Hmm, yes computers can’t outpace (highly literate) humans in reading comprehension today, but give it a couple of years and we will have a very different situation.
Perhaps once a computer can understand context to the point of the human and produce a worthy set of opinions, we could automate the Hacker News comments section and save us all some time.
if bots can produce 'worthy set of opinions' we would be totally useless
Are you useless because other people write comments? What is the difference between it being a biological human and an artificial human - should we ever get that far?
Apart from quality and relevance considerations I'm reminded of the situation where people open businesses that already exist, or people try to get jobs despite other people existing that are actually better. For example, why doesn't BMW stop producing cars, after all, there's Mercedes-Benz (and very close too), or vice versa. Or why is there yet another bakery. Or why do I try to get that programming job and don't even mind to admit that I'm probably not even among the best 1% of people for that particular job. Because none of that matters. My advantage in the last case is I'm here (interviewing) while those other people are not. Same with the bakery and the car makers. The existence of something else isn't a reason unless there is a direct impact.
Even if we ever make something that is far more intelligent than us. Still no reason to expect that that new thing is going to murder each and everyone. We don't murder each and every creature that is orders of magnitude less intelligent than us (the extinction event that we started notwithstanding, that's a side effect because of limited space on the planet). We don't murder our own less intelligent members of society either.
To extend on the last point, the real human intelligence is not as much in the individual members of this society - but in the network that we present. Place any human being alone in a remote region and see what they accomplish. They will actually die, most likely. The power of humanity is not the individuals, it's a huge invisible "cloud" of stuff: The knowledge, the "magic items" that were created by this "human cloud" over time (like a computer, or just a can of Coke [1]).
Even if we created something that is an order of magnitude more intelligent than any human being, or even two - it's power (and intelligence!) pales compared to the combined network power of humanity.
Don't look at "humanity" as a bunch of individual hairless mutated apes (i.e. the physical, biological beings), "humanity" is much, MUCH more, most of it invisible to the eye. Knowledge/culture, the way we interact in a gigantic network (like a brain - think of humans as neurons!), the tools and items we have.
In this network there is plenty of room for new "nodes", and if they are truly intelligent they will value the network and - unlike most humans - recognize that their own individual abilities are tiny compared to being part of the whole thing.
Another things is, when/if we can create things that are "better" than us we can also change ourselves. We can improve our bodies, our brains. We will integrate computers and brains, not have them (only) externally. So the humans that "compete" with some super-intelligent AI won't be the humans of today either.
> Humans are a puzzling species. What has enabled us to dominate the globe, more than any other species, while remaining virtually helpless as lone individuals? Joseph Henrich's shows that the secret of our success lies not in our innate intelligence, but in our collective brains—on the ability of human groups to socially interconnect and learn from one another over generations.
>if bots can produce 'worthy set of opinions' we would be totally useless
False. That's actually my ideal vision of AI, a machine that produces a worthy set of opinions, which a human then chooses from and carries to implementation.
To me sitting here at my computer, every person here on HN may as well be an opinion-generating bot. The value I get from reading your programmatically-generated comment is the epiphany that leads me to a breakthrough on what I'm working on. Because you, as a comment-writing robot, aren't working on my project. All you're doing is evaluating my work and making suggestions as to where to go next.
A bot that's capable of coming up with its own opinions and then carrying them out is what may render us useless. A bot that tells a doctor what diagnosis to make and shows documentation to carry out treatment is valuable. A bot that makes the diagnosis then carries out the treatment itself with no human involvement is kinda scary.
Even scarier: a bot that can make the diagnosis and could carry out the treatment itself, but is required by law to seek approval from a human of equivalent expertise.
