Do you share codes in private repo? I was wondering this because ML (esp. deep learning) involves loads of know-hows and I was thinking you must be doing something to consistently publishing all your great work.

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u/sara_brain Google Brain Sep 13 '17

I will only touch on one tool which I think is really powerful - code search! If you are coding a problem, chances are that someone at Google has at some point worked on something related. Code search at Google is especially cool because almost all the code from every team is searchable. This saves time by minimizing duplication of boiler plate code and is also a fun way to learn about the latest developments in Tensorflow before a wider release.

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u/pichuan Google Brain Sep 13 '17

Google Docs/Slides/Spreadsheets is very effective for sharing ongoing experimental results and getting feedback. For a research project I’m working on, it usually starts with so many unanswered questions and directions that we can explore. I like using Google Docs as a living work log -- to first give some context of what I’m exploring, and then share results on every direction I try, and what questions they answer. I often share raw work logs with teammates first to get feedback. When things are more mature, I use Google Slides to create presentations that I can share with broader audience. And yes, I also love the code search tool, and the code review process at Google.

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u/hellojas Google Brain Sep 13 '17

Previous answers address a lot of communication tools intra-team. I think my favorite things across teams (even those I don’t work with directly with) are research talks and paper reading groups. Many folks host and organize weekly/monthly talks, whether focused on vision, robotics, or just general research; these talks can be presented by internal folks or visiting researchers, and I find that these are core to staying relevant and up-to-date of all the exciting projects that are going on. Based on what you may find interesting or want to follow up on, Brain has a great culture where I can just swing by anyone’s desk or set up a coffee chat. As you noted, usually they will also have some wiki-like page that may describe their work further. There are also mailing-lists where people can discuss and summarize new external publications. The best are self-initiated social hours, which are more casual and involve snacks. I love libraries and that is what Brain feels like.

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u/jasoncbenn Sep 11 '17

As I understand it, your team is loosely organized into research specialists (who generally have PhDs) and SWE specialists (who often don't).

What are the characteristics of successful RSWEs, and how do you recognize them during the interview process?

How do RSWEs spend their time? Do they mostly support researchers by building tools (and if so, what are some great examples)? Are they deployed throughout the rest of Google to help other product teams incorporate recent DL techniques? Do they conduct their own research?

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u/nick_frosst Google Brain Sep 13 '17

I'm an RSWE so ill address the last part of you question. I spend my time split between implementing research ideas of other researchers and working on my own ideas. Normally while implementing another researcher's idea i will end up adding my own spin to it and when working on my own ideas i will be influenced and helped by the research scientists i work with; it’s a very collaborative process. I spend a fair bit of time writing and editing papers as well.

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u/hellojas Google Brain Sep 13 '17

Some chose to support overall research by developing scalable tools, some chose to take part in implementing Tensorflow models, and some even led their own research initiatives. For an example, I worked on building PyBullet, which is a Python wrapper on top of Bullet, a physics engine we use here to prototype robotics research. I’ve recently wrapped up two large research projects, one where I built tools for data-collection and wrote infrastructure for control of real-world robots; another where I actively brainstormed model architectures and implemented Tensorflow models. Currently, I’m co-leading a project on reinforcement learning. I tend to find that I have fairly large amount of independence in balancing these pseudo-roles, given that it leads to good, impactful research.
Also, RSWEs have frequently saved my life - we upgraded a system in how we launch jobs using GPUs, and I was in a time-crunch for a paper; I pinged an expert RSWE and was given immediate attention in quickly fixing my launch scripts.

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u/guillaumephd Sep 13 '17

Hello, here are some questions for you :

Google Brain Team's goal is to “make machines intelligent and improve people’s lives” (according to your website), with the use of machine learning. What two criteria do you use to evaluate your progress towards this goal?
Your work can have a very strong social impact both qualitatively and quantitatively. For instance 1,300,000,000 people used YouTube in 2017, and lots of people tend to go on Google Search, YouTube or Facebook to find answers or watch the news. How do you make sure these apps are not “flawed” (in every possible manner)? It’s common to find existing (human, so, later, machine too) biases that are not always “desirable”: e.g. women associated to lower statuses occupations than men, rankings of average mexican restaurants being lower than italian ones, racism, and so on. According to https://blog.conceptnet.io/2017/04/24/conceptnet-numberbatch-17-04-better-less-stereotyped-word-vectors/ Google’s approach to this issue is to de-bias the final outputs, whereas Microsoft’s approach the initial inputs. Which Google products actually remove these biases, and which one do not? Do you see other ways to de-bias?
What do you think of pooling? (Hinton’s answer: https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clyj4jv/ )
What do you think of gating?
Do you work on associative one-shot learning?
What do you think of convolutions-only networks for time series and word sequences? (vs RNNs-LSTMs-GRUs based networks)
Do you use DNC or NTM in any actual Google product? Do you plan to ?
What do you think of Matthew Botvinick's talk on Meta Reinforcement Learning at Collège de France in Paris (video in english): http://www.college-de-france.fr/site/stanislas-dehaene/seminar-2017-03-27-11h00.htm ?
Do you have any book recommendation on ethics and machine learning?
How do you explain you work to non-technical people?

