Hacker News Reader

Comment by jpatokal | Hacker News Reader

I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype.

And you're trying to rebut this by referencing an AI-generated Christmas jingle. I think the author rests their case...

View on HN

A lot of revolutionary technologies begin as toys. In the 9th century two brothers created a machine called, "the instrument which plays by itself." It uses a rotating cylinder with "teeth" that triggers keys which were encoded to play music. One can say it's a programmable music machine.

Fast forward to the 1700s and artisan engineers in France were creating similar devices that automatically played music. Toys for the entertainment of the aristocracy. Some insightful engineer noticed the use of pinned cylinders to recreate music, and realized the device might also be used to create textiles with a loom. You could program a machine to create textile patterns. While the initial implementations didn't work very well, eventually Joseph-Marie Jacquard created a version that revolutionized textile manufacturing in the early 1800s, relying on punch cards instead of expensive-to-create cylinders.

By the mid-1800s Charles Babbage became interested in Jacquard's work using punch cards to create complex textile patterns. "The major innovation was that the Analytical Engine was to be programmed using punched cards: the Engine was intended to use loops of Jacquard's punched cards to control a mechanical calculator, which could use as input the results of preceding computations." - https://en.wikipedia.org/wiki/Charles_Babbage#Analytical_Eng...

Toys are amazing for inspiration and innovation.

I think your story does more to reinforce the parent post's point. The chain of events you outline spans an entire millennium. Over that time, yes, a lot of technology developed, and eventually we got to computers, and there was even the movement that gives us the term Luddite. But it was still one long chain of gradual, evolutionary developments over the course of a very long expanse of time. At no point was there anything like the kind of sudden turmoil that futurists like to claim that deep learning will produce.

We never know, that is what the original parent comment was about. Regarding gradual progression, remember - we are now in a highly connected world, where things move at tremendous pace. And a lot more population have access to the latest information. Just like the invention of the wheel, or the invention of steam engines, or the computers, transistors etc, there could be a tipping point, which could happen today as I write or tomorrow or even after a decade. We can only guess. But looking at the speed of things, I would say the probability of it happening very near is very high and is worth the hype.

If the world evolved in linear terms, you would be right. However, the rate of scientific advancement has multiplied exponentially over the history of mankind: our scientific understanding has advanced more in the last 100 years than it has in the previous 10000 combined (nuclear physics, quantum physics, molecular biology, information technology, astronomy/cosmology - you name it.)

Remember, 10 years ago smartphones were in the realm of science fiction. Now, everyone has one in their pockets.

Today we can train a computer for a week to generate a cute little christmas jingle. How many years do we need to train a human child to do the same? AGI won't happen overnight, sure, but its effects of that research will be felt continuously along the way.

The '40% of jobs lost' figure is reachable without any Singularity-style event. That's what you can get just by applying modern automation techniques to low hanging fruit. Self-driving vehicles, automatic checkouts at supermarkets, fully automated fast food places, robotic warehouse storage/retrieval etc.

If you used to have 10 people working at a supermarket and you now have 6 with self-checkouts, that's your 40% jobs lost (for that supermarket) right there.

Self driving vehicles are not a low hanging fruit. We are far, far away from fully autonomous vehicles that don't need a human backup driver. This is despite massive investment by Google and others.

True, I shouldn't have grouped it in with self checkouts etc. When we do achieve reliable self driving capability it will be arguably the single most impressive engineering feat we've accomplished.

Tesla are describing their 'hardware 2' self driving platform, fitted to all of their new cars, as "full self-driving hardware", and they've posted several videos recently of fully autonomous journeys on public roads. They've said they plan to roll out an OTA update to enable full self driving next year.

Mercedes-Benz have a limited self-driving mode on their 2017 model.

Uber-owned Otto, a company focusing on self-driving technology for trucks, plans to start offering its services next year.

And of course Google is quietly continuing their self-driving project, although they've gone a bit quiet recently.

Now, it does look to me like we've still got a fair few corner cases to straighten out, but either all of these companies are badly wrong, or we'll be seeing self-driving vehicles on sale to the public in the next few years (even if not next year).

No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.)

IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869)

Obviously it is hard to know when that magic moment will happen that some kind of general AI is created that can learn in some sense similarly to how humans do. My every indication and astonishment at the results that are being produced strongly suggests "at any moment". The results are absolutely astonishing every day and we have vastly more than enough firepower.