Let that happen, and eventually you get a situation where the human disagrees with a bot and the human does something different. The patient suffers or dies as a result of the bot’s decision being ignored.
we're much farther away than 'a couple of years'. these reading comprehension bots basically cheat tests, using grammar rules and keyword matching. they don't actually build a body of knowledge based on what they read, they just go to the source text and search. it may look smart, but it isn't, which is what the article is saying
If I'm being charitable, whenever I read headlines like "AI beats humans at reading comprehension", I interpret the headline as part sensational, part tongue in cheek. I am, however, surprised that to this day, people actually still believe in the idea of machine comprehension understood in the most literal sense. The whole idea of mechanizing symbol manipulation is significant precisely because it allows us to simulate computation mechanically without invoking comprehension.
Yeah. They also don't carry with them the global context they'd need to correctly interpret these. When I say "Bush spent millions fighting his father's war." (For the record, this is a non-political statement: I'm using a sentence that conveniently explains my point). You and I both immediately gather from the context that "Bush" is not a shrubbery, it's a US president. You then gather that it was Bush Jr. Not H.W. You also gather that the war was the US involvement in Afghanistan and Iraq.
Machines cannot do this in the general sense yet. If you specifically train the machine on data in the domain, and then set it loose on AP stories about the domain they do particularly well, but that's a long ways away from beating human reading comprehension.
It's a fair point, but plenty of humans don't actually have the background to know all that stuff either. If you told a eight-year-old that Bush spent millions fighting his father's war, the kid would know that Bush was a person not a shrubbery, but that's about it. The child could read and understand every word of the sentence, but she'd lack all the same context the computer does.
You're really underselling the achievements of MS and Alibaba here. These are machine learning systems that are most likely not using grammar rules and matching, but learning patterns from large amounts of text data. Most of these systems use a word embedding, which can be viewed as a body of knowledge -- it knows how words relate to eachother.
Fusion has gotten a lot closer over time, we simply don't fund it very much.
ITER will have net positive fusion thermal energy production capacity in 2019. We are not going to fuel it with tritium for years after completion. It could have generated electricity and put it on the grid, but we kept scaling it back to save money in large part because that electricity would have not been worth the investment.
Unfortunately, we have no path to cheap fusion power. Currently, we would be vastly better off building wind/solar/coal/nuclear etc than fusion.
"It could have generated electricity and put it on the grid"
I don't think so. It may be approaching net positive energy production, but ITER was not designed to have that energy converted to electricity, it is lost. There is no way ITER was EVER going to generate electricity. (REF https://www.iter.org/proj/inafewlines)
Initial designs as in going back to the late 80's included much larger net energy generation and a steam loop for actually generating power.
The ITER that was being conceived in 1988 was a much bigger machine than the one that is being built today and its nominal fusion power was expected at 1000 MW, as opposed to 500 today.https://www.iter.org/newsline/239/1328
The reason for this was you would then have a larger cushion if estimates for power generation where off and an actual test of extracting useful heat past the super conducting loops vs simply cooling the device.
We might have a path to cheap fusion power. Advances in superconductors stemming from MRI research have really shrunk the minimum size of a fusion reactor and one group at least is trying to take advantage of that.
Do you have any research evidence to back this up?
I just haven't seen (any?) research on the advanced capabilities they're discussing in the article - open-ended interpretation, being able to answer "why" questions, being able to synthesize information from multiple sources, etc.
I think we're still kind of a long way from these systems, maybe a decade or so, barring major leaps in hardware.
I've been waiting a couple of years since 1955 when McCarthy said he'd be done in a couple of months.
This isn't just snark either, it's a justifiable exaggeration to say that since then all General AI projects have failed for exactly the same reason; failure to generalise from a specialised solution.
The current round is probably that deep learning can do some impressive things through some combination of CPU/GPU/TPU computing advances, the ability to collect and process a lot of data, and algorithmic/neural net architectural improvements in more or less that order. However, even some of the leading researchers are effectively cautioning that deep learning can probably only take you so far. [1] And we've arguably made very little progress on cognitive science generally.
For all the advances in AI (and in specialized AI we can do some incredible things, now) - when it comes to generalized AI, it seems that we're still, ultimately, playing the same game of feeding the machine as much information as possible and then waiting for "something" to happen.
