The Google Brain Team – Looking Back on 2016

https://research.googleblog.com/2017/01/the-google-brain-team-looking-back-on.html

by stablemap • 10 years ago

209 61 10 years ago

research.googleblog.com

rawnlq2• 10 years ago

Is Brain completely separate from DeepMind?

tcai• 10 years ago

Yes - they are independent groups with separate projects in different locations (Brain is in Mountain View, DeepMind in London).

jrheard• 10 years ago

Is there an easy way of characterizing the difference between the two groups - eg brain focuses on A, deepmind focuses on B?

zengid• 10 years ago

As a student hoping to become a software engineer, I'm a little nervous about Googles aggressiveness towards applying ML across many of their products. It makes me wonder: Will the field of software engineering be reduced to plugging ML nodes into client-side interfaces? Could it shrink the demand for engineers?

jrockway• 10 years ago

It's a logical concern. There is no reason to assume that today's software engineers are any different than the 1950's coal miners. The world changes, bit by bit, there is no such thing as perfect job security for life.

plessthanpt05• 10 years ago

Not really true...tenured professors pretty much have perfect job security for life.

ilaksh4• 10 years ago

Personally I think that within X years (could be only 10, or 5, or probably less) we will see human-level AGI. This is because the grounded (based on granular sense/motor) deep learning systems such as Deep Mind are being applied in varied virtual environments and some with gradual development of skills and knowledge. This type of approach will enable truly general agents.

So we better hope that the BCIs develop quickly as well.

fooker2• 10 years ago

Or there will be another AI winter. There is no way to tell now.

lend0005• 10 years ago

(could be only 10, or 5, or probably less)

I don't know when, but I can guarantee it won't be within 5 years. The reason: digital hardware improvements are slowing down. Today's best hardware can only run a model with the number of nodes in a human brain at 1/2400th speed, and probably with an oversimplified model: http://www.telegraph.co.uk/technology/10567942/Supercomputer...

I don't think we will reach human level AI on digital hardware; it just doesn't really make sense with the Von Neumann bottleneck. Parallel, analog computation is the way forward and that has yet to be commercialized, so we probably have a ways to go.

That being said, AI is getting very impressive on the software side, and with the use of TPU's, GPU's, and FPGA's, we can definitely look forward to compelling use cases where these algorithms have apparently creative intelligence, but only within an extremely specific category (when compared to the generalities of biological analog brains).

maverick_iceman2• 10 years ago

The singularity is far.[1]

[1] http://www.scottaaronson.com/blog/?p=346

fat-chunk• 10 years ago

I work in AI, specifically in the field of deep learning applied to computer vision. Not trying to discredit your comment, but can you provide some sources indicating that the type of models being trained which you have mentioned are approaching the notion of a general agent?

I haven't read much of the literature around deep learning, mostly only what has been applied to computer vision. But from what I understand, the general consensus is that the current crop of state-of-the-art deep learning models are very good at performing specific tasks (machine translation, object recognition in images, etc.), but are not so good at generalising across multiple fields. This seems more relevant to the domain of reinforcement learning (which does indeed include deep learning), which has proven to be a very difficult problem to solve.

jkestelyn• 10 years ago

IMO, the same question could be asked with respect to any abstraction that hides complexity. The answer will always be/has always been: No, it won't. Rather, engineers who can think creatively about the role of pre-built ML models in mainstream applications will be in high demand.

closed• 10 years ago

Agreed. That made me think of how statisticians 70 years ago were often good at doing all kinds of tests by hand. Once the doing things by hand part was abstracted away by modern computing they just focused on other parts of statistics.

maverick_iceman2• 10 years ago

That would be a great day, when no humans will need to program any more. But I don't think it's anywhere near.

eva19842• 10 years ago

Much of so called software engineering is about assembling/wrapping other API/libraries anyway.

I don't see how ML change the scenario, instead of querying some db, you are now querying some trained models. Until we have an algorithm that could write correct code given requirement specified in natural language, the demand or prospect of software engineer as a profession will not be affected by ML/AI too much. However it is hard to predict when such technique will emerge, 10 years? 20 years? maybe 50 years.

rawnlq2• 10 years ago

The blog post specifically mentioned Program Synthesis which is exactly what you're talking about. The article they published was called Neural Programmers but I haven't read it: https://arxiv.org/abs/1511.04834

So at the very least they are working on it!

pinouchon• 10 years ago

The jury is out on this one. The probability distribution would be the surface of a mattress with a peanut under it (very flat).

This is the branch of IA/ML that turns me on the most. When we get there, it will redefine many jobs (as well as create an existential thread as a side effect).

I think it is sad that the terms AI and Machine learning do not make a clear difference between narrow and strong AI, this is the source of much confusion.

dgacmu• 10 years ago

Speaking as a part timer on Brain who's not a machine learning person, I've found and believe that MI just opens up more opportunities for good programmers. There are a ton of problems out there that we couldn't solve well with computers without MI, and so didn't even attempt. Now we can attempt them, and they're both fun and hard.

