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The Singularity - Official Thread

Singularity AI Deep Learning Technology Artificial Intelligence Future Science Culture Government Computers

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Poll: The Singularity (76 member(s) have cast votes)

How do you feel about the Singularity

  1. Voted Excited (48 votes [50.00%] - View)

    Percentage of vote: 50.00%

  2. Scared (11 votes [11.46%] - View)

    Percentage of vote: 11.46%

  3. Skeptical (24 votes [25.00%] - View)

    Percentage of vote: 25.00%

  4. Angry (3 votes [3.12%] - View)

    Percentage of vote: 3.12%

  5. Neutral (5 votes [5.21%] - View)

    Percentage of vote: 5.21%

  6. What's That? (1 votes [1.04%] - View)

    Percentage of vote: 1.04%

  7. Other (4 votes [4.17%] - View)

    Percentage of vote: 4.17%

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#81
Alislaws

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Is this "when we will have the technological capability to automate this" or "when humans will have been mostly replaced in this task",

 

Because the idea that folding laundry will be mostly done by machines in 2021 seems absurd, people don't all have washing machines and dishwashers today, no way we're all buying laundry folding robots that fast. 

 

At the same time the idea that it will take till 2026 to have the technological capability to automate truck driving is equally absurd. the hard part of driving is interpreting and understanding the world around you, not manoeuvring the vehicle. Trucks will not be much more difficult than cars in this respect. 

 

EDIT:

Computers can already read text aloud?

"beat the fastest human in a 5k race" is only hard if your robot/AI cant use wheels, or flight, which is exactly what you would want for any practical application. 


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#82
funkervogt

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See: https://kk.org/thete...he-singularity/



#83
Alislaws

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That's an interesting article, but it assumes Ray Kurzweil has arrived at his date for the singularity by plotting charts and picking a point that looks vertical. 

 

Instead his target date for the singularity is shortly after he expects us to have:

 

A) sufficient processing power available to equal the human mind in raw power (for a reasonable price). 

B) sufficiently high resolution brain imaging technology to observe in great detail the functions of the human brain. 

(assuming no major changes to the exponential growth in price/performance of computers, and brain imaging resolution)

 

The singularity is not some kind of natural function caused by exponential growth, you wont see anything growing exponentially eventually hit a "singularity" moment.

 

It is specifically when we are able to create intelligence that is equal to our own that the singularity theoretically begins, and it begins because of the great differences in how computerised and biological intelligence work. (i.e. you cannot copy and paste biological intelligence (well not yet anyway!)) or copy and paste knowledge and experience between biological minds. 

 

So computers that are equal to humans, will result in a huge intelligence explosion. (even if they couldn't make smarter computers, we could still make billions of human level AIs all linked together sharing experience and knowledge which would have a similar effect. It may not result in a world beyond our ability to comprehend, but it would make humanity obsolete. 


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#84
wjfox

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Peter Diamandis | The Future Is Faster Than You Think | Global Summit 2018 | Singularity University

 

https://youtu.be/FTTgdtl8FvM


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#85
Yuli Ban

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"I hear LFADS has become conscious... beware..."
 
From Starspawn0:


First, the guy is a Stanford Neuroscience professor, and this is his only Twitter post (aside from replies).
 
A Deep Learning method to model the primate motor cortex using brain activity recordings. When the models were trained, they found them to be shockingly accurate at reproducing neural population response patterns. For the first time, Deep Learning has crossed over from simply modelling sensory areas (like the visual and auditory cortices), towards higher cortical areas. The next things to be tried might be the higher cognition parts of the frontal cortex -- if that succumbs to a Deep Learning modelling approach, it would be powerful evidence that one might be able to model the whole brain with Deep Learning.
 
We stand between the worlds of possibility...


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And remember my friend, future events such as these will affect you in the future.


#86
Yuli Ban

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Artificial General Intelligence Is Here, and Impala Is Its Name