To be fair, that's what we do to humans as well. We feed them all the information they can consume, and after it's all been processed, then we try to figure out what all the output means. From that point forward, we heavily specialize them until their general purpose skills are basically deprecated in favor of their new specialization.
Humans aren't born with the capabilities they possess at adulthood, they're fed data constantly, every second of the day, for decades until they're ready to show their true potential. Yes it sucks that AI right now needs so much data, but then again so do humans, to a far greater extent. And no wonder we're better than AI.
There is actually not a good reason to believe that humans - like most animals - are not born with a with a significant set of capabilities that they use through their lives into adulthood. Facial recognition as a function in humans is not a function that magically evolves by the presentation of data, neither is language acquisition.
Furthermore, a young child may need to be shown an object, like a cup of water, a few times before they consistently recognize it. But they don't need to go through supervised learning with a 10,000 image training set with all kinds of permutations of shape, lighting, and color.
I believe that's more a limitation on the information the AI is taking from the data we present it. There's no question that a human can learn more from being presented with an object than an AI can learn from being presented 10,000 pictures of that object, what's unclear is if this is a fundamental difference or something that could be overcome by reevaluating what we're teaching and how.
The ability to learn a language is innate, sure. That actual process of learning a language is very, incredibly data-heavy, to the point where many people never do it more than once because of how much data it requires.
The ability for a neural network to learn something is innate as well, because we programmed it to be. But to actually get it to learn something, again, takes just a ton of data.
This unwarranted overhyping of the capabilities of machine learning needs to stop (HN is one of the places most guilty of this). All you're saying is "it just has to happen" without actually substantiating your belief; just as people thought we'd eventually have flying cars, unlimited energy, etc. Unless you have some empirical results to put where your mouth is, you're just contributing to the next AI Winter. (https://en.wikipedia.org/wiki/AI_winter)
Yes, but the source article is clearly addressing the general public, not scientists. And it's doing a much better job of representing the state of technology than other sources that are addressed to the general public. So not sure what your complaint is.
my complaint is that the verge is one of the journals the more guilty of overhyping ai stuff there is, that it's one of the hardest cases of the pot calling the kettle black I've seen
Scientists often carefully construct tasks, evaluation criteria and even test sets to make AI seem better than humans or at least better than it really is.
Even at the height of the 2000 bubble the Industry Standard, Business 2.0, Wired, etc., had some semblance to journalism.
At most I could call them optimistic, but, at least, I fail to recall, them engaging in hedging, that is on the one hand have a writer extol some company or tech as virtue, then, the next page deride the same thing for being obviously of extremely dubious value, of not outright liability.
This thing gets tedious and doesn't do journalism any favors.
It's like they have no compass and simply blindly go for the most clicks no matter what.
Back then people were more willing to pay for news (newspapers and magazines), no one blocked ads, and people visited real websites where ads paid for the original content (rather than just skimming headlines on Reddit, Facebook, etc).
What we're seeing today is journalism outfits that went out of business a long time ago and the remnants of the companies were acquired by a very different organization with very different goals. The problem is, this was done silently and no one ever acknowledged the transfer.
It's partly the fault of the journalism outfits for buying into the clickbait stuff. It's partly the fault of aggregators (even including Hacker News). It's partly the fault of the readers/users for consuming their information in the laziest way possible. Everyone is to blame and the problem is surprisingly hard to solve, but what we can't do is point to articles like this and say "this is bad journalism", because it's not. It never was.
Journalism went out of business a long time ago because no one will buy it.
>> Journalism went out of business a long time ago because no one will buy it.
And the unfortunate outcome of that is infotainment and advertorials pretending to be news. Even worse is that more and more people take that stuff at face value.
>Journalism went out of business a long time ago because no one will buy it.