(I wrote a longer-form answer to a very similar question a few months ago: http://da-data.blogspot.com/2016/05/dont-quit-that-programmi... )

EternalData• 10 years ago

I've noticed that the number of applications MI can be embedded into is nowhere near its full potentiality. There's a lot of potential for people who are able to think on their feet product-wise and incorporate MI, even if MI is a black box to them. Echo your opinion.

Swizec• 10 years ago

Your real job as a software engineer isn’t to write code. It’s to translate hand-wavy business requirements into detailed specs that a computer can follow.
Your job is to ask questions and to find edge cases that the product people didn’t think of. Your job is to help operations define processes well enough to be automated.

https://medium.com/swizec-s-nightowls/what-should-you-learn-...

Right now it takes 5 to 10 years to train a human who already knows how to code to do this. Some never learn.

I think we're safe for a while.

Engineers don't "write code", they build solutions. We just happen to use code to do it. And we happen to manually type that code. Even if an AI could type it for you, you'd still need someone to build the solution.

Roboprog2• 10 years ago

> Some never learn.

Even with currently available programming techniques, simply getting many coworkers to move from "WET" to "DRY" can be a daunting challenge.

Many people just hate abstractions. ML / AI tools seem to often just boil down to larger aggregations of abstractions (which require some training and/or experience to learn). I guess the "we enjoy [lots of] typing" crowd will go the way of the dodo as the tooling moves up abstraction levels. I hope they don't suck me down with them :-(

(yes, I'm glossing over the requirements gathering aspect of the comment to carp about the construction aspect, but the same strategy of abstraction/investigation, or not, applies)

deepnotderp2• 10 years ago

As someone who does ML research for a living, I can wholeheartedly assure you that programmers will be even more in demand as a result of ML.

Sargos• 10 years ago

But why? Programmers right now do everything by hand. In a ML future less of those by hand things will be needed. It seems like less programmers will be needed as a result.

thavalai• 10 years ago

Lots of commenters seem to think that programmers are safe for a while. As a programmer, I sincerely hope they are right.

But, the majority of developers I know, including myself, are ultimately writing software to be used by humans, typically presenting data that is comprehensible to humans, so that they can make decisions.

If AI advances to the point where similar decisions can be made by machines, looking at the same data, then I fear that entire classes of applications will disappear, and with them, the need for developers who build them.

Obviously, we'll still need developers, though perhaps far fewer than are needed today. Perhaps software development will go through the same revolution that agriculture did, where from we went from > 90% to < 3% of the population being involved.

eitally• 10 years ago

This is already nascent, but growing quickly, in a field typically called things like "robotic process automation" or "cognitive computing". Essentially, eliminating the need for wide swaths of CRUD and business process enterprise applications, and the developers who create them (and the sysadmins who support them, etc etc).

logicallee• 10 years ago

Put another way, the downside of utopia is that it's very hard to make a living sweeping up excrement off of the streets anymore - those jobs are simply gone. And they're not coming back, either.

The reason I make this analogy is that programming isn't fun†. It's maddeningly difficult. As far as I'm concerned, plugging ML nodes into client-side interfaces can't get here fast enough.

† I don't care if you "think" you like it - just like someone might think they like sweeping excrement all day.

pizza• 10 years ago

I see the future kind of like this:

1 - introduction of automation vastly increases throughput of industry, either eroding a traditional

2 - industry vastly increases demand of technically and scientifically literate workers

3 - folks compete for contemporary educational/employment opportunities in industry

4 - an engineer acclimates to their niche

If you're already in a stage 4 situation, it may seem like there's always a new blockbuster industry in stage 1. But, the transition into stage 3 for a new technology may seem insurmountable. That said, technologies rarely meet their hype..

Either way, engineering is a subset of problem solving, and people will always have problems. It would be great to assimilate all kinds of problem solving skills that you can.

bsn54• 10 years ago

It's. funny and amazing at the same time to see software engineers driving themselves into oblivion by meddling with this AI and ML.Godspeed!

dmix• 10 years ago

This post referenced a NYTimes article about Google's AI work on Translate, and the brain team in general, that I missed previously. It's a good read.

"The Great AI Awakening"

The new incarnation, to the pleasant surprise of Google’s own engineers, had been completed in only nine months. The A.I. system had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime.

https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awa...

NumberCruncher• 10 years ago

This is a really great article.

philip142au• 10 years ago

I wish they would make a society of AI brains inside the computer which talk to each other by sending documents around, one AI brain might have a job such as checking the document is correct. A large society of AI brains.

Secondly, a single AI brain is composed of many small AI brains in a society. Also those brains, recursively down forever recursive.

johnsmith21006• 10 years ago

Google voice is rumored to be getting a major refresh. Will we get the new translate capabilities built-in to Google Voice in real time. Basically it would be the universal communicator.

I do not need the functionality personally but I want to see it in my lifetime.

cercatrova• 10 years ago

Skype does that already I think

The Google Brain Team – Looking Back on 2016

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