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Comment · 9 years ago
Comment · 9 years ago
>Everyone talks about extending life and eventually becoming immortal, yet no one asks what happens when we are immortal.

I see the opposite picture when these discussions pop up: everybody tries to find a downside in increased lifespan, be it personal or social. It looks like a pretty obvious defense of status-quo to me.

What is really-really sad and what really bothers me is that this attitude is so prevalent in the modern West, when the West is the only society on planet Earth that has knowledge and resources to make some version of life extension happen. Other parts of the world are de-facto firmly in the survival mode.

Comment · 9 years ago
Comment · 10 years ago
There was a couple of interesting threads on HN about ultrasound machines recently: https://news.ycombinator.com/item?id=13230741 https://news.ycombinator.com/item?id=13241295

I stumbled upon this startup https://www.clarius.me/ which provides an existence proof for a decent ultraportable stethoscope-like ultrasound machine.

Any thoughts on this? How does it change the arguments from these earlier threads?

Comment · 10 years ago
Comment · 10 years ago
Comment · 10 years ago
Comment · 10 years ago
Looks like the "UNREAL" (https://arxiv.org/abs/1611.05397), "Learning to reinforcement learn" (https://arxiv.org/abs/1611.05763) and "RL^2" (https://arxiv.org/abs/1611.02779) are state of art in pure RL for now.

Finally there is a trend of using recurrent neural network as a top component of the Q-network. Perhaps we will see even more sophisticated RNNs like DNC and Recurrent Entity Networks applied here. Also we'll see meta-reinforcement learning applied to a curriculum of environments.

Comment · 10 years ago
Comment · 10 years ago
You have a lot of solid points, but note that Linux is currently being used by SpaceX in an even more safety-critical aerospace setting.

Also note that interpreters have their place in safety-critical aerospace as well: some satellites run Forth.

Comment · 10 years ago
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?

Comment · 10 years ago
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.

Comment · 10 years ago
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.

Comment · 10 years ago
The unpleasant truth is that this dystopia isn't coming out of nowhere, it is being built with our own hands. Our everyday desires, decisions, actions and inaction decide what will be built (more apps) and what won't (nextgen cures).

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