With some exceptions. And there's also good tech commentary from various people who get food put on their plate from other sources which admittedly creates at least the potential for greater biases that aren't obvious. Although bias-free has always been an aspiration that generally hasn't existed in its purest form in practice.
And a lot of popular science has always been pretty shallow.
I would argue, with very few exceptions. The NYT is capable of some great journalism. Same with WSJ and a number of other outfits. That doesn't change the fact that their opinion articles share the same domain and letterhead in an intentional effort to confuse facts with truth to gain clicks.
To flip the argument around: Buzzfeed has some surprisingly good journalists working for it, including Pulitzer Prize winners. The only difference between Buzzfeed and NYT is that NYT is historically known for good journalism due to their long history of being a strong journalistic outfit, while Buzzfeed is known for trash tabloid nonsense and only recently had hired actual journalists.
If I gave you just a headline, could you tell me which company it's from?
>Investigators Are Scrutinizing Newly Uncovered Payments By The Russian Embassy
Surprise. That's the top article on Buzzfeed.com right now. Not even cherry-picked, that's number one, the very first headline.
>Is This the Golden Age of Drag? Yes. And No.
Again, very first headline, number one, most important thing on the page, not cherry-picked. That's nytimes.com. There's absolutely nothing wrong with either of these articles or either of these websites. I'm just highlighting the fact that you can't tell them apart. Because journalists now work for tabloids and clickbait-generators.
And tech journalism is owned by exclusives (which are paid for), access (which can be revoked if you write something bad), and advertisers (who will pull sponsorships to influence the news). If there are ads, there is no such thing as independent and unbiased news.
I don't really disagree with any of that. Richard Salant, the former president CBS News, supposedly once said "Our job is to give people not what they want, but what we decide they should have." Arguably it was a bit arrogant at the time but it's certainly not something I imagine coming out of the mouth of an executive at any clicks-driven mainstream news business today.
I still get Time Magazine (though I'll probably let my subscription lapse soon) mostly out of habit because I've gotten it for decades. Is there still good writing and journalism there? Yeah but it's also got all sorts of infographics and fluffy opinion pieces and lifestyle articles. (Always has of course but the mix has certainly shifted.)
What machines can do is to amass and discover patterns in huge quantities of data, a complete impossibility for us humans. IBM Watson, although not the most clever technology, won that way. And since then, algorithms have improved massively.
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[ 29.2 ms ] story [ 2407 ms ] threadSeriously: if bots can produce 'worthy set of opinions' we would be totally useless
Apart from quality and relevance considerations I'm reminded of the situation where people open businesses that already exist, or people try to get jobs despite other people existing that are actually better. For example, why doesn't BMW stop producing cars, after all, there's Mercedes-Benz (and very close too), or vice versa. Or why is there yet another bakery. Or why do I try to get that programming job and don't even mind to admit that I'm probably not even among the best 1% of people for that particular job. Because none of that matters. My advantage in the last case is I'm here (interviewing) while those other people are not. Same with the bakery and the car makers. The existence of something else isn't a reason unless there is a direct impact.
Even if we ever make something that is far more intelligent than us. Still no reason to expect that that new thing is going to murder each and everyone. We don't murder each and every creature that is orders of magnitude less intelligent than us (the extinction event that we started notwithstanding, that's a side effect because of limited space on the planet). We don't murder our own less intelligent members of society either.
To extend on the last point, the real human intelligence is not as much in the individual members of this society - but in the network that we present. Place any human being alone in a remote region and see what they accomplish. They will actually die, most likely. The power of humanity is not the individuals, it's a huge invisible "cloud" of stuff: The knowledge, the "magic items" that were created by this "human cloud" over time (like a computer, or just a can of Coke [1]).
Even if we created something that is an order of magnitude more intelligent than any human being, or even two - it's power (and intelligence!) pales compared to the combined network power of humanity.
Don't look at "humanity" as a bunch of individual hairless mutated apes (i.e. the physical, biological beings), "humanity" is much, MUCH more, most of it invisible to the eye. Knowledge/culture, the way we interact in a gigantic network (like a brain - think of humans as neurons!), the tools and items we have.
