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Vancouver Off-Topic / Current EventsThe off-topic forum for Vancouver, funnies, non-auto centered discussions, WORK SAFE. While the rules are more relaxed here, there are still rules. Please refer to sticky thread in this forum.
That dubstep is distractingly awful...and is far too fast paced to match the ridiculously slow Mantis. Fun project to make, expensive no doubt. But pointless.
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1999 Nissan Stagea RSfourS, White
1994 Honda CB1000, Black Previous Rides:
1992 Nissan President Sovereign, Black
1991 Nissan Skyline GT-R, Black
1989 Nissan Skyline GTS-4, Black
1986 Porsche 944, Black
There are around 377 million results on Google.com for the query “Can I live forever?” Ask that question to company’s top engineer, though, and you’re likely to hear an answer that’s much more concise.
Simply put, Google’s Ray Kurzweil says immortality is only a few years away. Digital immortality, at least.
Kurzweil, 64, was only brought on to Google late last year, but that hasn’t stopped him from making headlines already. During a conference in New York City last week, the company’s director of engineering said that the growth of biotechnology is so quickly paced that he predicts our lives will be drastically different in just a few decades.
According to Kurzweil, humans will soon be able to upload their entire brains onto computers. After then, other advancements won’t be too far behind.
SAN FRANCISCO — BigDog, Cheetah, WildCat and Atlas have joined Google’s growing robot menagerie.Google confirmed on Friday that it had completed the acquisition of Boston Dynamics, an engineering company that has designed mobile research robots for the Pentagon. The company, based in Waltham, Mass., has gained an international reputation for machines that walk with an uncanny sense of balance and even — cheetahlike — run faster than the fastest humans.
It is the eighth robotics company that Google has acquired in the last half-year. Executives at the Internet giant are circumspect about what exactly they plan to do with their robot collection. But Boston Dynamics and its animal kingdom-themed machines bring significant cachet to Google’s robotic efforts, which are being led by Andy Rubin, the Google executive who spearheaded the development of Android, the world’s most widely used smartphone software.
The deal is also the clearest indication yet that Google is intent on building a new class of autonomous systems that might do anything from warehouse work to package delivery and even elder care.
Boston Dynamics was founded in 1992 by Marc Raibert, a former professor at the Massachusetts Institute of Technology. It has not sold robots commercially, but has pushed the limits of mobile and off-road robotics technology, mostly for Pentagon clients like the Defense Advanced Research Projects Agency, or Darpa. Early on, the company also did consulting work for Sony on consumer robots like the Aibo robotic dog.
Boston Dynamics’ walking robots have a reputation for being extraordinarily agile, able to walk over rough terrain and handle surfaces that in some cases are challenging even for humans.
A video of one of its robots named BigDog shows a noisy, gas-powered, four-legged, walking robot that climbs hills, travels through snow, skitters precariously on ice and even manages to stay upright in response to a well-placed human kick. BigDog development started in 2003 in partnership with the British robot maker Foster-Miller, NASA’s Jet Propulsion Laboratory and Harvard.
The video has been viewed more than 15 million times since it was posted on YouTube in 2008.
More recently, Boston Dynamics distributed a video of a four-legged robot named WildCat, galloping in high-speed circles in a parking lot.
Although the videos frequently inspire comments that the robots will evolve into scary killing machines straight out of the “Terminator” movies, Dr. Raibert has said in the past that he does not consider his company to be a military contractor — it is merely trying to advance robotics technology. Google executives said the company would honor existing military contracts, but that it did not plan to move toward becoming a military contractor on its own.
Under a $10.8 million contract, Boston Dynamics is currently supplying Darpa with a set of humanoid robots named Atlas to participate in the Darpa Robotics Challenge, a two-year contest with a $2 million prize. The contest’s goal is creating a class of robots that can operate in natural disasters and catastrophes like the nuclear power plant meltdown in Fukushima, Japan.
“Competitions like the Darpa Robotics Challenge stretch participants to try to solve problems that matter and we hope to learn from the teams’ insights around disaster relief,” Mr. Rubin said in a statement released by Google.
Boston Dynamics has also designed robots that can climb walls and trees as well as other two- and four-legged walking robots, a neat match to Mr. Rubin’s notion that “computers are starting to sprout legs and move around in the environment.”
A recent video shows a robot named Cheetah running on a treadmill. This year, the robot was clocked running 29 miles per hour, surpassing the previous legged robot land speed record of 13.1 m.p.h., set in 1999. That’s about one mile per hour faster than Jamaica’s Usain Bolt, the two-time Olympic gold medalist in the 100-meter dash. But it’s far short of a real cheetah, which can hit 65 m.p.h.
Google’s other robotics acquisitions include companies in the United States and Japan that have pioneered a range of technologies including software for advanced robot arms, grasping technology and computer vision. Mr. Rubin has also said that he is interested in advancing sensor technology.