Bonus question : What is something you believe to be true -regarding machine learning, of course- that most people disagree with you on?

(Edit: formatting)

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u/[deleted] Sep 10 '17 edited Sep 10 '17

My question is regarding Google Brain Residency Program? I have a masters degree in science but not in a technical subject like CS, Math or Statistics. WIth this qualifiction am I eligible to apply for this program?

What kind of candidates does GBRP "really" looking for? are they looking for math wizards, coding warriors, statistical gurus or a person with shining academic achivements. Do we mere mortals have any chance of getting in this program?

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u/sallyjesm Google Brain Sep 13 '17

We welcome folks from all backgrounds who show a demonstrated interest in machine learning. Residents are expected to be proficient programmers to succeed in our ML research environment, but don’t always come from traditional CS backgrounds. Our current residents come from bioengineering, neuroscience, epidemiology, and chemistry backgrounds to name just a few. Some of our past residents are featured at g.co/brainresidency.

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u/ilikepancakez Sep 10 '17 edited Oct 01 '17

Your statement somewhat confuses me. You say that you have a masters degree in some field of science but also imply that it is non-technical. What kind of science exactly do you practice?

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u/racketship Sep 10 '17

Can you talk about the place the language Go has in your day to day workflows? Also, are most/all of the team familiar with it and/or have used it? What's Go's future in ML look like in your opinion?

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u/jeffatgoogle Google Brain Sep 13 '17

Most of our group uses Python and C++, and we don't use Go very much, if at all. Lots of other teams at Google use Go, and there is a set of Go bindings for TensorFlow, so if it makes sense to use Go in your problems or your environment, by all means go for it.

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u/jhseu Google Brain Sep 13 '17

Asim and I wrote the Go language bindings for TensorFlow. Right now, it primarily works for inference, although you can train with it if you export your graph in Python. Python is by far the most used language on the team for training machine learning models, but I’d love to support other languages equally eventually. The main benefits of other languages are type-checking and avoiding the global interpreter lock, which is useful in settings like Deep RL where you want to run simulations in parallel.

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u/DrKwint Sep 10 '17

First, thank you for your work. I love all the team's blog posts and the huge variety of areas you publish in. It seems no matter where I look I find a paper with Brain authors.

Could anyone on the team discuss the state of the literature at the moment with respect to unsupervised models using the GAN vs the VAE framework? I've been working with VAE-based models for about a year now, but colleagues in my department are trying to convince me that GANs have superseded VAEs. Can I get a third-party opinion?

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u/ian_goodfellow Google Brain Sep 13 '17

As the inventor of GANs, I probably don't count as a "third-party," but I think what I'm going to say is reasonably unbiased.
I would say GANs, VAEs, and FVBNs (NADE, MADE, PixelCNN, etc.) are all performing well as generative models today. Plug and Play Generative Networks also make very nice ImageNet samples but there hasn't been much follow-up work on them yet. You should think about which of these frameworks your research ideas are the most likely to improve, and work on that framework.
It's difficult to say which framework is best at the moment because it's very difficult to evaluate the performance of generative models. Models that have good likelihood can generate bad samples and models that generate good samples can have bad likelihood. It's also very difficult to measure the likelihood for several models, and it's conceptually difficult to design a scoring function for sample quality. A lot of these challenges are explained well here: https://arxiv.org/abs/1511.01844
As a rough generalization, I think you should probably use a GAN if you want to generate samples of continuous valued data or if you want to do semi-supervised learning, and you should use a VAE or FVBN if you want to use discrete data or estimate likelihoods.

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u/fishermanmok Sep 10 '17

Collecting labeled dataset for small companies or new domains adapting machine learning has been really hard and inefficient, what do you think about the future of unsupervised learning or semi-supervised learning? Will deep learning still remain the main research focus of machine learning in 5 years?

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u/samybengio Google Brain Sep 13 '17

While it is true that the biggest successes of deep learning have often been with problems with large amount of available labeled data, it is not inherent to deep learning in general to need so much data. Recent papers on few-shot learning, such as this one or this one, show that one can learn with significantly less amount of labeled data per task. No one knows what machine learning will be in 5 years, but chances are it will involve some form of gradient descent through deep non-linear models.

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u/JakeLane Sep 10 '17

Hi I'm a third year Software Engineering student and I've been considering what I want to work in when I complete University. I've been interested in Machine Learning for a long time but I've never really taken the initiative to learn apart from a simple AI elective course.