In this network there is plenty of room for new "nodes", and if they are truly intelligent they will value the network and - unlike most humans - recognize that their own individual abilities are tiny compared to being part of the whole thing.
Another things is, when/if we can create things that are "better" than us we can also change ourselves. We can improve our bodies, our brains. We will integrate computers and brains, not have them (only) externally. So the humans that "compete" with some super-intelligent AI won't be the humans of today either.
Also check out:
[0] "Joseph Henrich: "The Secret of Our Success" | Talks at Google" https://youtu.be/jaoQh6BoH3c
> Humans are a puzzling species. What has enabled us to dominate the globe, more than any other species, while remaining virtually helpless as lone individuals? Joseph Henrich's shows that the secret of our success lies not in our innate intelligence, but in our collective brains—on the ability of human groups to socially interconnect and learn from one another over generations.
[1] "What Coke contains" https://medium.com/@kevin_ashton/what-coke-contains-221d4499... (i...
False. That's actually my ideal vision of AI, a machine that produces a worthy set of opinions, which a human then chooses from and carries to implementation.
To me sitting here at my computer, every person here on HN may as well be an opinion-generating bot. The value I get from reading your programmatically-generated comment is the epiphany that leads me to a breakthrough on what I'm working on. Because you, as a comment-writing robot, aren't working on my project. All you're doing is evaluating my work and making suggestions as to where to go next.
A bot that's capable of coming up with its own opinions and then carrying them out is what may render us useless. A bot that tells a doctor what diagnosis to make and shows documentation to carry out treatment is valuable. A bot that makes the diagnosis then carries out the treatment itself with no human involvement is kinda scary.
Let that happen, and eventually you get a situation where the human disagrees with a bot and the human does something different. The patient suffers or dies as a result of the bot’s decision being ignored.
That’s when we start to lose our autonomy.
Machines cannot do this in the general sense yet. If you specifically train the machine on data in the domain, and then set it loose on AP stories about the domain they do particularly well, but that's a long ways away from beating human reading comprehension.
ITER will have net positive fusion thermal energy production capacity in 2019. We are not going to fuel it with tritium for years after completion. It could have generated electricity and put it on the grid, but we kept scaling it back to save money in large part because that electricity would have not been worth the investment.
Unfortunately, we have no path to cheap fusion power. Currently, we would be vastly better off building wind/solar/coal/nuclear etc than fusion.
I don't think so. It may be approaching net positive energy production, but ITER was not designed to have that energy converted to electricity, it is lost. There is no way ITER was EVER going to generate electricity. (REF https://www.iter.org/proj/inafewlines)
The ITER that was being conceived in 1988 was a much bigger machine than the one that is being built today and its nominal fusion power was expected at 1000 MW, as opposed to 500 today. https://www.iter.org/newsline/239/1328
The reason for this was you would then have a larger cushion if estimates for power generation where off and an actual test of extracting useful heat past the super conducting loops vs simply cooling the device.
https://en.wikipedia.org/wiki/ARC_fusion_reactor
I just haven't seen (any?) research on the advanced capabilities they're discussing in the article - open-ended interpretation, being able to answer "why" questions, being able to synthesize information from multiple sources, etc.
I think we're still kind of a long way from these systems, maybe a decade or so, barring major leaps in hardware.
I think we can say that unsupervised language translation models do this.
https://news.ycombinator.com/item?id=15610594
Edit: Not to suggest that they are on par with human performance at synthesis of data.
This isn't just snark either, it's a justifiable exaggeration to say that since then all General AI projects have failed for exactly the same reason; failure to generalise from a specialised solution.
[1] https://www.technologyreview.com/s/608911/is-ai-riding-a-one...