One of the most significant AI milestones in history was quietly ushered into being this summer. We speak of the quest for Artificial General Intelligence (AGI), probably the most sought-after goal in the entire field of computer science. With the introduction of the Impala architecture, DeepMind, the company behind AlphaGo and AlphaZero, would seem to finally have AGI firmly in its sights.
Let’s define AGI, since it’s been used by different people to mean different things. AGI is a single intelligence or algorithm that can learn multiple tasks and exhibits positive transfer when doing so, sometimes called meta-learning. During meta-learning, the acquisition of one skill enables the learner to pick up another new skill faster because it applies some of its previous “know-how” to the new task. In other words, one learns how to learn — and can generalize that to acquiring new skills, the way humans do. This has been the holy grail of AI for a long time.
As it currently exists, AI shows little ability to transfer learning towards new tasks. Typically, it must be trained anew from scratch. For instance, the same neural network that makes recommendations to you for a Netflix show cannot use that learning to suddenly start making meaningful grocery recommendations.  Even these single-instance “narrow” AIs can be impressive, such as IBM’s Watson or Google’s self-driving car tech. However, these aren’t nearly so much so an artificial general intelligence, which could conceivably unlock the kind of recursive self-improvement variously referred to as the “intelligence explosion” or “singularity.”
Those who thought that day would be sometime in the far distant future would be wise to think again. To be sure, DeepMind has made inroads on this goal before, specifically with their work on Psychlab and Differentiable Neural Computers. However, Impala is their largest and most successful effort to date, showcasing a single algorithm that can learn 30 different challenging tasks requiring various aspects of learning, memory, and navigation.


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And remember my friend, future events such as these will affect you in the future.


#87
wjfox

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10 years ago this month, I read this book.

 

I can honestly say it changed my whole outlook on life. :)

 

 

singularity-is-near-book-amazon.jpg


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#88
Zaphod

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Did anyone else sort of come up with an approximate idea of the singularity without really knowing about it?

 

I remember just having a thought experiment that simply was: if you create a computer capable of designing a more powerful computer and that resultant computer can do the same, then given enough iterations you will quickly get a superintelligence. It was only after thinking that when I realised there were people taking this idea seriously and had called it the singularity. 


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#89
TranscendingGod

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I rate it as the best book I've read fiction or otherwise. It expanded my mind and colored my world. Thank you Raymond Kurzweil. It also gave me chronic anxiety because i'm afraid i'm going to die before we get a grip on death. Damn you Raymond Kurzweil. 


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The growth of computation is doubly exponential growth. 


#90
Futurology2039

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Actually I see the end result of scientific technology looking more like Human's incorporating it into themselves  "AIhumans" ..completely human but assisted by technology specifically in the utilization of a huge percentage of the HUMAN BRAIN  which we don't seem to be able to access completely at this time... We will never become GOD but we will always  make and attempt to become gods by  better utilization of our  brains . Obviously TECHNO-HUMAN is not a new idea or term :I prefer AIHUMAN

 

techno-humanism : The merging of technology and humans. Also known as "posthumanism" and "transhumanism," although there are nuances implied in these and other similar theories.

 

     Why don't I see AI by AI's self :  Human EGO... most of us present  three normal multiple personalities to some quantitative extent ..   To be exact …seeker of recognition, daytime dreamer ,hedonist.....   All three of them don't allow or actually force almost all of us to refuse to see our selves as second or inferior..   Undoubtedly our major stumbling block to a total acceptance of GOD.  BUT GOD we can't change or control…  Limiting AI in Robotic machine forms we can.  I do not believe our DEEP need for dominance and our  Universe sized egos  would allow our humanism to be supplanted by something initially of our own invention.  But at some point I think almost all of us would accept technology that would make us literally mega times more cerebral,  with out removing our humanity,  and  in the process capaciously feeding our egos

 

      Because of this opinion I don't  concern myself with the ethics of AI although it may be both naïve and cheeky of me.

 

 

 
 



#91
wjfox

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#92
Yuli Ban

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What's funny is that it's still going to look flat compared to the wealth created by asteroid mining and nanofabrication.


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And remember my friend, future events such as these will affect you in the future.


#93
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The Singularity - feat. Ray Kurzweil & Alex Jones [RAP NEWS 28]

 

https://youtu.be/dHVtUw5wToA



#94
caltrek

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What's funny is that it's still going to look flat compared to the wealth created by asteroid mining and nanofabrication.

 

Also, this may not be a very good way to measure actual wealth. That is to say, the chart  may a be a better indication of the success of capitalism.  Capitalism  turns everything into commodities.  So, more and more wealth is measured by $s.


The principles of justice define an appropriate path between dogmatism and intolerance on the one side, and a reductionism which regards religion and morality as mere preferences on the other.   - John Rawls


#95
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Moore's Law, 1970-2100

 

https://www.futureti...ta-trends/9.htm

 

 

moores-law-future-timeline-2020-2030-204


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#96
wjfox

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#97
tomasth

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How long is that future ? Who's time is that afterward ?

#98
Yuli Ban

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I can't help but notice how often we just assume we'll use futuristic technology for their intended purposes.