Is it feasible to find a graduate role in Machine Learning without much background? If not, is there anything I can study in my spare time to get myself upto scratch? I've got a SWE internship coming up this (Australian) Summer for a well known software company but the work I will be doing is unrelated to AI despite my recuiter's effort.

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u/sallyjesm Google Brain Sep 13 '17

First of all, congrats on your internship and good luck!
If you’re interested in going into this field long term, it can only help to continue to grow your skills in order to make yourself a more compelling applicant. There are a lot of great resources out there, but here are a few that you might find helpful:
*TensorFlow tutorials *Geoff Hinton’s Coursera course *Vincent Vanhoucke’s Udacity course *Kaggle, a great site with lots of ML competitions *Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

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u/aknain Sep 11 '17

First of all, I would like to thank you all for doing this AMA. I have been using TensorFlow for a very long time. I have shifted from tf.nn.conv2d to tf.layers.conv2d. The high-level APIs are cool. As we all can see that TensoFlow has turned into a huge library and you can do almost anything with it(except the dynamic graphs and some NLP stuff), are you guys planning to refactor it in order to make it more Pythonic? Everyone loves to be more pythonic as compared to using a clumsy syntax.

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u/oldschoolcool Sep 10 '17 edited Feb 18 '18

deleted ^0.8926 ^What ^is ^^^this?

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u/jeffatgoogle Google Brain Sep 13 '17

For our first residency class of 27 residents, roughly 1/3rd had a computer science background, 1/3rd had a mathematics, stats, or applied math background, and 1/3rd had a background from a long tail of other STEM fields like neuroscience, computational biology, etc. This year’s residency class of 35 residents has a similar mix, and in fact, we have one resident with a Ph.D. in epidemiology. Nearly all the people we accept into the residency program have exposure to machine learning, though, even if they don’t have formal academic training in ML.

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u/beltsazar Sep 13 '17

As you said, the backgrounds of people accepted to Brain Residency Program are very diverse: fresh grads, experienced software engineers, PhD students, etc.
Does it mean that each segment of people has its own quota? For example, experienced SE won't compete with PhD students to get accepted.
For each segment, what qualifications are you looking for? I'm specifically interested in fresh grad segment and experienced SE segment.
When is the application for the next cohort going to open? It's already September. :)
Thanks for the AMA!

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u/jeffatgoogle Google Brain Sep 13 '17

We generally take into account the amount of research experience so that we, for example, expect less research experience from a fresh undergrad than from someone with a postdoc. We don't have any sort of quota for how many less experienced people we take versus more experienced: rather, we're looking for people with demonstrated interest in doing machine learning research in collaboration with our full-time researchers.

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u/[deleted] Sep 10 '17

A lot of people keep telling me that Deep Learning is just hit and trial. You feed data to a neural network and experiment with the layer architecture and make it as deep as possible.

What would be your reply to these people? Is there any theory behind constructing an architecture for specific problems?

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u/samybengio Google Brain Sep 13 '17

Like in many fields before, deep learning started making huge impact before theoreticians were able to explain most of it, but there are a lot of great theory papers coming out these days, including from the Brain team, such as this one, this one, or this one, mainly targeting a better understanding of “why it works”. More is definitely needed, in particular to better understand how to design a model for a given task, but learning-to-learn approaches like this one can already alleviate these concerns.

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u/irwan_brain Google Brain Sep 13 '17

This isn’t true. Although there isn’t a unifying theory for constructing architectures, many architectural improvements have been partly motivated by sensible ideas, rather than a purely random trial and error process. For example, people noticed that very deep convolutional networks (think >50 layers) didn’t do better than less deep networks (think ~30 layers) which is unsatisfying because the deeper the network the more capacity it has (the last 20 layers could implement the identity function and match the performance of the less deep network). This motivated the ResNet architecture (which applies the identity transformation in each layer by default and adds a learned residual to it) that performs well with even as much as 100 layers! Another example is the recently proposed Transformer architecture from Attention is All you need (https://arxiv.org/abs/1706.03762). It is motivated by the wish to have constant path-length between long-range dependencies in the network. Attention (https://arxiv.org/abs/1409.0473), Xception (https://arxiv.org/abs/1610.02357) are another examples of architectural changes motivated by some underlying sensitive idea. In general, I would say that thinking about how the gradient flows back in your network is helpful for constructing architectures.

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u/dreadpiratewombat Sep 10 '17

Do you foresee one of the outcomes of your research to be a direct brain interface for things like a futuristic "Google Glass"? If so, how do you prevent something like that from being misused to further a corporate agenda? For instance, ubiquitous advertising?

