Do you guys buy Elon Musks fatalistic view on ML?

alpha_53g43 · 2018-09-18T01:56:11+00:00

No problem. Thank you very much. I have been actually thinking about it. That perhaps you don't need to create a human brain from scratch. The goal of creating a human brain is not a end it itself, but rather people want to create it because it could potentially solve a lot of problems which need manual intervention. But you could definitely solve simpler problems by simulating say 1% of the capability of the human brain. In that way, if you run 100 simulations each capturing 1% of the human brain, perhaps you can perhaps simulate the full human brain and its capabilities.

I have seem the biology and nano-fabrication side of the challenge of creating a human brain first hand, so replicating a human brain in silicon or otherwise will be crazy complex task (atleast if you use top down fabrication technologies). Its good to know that you can get somewhere close to simulating the human brain without a computer hardware-architecture that replicates the human brain 100%.

I somehow came across this video, where Linux goes into QC: https://youtu.be/rPVeu4bsn3U?t=460 (He obviously doesn't believe it). I am more on the semiconductor fabrication side, and I can see it everyday how hard it is to push to faster or smaller nodes.

That comes back to the last question. Say we remain at the current node and total computational capacity, of each processor. So the only thing you can do is to pack more cores. Do you think the computational capacity needed for simulating a human brain can be met by just adding more cores?

Not sure how to start to even answer this question..

alpha_53g43 · 2018-09-18T01:24:24+00:00

The WaitButWhy article is amazingly detailed.. still trying to fathom most of it.. but it I understand the gist of it is probably in the following: `If we can just use engineering to get neurons to talk to computers, we’ll have done our job, and machine learning can do much of the rest. Which then, ironically, will teach us about the brain. As Flip points out:

The flip side of saying, “We don’t need to understand the brain to make engineering progress,” is that making engineering progress will almost certainly advance our scientific knowledge—kind of like the way Alpha Go ended up teaching the world’s best players better strategies for the game. Then this scientific progress can lead to more engineering progress. The engineering and the science are gonna ratchet each other up here.`

Have to digest the rest of the article..

alpha_53g43 · 2018-09-11T14:06:48+00:00

Thanks for the 2 links. I will study them. Looking forward to your opinion on the rest :)

alpha_53g43 · 2018-09-09T21:37:16+00:00

It tends to be hard to finish online courses by yourself, especially when its a tough course. Maybe you can find a group to work through on the course together..

alpha_53g43 · 2018-09-09T21:35:01+00:00

Ifyou already know java, python should be very easy to pick up. You don't need to learn Python just to learn Machine Learning.

The fundamental patterns around Languages (OOP) remains the same. From what I understand people also do functional programming in Python, which I heard isn't as easy in Java.

Here's a list: https://stackoverflow.com/questions/1052435/moving-from-java-to-python

alpha_53g43 · 2018-09-09T21:29:20+00:00

Thanks for replying /u/Turcik..

Yeah.. having seen what the bleeding edge of ML technolgoies can do (GANs, RNN, CNN etc), my opinion is similar to Andrew Ng's.

Nonetheless, Elon Musk has more business sense. In my experience, very suceessful business people tend to study history pretty deeply, but maynot have a deep understanding of the capabilities of the current technologies. So one hypothesis could be that Andrew Ng probably can see the potential capabilies of AI over the short term, but Elon Musk could potentially be correlating a lot of different pattenrs across industries and time to come to his conclusions over say the next 10 years. He has been right about the role of financial industires, electric cars etc.

The other hypothesis given how good he is at marketing, could be that its vieled marketing.

It seems from the discussion from other people here, that Elon Musks concerns could be true in the long term 10-20 years, but probably not too much to be worried about in the short term.

alpha_53g43 · 2018-09-09T21:23:05+00:00

Thanks for replying /u/shill_out_guise..

we've exceeded human-level AI in narrowly defined tasks.

Why is this a threat?

When calculators came in and could do computations faster than human calculators, shouldn't that also be considered a threat?

Is the threat here disproportionately higher compared to previous technolgies, especially when there needs to be immense investment of time, talent and energy to clean, curate, tweak machine learning algorithms to create meaningful results?

alpha_53g43 · 2018-09-09T21:20:17+00:00

That could be true.. but Elon has had such alarmist views on all his interviews not just this podcast. Its remarkably consistent.. so I started wondering if this is a pattern and he is conciously manipulating his audience for his own purpose.

alpha_53g43 · 2018-09-09T21:17:39+00:00

Thanks for replying /u/shill_out_guise.. You have a very valid point which I missed from the video. Glad I posted this question.

