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Comment by sapphireblue | Hacker News Reader

DeepMind looks like a hilariously wrong project to criticize because it is a true moonshot, something very different from the majority of other SV projects. If hiring hundreds of PhDs to create a general purpose learning agent, all while publishing all the intermediate results in freely available papers isn't a moonshot with socially beneficial outcome, then I don't know what is. Also note that DeepMind went even further than that, there is DeepMind health division aiming at using this technology to help doctors and patients directly.

If I were the author I'd choose some social media unicorn or an ad network as an example of inherent misallocation of human talent.

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I think part of the issue is that the results of DeepMind, while enormously impactful, do not have the sort of tangible quantity that the Manhattan Project or the Apollo Program did. It's easy for critics to dismiss them when the most relatable thing to the layperson is "This thing plays Go". It's a common lament in the scientific community, especially in the theoretical space. I don't see an easy answer for it, other than "Wait for it."

To me, the more apt comparison isn't say, DeepMind to the Manhattan Project, it's DeepMind to the early physics experiments, or Chicago Pile 1. You can imagine in the future, a letter being written much like Einstein's to FDR, pointing to an early and modest project saying "because of this, we can now create <X>".

(I'm ignoring the wartime necessities and issues surrounding that letter in this example as they're outside of the discussion.)

> If hiring hundreds of PhDs to create a general purpose learning agent, all while publishing all the intermediate results in freely available papers isn't a moonshot with socially beneficial outcome, then I don't know what is.

I'm sorry, I don't buy it. What exactly are the social benefits of general purpose learning agent?

It's an impressive technical and scientific challange, agreed, but most applications that I can immediately think of are harmful to society (reduction of white-collar jobs, increased potential for surveillance and profiling, etc) - so how would such an agent be used to actually improve society?

An example was already given. Medical professionals screw up all the time. Having a highly intelligent entity capable of providing advice and even oversight could greatly speed diagnosis and reduce mistakes in treatment.

Asking how a general purpose learning agent could be useful seems kind of like asking how an intelligent person could be useful. The ways are countless.

A general purpose reinforcement learning (RL) agent is a machine that can be taught to perform any task from a very wide range of tasks via sparse rewards given by a human or software trainer.

The agent can, like any software, be snapshotted, saved, loaded and copied, creating as many identical agents as needed (given hardware, of course). Agents can and will be trained to perform various tasks, and their snapshots will be sold or made available for download over the Internet.

By saying that your main concerns are technological unemployment of white-collar demographic and increased state surveillance you make it clear that your views reflect that of an upper-middle class western person. On the global scale affluent westerners are a minority.

So, How would such an agent be used to actually improve society? Consider universally valued, life-critical services: healthcare and education. Only the western people have access to high-quality medicine and education due to a whole lot of reasons (global economical inequality, a very long and hard path to become a doctor or a professor, a very long time needed to establish the necessary social institutions, lack of social stability outside the west, ...).

If we had a general RL agent we could train several variants of it to perform high-quality work in the fields of Diagnosis, Radiology, Paediatry etc. We could also train artificial education agents for many subjects. The training needs to only be done once. Given sufficiently powerful mass-produced hardware (smartphone SoCs with Nervana-like NN accelerators?) these agents could be given almost for free to billions of people that wouldn't be able to afford such services in any point of their lives otherwise.

How could one be against giving essential high quality services to every human with a smartphone?

And if even that is not enough to justify the utility of RL agents, then consider how much progress in molecular biology and medicine could be done if thousands of agents trained to do life science research worked around the clock to push the state of art further. How many people with debilitating diseases could be cured by such an effort?

And then consider how we could make our currently-crumbling cities and infrastructure permanently well-attended by RL agents inside simple robots. The world certainly could use more smart attention everywhere. I guess the quality of life in such a world would be remarkably different.

The social benefits are that our knowledge develops. You can argue that that is not a good thing because of the social upheaval it causes, but everyone who has argued that since we were cavement have been proved wrong.

For example, humanity's current best attempt at sustainable society is an unstable mix of democracy and capitalism, and in many countries that isn't working out too well - particularly for blue collar workers, but increasingly for your white collar workers too.

It isn't inconceivable that deep mind could design a better political system for the US, for instance, that resulted in broad consensus instead of virtual civil war. Or design a fairer tax system that meant more people could have fulfilling and enjoyable lives.

Whether deep mind's masters would apply it to those questions is moot, but the parent's point is that the huge resources being poured into Facebook makes it a much better example of the squandering of the efforts of our brightest and best.

Note that Facebook also has a formidable AI research group called FAIR and they are pursuing goals close to DeepMind's, while openly publishing their results and tools. There is a lot of social media unicorns that don't contribute much to research which are not Facebook.

Who knows, maybe there is no real need for a dozen of global social media companies that provide roughly similar features to the same users?

> but most applications that I can immediately think of are harmful to society (reduction of white-collar jobs, increased potential for surveillance and profiling, etc)

Those are not applications, those are side-effects to _some_ applications.

Also note that DeepMind went even further than that, there is DeepMind health division aiming at using this technology to help doctors and patients directly.

Thing is, there is plenty of existing AI research that could hypothetically revolutionize medicine. Even in the eighties there were expert systems that outperformed doctors in certain areas. The issue with it all isn't that the algorithms are not fancy enough or accurate enough yet, it's the practical application.

I believe the biggest challenge of today's AI research is making AI and ML accessible. Not just for consumption, but for actual training and open ended use.

I don't see Deep Mind doing much in *that" regard.

>If I were the author I'd choose some social media unicorn or an ad network as an example of inherent misallocation of human talent.

Google acquired DeepMind using money that was ultimately the result of ad revenue.

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