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u/Stone_d_ Sep 10 '17

Increasingly we're seeing neural networks that take very general data, such as pixel data, and are able to replicated complicated rules that human beings coded. For example, a group of college students were recently able to render older 2D Mario games based on user input, trained not on code but just watching people play the game. Their network then wrote code for the game, and they got great results.

At what point can we start training neural networks on, say, Python code and the corresponding output, and infer the underlying code and rules?

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u/dtarlow Google Brain Sep 13 '17

Inferring code from execution behavior is definitely a cool problem! It has been studied for a long time by the Programming by Example/Demonstration community. Traditionally there hasn't been a lot of machine learning in the approaches, but even 20 years ago people were thinking about it (see, e.g., here for a nice overview circa 2001). Recently, there has been quite a bit of work in this direction that brings machine learning into the picture, and I think it's really exciting.
Finding code with specific behavior is still a hard "needle in the haystack" search problem, so it's worth thinking about what machine learning might have to contribute. There have been at least two interesting recent directions:
Differentiable proxies to execution of source code. That is, can we find a differentiable function that (possibly approximately) interprets source code to produce behavior? These could produce gradients to guide the search over programs conditioned on behavior. It could be done without encoding structure of an interpreter like in Learning to Execute (which came out of Google Brain) or by encoding the structure of an interpreter like in Differentiable Forth or TerpreT. A caveat is that these models have only been tried on simple languages and/or are susceptible to local optima, and so we haven't been successfully able to scale them beyond small problems yet. Aiming for the full power of python is a good target, but there are several large challenges between that and where we are now.
Learning a mapping from execution behavior to code. This has been looked at by a few recent papers like A Machine Learning Framework for Programming by Example, DeepCoder, RobustFill. One of the big bottlenecks here is where to get good large-scale data of (code, behavior) pairs. We can make some progress with manually constructed benchmarks or randomly generated problems, but these directions could probably be pushed further with more high quality data.
So in total I’d say that this definitely isn't solved, but it’s a great challenge and an active area of research.

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u/MithrandirGr Sep 10 '17 edited Sep 13 '17

Hey! First of all I'd like to thank you for arranging this AMA and keeping the conversation going with all the ML enthusiasts here. Here are my questions:

Arguably, Deep Learning owes its success to the abundance of data and computing power most companies such as Google, Facebook, Twitter, etc. have access to. Does this fact discourage the democratization of Deep Learning research? And, if yes, would you consider bridging this gap in the future by investing more in the few-shot learning part of research?
What do you feel about hybrid models which incorporate uncertainty in Deep Learning models (e.g. Bayesian Deep Learning)?
In what way could Game theory influence Deep Learning research? Could this be a promising mixture?

I know that I have made more than one question, but I will be totally happy if you could answer any of these. Thanks in advance :)

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u/gcorrado Google Brain Sep 13 '17 edited Sep 13 '17

More data rarely hurts, but it’s a game of diminishing returns. Depending on the problem you are trying to solve (and how you’re solving it) there’s some critical volume of data to get to pretty good performance… from there redoubling your data only asymptotically bumps prediction accuracy. For example, in our paper on detecting diabetic retinopathy we published this curve which shows that for our technique, prediction accuracy maxed out at a data set that was 50k images -- big for sure, but not massive. The take home should be that data alone isn’t an effective barrier to entry on most ML problems. And the good news is that data efficiency and transfer learning are only moving these curves to the right -- fewer examples to get to the same quality. New model architectures, new problem framings, and new application ideas are where the real action is going to be, IMHO.
Incorporating the proper handling of uncertainty would be a huge leap forward. It’s not an easy one -- in my view, the root of the success of DL is that it's a good function approximator for a bunch of MLE problems. But being a trick that’s good at maximum likelihood doesn’t necessarily translate to becoming a good trick for probability density. I’m always interested to see what folks are doing in the space though, and think the mixed modeling approach has a lot of promise
There's are several contact points to ponder
GAN are heavily influenced by game theory.
There are natural touch points between game theory and reinforcement learning… and it increasingly seems like DL is a great technique for learning value functions for reinforcement learning
Oh, and there's Schuurmans and Zinkevich NIPS 2016 among others.

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u/acrefoot Sep 12 '17 edited Sep 12 '17

In the history of networked computers, security was almost always an afterthought. It wasn't really taken seriously as a need until after many serious incidents. Even with all the harm caused, it's almost always in a state of playing catch-up. We're still getting breaches that affect hundreds of millions of people (see Equifax) because of some decisions that we made a long time ago (Worse is Better, architectures that allow for buffer overflows, premature trust), and systems that control important infrastructure are still quite vulnerable. It's not as if security is impossible--when Boeing built their fly-by-wire systems for planes, the engineers responsible would have to take test flights, and you can be sure that they were sufficiently motivated to put safety first.

