Rise of the Robots | Machine Learning

[Heracleum Persicum]

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Robot inside you

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[Heracleum Persicum]

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I bought a Chinese Xiaomi Mi Robot Vacuum

Shipped directly from China to Vancouver

Excellent idea, cleans perfectly, maps your home, very smart.

.

[Typhoon]

Wired | Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning

According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow.

The systems are greedy because they demand huge sets of training data.

Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks.

They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases.

Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.”

In my experience, these reservations are accurate.

[noddy]

neural nets are all those things and less.

still.

you cant overstate the act of the simple refinement over all the data they have access too.. even a string.find(keywords) is mind boggling powerful in the context of the modern world and turning millions of millions into subsets of thousands.

i dont have the language for how basic and laughable it all is versus how terrifying and powerful it all is.. at the same time.

[Typhoon]

neural nets are all those things and less.

still.

you cant overstate the act of the simple refinement over all the data they have access too.. even a string.find(keywords) is mind boggling powerful in the context of the modern world and turning millions of millions into subsets of thousands.

i dont have the language for how basic and laughable it all is versus how terrifying and powerful it all is.. at the same time.

The algorithmic equivalent of an idi*t savant.

[noddy]

+1

[kmich]

Hamilton 68-Tracking Russian influence operations on Twitter

[NapLajoieonSteroids]

neural nets are all those things and less.

still.

you cant overstate the act of the simple refinement over all the data they have access too.. even a string.find(keywords) is mind boggling powerful in the context of the modern world and turning millions of millions into subsets of thousands.

i dont have the language for how basic and laughable it all is versus how terrifying and powerful it all is.. at the same time.

Behold! The calculator! *(works pretty well)*

[Mr. Perfect]

Hamilton 68-Tracking Russian influence operations on Twitter

I wonder why they don't track the other foreigners influencing twitter.

[Typhoon]

guJnFY1R4I0

[Zack Morris]

Wired | Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning

According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow.

The systems are greedy because they demand huge sets of training data.

Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks.

They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases.

Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.”

In my experience, these reservations are accurate.

Maybe the neural networks are shallow because, well, they're physically still shallow. Who's to say that a neural network of sufficient density could not outperform human cognition? We simply don't know.

[Doc]

Wired | Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning

According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow.

The systems are greedy because they demand huge sets of training data.

Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks.

They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases.

Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.”

In my experience, these reservations are accurate.

Maybe the neural networks are shallow because, well, they're physically still shallow. Who's to say that a neural network of sufficient density could not outperform human cognition? We simply don't know.

I suspect eventually they will. However this is not quite the same thing but it demonstrates a point:

https://gizmodo.com/facebook-decides-to ... 1825027429

One example of how this could have worked, described by CNBC, would have had hospitals send home nurses to patients recovering from major heart surgery deemed to have no nearby friends or family based on their Facebook profile.

OK so what they are saying that it is preferable to take everyone's personal non medical information and combine it into a big database. Which not only could be abused by say the health insurance companies, but also be prone to being hacked. As opposed to just asking the patient if they have anyone at home to take care of them? REALLY?

And yes I do get it that databases can find things that are not otherwise visible Add AI to that and it can be even more powerful. But lack of cognition means that whatever the programmer did not think about, the odd exceptions, are exactly what are interesting to have a AI find. That inherently AI can not think outside the box because it is in the box and no where else(so to speak)

As an Engineer I can come up against a seemingly intractable problem, that I am making no progress on. More often than not, when I put it aside for the day go home and go to sleep, the next morning when I wake up I know the solution. I know others that say the same thing. Plus I have seen it written about.

So I am thinking that maybe to get truly cognitive machines requires an better understanding of what our brains really do while we are sleeping. Is it a sorting algorithm ? Or more like working out a jigsaw puzzle. IE First you semi sort the pieces by edge and color. Then you try to see which fit where based on the shapes on the images and the physical shape. Or something else like our neurons somehow reset their interconnections, discarding dead end solutions, giving voice to the paths less traveled???

[noddy]

I wouldnt have thought post processing that improves linkages and whatnot was much of an issue for theoretical future AI.

it works for us because our puny little flesher brains can only do so much in realtime before they get overloaded and we have long downtimes whilst asleep in which such things can happen.

their is no algorithmic reason these things couldnt happen in realtime - compare old db engines which needed maintenance after hours to modern ones that maintain whilst they are running.

