marshallp

That's so foolish, he should be investing it into the ultimate medical technology of this generation, focus ultraound http://www.fusfoundation.org/MRgFUS-Overview/about-focused-u...

Hopefully he invests in immortality research. The trend of billionaires dying without even trying to engineer a solution is retarded.

He should start with focus ultrasound. It allows to grow/shrink any part of the body and so essentially reach immortality.

I see where you're coming from, but ultrasound is not like gamma knife, it truly is revolutionary (no sides effects + very cheap), and I'd put Norvig inside the science community of AI, he's head researcher of the premier AI company after all. These are current technologies, not futuristic, that are enabled by moore's law. You're assuming that what the crowd sees is what's right, and that isn't always right. The crowd didn't see search engines as important in the 90s but they were wrong.

Anyway, I'm not offended by gmail news, just wanted to get some ultrasound and some ai in there and put things in context.

The failed cancer cures were biomolecules. This is about literally cooking it with beams, something hackers with a few hundreds in parts could attempt to do.

The AI advance is seen as an advance by Peter Norvig (head of Google Research) himself.

Both of them are hacker relevant potentially profitable business with huge upsides. Real advances can be made, especially by the hacker crowd. There's too much of a web 2.0 filter bubble going on here.

Why is this so important that it reaches top spot in minutes. My links to curing cancer with ultrasound or creating ai get nothing, but the big G opens a new window for compose and it's a giant leap for humanity!

Peter Norvig gave singularity summit talk on exciting work they're doing http://fora.tv/2012/10/14/Peter_Norvig_Channeling_the_Flood_... , why isn't that top spot?

HN is kowtowing to the pseudo-brogrammer crowd.

Just flip that around. First consider whether you can get data to solve a problem, and if you can get sufficient data, do machine learning. What you basically get is test-driven development with the development part being done automatically.

If you can't get data cheaply enough, only then consider your normal choices. It's easier and less creative to just use the data.

To be fair, microsoft did kinect, a computer vision product that is in millions of hoees. It wouldn't be hard for google or apple to this but they haven't for some reason.

The problem with microsoft has been that is ran it's research wing separate from the business, while google runs research like a business http://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-a...

A full shift to the realization that machine learning isn't research and should actually be the majority of what everyone does hasn't sunk in it. The question not be "where can we apply machine learning" but instead "where should machine learning not be applied". This type of thinking should occur even down to the individual person (quantified self) http://quantifiedself.com/ .

That's all about bulk cargo though. For people movement and last mile delivery like pizza, energy isn't the major consideration. And airships don't compete with jets. They're either bulk cargo or scenic travel. If airships are commonplace some people will choose to live in airships permanently, gliding with magnificent views, like some people currently do with boats and rv's.

It's not as simple as that. People fly now as well. If everything's about energy efficiency, people wouldn't leave their house. If by flying you can commute from a much cheaper place than it's worth it. Also, you have to factor in the road construction/maintenance costs which flying doesn't have. Especially in the third world where roads aren't built yet. Also, the flying can be in the form of airships which are energy efficient.

Everyone's missing that drones (flying cars) might take off too at roughly the same time. The challenge of making driverless cars completely safe (intelligent sensing and control) is also the challenge for flying cars. Flying cars would beat out road cars and so the reenvisioning of society is greater than what people like Brad Templeton have done. With flying cars, all investments in roads become useless and huge swathes of land become inhabitable. Real estate prices should consequently plummet and a worldwide financial panic occurs (this can all happen within 5 years). However, this will be a good thing since it will clean the current mess of unproductive activity that is land owning and leasing.

What the mainstream media also aren't realizing is that the technology for driverless cars is also the same technology that automates away most factory/farming/mining/retail/distribution jobs. The effect in the west might not be so large, but in the rest of the world it could be devastating, leading to riots and civil wars.

Your argument doesn't really make sense though.

Nonworking -> working can describe early soviet industrialization, but it was far more impressive than you're making out - they were first in space for everything up till 69.

Also, going from the everyday puddling to big science projects can also be described as nonworking->working. If government taxes landowners more thoroughly and invests in big science instead, you can consider that as nw->w.

Focused growth. Instead of factories churning out designer handbags and pet food, have them churn out biotech experiments for brain-computer interfaces.

There are huge risks that nick bostrom talks about http://www.nickbostrom.com/

The only difference is between that and the cold war is that people don't give them due attention because they are non-anthropomorphic (a fight with nature instead of against other humans). If it was the soviets who were behind the asteroid threat, and they were causing old age and cancer, and they were behind worldwide poverty, the US government would be mobilizing trillions of dollars to tackle those problems.

But there's also a place for big science. Manhattan project, Apollo program, Soviet industrialization.

The fastest way to degrowth is creation of brain-computer interfaces that have realistic feel like in the matrix. Degrowthers should join the hacker movement in advancing that technology. The way to most quickly advance that technology is through computer vision technologies that allow the running of biotech experiments at an industrial scale. The key to computer vision is the application of machine learning (neural nets and random forests). The key to that is the creation of classifiers on image datasets on cloud computing infrastructure. Therefore, projects like microsoft kinect and google goggles in addition to vision guided industrial robots are the best degrowther strategies.