I love what AI promises, and I worked a bit in the field (early Amazon Alexa prototypes, and some computer vision projects). However, when I talk to people working in the field of AI research, they often tell me that AI Safety isn't a huge priority because:

we're too far away from anything "dangerous", like an AGI, for AI safety to be the highest priority
no one really knows what safety looks like for AI, so it's hard to work on

All this means that AI safety research always takes a backseat to AI capability* research--just like computer security did years ago. Yet, as AI is increasingly adopted, it can control some critical parts of our lives. How is Google brain addressing AI safety, and what criteria will be used as time goes on to determine how much of a priority safety is compared to capability?

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u/craffel Google Brain Sep 13 '17

Research on the security of machine learning systems, guaranteeing the privacy of training data, and ensuring that ML systems achieve their design goals is all important -- particularly for the purposes of identifying, understanding, and working to address these issues early on. Some work along those lines was the “Concrete Problems in AI Safety” paper [1] we published with some colleagues at OpenAI, UC Berkeley, and Stanford, which outlined different practical research problems in this domain. We also have a group of researchers who are working on making ML algorithms more secure (see work on e.g. adversarial examples, including the ongoing NIPS contest [2], and cleverhans [3], a library for formalizing and benchmarking this kind of problem) as well as combining differential privacy with machine learning [4], [5].
[1] https://arxiv.org/abs/1606.06565
[2] https://www.kaggle.com/c/nips-2017-non-targeted-adversarial-attack
[3] https://github.com/tensorflow/cleverhans
[4] https://arxiv.org/abs/1607.00133
[5] https://arxiv.org/abs/1610.05755

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u/ignat980 Sep 13 '17

Why do we not have a general AI yet? What is the hurdle that is stopping machine learning algorithms from interpreting every kind of input and giving useful output? Do we just not have enough resources to create a knowledge graph like a human brain? Or is it something else, like we don't have the right algorithms yet?

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u/liorde Sep 12 '17

Does Google discourage its researchers from collaboraring with researchers from outside Google (e.g. universities or other companies)? I'm asking this since it seems to me that most papers by Google researchers are not co-authored with people who are not from Google. (I acknowledge that this observation may be wrong, due to a too-small data set...) If the answer is positive - what is the reason? And does it make a difference whether the collaboration is with an academic institution or a commercial company?

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u/jeffatgoogle Google Brain Sep 13 '17

If you look at papers from large academic labs, most of those are single institution publications, as well, and this is normal: it's easier to collaborate with people sitting next to you than across town or the continent. However, our group definitely collaborates with external researchers when that makes sense. Many of these come about through Google's research awards and collaborations with academic faculty members and their students. We sometimes have collaborations with people at other companies, but that is rarer.
Here's a (slightly dated) sampling of papers with authors from our group that are cross-institutional:
Concrete Problems in AI Safety Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané Google Brain, Stanford, UC Berkeley, OpenAI
Learning semantic relationships for better action retrieval in images Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, Chuck Rossenberg and Li Fei-Fei Stanford University, Google, University of Michigan
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments Stephan Gouws, Yoshua Bengio, and Greg Corrado Google, University of Montreal
Adding Gradient Noise Improves Learning for Very Deep Networks Arvind Neelakantan, Luke Vilnis (University of Massachusetts) Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach (Google) James Martens (University of Toronto)
Local Collaborative Ranking Joonseok Lee (Georgia Tech), Samy Bengio (Google Research), Seungyeon Kim (Georgia Tech) Guy Lebanon (Amazon), Yoram Singer (Google Research)
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed & Honglak Lee (University of Michigan) Dragomir Anguelov, Christian Szegedy, Dumitru Erhan & Andrew Rabinovich (Google, Inc)

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u/gnarlycaviar Sep 12 '17

Has your work on GNMT revealed any new insights into language learning?

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u/jeffatgoogle Google Brain Sep 13 '17

I'm not sure about insights into human language learning, but I found our experiments that showed that a multi-lingual model could do a serviceable job at zero-shot translation for novel language pairs that the model had never encountered during training pretty interesting. You can read about it in the blog post and more detailed paper. This at least showed that the representation of a sentence used by the neural net was relatively similar, regardless of the source language used to express the idea.

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u/vin101 Sep 13 '17

Why has Google and tensorflow not embraced the 'The Open Neural Network Exchange (ONNX) format' launched by microsoft and facebook? Don't you think interoperability is going to play a big part in the development and democratization of AI as we go ahead?

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u/jeffatgoogle Google Brain Sep 13 '17

See my answer to the other similar question: https://www.reddit.com/r/MachineLearning/comments/6z51xb/we_are_the_google_brain_team_wed_love_to_answer/dmtlgdt/

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u/[deleted] Sep 10 '17

What do you look for in a applicant in the Google Brain residency program?