[Doc]

I wouldnt have thought post processing that improves linkages and whatnot was much of an issue for theoretical future AI.

it works for us because our puny little flesher brains can only do so much in realtime before they get overloaded and we have long downtimes whilst asleep in which such things can happen.

their is no algorithmic reason these things couldnt happen in realtime - compare old db engines which needed maintenance after hours to modern ones that maintain whilst they are running.

Perhaps. I am just thinking that maybe we are missing something. But maybe Zack is right. Maybe we aren't building networks that are large enough. The human brain is divided into different parts that specialize in different things. Each part seems to be pretty powerful by itself. Also each part develops back to front, and doesn't finish until we are 24 years old or so. That is a lot of "greedy" learning and training time.

[Typhoon]

Wired | Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning

According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow.

The systems are greedy because they demand huge sets of training data.

Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks.

They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases.

Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.”

In my experience, these reservations are accurate.

Maybe the neural networks are shallow because, well, they're physically still shallow. Who's to say that a neural network of sufficient density could not outperform human cognition? We simply don't know.

The

We're going to need a large computer.

fallacy.

[Doc]

Another warning

qsKsBualNT8

[Parodite]

Wrong topic, Doc but that is your best meme.

Indeed.. I think it belongs here.

[Typhoon]

Another warning

qsKsBualNT8

Given that the track record of experts predicting the future is no better than the O-mikuji random fortunes at the local Shinto shrine, I won't spend time worrying about it.

[Doc]

Robo sales. Robot Con. Robot lawyers. Sounds like a future John le Carré novel

http://journals.plos.org/plosone/articl ... 581#sec026

Do a robot’s social skills and its objection discourage interactants from switching the robot off?

[Miss_Faucie_Fishtits]

Keeko is just 45 centimeters tall, or one-foot seven inches, and weighs only 45 kilograms, roughly 99 pounds. Gliding across the room to the amazement of starry-eyed five-year-olds, it rolls its head and tells the transfixed children “remember to wash your hands before you eat.”

http://www.atimes.com/article/artificia ... -in-china/

[Doc]

So what happens after AI takes away most of the jobs and Universal income is the norm ? The Late Great(or not so great) Human race?
NgGLFozNM2o

Note in the experiments not all the territory was used up even though Food Water shelter and material was provided in an unlimited supply? People claim that with Universal Income people will be free to do whatever they want without working towards the purpose of fulling their basic needs. The basic implication seems to be, is that when survival of the fittest is taken away, the end game is no one is fit to survive.

[noddy]

nar, we will just start filtering on being self motivated.

anyone who goes self destructive when bored will remove themselves from the gene pool.

personally, i have enough hobbies and interests to last 10 lifetimes, so if minimum income happened, and was survivable, id quit work yesterday.

long term consequences and the health of the nation be damned, thats their problem. (the true western spirit)

[Miss_Faucie_Fishtits]

The Mouse Utopia Experiments | Down the Rabbit Hole

Doc, that's scary as hell. In a similar vein consider Eve Tushnet as she upends what we should consider conventional, and unquestionable wisdom:

The overwhelming majority of my clients believe the most secure path to a lasting marriage is sex first, then cohabitation once you can afford your own place, then marriage once you’re both economically stable. Delaying sex until marriage is not merely unrealistic—not merely prudish—but risky. To rush to the altar before you’ve “tested the relationship” is irresponsible: the character trait my clients fear most.

https://ifstudies.org/blog/whats-wrong- ... s-sequence

[noddy]

Im struggling to see any potential doom for humans right now that isnt humans.

http://www.worldometers.info/world-population/

we could lose 80% of the population and still be dominating the planet in numbers never seen before the last century.

[Doc]

The Mouse Utopia Experiments | Down the Rabbit Hole

Doc, that's scary as hell. In a similar vein consider Eve Tushnet as she upends what we should consider conventional, and unquestionable wisdom:

The overwhelming majority of my clients believe the most secure path to a lasting marriage is sex first, then cohabitation once you can afford your own place, then marriage once you’re both economically stable. Delaying sex until marriage is not merely unrealistic—not merely prudish—but risky. To rush to the altar before you’ve “tested the relationship” is irresponsible: the character trait my clients fear most.

https://ifstudies.org/blog/whats-wrong- ... s-sequence

I think that says more about the expectations of western culture than anything else. Poor in the third world tend to have as many children as possible so there are many to take care of them in old age. As many children are expected to die before they grow up.

In the west the average number of children per woman is much smaller irregardless of the infant mortality rates being much lower. It seems to me not a decision based on economics but on the amount of time and work related to raising children. Like the rats in the study that took poor care of their offspring so they could participate more in "social activities" Humans anticipate this desire and have fewer children in response to it.