Yeah, that wrong someone is you. You'll have to eat your words within 5 years at the most.

That kind of happened if you frame it in their era - HTML, CSS, PHP, SQL.

2011 and 2012 have been watershed years. IBM watson, speech recognition using neural nets beating speech researchers, neural nets on imagenet beating computer vision researchers. The trend is that automated machine learning wins. You can be on the right side of history (automated machine learning) or take opposite side (and be wrong).

This is not about the singularity. This is about data vs programming.

And your attacks on the singularity are misguided. You think singularity people are retarded and your anti-singularity possie are holier, yet you don't have a coherent anti-singularity argument.

Any fool can tear down a barn, but it takes a carpenter to build one.

I think most people are interested in practically solving problems. If the black box can solve it, there's no reason to care about "meaning". Once the machines have taken over all jobs, you can sit around and understand the meaning of things to your satisfaction at your own leisure surrounded by the luxury the machines have created for you.

You can read up presentations by Jeff Dean and also look at what those companies are doing. Listen to Craig Mundie from microsoft for example. See what's happening in competitions like kaggle or what are state of the art on difficult computer vision challenges like imagenet. Machine learning as used in android speech recognition and microsoft speech recognition has already decimated speech recognition researchers and computer vision researchers are next (in the sense that automated machine learning takes over feature engineering). Read all the big data hoopla. There's your citations. It's not all that difficult to figure this all out if you look into it.

Programming bubble will pop. Automated Machine learning is taking over. Even at google, Jeff Dean has recently stated that his Perceptual AI project is aimed at reducing feature engineering in their machine learning projects. In plain terms this means eliminating the need for programming in their search engines and other machine learning focused projects like google now and google goggles (the mobile app, not google glass). The only programmers left will be to manage cloud computing infrastructure and front end programmers. As things like siri and google now take off, front end programmers will also be in less demand. Microsoft, Apple, and Amazon are also increasingly focusing on machine learning. Automated analysis of text will take over the role of sql databases as demonstrated in IBM watson. In manufacturing, the role of programming also diminishing as vision guided robots like Baxter by Rodney Brooks's team take off.

All this is a good thing though, programming is slow and data is agile, as talked about by Peter Norvig in his startup school 2008 speech http://www.youtube.com/watch?v=LNjJTgXujno

Startups should aim at doing as little programming as possible and leaving it to data crunching.

It was 85% accuracy on imagenet of pascal voc, using just a couple of computers with nnets vs 1000 for that google brain. And both of these were just experiments run by grad students, a proper industrial effort would solve it.

What do you mean by that. If you mean progress, then that has occurred this year, basically the object recognition problem looks like it can be solved by neural nets (google brain cat detector and the latest imagenet challenge in pascal voc).

If applications, then they are literally immense. Robots that can perform any manual labor, thus increase in mnaufacturing ability (plus ability to conduct scientific experiments) by an immense amount. Marshall Brain has good articles about what computer vision technology can enable, including the short story Manna. Computer Vision is easily the most disruptive technology since the invention of the steam engine (it multiplies the work capacity of humanity by that much). It's a new gold rush bigger than the internet (robocars and humanoid robots including Baxter from Rethink Robotics are signs of that beginning). Startups should be all over it, web 2.0 and social networking are miniscule in comparison.

Yeah, but they haven't put it in the framework of an actual benchmarked test like machine learning problems. They futzed around with prolog and other stuff rather than have any kind of metrics.

Machine learning has cracked any problem put in it's path. AI hasn't been done yet because no one's actually tried. There's the Loebner Prize but it's too easy (needs to be multi-month and with difficult challenges like write an essay or computer program) and there's no heavyweight turing tests with heavyweight teams (in the ballpark "heavweightness" are search engines and ibm watson).

Big problems are quite easily solvable if more X prize and darpa challenges were promoted.

Some big problems easily solvable today

- Create artificial general intelligence

- Create an immortal mice using focused ultrasound technologies (non-invasive surgery and localized drug delivery)

- flying cars (drones) including air traffic control for everyone on earth (+ cargo drones, so 1 trillion air vehicles or more)

(That's in addition to the important prizes already like x tricorder and darpa humanoid)

Up the prize money to $1 billion each (that's nothing compared to the bailouts) and you'll see all of the above done within 1 year, they really aren't difficult if you put a little thought to it. There's more than enough money and the technological pieces are in place. It just requires some bold thinking.

No one holds people to account for opportunity costs. The robocar challenge showed that robocars were possible. Governments and car companies should be held to account to account (sued in court) for not working on it sooner. That delay is millions of deaths. The same is true for other techs as mentioned above. Some economic indicators need to be put in place for tech development that isn't being done (opportunity cost accounting).

They'll have to. The future is local mobile social.

I'm actually anti-ROR and not a frontend guy, but information systems can tackle all problems. If there is a problem that can be solvable, the critical step is designing the database (it's why ERP runs all big busineses).

You've missed the point completely. It's not to make money, it's to deal with the OP's problem of get looked over by sales reps. People don't like being treadted like that, that's why they hate traditional car dealers.