61–70 of 189 posts
Of course the fixed graphical models we use have their own problems. For instance, we can't even effectively model a neural network with a variable number of inputs.
Click-baity. AI tech isn't stuck. There are many forthcoming breakthroughs, particularly in medicine, which should really benefit humanity. Radiology is poised to let CNNs make radiologists a lot more efficient. We just need to build the labeled datasets. If we invest heavily in some AI tech, let it be to produce huge medical datasets. The software and hardware is ready. We're only lacking sufficient data to make mor…
I honestly don't see the added value of having a "more efficient" human once the classifier is good enough.
- Not staffing or structuring like you understand or respect what General Intelligence is results in a narrow and specialized mindset among your employee base that produces narrow and specialized solutions.
It's called a local minimum. It's where you land when you don't focus on the bigger picture.
> How to move forward? There's several techniques for that. I don't see them being used. Which either means they don't understand they're stuck or they know they're stuck and don't care. Why would the latter mindset be willfully chosen?
Current models and methods for training AI require huge data sets and computational power to be effective. Who currently maintains such resources? Whose fueling and molding the perception and direction of current efforts? See the conflict of interest?
Furthermore, given how convoluted the approaches/math are, it lends itself to specialized individuals...PhDs. A match made in heaven that allows the market to be narrowed and segmented to a specialized group of people. The problem with this is : It results in narrow and Weak AI.
> Not knowing you're stuck Enough people have made sound arguments. You either grasp them and change or, given how comfortable you are, stay the course. It could be, even with a PhD and clout, that you're just not that intelligent enough to grasp the sound arguments... But, if this is the case, do you really think you're going to solve general intelligence?
Those that (truly) seek to move forward have been moving forward with (AGI).
> Those attempting to preserve old business models with a fresh top layer coating of the new. > Specialist who refuse to respect anything beyond their group's chosen methods and thus respect the scope of AGI. > Well-funded groups who exclusively hire from a narrow scope and narrow specialized focus > VC groups that only invest in low hanging fruit applied engineering ventures > VC groups that don't give those in (true) pursuit of this funding Will just get let behind with the new wave. It's the same as it's always been. You maybe can fool yourself and others. However, you can't fool the laws of nature and the universe.
Enough people have spoken. Enough hints have been given. Enough people have taken and borrowed concepts of the small fry and called it their own only to find themselves lost in what it meant.
Enough time has elapsed. If you're not acting and steering your resources in accordance with the new, you just get left behind grasping the old.
Same as its always been... (True) disruption.
It's on the horizon. It's coming. So, keep your eyes peeled.
The author is just spouting off on a topic he doesn't understand. It's just a rehashing of Chomsky's hatred of statistical NLP. He pulls off the neat trick of approximating knowledge of artificial intelligence by hoodwinking the New York Times, but he doesn’t have insight into the topic he's talking about.
I believe he has much more insight than most AI researchers/engineers. What's going on in AI right know looks exactly like what happened in the 1960s, sucking all the resources into something (i.e Deep Learning) without looking at the big picture, without looking at epistemological questions, and not training new grads on solid AI foundations anymore like probability theory and logic. Not asking the right questions i…
But 10 years is a long time in internet years. I would not be at all surprised if there's another word2vec (or two) arose before then to displace the tectonic plates yet again.
Earlier quoted context omitted.
Can you describe what you mean by "he doesn't understand"? His background is in psychology and neuro science so his view on intelligence is probably quite different than person coming from computer science. For me he puts words onto something I've felt recently, that what we're doing is cool and all, but just doesn't feel like the right way to approach it. We're just putting loads of data and computing power into som…
I'm riffing on his writing -- > Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn’t understand a word of what it is translating. This is just another incarnation of "AI is the thing we haven't done." He's parroting Chomsky's disdain for statistical models and John Searle's fundamental misunderstanding of AI. For the forme…
>This is just another incarnation of "AI is the thing we haven't done."
I don't think so - it appears to be an objectively correct assessment of the current state of the art.
> Otherwise we're just moving goalposts.
The first movement of the goalposts was to call '80s technology AI. Now they are drifting back to where they started.
On the other hand, I am surprised by the claim that AI is stuck; my outsider's impression is that progress has accelerated. Perhaps the impression of being stuck comes from more people realizing how difficult a problem it is.
Click-baity. AI tech isn't stuck. There are many forthcoming breakthroughs, particularly in medicine, which should really benefit humanity. Radiology is poised to let CNNs make radiologists a lot more efficient. We just need to build the labeled datasets. If we invest heavily in some AI tech, let it be to produce huge medical datasets. The software and hardware is ready. We're only lacking sufficient data to make mor…
I agree with the fact that AI isn't stuck (flagged the article). I would say the research is ready, but there's still a lot of infrastructure work for integrating with data sets, learning and serving cheaply on high scale. Still, lots of people are making hard to productionize the research results.
A match made in heaven. Two peas stuck in local minimum pot vacuuming up money and resources.
One pea says : > there's still a lot of infrastructure work for integrating with data sets, learning and serving cheaply on high scale. The other pea says : > Lots of people are making it hard to productionize the research results. Both peas agree : This is how I make my money and stay on-top.
You get what you get for reasons. If neither of them wants to agree their stuck. That's fine with those ushering in the new wave.
Enjoy the party while it lasts.
The author is just spouting off on a topic he doesn't understand. It's just a rehashing of Chomsky's hatred of statistical NLP. He pulls off the neat trick of approximating knowledge of artificial intelligence by hoodwinking the New York Times, but he doesn’t have insight into the topic he's talking about.
Earlier quoted context omitted.
Can you describe what you mean by "he doesn't understand"? His background is in psychology and neuro science so his view on intelligence is probably quite different than person coming from computer science. For me he puts words onto something I've felt recently, that what we're doing is cool and all, but just doesn't feel like the right way to approach it. We're just putting loads of data and computing power into som…
I'm riffing on his writing -- > Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn’t understand a word of what it is translating. This is just another incarnation of "AI is the thing we haven't done." He's parroting Chomsky's disdain for statistical models and John Searle's fundamental misunderstanding of AI. For the forme…
Earlier quoted context omitted.
> Clearly expectation has to catch up to reality. Woah, you feel expectations are behind reality? I feel there's a lot of news lately predicting AGI.
I actually do feel that expectations are behind reality, at least amongst those who are just barely too smart for their own good. I still see comments daily on HN or Reddit that promote the narrative that there is no AGI, people only work on ML, and all ML is a narrow party trick. And I think that is a terrible characterization of what, e.g., the computational neuroscientists are doing. Peruse some of the research ha…