In the future, we'll have flying cars!
But idiots will crash them everywhere and fly drunk.
In the future, we'll have food in the form of pills!
And idiots will overdose on these pills.
In the future, our houses will be fully automated!
So idiots can get butchered on automatic robotic utensils.
In the future, we'll go to the moon for work and be back home in time for supper!
Just in time for idiots to take control of their space cars and veer off into the void, drifting forever more away from Earth into nothingness.
In the future, we'll use ray guns!
All so idiots can blind themselves and drivers and pilots.
In the future, robots will do all our work!
Cue idiots with nothing to do deciding to take out their boredom droog-style.
In the future, skyscrapers will reach ten miles in height!
Idiots will fuck up and the entire thing will come crashing down, wiping out fifty city blocks.
In the future, techno music will be the only thing people listen to!
Unfortunately, idiots use it to create an endless amount of shitty dance music with electronic songwriters completely ignored by the mainstream.
In the future, computers will create classic works of literature and art!
Giving idiots enough opportunity to bring their Edgy the Edgehog grimdark My Evanescent Linkin Grace fanfictions to life. That or loads of lolicon.

 

Obviously I'm for all of these things— but you can really figure out why the Future as it exists now (what with smartphones and domestic single-use robots and VR and automatic doors and smartwatches) feels so mundane or disappointing. Even when we have sci-fi tier tech, it'll feel disappointing.
Sci-fi, even dystopian sci-fi, assumes that we'll all use technology in their intended ways. Even when the street finds its own uses for things, the street still seems to keep holy the traditions of that technology. Smartphones in sci-fi are either microcomputers used to hack into UNATCO or transcendental datacrystals that citizens use to become super-enlightened and informed.
In real life, smartphones are either your alternative desktop to browse dank memes or a particularly slabby dildo. I mean, why not? We develop a real life ray gun in the vein of Flash Gordon or Buck Rogers? First thing we're gonna do with it is stick it up our assholes. It shouldn't go there, but fuck you. Then, afterwards, we'll use it to light shit on fire. Usually by accident. Hey, can this ray gun charge my smartphone? Let's find out. Spoiler: no, it can't, but it got us 7 million views!


And remember my friend, future events such as these will affect you in the future.


#99
Yuli Ban

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For my 20,000th post, I figured I ought to post this old thing from 2015.
 
What's the smallest chip that can do exaflop calculation?
 
h3half

When people say "exaflop computer" what's usually meant is "a computer that can do at least one exaflop per second."
This site says that, currently, the Chinese supercomputer Tianhe-2 is the fastest computer in the world, at 33.86 petaflops/second. What I'm going to do is figure out how many transistors are in it, find a transistor-to-flop ratio, and calculate the size of a theoretical exaflop chip based on that.
This Forbes article says that Tianhe-2 consistes of 16,000 nodes, "which each contain two Xeon IvyBridge processors and three Xeon Phi processors." That's good information, because we can find out how many transistors are in Ivy Bridge and Phi processors. This makes for a total of:
 
2 Ivy Bridge/node * 16,000 nodes = 32,000 Ivy Bridge processors
3 Phi/node * 16,000 nodes = 48,000 Phi processors
 
We don't have information on exactly what processor models Tianhe-2 is using (which I would guess is probably a Chinese state secret), but I'll assume that the most powerful/largest/highest transistor count models are used.
For Ivy Bridge, the highest transistor count model is the Ivy Bridge-EX, clocking in at 4.31 * 109 (4.31 billion) transistors.
For Phi, the highest transistor count model is the Xeon Phi SE10X, with 5 * 109 (5 billion) transistors.
So the Tianhe-2 has:
 
32,000 Ivy Bridge processors * 4.31 * 109 transistors/Ivy Bridge processor + 48,000 Phi Processors * 5 * 109transistors/Phi processor = 3.7791 * 1014 transistors
 
These 3.7791 * 1014 transistors are able to produce 33.86 petaflops/second, which is equivalent to 3.386 * 1016flops/second.
To find the ratio of transistors to flops:
 
(3.386 * 1016) / (3.7791 * 1014) = 3,386,000 / 37,791 flops/second/transistor
 
That's not a very round number, but it's roughly equal to 89.5981 flops/second/transistor.
Our goal is a computer that can do 1 exaflop/second, equivalent to 1 * 1018 flops/second.
Using our current flops/second/transistor ratio, this would take:
 
(1 * 1018 flops/second) / 89.5981 flops/second/transistor = 1.1161 * 1016 transistors
 
This is roughly the point in my calculations that the unit "flops" starts to sound pretty silly.
So we'll need a total of 1.1161 * 1016 transistors. That's 1.1161 quadrillion. That's a lot.
In your post you specified that each transistor should be three atoms large. I don't think that's super feasible with current technology (barring some hardcore cooling to keep everything working properly) but hey - nobody has made an exaflop computer yet either so we're talking hypotheticals anyway.
Ninja edit: Apparently three-atom transistors are totally a thing. The paper is behind a paywall so I can't access it but still; cool.
Another issue is that transistors need space between them in order to work properly. I can't really find how much space, but we'll assume that since our transistor technology has advanced to the reliable three-atom range, we need two atoms of space between each transistor. We'll also assume that each transistor is 3 atoms long by 1 atom wide. This means that each transistor takes up an area of:
 