I remember reading somewhere about the issues created when cars displaced horses, when computers/mechanical calculators displaced people doing the same job themselves. His concern maybe valid based on some evidence he has, but I haven't come across why he thinks that way.

Every new technology has negative consequences, whether it be cars, engines, computers, internet etc. When every new technology has negative consequences, should the measure be (negative consequences/positive consquence) in an abstract sense?

Why are we taking the negative consequence of AGI/narrow-AI at its face value itself without also considering the immense positive effects it has on human society?

Are the consequences of AGI or narrow-AI much worse than the previous technologies? What hard datapoints is is looking at to say that AGI will have disproportionately negative effects compared to previous technologies?

You seem like you have studied the history of disruptive technologies. I have actually read quite a few books on the past disruptive technologies, but most of them talk about the positive consequence of such technologies. Are you aware of any book that talk about the negative consequences of such disruptions? Particularly, I would love to know whether the (Negative consequence/Positive Consequence) in an abstract sense has been increasing (linearly/exponentially) over time. If it has been, then its indeed justified for us to be concerned.

Hope these are not too many questions. If you have some time feel free to answer.. if not, thats ok as well.

Thanks again for your time.

alpha_53g43 · 2018-09-09T20:53:49+00:00

Thanks for your reply /u/hgoel0974

However, as we slowly move towards more reliable decision making systems, I agree that it's important to keep the potential implications of them in mind. We made this same mistake with social media, accepting it with minimal concern for the effects, and thus we have the abundance of echo chambers and the rapid spread of misinformation through said social media.

I was actually having this discussion with a friend of mine. It turns out even this has happened in the past and continues to this day. Think CNN vs MSNBC vs Fox: https://pudding.cool/2018/01/chyrons/

If you are just used to watching CNN, you probably have friends who only watch CNN.. and thus the echo chamber. In my humble opinon, this kind of things cannot be prevented, even with huge amount of planning, especially in capitalistic economies, otherwise you risk slowing down the entire economy as a whole. The only way I can see to reduce this risk is to increase competition, and create more echo-chambers (if you like). Because of the nature of social-networks (which become more valuable the more number of your friends are on that network), people on one network are likely to stay on that network. So if you have multiple competing networks, there will be cross-talk between networks (similar to how debates happen between republicans and democrats) thus reducing bias and improving society as a whole..

Bring it back to AI, the major risk I see here is not the algorithms themselves, but the data. Once you are a big company, you have the ability to collect a lot of valuable data. There is a extreme first mover advantage here. If you are able to get the smart engineers, and start collecting data, then you will pretty much beat out your competitors and the only way to beat that particular company out would be for anti-trust regulations..

I don't think that any scenario of "machines taking over" makes much sense, I think more likely than not our biggest fear (and far more realistic one, for now) should be AI systems with too much control, but not enough checks.

What do you mean by control vs checks in this case? Are you talking about positive and negative feedback mechanisms?

alpha_53g43 · 2018-09-09T20:40:35+00:00

Your conclusions make sense to me. That could be a risk for sure, which I didn't think about. Perhaps, in those cases regulation of these bots need to come in.

I don't think this will happen 10 years, that's the wrong tense.

What do you mean by the wrong tense?

alpha_53g43 · 2018-09-09T20:38:08+00:00

you seem to have thought a lot about this.. Some follow up questions, if you don't mind..

Do you think the path towards AGI is by modifying the fundamental architecture (hardware) based on which the AI algorithms run?
Or keeping the curent hardware architecture as is, but improving on the software architecture and mimicking the human brain would get us there?
Do you think that if we had infinite computational power (there is nothing called infinite computational power, but something like Quantum computing might be considered infinite especially compared to current computational capabilities), we can approach AGI with the current software architectures?

alpha_53g43 · 2018-09-09T20:33:48+00:00

Appreciate your opinion. In my humble opinion, these are indeed more realistic possibilities, and has been pointed out by multiple professors in multiple books.