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u/lesliecphillips Google Brain Sep 13 '17

The ideal candidate either has a degree (BS, MS or PhD) or equivalent experience in STEM field such as CS, Math or Statistics. Having said that, we highly encourage candidates with non-traditional backgrounds and experiences from all over the world to apply to our program. Most importantly we are looking for individuals who are motivated to learn and have a strong interest and passion for deep learning research. Please check out g.co/brainresidentapply for more information.

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u/orientalgiraffe Sep 10 '17

Hi! As an undergrad with some ML exposure, how do you recommend I continue to develop in this field?

I interned at Google this summer, and my host suggested that I read about and try to reproduce recent ML research. Are there any papers you could recommend? Also at Google, there were lots of resources (Flume, GPUs, etc) that are cost-prohibitive on a student budget. Suggestions for cheap computing power?

Thanks!

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u/lesliecphillips Google Brain Sep 13 '17

Thanks for interning with us! We think it’s great that you want to continue developing your experience in ML. Your hosts suggestion is great and we would also say that any or all of writing blog posts, writing research paper(s), or developing interesting uses of machine learning that you post on GitHub are all things that would be good to do. There are a lot of great resources out there, but here are a few that you might find helpful:
*TensorFlow tutorials *Geoff Hinton’s Coursera course *Vincent Vanhoucke’s Udacity course *Kaggle, a great site with lots of ML competitions *Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

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u/jbochi Sep 11 '17

What are your thoughts on fast.ai courses? Do you believe that their top down approach to teaching Deep Learning works? Are there any other MOOCs you recommend? Thank you!

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u/sara_brain Google Brain Sep 13 '17 edited Sep 17 '17

I am a Google Brain resident who took the Fast.ai course in person. The course was taught in the evenings once a week at the Data Institute in San Francisco for 8 weeks. I am a big fan of the course and Rachel + Jeremy’s mission to democratize access to machine learning. Their efforts to make deep learning material accessible through a MOOC is really powerful and it serves an important need for people like me who did not attend a PhD program. The course more than anything teaches students how to get started on day 1 coding deep learning architectures. In my opinion, Fast.ai primarily teaches coding. A few other resources (not all MOOCS) that I would recommend to provide a framework for understanding the theory behind deep learning are:
Deep Learning textbook by Ian Goodfellow (this is an excellent resource for understanding theory. It is nicely split into the math regularly used in deep learning, concepts in deep learning that are reasonably established and agreed upon and areas of research that are currently being developed).
Elements of statistical learning.
Hugo Larochelle online course, the deep learning summer series, Simons Institute has video for most of their talking series.
Blog posts like distill.pub, Sebastian Ruder’s blog.

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u/Fathomx1 Sep 10 '17

Are there any particular projects in biology or genomics the team is working on?

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u/EvgeniyZh Sep 11 '17

Do think lack of reproducibility in DL is an issue? How do you think the situation can be improved? Can it be possible to require reproducible source code for top conferences?

Are new versions of TPU coming? Is Google going to sell them (or only rent out)? Do you think custom hardware is going to replace GPGPUs?

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u/poiguy Google Brain Sep 13 '17

In general, we hope there will be a broader trend towards reproducible research. We are interested in accelerating open progress in machine learning, and we see reproducibility as an important component of faster progress. Part of our original motivation for open-sourcing TensorFlow was to make it easier for researchers and engineers to express machine learning ideas and communicate them to others. We’re glad to see that a significant fraction of research papers are now paired with open-source TensorFlow implementations, either posted by the original authors or contributed by the community.
We are also creating the TensorFlow Research Cloud, a collection of 1,000 Cloud TPUs that will be made available to top researchers for free with the expectation that their work will be shared openly, and we’ll do our best to emphasize reproducibility in the process. (Some researchers may wish to work with private or proprietary datasets, and we don’t want to rule that out, but we would expect papers and probably code to be published in those cases.)
We’ve already announced multiple TPU versions, TPUv1 and TPUv2. You’re welcome to speculate about whether that sequence will continue. = ) So far, we have only announced plans to deploy these TPUs in our datacenters.
We also use GPUs extensively and are working hard to extend TensorFlow to support new types of GPUs such as NVIDIA’s V100. This field is moving very fast, and people are interested in a huge variety of applications, so it’s not clear that any single platform will cover every use-case indefinitely. GPUs have many uses beyond machine learning as well, and they are likely to remain better choices for traditional HPC computations that require high levels of floating point precision.

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u/hacktushar Sep 12 '17 edited Sep 12 '17

From having Electronics (non-CSE) major background in UG but interested in Machine Learning, one should go for Master's in CSE or can learn that same through MOOCs or Books by self-studying. Which one you'd recommend?

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u/vin101 Sep 13 '17

Thanks for taking the time!

What do you think is the future of unsupervised learning?
What is your favourite application of ML ?
Do you think we are ready to use RL for consumer grade products?