Mr. Rubin has called his robotics effort a “moonshot,” but has declined to describe specific products that might come from the project. He has, however, also said that he does not expect initial product development to go on for years, indicating that Google commercial robots of some nature could be available in the next several years.
Google declined to say much it paid for its newest robotics acquisition and said that it did not plan to release financial information on any of the other companies it has recently bought.
Dr. Raibert is known as the father of walking robots in the United States. He originally created the Leg Lab, a research laboratory to explore walking machines at Carnegie Mellon University in 1980. He then moved the laboratory to M.I.T. before leaving academia to build engineering systems for the military and Sony.
His research in walking robots began with a pogo-stick project called “the hopper,” which he used to test basic concepts.
“I am excited by Andy and Google’s ability to think very, very big,” Dr. Raibert said, “with the resources to make it happen.”
Artificial intelligence, machines and software with the ability to think for themselves, can be used for a variety of applications ranging from military technology to everyday services like automated telephone systems. However, none of the systems that currently exist exhibit learning abilities that would match the human intelligence. Recently, scientists have wondered whether an artificial agent could be given a tiny bit of human-like intelligence by modeling the algorithm on aspects of the primate neural system.
Using a bio-inspired system architecture, scientists have created a single algorithm that is actually able to develop problem-solving skills when presented with challenges that can stump some humans. And then they immediately put it to use learning a set of classic video games.
Scientists developed the novel agent (they called it the Deep Q-network), one that combined reinforcement learning with what's termed a "deep convolutional network," a layered system of artificial neural networks. Deep-Q is able to understand spatial relationships between different objects in an image, such as distance from one another, in such a sophisticated way that it can actually re-envision the scene from a different viewpoint. This type of system was inspired by early work done on the visual cortex.
Scientists considered tasks in which Deep-Q was able to interact with the environment through a sequence of observations, actions, and rewards, with an ultimate goal of interacting in a way to maximize reward. Reinforcement learning systems sound like a simple approach to developing artificial intelligence—after all, we have all seen that small children are able to learn from their mistakes. Yet when it comes to designing artificial intelligence, it is much trickier to ensure all the components necessary for this type of learning are actually included. As a result, artificial reinforcement learning systems are usually quite unstable.
Here, these scientists addressed previous instability issues in creating Deep-Q. One important mechanism that they specifically added to Deep-Q was “experience replay.” This element allows the system to store visual information about experiences and transitions much like our memory works. For example, if a small child leaves home to go to a playground, he will still remember what home looks like at the playground. If he is running and he trips over a tree root, he will remember that bad outcome and try to avoid tree roots in the future.
Using these abilities, Deep-Q is able to perform reinforcement learning, using rewards to continuously establish visual relationships between objects and actions within the convolution network. Over time, it identifies visual aspects of the environment that would promote good outcomes.
This bio-inspired approach is based on evidence that rewards during perceptual learning may influence the way images and sequences of events or resulting outcomes are processed within the primate visual cortex. Additionally, evidence suggests that in the mammalian brain, the hippocampus may actually support the physical realization of the processes involved in the “experience replay” algorithm.
It takes a few hundred tries, but the neural networks eventually figure out the rules, then later discover strategies.
Scientists tested Deep Q’s problem-solving abilities on the Atari 2600 gaming platform. Deep-Q learned not only the rules for a variety of games (49 games in total) in a range of different environments, but the behaviors required to maximize scores. It did so with minimal prior knowledge, receiving only visual images (in pixel form) and the game score as inputs. In these experiments, the authors used the same algorithm, network architecture, and hyperparameters on each game—the exact same limitations a human player would have, given we can't swap brains out. Notably, these game genres varied from boxing to car-racing, representing a tremendous range of inputs and challenges.
Remarkably, Deep Q outperforms the best existing systems on all but six of the games. Deep Q also did nearly as well as a professional human games tester across the board, achieving more than 75 percent of the human's score on the majority of the games.
The scientists also examined how the agent learned from contextual information using the game Space Invaders. Using a special technique to visualize the high-dimensional data, scientists saw that the situations that looked similar mapped to nearby points, as you'd expect. But Deep Q also learned from sensory inputs in an adaptive manner: similar spatial relationships within Deep Q’s neural network were found for situations that had similar expected rewards but looked different. Deep Q can actually generalize what it has learned from previous experiences to different environments and situations just like we can.
This bio-inspired approach suggests that modeling artificial intelligence systems on the mammalian brain and neural system could be a successful avenue to the development of artificial intelligence systems. So now all that’s left is to ask ourselves one question: do you think you could beat Deep-Q’s scores?
