sapphireblue

>But neither the NN nor the researcher will be able to give you a rule like: "F=(G m_1 m_2)/(r^2)" to explain the underlying reasons for the object-trajectory dynamics.

It is not impossible. There is a combination of a special neural network architecture and strong sparsity-inducing regularization that makes it possible to learn equations from dynamics dataset: https://openreview.net/pdf?id=BkgRp0FYe

>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.

This is very important. It seems that the automation of labor is underexplored due to political and economical inertia (I know that industrial robots do somewhat improve, but they still are very costly, very proprietary and require experts to use them. Could this be done another way? Companies like Rethink Robotics show that maybe the answer is "yes"). There needs to be more fresh thinking in this space.

There was an ambitious NASA project 35 years ago: a self-replicating lunar factory http://www.nss.org/settlement/moon/library/1982-SelfReplicat... . The engineers tried to design a manufacturing system aiming for almost full parts closure. The project was too ambitious (e.g. it looked optimistically at AI's capabilities), and didn't went past design study stage then;

But maybe now, 35 years later, the technology is good enough for something similar to be viable?

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?

I agree with the first sentence, but I'd like to note that there are practical (though weak) approximations of AIXI that preserve some of its properties, and while not turing-complete, prove to be more performant when compared to other RL approaches on Vetta benchmark. See [1].

Also there is a turing-complete implementation of OOPS, a search procedure related to AIXI that can solve toy problems, programmed by none other than Jurgen Schmidthuber 10 years ago [2]

Even more important: there is a breadth of RL theory built around MDPs and POMDPs. There are asymptotical, convergence, bounded regret, on-policy/off-policy results, etc. Modern practical Deep RL agents (the ones DeepMind is researching) are developed on the same RL theory and inherit many of these results.

From my POV it looks unfavorable to researchers that produced these results over decades of work when the comment's grandfather (and grand-grandfather) write that there is no definition and theory about AI, and that AI is like alchemy.

1. https://www.jair.org/media/3125/live-3125-5397-jair.pdf 2. http://people.idsia.ch/~juergen/oops.html

Actually there are at least two decades-old branches of computer science/mathematics that have formulated precise definitions of AI, and proved many theoretical results that gave way to lots of practical applications. These branches of CS are called "Reinforcement Learning" and "Universal AI".

While Gwern has already mentioned Reinforcement Learning, UAI is a less known (but even more rigorous and well received) mathematical theory of general AI that arose from Marcus Hutter work [1].

My point here is how can one say that there is no definition of AI when there are several precise mathematical definitions available with many theorems proven about them?

1. http://www.hutter1.net/ai/uaibook.htm

Meta- reinforcement learning could prove to be such breakthrough, see [1],[2]. Also next generation ASIC accelerators (Google's TPU, Nervana) can give 10x increase in NN performance over a GPU manufactured on the same process, with another 10x possible with some form of binarized weights, e.g. BNN, XNOR-net. There are also interesting techniques to update the model's parameters in a sparse manner.

So, there certainly is a lot of room left for performance improvements!

1. https://arxiv.org/abs/1611.05763 2. https://arxiv.org/abs/1611.02779

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.

Is he a CEO though? Wikipedia and other press articles say that Hassabis is the CEO: https://en.wikipedia.org/wiki/DeepMind and Suleyman is Chief Product Officer, the head of applied AI at DeepMind.

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.

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?

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.

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.

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).

Turing test is relatively easy to pass if you creatively design your program to roleplay an irrational character. See this program https://en.wikipedia.org/wiki/Eugene_Goostman

Obviously such programs, while being works of art, are not interesting from AI/Machine Learning point of view.

I very much agree with this point of view. It seems to me that dry metaphysical debates (i.e. questioning the nature of these conversation models) are obscuring interesting practical questions that could be asked and tested empirically.

Given modern deep learning conversation models it is already possible to recreate some basic patterns of human dialogue with a recurrent neural network trained on a large corpus (see Google's "A Neural Conversational Model" paper and someone's implementation of it: https://github.com/macournoyer/neuralconvo ).

It would be interesting to experiment with it and see, just how far can we push this modeling approach? How much data is needed? Is it possible to train the model on a huge corpus of human dialogues, and then to finetune it on small amount of data from one specific person?

I liked Anathem, but it is more of a philosophical work. Personally I think The Diamond Age is Stephenson's hard sci-fi masterpiece.

Hannu Rajaniemi, Ted Chiang and Neal Stephenson come to mind as hard sci-fi authors worthy of comparison to Greg Egan.

Well said. Political and intellectual elites tend to attach negative connotations to the word "utopia". Perhaps they feel that the very concept of utopia attracts undesirable attention to the bleakness of our current status quo as experienced by the average person.

I disagree. There exist examples of system displaying very non-trivial, open-ended behavior, and yet they are still not agi. A good example of this is https://arxiv.org/abs/1603.01417 . When trained it is able to answer free-form questions about images.

Even if we relax hardware requirements, e.g. assume a Xeon+Titan X hardware, it is still a question if a viable conversational agent that uses deep learning to generate conversations and adapt to surroundings can be developed around it. Maybe it is possible to use DL to extract some meaning vector (or textual description) that is later used by conventional NLP chatbot to converse with user. I wonder if the quality will be good enough.