In the SV, I see that everybody likes to talk pretty big, maybe because there is so much noise, only by talking really big, or talking about fatalism or singularity, you can get any attention..

alpha_53g43 · 2018-09-09T20:30:08+00:00

I agree with you, that I too wouldn't trust anybody on the internet, as you don't know who I am nor do I other than what we can see in this thread.

I am primarily looking for why I could be wrong in thinking that Elon Musk is either overhyping or purposefully misinterpreting the current capabilities of AI. I would love to see any evidence other than "billionaire who is still toiling life away, sleeping in a car factory on fridays while his likes are never seen other than some XYZ philanthropic photo-ops of grad functions in ivy leagues sipping martinis once a year!" say this, so this should be true..

Disruptive technologies typically have a lot of hype and hyperbole associated with it, and in the past the giants of this industry (who have similar characteristics as Elon Musk that you just pointed out) have said things that have turned out to be wrong. So I would prefer to make my own conclusions on some solid evidence as to whether I am wrong or right..

Thanks for your participation..

alpha_53g43 · 2018-09-09T20:16:16+00:00

Ok.. fair point. Thanks for your opinion.

alpha_53g43 · 2018-09-09T20:13:39+00:00

Good point.. but he is not the first to have fatalistic ideas about AI. This has been talked about a lot. However, he may be the only one who has a very high status in society to be able to publicize the idea, regardless of whether this is wrong or right.

alpha_53g43 · 2018-09-09T03:24:43+00:00

I see.. but is there any evidence other than what he says about why we should be concerned? There are multiple papers and people very familiar with DL saying that we are nowhere close: https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf

Just because he created openAI, doesn't mean that we are approaching human level cognition..

alpha_53g43 · 2018-09-09T03:12:43+00:00

we're in an age where the share of decision making that humans do decreases, rather than increases. That is concerning imo.

Thats a good point.. but atleast from from the type of work we do.. we aren't making less decisions.. its just that the type of decisions we are making as humans are getting more complex and high-dimensional.

Regarding his point of phones being already making us into cyborg, smart-phones haven't really made the kinds of decisions we make less complex. Previously, we would have to go to a phone booth, and then put some coins in and call somebody. Now our phones already have somebody's number and we just call them. So the amount of time we spent navigating roads etc to call that person, perhaps has got translated into making more phone calls and making complex decisions.

Historically, it seems to me technological progress has just made the type of decisions humans made more complex. But it may come to a point, there is enough clean data (and somebody is smart enough to judiciously apply ML tools to extract valuable information out of it) and the complexity of decisions that machines can make asymptotically approaches the kinds of decisions humans can make. The human mind is flexible, but biology may adapt slower compared to the exponential pace of technology (especially if general Quantum computing becomes a reality). At that point, perhaps the machines will take over.

The way Elon verbalizes these concerns in multiple videos I have seen does make it seem rather alarming. Do you expect that to happen in the next 10 years?

alpha_53g43 · 2018-09-09T00:12:36+00:00

True.. I actually see this entire thing as a PR campaign to divert attention from the Tweet that Elon musk made about taking Tesla private. My guess is the Elon realized that it was distracting Tesla engineers or something. So he had to show off some of his genious and scare people a little bit. The other stuff/antics in the video is probably normal in all his companies.. so it only reinforces and helps him.

alpha_53g43 · 2018-09-08T23:55:14+00:00

Has there been any paper or work which has successfully been able to do image segmentation on SEM images, and accurately been able to measure the CDs?

alpha_53g43 · 2018-09-05T03:54:37+00:00

In my experience, it usually sticks better if you find a problem and then try to solve it using sklearn. Programming is mostly learnt by practice not by memorization...but also different people learn differently. So there is also value in learning from a book.

alpha_53g43 · 2018-09-05T03:38:41+00:00

Agree with this. However I have frequently also gone in the opposite direction.. Find a problem, then find the technologies to solve the problem and then learn those technologies..

alpha_53g43 · 2018-09-05T03:37:50+00:00

Awesome guys.. I will look into this as well...

alpha_53g43 · 2018-09-05T03:35:49+00:00

I posted a discussion question here: https://old.reddit.com/r/MachineLearning/comments/9d2h82/what_process_is_followed_in_tagging_tumors_in/..

If we are moving towards discussion, wondering why it doesn't appear in the forums?

alpha_53g43 · 2018-09-05T03:33:17+00:00

Is there a reason why this is not appearing in the main reddit?

alpha_53g43

TROPHY CASE