So when AI becomes self aware and realizes in order to survive humans would need to go stasis mode like the matrix to stop population growth but to also preserve the human race. Other wise humans keep multiplying and consuming all natural resources. Or it could go Stephen hawking way and just destroy all humanity in fear of being offlined or cleansing the planet of this virus that's destroying the planet. There's also a chance AI and humans work together and can solve problems like quantum physics and help you create technology like worm hole portals. What do you guys think?
I like Linus' response the best and you know what Torvald and Ng are people who are actually mucking with code, instead of all the armchair quarterbacks, like Musk and even Hawkings. Linus' Q&A is here Also Andrew Ng's answer
Quote:
I don’t know what’s going to happen five years from now. The reason I say that I don’t worry about AI turning evil is the same reason I don’t worry about overpopulation on Mars. Hundreds of years from now I hope we’ve colonized Mars. But we’ve never set foot on the planet so how can we productively worry about this problem now?
Another key take home point is last week all the fear is based on we will follow the Moore's law pretty much until AI comes around.. Intel acknowledged Moores law is actually no longer true in a commercial sense and they are scaling back their aggressive time table. We are down to the last 2 shrinks.. 10nm, 7nm.. then it is all over. The scary thing is we don't have anything to replace that... I am not sure we can fit in all the shielding that a D Wave requires into an Apple Watch.
Quote:
alleged danger of Artificial Intelligence
by peter303
Some computer experts like Marvin Minsky, Larry Page, Ray Kuzweil think A.I. will be a great gift to Mankind. Others like Bill Joy and Elon Musk are fearful of potential danger. Where do you stand, Linus?
Linus: I just don't see the thing to be fearful of.
We'll get AI, and it will almost certainly be through something very much like recurrent neural networks. And the thing is, since that kind of AI will need training, it won't be "reliable" in the traditional computer sense. It's not the old rule-based prolog days, when people thought they'd *understand* what the actual decisions were in an AI.
And that all makes it very interesting, of course, but it also makes it hard to productize. Which will very much limit where you'll actually find those neural networks, and what kinds of network sizes and inputs and outputs they'll have.
So I'd expect just more of (and much fancier) rather targeted AI, rather than anything human-like at all. Language recognition, pattern recognition, things like that. I just don't see the situation where you suddenly have some existential crisis because your dishwasher is starting to discuss Sartre with you.
The whole "Singularity" kind of event? Yeah, it's science fiction, and not very good SciFi at that, in my opinion. Unending exponential growth? What drugs are those people on? I mean, really..
It's like Moore's law - yeah, it's very impressive when something can (almost) be plotted on an exponential curve for a long time. Very impressive indeed when it's over many decades. But it's _still_ just the beginning of the "S curve". Anybody who thinks any different is just deluding themselves. There are no unending exponentials.
My bookmarks are Reddit and REVscene, in that order
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Technological innovation over time seems to be on overlapping S-curves.
Yes, we're getting close to maxing out our capabilities of silicon manufacturing. But that's because we went from an emerging technology, to innovative spark, exponential growth and now we're slowing down because the technology is mature.
It doesn't have to be infinite exponential growth in Moore's law for transistors forever.
An AI only needs to climb to be 2-3 steps smarter than us, and it will be as far us as we are to a chicken.
This isn't even accounting for the fact that an AI's performance and intelligence can scale with no biological limitation.
- Perfect recall
- 24 hour functionality
- Scales by adding new hardware
- Can re-write it's own code for self improvement.
My bookmarks are Reddit and REVscene, in that order
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As for Ng's interview
Quote:
I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel.
The analogy to deep learning [one of the key processes in creating artificial intelligence] is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.
If huge engine = deep learning models
and fuel = massive amount of data
People like Kurzweil are already attempting to do that.
They are building their neural nets for pattern recognition (voice, image, text). The days of people of doing manual coding to directly influence an AI is over. Yes, we have narrow AI, but the are teaching the AI more and more generalized concepts.
And Kurzweil sitting as Google's director of tech engineering has access to essentially all the data in the world (fuel),
and the processing power required to churn through it all.
Honestly Kurzwell is all talk.. he has been talking about this since his grad school days if you read his work, it is always updated for the day it will happen. His and others like Pinker's, it is basically their grad school thesis recycled and we are still not there. They are in the 60s and we are still waiting.
As I say, I prefer listening to people who are actually doing the work for their opinions on when events might happen, vs someone who just thinks about it theoretically. I know you need both, but I find the first group gives a better estimate than the latter.
AI up to now is programmatically inefficient.. there is no way around that with our silicon based systems. Functional programming might provide enough abstraction for programs to change on the fly for it to "evolve", but it is slow as heck.
There is a good chance of something down the road that might change that.. that will be way beyond 20-30 years (see my next post).. don't think I will be around to contribute to that.