Also we don't know if the company will really develop their product to fruition, they may simply develop a good (but not viable as a product) demo and be acqui-hired by one of big players.

Am I the only one who thinks that dystopian scifi got boring a decade or two ago, while utopian scifi is an almost entirely neglected genre?

I prefer a techno-optimistic point of view shown here http://foundersfund.com/anatomy-of-next/

>Promising to deliver an AI that people could see as a friend is absolutely insane though. I don't see people being friends with something that couldn't complete the Turing Test

This is an interesting case. Turns out, given a creative approach it is possible to persuade a human that there is another human behind the screen. See ELIZA, "Turing tests". The methods are quite similar: constrain the domain and/or creatively manipulate human's expectations (e.g. the program that "passed" the Turing Test pretended to be a 13-year boy, so human jury tolerated its errors). The question is not how to fool humans but how to make such product non-trivially useful.

I think that the best approach currently available is applied in facebook M - use human workers to interact with customers while storing all interaction data and experimenting with training state of art ML models on it to eventually replace human workers.

... but without new Hardware there will be no new future. Just more of the same.

We really need to embrace hardware more.

Maybe it is possible to design a robot-friendly rug or a rug-friendly robot locomotion mechanics!

I think the problems you described could be solved if we made tiny adjustments to our lifestyles and environments to make it much easier for automated systems to navigate and interact with it. For example make a choice of not having rugs, having QR codes in rooms, having a track on the ladder in your home, etc. The clothing and dishes could be customized to faciliate handling as well.

It seems to me that western people have become too entrenched in their familiar lifestyle and it is too hard for current technology to adapt to it as is. And so we live in a status quo where a tiny rich minority can afford to hire human servants to do their chores while middle class can neither hire human servants nor buy robotic ones.

>The skills for building a robot are quite different from the skills for building "classical" software.

I'd object that on the contrary, arduino and http://www.espruino.com/ require mostly the same skills (plus some basic maker-tier hardware skills which are easy to gain). For example Espruino is programmed in javascript. I have bought ESP8266 boards for 3.5$ each and with espruino firmware that gives me a wifi-enabled computer for IoT or smart home tasks. I have done some simple smart home projects that provide a web interface (hosted on espruino!) to some functionality.

Of course there are robotics specifics - computer vision, motion planning (also forward, inverse kinematics), but these functions in principle could be hidden behind an opaque DOM-like APIs, while being implemented in a very advanced manner (e.g. trained deep learning models for vision, best SLAM algorithms, best planning algorithms). Just like the browser doesn't require you to draw webpages pixel-by-pixel on raw framebuffer and provides you with fonts, block model, events etc.

>For example for classical software one can do correctness proofs/analysis, while for robots one can only do empirical tests or correctness proofs relative to a strongly abstracted world model.

Objection #1: Almost nobody does correctness proofs in application and even in system software (e.g. the linux kernel), and yet these software projects work quite well, for example it is known that SpaceX uses Linux (with various patches) as a platform for its in Dragon and Falcon. In fact correctness proofs are mostly done by hardware companies, for some functional blocks, and maybe by the military. If you are interested in exploring this question further you can read "How did software get so reliable without proof?" by C.A.R. Hoare [1].

Objection #2: Proofs of correctness in logical or probabilistic sense are being done for various real systems by Cyber-Physical Systems community and by Machine Learning community.

>So in other words: Applying their (existing) skills into that direction would not have much value.

I still think that given good blackbox abstractions and familiar API much could be done. We can see beginning of it with maker community, arduinos and espruinos. More should be possible.

>but who of this group is really willing (and can afford) to do so?

Myself !

I think programmers, investors and customers should be less averse to hardware. We could live much more pleasantly if it were true. Underautomated status-quo is daunting.

1. https://www.gwern.net/docs/math/1996-hoare.pdf

I also wonder how we manage to stay busy given that most home tasks (dishwashing, laundry, cleaning (roombas) and even cooking - see multicooker devices) are already automated, with "doing laundry" meaning merely loading and unloading the washing machine.

I think you have a good point about idle time being gadgeted away.

Also I think there is a tremendous potential in automation of physical labor (including remaining daily tasks). I wonder how much more free time we would have, if only 10% of workforce that currently does web/mobile/game apps (including myself) would apply their skills to automating their daily life with simple robots.

Also, imagine relatively cheap mass-produced robots that have embedded computer vision and motion planning accessible via DOM-like API (with javascript, of course), and what would millions of web developers do with that, with their current javascript/DOM skills directly applicable to manipulating the physical world. Does it sound too good to be true? I don't know.

Building a shack isn't that hard, see https://www.youtube.com/watch?v=P73REgj-3UE

Try building it in the forest though - it is most likely illegal due to some rentier (or government) already owning it. I think the grandparent has some truth to their words.

Existing outside the dwelling is illegal. Dwellings are owned by the elite rentier class, so you need some money just to exist (unless you are a rentier).

>A CPU for games would have very fast cores, larger cache, faster (less latency) branch prediction,

The CPU industry stayed on this path for as long as it were physically possible, even long after the time when it hit diminishing returns on single thread performance divided by (area*power). Pentium 4 was the last CPU of this single-core era.

If you look closely at the microarchitecture of the modern desktop CPU, the out-of-order execution, caches and branch prediction are already maximized (to the point that >2/3ds of die area is cache). Multi-core has become mainstream only after all other paths became exhausted.