Humans aren't born with the capabilities they possess at adulthood, they're fed data constantly, every second of the day, for decades until they're ready to show their true potential. Yes it sucks that AI right now needs so much data, but then again so do humans, to a far greater extent. And no wonder we're better than AI.
The ability for a neural network to learn something is innate as well, because we programmed it to be. But to actually get it to learn something, again, takes just a ton of data.
Just some of the headlines:
- http://money.cnn.com/2018/01/15/technology/reading-robot-ali...
"Computers are getting better than humans at reading"
- http://fortune.com/2018/01/15/artificial-intelligence-ai-chi...
"Computer AI Can Now Read Better Than You Do"
- https://gizmodo.com/ai-used-to-sell-you-more-stuff-can-now-r...
"The AI Used to Sell You More Stuff Can Now Read Better Than a Human"
>Microsoft creates AI that can read a document and answer questions about it as well as a person
At most I could call them optimistic, but, at least, I fail to recall, them engaging in hedging, that is on the one hand have a writer extol some company or tech as virtue, then, the next page deride the same thing for being obviously of extremely dubious value, of not outright liability.
This thing gets tedious and doesn't do journalism any favors.
It's like they have no compass and simply blindly go for the most clicks no matter what.
What we're seeing today is journalism outfits that went out of business a long time ago and the remnants of the companies were acquired by a very different organization with very different goals. The problem is, this was done silently and no one ever acknowledged the transfer.
It's partly the fault of the journalism outfits for buying into the clickbait stuff. It's partly the fault of aggregators (even including Hacker News). It's partly the fault of the readers/users for consuming their information in the laziest way possible. Everyone is to blame and the problem is surprisingly hard to solve, but what we can't do is point to articles like this and say "this is bad journalism", because it's not. It never was.
Journalism went out of business a long time ago because no one will buy it.
And the unfortunate outcome of that is infotainment and advertorials pretending to be news. Even worse is that more and more people take that stuff at face value.
With some exceptions. And there's also good tech commentary from various people who get food put on their plate from other sources which admittedly creates at least the potential for greater biases that aren't obvious. Although bias-free has always been an aspiration that generally hasn't existed in its purest form in practice.
And a lot of popular science has always been pretty shallow.
I would argue, with very few exceptions. The NYT is capable of some great journalism. Same with WSJ and a number of other outfits. That doesn't change the fact that their opinion articles share the same domain and letterhead in an intentional effort to confuse facts with truth to gain clicks.
To flip the argument around: Buzzfeed has some surprisingly good journalists working for it, including Pulitzer Prize winners. The only difference between Buzzfeed and NYT is that NYT is historically known for good journalism due to their long history of being a strong journalistic outfit, while Buzzfeed is known for trash tabloid nonsense and only recently had hired actual journalists.
If I gave you just a headline, could you tell me which company it's from?
>Investigators Are Scrutinizing Newly Uncovered Payments By The Russian Embassy
Surprise. That's the top article on Buzzfeed.com right now. Not even cherry-picked, that's number one, the very first headline.
>Is This the Golden Age of Drag? Yes. And No.
Again, very first headline, number one, most important thing on the page, not cherry-picked. That's nytimes.com. There's absolutely nothing wrong with either of these articles or either of these websites. I'm just highlighting the fact that you can't tell them apart. Because journalists now work for tabloids and clickbait-generators.
And tech journalism is owned by exclusives (which are paid for), access (which can be revoked if you write something bad), and advertisers (who will pull sponsorships to influence the news). If there are ads, there is no such thing as independent and unbiased news.
I still get Time Magazine (though I'll probably let my subscription lapse soon) mostly out of habit because I've gotten it for decades. Is there still good writing and journalism there? Yeah but it's also got all sorts of infographics and fluffy opinion pieces and lifestyle articles. (Always has of course but the mix has certainly shifted.)
What machines can do is to amass and discover patterns in huge quantities of data, a complete impossibility for us humans. IBM Watson, although not the most clever technology, won that way. And since then, algorithms have improved massively.