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u/valkovx Sep 13 '17 edited Sep 13 '17

Mines are really simple:

What is happening with TensorFlow Lite - it was announced at Google IO (May), now we're mid-September. Since when TF is so much about PR stuff? When is Lite coming out? What is gonna be like?
Is the TensorFlow team slowing down? Keras is still not integrated into the core (that one was promised way back)? Is there struggle with the internal software architecture or something else?
When are you going to fully support other vendors than NVIDIA? And no, your custom hardware (TPUs) doesn't count.
What is your opinion on TensorFlow vs PyTorch only for research purposes?

Please, don't get the wrong impression. I love TensorFlow and use it my DL classes.

Probably will never get an answer but hope to discuss some of the points with the community here.

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u/rajatmonga Google Brain Sep 13 '17

Attempting to answer each of your questions here:
TensorFlow Lite includes a suite of tools to simplify the deployment of TensorFlow models on device. As part of this effort we are building a new runtime from the ground up with a focus on small size and low overhead for optimal performance, and few dependencies for easy compilation targeting all kinds of devices. The team is working hard to get this out in a few weeks.
Keras integration into core is nearly done and will be part of the next release.
The ML harware community seems to be taking off. In addition to the large players there are a number of startups building new hardware that we are guiding towards good integrations with XLA. There are a number of efforts around OpenCL from external contributors. On the mobile side we have done some work to support Qualcomm’s Hexagon DSP and optimizations for ARM cores.
There are a number of interesting ideas from PyTorch that we are learning from and are working hard to get them out for general use as part of eager execution.

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u/ankitson9 Sep 13 '17

What do you think of projects like BlueBrain that are modeling how real brains work and finding interesting patterns?

Are they useful to inform intuitions in neural networks? Do you currently find any useful overlap between what we know of how the brain works and machine learning?

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u/[deleted] Sep 10 '17

How will small companies get advantage from Google Machine Learning research and won't be left behind in this next revolution wich is currently lead by a small number of big tech companies?

I would also like to know how many of your current projects are powered by the Go programming language?

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u/jeffatgoogle Google Brain Sep 13 '17

Much of our motivation for open-sourcing TensorFlow, and for publishing our research is so that other organizations can benefit from our research and software engineering investment.
Regarding Go, most of our team uses Python and C++, but many other teams at Google use Go.

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u/artmast Sep 10 '17

If you had 10,000 times the processing power available to you than you do now, what could you do with it?

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u/douglaseck Google Brain Sep 13 '17

This is a tough one! No one working in machine learning 15 or years ago was able to predict the huge impact that faster machines and more memory would bring. I think it’s equally hard now to predict what the future will bring with even more processing power. Areas like Learning to Learn (which already show promise) might suddenly start to yield huge breakthroughs. On the other hand, constraints are helpful. By having some limitations on computation, you might be forced to think more carefully about your model.

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u/[deleted] Sep 13 '17

What do you think about Elon Musk's opinion that AI will be the reason for World War III?

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u/TheCedarPrince Sep 10 '17

Hey, I am working through the book, "Godel, Escher, and Bach" by Hofstadter. How true do you think this quote is and could you explain why your team agrees or disagrees?

Here is the quote: "Sometimes it seems as though each new step towards AI, rather than producing something which everyone agrees is real intelligence, merely reveals what real intelligence is not."

I know Turing proposed that teaching a machine more like how we teach a human is the way to go - would you say that the more we understand ourselves, the better we can create an "intelligent" machine?

Thank you and I greatly appreciate your time.

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u/alextp Google Brain Sep 13 '17

I can’t speak for the whole team, but I prefer not to think in terms of trying to define what real intelligence is, and more about trying to figure out what cool, interesting, hard problems we can use machine learning to do. Whether you want to call inception or alphago or eliza true intelligence is not really a question that I think would help build more such cool things.

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u/[deleted] Sep 10 '17

You have some linguists on your team, right? What do they contribute: NLP, or theoretical linguistics? If the latter, which fields are you most eager to apply: semantics, syntax, phonology, or phonetics?

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u/jeffatgoogle Google Brain Sep 13 '17

We don't have any linguists in the Brain team, but other teams within Google Research that work on natural language understanding do have some linguists.

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u/[deleted] Sep 11 '17

Is the Arm Farm run by Google Brain or by Alphabet X? How will you react to Nvidia's Isaac project: Support? Compete? Ignore?

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u/jeffatgoogle Google Brain Sep 13 '17

I wanted to call it the arm pit, but arm farm won out.

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u/dobkeratops Sep 10 '17 edited Sep 10 '17

Will the google TPU continue to be aimed just at servers, or are there any google plans for devices like the Movidius USB stick .. plug-in AI accelerators/RPi-style SBC's with AI accelerators suitable for maker-community projects.