Maybe I am just old, I cut my teeth with genetic programming and algorithms with old school Silicon Graphics machines (still have a couple of the mini fridges in my townhouse basement).. so my perspective is it won't happen any time soon. For AI to truly work, you will need to change the whole computing paradigm to include some type of stochastic process on every level.
Quote:
Originally Posted by Culverin
As for Ng's interview
If huge engine = deep learning models
and fuel = massive amount of data
People like Kurzweil are already attempting to do that.
They are building their neural nets for pattern recognition (voice, image, text). The days of people of doing manual coding to directly influence an AI is over. Yes, we have narrow AI, but the are teaching the AI more and more generalized concepts.
And Kurzweil sitting as Google's director of tech engineering has access to essentially all the data in the world (fuel),
and the processing power required to churn through it all.
Silicon might be our first foray.. and might not be our last.. BUT it relies on fabrication technologies that are being developed and we can foresee what is in the pipeline a decade or 2 down the road.. right now our pipeline beyond 7nm is empty, that's a decade down the road on a good day. We are having problems with cross talk with 10nm. Innovation that scales that's a heck of a long time, especially it is in the physical realm, people still need to build things like fabs.
AI might scale well when it happens. But right now today or for a few decades: manufacturing of silicon still requires human labor to design, lay down the etching, take the wafers out of the oven and whole bunch of sundries.. having a big brain to create its own chip to take over the world all by themselves or manufacture stuff themselves? We are not there.
Silicon wise, we can do Group 4 dope but that's extremely expensive and no way we can do it at the current price and at quantities we want to keep up the computing power growth we have. Basically our computing power is plateauing out, we are also at a power crunch.
You might say what about photonics or spintronics? We are having a rough ride with those too. We might be getting some interface work offloaded to them, but processor? in say a decades? doubt it.
I always assume any human can intellectually contribute to science for 50 years. Actively working on it like, Ng or Linus probably 30. At this stage of technology of what we have right now.. AI/ Singularity.. probably for the next, next generation to figure out.. and honestly we shouldn't control / dictate what future generations want to do. We can only educate the next generation, not change the world for them. I am pretty sure I won't be around to see it.
Just like I won't see flying cars, but I am glad that self driving cars might happen. I won't worry about AI, just as I won't worry about 20M tall Mobile Suits that can fly, swim, muck around town and have kendo fights, after seeing the DARPA challenge up close as a spectator.
Quote:
Originally Posted by Culverin
Technological innovation over time seems to be on overlapping S-curves.
Yes, we're getting close to maxing out our capabilities of silicon manufacturing. But that's because we went from an emerging technology, to innovative spark, exponential growth and now we're slowing down because the technology is mature.
It doesn't have to be infinite exponential growth in Moore's law for transistors forever.
An AI only needs to climb to be 2-3 steps smarter than us, and it will be as far us as we are to a chicken.
This isn't even accounting for the fact that an AI's performance and intelligence can scale with no biological limitation.
- Perfect recall
- 24 hour functionality
- Scales by adding new hardware
- Can re-write it's own code for self improvement.
Over 1,000 high-profile artificial intelligence experts and leading researchers have signed an open letter warning of a “military artificial intelligence arms race” and calling for a ban on “offensive autonomous weapons”.
The letter, presented at the International Joint Conference on Artificial Intelligence in Buenos Aires, Argentina, was signed by Tesla’s Elon Musk, Apple co-founder Steve Wozniak, Google DeepMind chief executive Demis Hassabis and professor Stephen Hawking along with 1,000 AI and robotics researchers.
The letter states: “AI technology has reached a point where the deployment of [autonomous weapons] is – practically if not legally – feasible within years, not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms.”
The authors argue that AI can be used to make the battlefield a safer place for military personnel, but that offensive weapons that operate on their own would lower the threshold of going to battle and result in greater loss of human life.
Should one military power start developing systems capable of selecting targets and operating autonomously without direct human control, it would start an arms race similar to the one for the atom bomb, the authors argue.Unlike nuclear weapons, however, AI requires no specific hard-to-create materials and will be difficult to monitor.
“The endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow. The key question for humanity today is whether to start a global AI arms race or to prevent it from starting,” said the authors.
Toby Walsh, professor of AI at the University of New South Wales said: “We need to make a decision today that will shape our future and determine whether we follow a path of good. We support the call by a number of different humanitarian organisations for a UN ban on offensive autonomous weapons, similar to the recent ban on blinding lasers.”
Musk and Hawking have warned that AI is “our biggest existential threat” and that the development of full AI could “spell the end of the human race”. But others, including Wozniak have recently changed their minds on AI, with the Apple co-founder saying that robots would be good for humans, making them like the “family pet and taken care of all the time”.
At a UN conference in Geneva in April discussing the future of weaponry, including so-called “killer robots”, the UK opposed a ban on the development of autonomous weapons, despite calls from various pressure groups, including the Campaign to Stop Killer Robots.
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