(3 atoms + 2 atoms) * 3 atoms = 15 atoms2
 
This brings us to, finally, the size of our chip:
 
15 atoms2 /transistor * 1.1161 * 1016 transistors = 1.67415 * 1017 atoms2
 
As far as I'm aware, transistors are still made of Silicon and our hypothetical 3-atom transistor should be no different.
Silicon's atomic radius is 110 picometers, or 1.1 * 10-10 meters. This datatable says that a Silicon-Carbon bond is 186 picometers (or 1.86 * 10-10 meters) long. We'll assume that a silicon-silicon bond is similar enough that the same numbers apply.
So in one direction (call it the 'x' axis) we have five atoms and four bonds, and in the other direction (call it the 'y' axis) we have three atoms and two bonds. Our transistor dimensions are then:
 
5 atoms * 1.1 * 10-10 meters/atom + 4 bonds * 1.86 * 10-10 meters/bond = 1.294 * 10-9 meters in the x direction
3 atoms * 1.1 * 10-10 meters/atom + 2 bonds * 1.86 * 10-10 meters/bond = 7.02 * 10-10 meters in the y direction
 
You can pretty much think of this as a rectangle, and the area of that rectangle is:
 
1.294 * 10-9 meters * 7.02 * 10-10 meters = 9.08388 * 10-19 meters2
 
So 9.08388 * 10-19 meters2 is the size of each transistor.
Finally, the size of our chip:
 
1.1161 * 1016 transistors * 9.08388 * 10-19 meters2 /transistor= 101.4 cm2
 
That's roughly the size of a CD.
So, now, why is that number so small? Honestly it surprised me too, but the math is correct. The key thing here is that you specified that each transistor should be three atoms big. I added onto that to try and simulate how transistor might actually be placed on a chip, but still. Three atoms is really really really small.
Obviously we're not at the point yet that we can reliably produce huge quantities of three-atom transistors (at least not in the scale this exaflop computer would need).
But this question, and subsequent answer, shows exactly why tech sites get so excited about news of smaller transistors - once we can produce transistors that are so ludicrously small we're open to such huge advances in processing size that it's almost unbelievable.

tl;dr: 101.4 cm2 , or roughly the size of a CD. See immediately above for explanation why it's so small.

 
ZorbaTHut figured out:

Wolfram Alpha informs me that a cubic millimeter of silicon is 5*1019 atoms. This means you could fit a brain into a cube roughly 0.1mm on a side.
 
This would technically be considered the right size for sand, although it's real close to being considered silt instead. Call it very fine sand.

 
Bagoole said:

If it's graphene instead of silicon, that's a power fold increase of 102 or 103 (or more). Let's be conservative. So at 1/100th the size, the chip now only has an area of about 96mm2. Round that up a bit (it'll need plenty of heatsinks I'm sure) and it's 10mm to a side. About the size of a coffee bean. An exaFLOPS coffee bean!
 
However, this is still not an exaFLOPS nanobot you seem to be hoping for. Fear not, the journey is not over. Firstly, I don't think every single nanobot in your blood would have to be exaFLOPS rated. A whole fleet of quadrillions of nanobots could form a distributed system.
 
Also, the computing rabbit hole goes deeper than graphene! The nanobots could have molecular locks, or it could be 3D graphene, or atomic computation, or even subatomic computation! This would be so much theoretical computational power, your blood wouldn't even know what to do with it and the nanobots might get bored.

 

Nanomachines really don't need any more than megaFLOPS to be useful. 

 

As for when we'll see such nanocomputers, well we're getting there. 

Recall this fun thing:

This computer is smaller than a grain of salt, stronger than a computer from the early '90s, and costs less than 10¢. 128 of them together is still much smaller than the tip of your finger.

Hwu4NXa_owPIlX0C-CYW961fHuPRFI1JNPUuExKj

 

Also recall, from 2014:

 

Engineers Build World's Smallest, Fastest Nanomotor

One nanometer thick graphene mimics two stroke combustion engines, but without the side effects


And remember my friend, future events such as these will affect you in the future.


#100
spartans2015

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Will singularity happen ??





Also tagged with one or more of these keywords: Singularity, AI, Deep Learning, Technology, Artificial Intelligence, Future, Science, Culture, Government, Computers

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