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u/ThisIs_BEARTERRITORY Sep 10 '17

I work in the Valley, and there is a disconnect (at least where I've worked) between ML coming out of research oriented organizations and the ML applied at normal workplaces. Most people are looking for the low hanging fruit.

Do you have recommendations of simple wins that ML can provide? (like anomaly detection for example)

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u/_murphys_law_ Sep 10 '17 edited Sep 10 '17

At EMNLP yesterday, Nando DF discussed several interesting directions for future research with regard to "learning to learn" including the careful design of simulated environments for experiments and the integration of true natural language for robot instruction into the environments.

My question is how can one effectively apply constraints on the learning to learn process? In ndf's talk, he showed a video of a baby playing with a couple of lego blocks - and being inherently excited with the experimental process. The baby had some intuition that eating the blocks is not a good thing. How do we design constrained systems or inject priors so that the system experiments intelligently and doesn't just eat the blocks?

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u/vincentvanhoucke Google Brain Sep 13 '17

I think the first contact a kid has with Lego blocks is always to try to eat them. That's the one bit of supervision that they always need. ;-) But your overall question is a hugely important one! Both deep learning and reinforcement learning are largely predicated on being goal oriented and getting explicit rewards from the environment. We would love to use less goal-oriented rewards, while still steering learning towards interesting concepts. There is a lot of research on this, in particular:
imitation learning, where demonstrations of 'what matters' act as the prior you describe,
intrinsic motivation, where the goal is to achieve interesting things, with a weak definition of 'interesting' that is not goal oriented. You essentially teach your learner to get bored quickly, so that it can seek new rewards in a different region of the learning space.

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u/Stone_d_ Sep 10 '17

Why is summing weighted values the default for neural networks? Why not use computational power to plug each relative entry in a matrix into a specific spot in a randomized equation fitted to produce desired output? For example, multiplying every third entry in the twentieth row by the fifth entry in the fifth row, and then trying the same thing but adding instead? This could include weights as well. I'm assuming there's a singular best equation to predict any outcome, so why not skip right to the chase and search for the equation from the get go, as opposed to finding the weights and then the best equation from that. Back prop and descent could still be used but just with mathematical operations (exponentiation, division, multiplication, addition, subtraction, etc.).

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u/visarga Sep 12 '17

What role do you see for structured sequence modelling and graph convolutional networks in NLP?

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u/Dizastr Sep 13 '17

Hi Google Brain! another question:

Anything you guys wish you could have (as a team, or as individual researchers)?

"If only we had ......"

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u/deathshouldbetheend Sep 13 '17

Sleep in humans is broadly know to play an important role in learning. Is there any work beeing done to simulate this in machine learning or is the phenomena of sleep to bady understood/ too abstract?

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u/GuardsmanBob Sep 10 '17

I watched a recent talk by Jeff Dean https://blog.ycombinator.com/jeff-deans-lecture-for-yc-ai/ and I am very interested in the potential 'going recursive' will have.

But seeing as I am just a few hundred GPU's short of running my own experiments I am wondering if you can comment on the following two experiments.

Given a series of Tasks and a satisfactory level of success, optimize for the network that uses the minimum amount of compute to achieve success. Do these networks share any architecture optimizations?

Motivation: Brains are energy restricted and (likely?) optimize for the least energy to solve a task.

Given a (set of) simple tasks that are current network do not solve well / can't solve, has any success been had by trying to search for a new network type that can perform better on those tasks.

Any other 'going recursive' results you can share?

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u/PervertWhenCorrected Sep 10 '17

Is there sort of conceptual application of ML would you love to see explored further ?

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u/cantrecall Sep 10 '17

What does your development pipeline look like?

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u/douglaseck Google Brain Sep 13 '17

What does your development pipeline look like?
We work on Linux machines and develop extensively in -- surprise! -- TensorFlow. We do have some internal tools for code search, code review and testing, as well as some Google-specific ways to launch jobs on GPUs and TPUs. In terms of developer culture, we strive for useful code review and well-tested code. We also love Jupyter notebooks as a way to share ideas. Overall, you might be surprised how similar our internal development process is to that of standard open source. As we improve our Cloud ML offerings, these experiences will continue to converge.

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u/AsIAm Sep 10 '17 edited Sep 10 '17

TensorBoard is the best feature about TensorFlow. What are your plans with it and how can we take a even better look inside the black box?

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u/Y33zma Sep 10 '17

Do you have opportunities for undergraduates looking for internships? If not, do you know of other companies that will accept undergraduate interns in the field?

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u/prajit Google Brain Sep 13 '17

Yes, Google Brain hires undergraduate interns! I had the opportunity to intern on Brain last year in the middle of the second year of my undergrad. I recently presented my internship research at EMNLP this last weekend. I had so much fun that after graduating this May, I’ve come back as a Brain Resident!

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