You might also be interested in this separate thread where I dealt with questions of consciousness and pain. I again reference it here:

https://news.ycombinator.com/item?id=13026020

and you can click through at the top to follow my reference.

We are way past the point of no return here, and in my estimation it is a question of years or at the most decades - not centuries.

absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167

Interesting. The concept seems to be very similar to this one: https://arxiv.org/abs/1611.01578

  > Obviously it is hard to know when that magic moment will happen that some kind of general AI
  > is created that can learn in some sense similarly to how humans do. My every indication and 
  > astonishment at the results that are being produced strongly suggests "at any moment".

As an IT guy with a basic but solid neuroscience education (which isn't even needed for what I'm about to say): Yep, you are on the hype train, and very deeply. I really like reasonable discussions, I have no idea what this is right here. We will see more amazing results, sure - but applications will be specialized narrow subjects. From creating "some kind of general AI is created that can learn in some sense similarly to how humans do" we are still very far away. Your statements remind me of 1960s "future" hype,a nuclear reactor in every car by the year 2000, stuff like that.

My hype is different, because in my estimation we already have the hardware. You write:

>As an IT guy with a basic but solid neuroscience education

-- could you go ahead and take a few minutes (maybe will take you 5-10) to read through my above-referenced links referencing my previous discussion and tell me whether I'm correct in your estimation on the bottom-up aspect - i.e. the amount of computation that human neural nets can likely be doing, and how it compares to server farms with fast interconnects today?

I'm not an expert in neuroscience so your feedback might be helpful there.

If P=NP then we already have the hardware to crack RSA encryption.

The above sentence is true, but it has no bearing on anything.

don't you think it would have a lot more bearing if you had 7 billion devices nonchallantly walking around cracking RSA every day using the same or less hardware? (but we couldn't reverse-engineer them, because they were obfuscated in biology)?

The fact that they weren't reverse-engineered (yet) would still have huge bearing on everything.

By 7 billion samples I mean the humans walking around. Your analogy with an RSA crack is fundamentally different beccause biology doesn't do it in 3 pounds of grey goo in seven billion different bodies already.

so you would have to come up with an analogy that uses something we cant use, to say, okay fine it exists and fine, we have the hardware to also do it, but the former doesn't have any bearing on us doing the latter.

Interesting observation. If brains routinely cracked RSA, that could be evidence that P=NP.

Still it wouldn't help us find the P-time algorithm in question. We could say "it seems to exist", but that would not imply "we'll discover it any day now".

  > If brains routinely cracked RSA

For brains numbers have a completely different meaning and internal representation than for computers. Brains don't "think in numbers". Doing the kind of math we invented is a major effort for the brain, it's not what it developed for, and it is very poorly equipped to do explicit numerical calculations (emphasis on "explicit"). So looking at brains to "crack RSA" seems like waiting for a hammer to be useful in driving screws.

How much computing hardware does it take to beat a human at chess or at go? (Less than you think, Deep Blue was hopelessly inefficient.) And how long did it take for those much simpler things to happen?

  > i.e. the amount of computation that human neural nets can likely be doing

We don't know nearly enough what they are really doing! We only know a few selected bits and pieces! You are basing your assumptions on nothing, so according to logic any conclusion is possible from a faulty premise. On which you promptly deliver spectacularly.

Also, the "computation" a brain does at one moment leaves out the time aspect: Lots of things lead to constant changes. The wiring changes all the time. The "computation" metaphor has little use for describing or understanding this major aspect of "brain". The more I learned about neuroscience the more unhappy I got with the computing metaphor that I had had going in (as a CS graduate, naturally, I think). The brain is so very, very different from my pre-neuroscience-courses notions.

How much neuroscience do you know? If the answer isn't at least an undergrad introductory course (the accompanying book is over a thousand pages), why do you get the idea you can make any predictions?

Read this to read about complexity in biology vs. engineering and what scientists in the field think how well we are dealing with it:

- http://biorxiv.org/content/early/2016/05/26/055624

- http://www.cell.com/cancer-cell/fulltext/S1535-6108(02)00133...

Test yourself: Do you understand what he's talking about? http://inference-review.com/article/the-excitable-mitochondr...

Fortunately you don't have to sign up at university these days just for such knowledge:

Free courses (if you ignore the certificate nonsense):

- https://www.mcb80x.org/ (This is linked to from edX as "The Fundamentals of Neuroscience" Parts 1, 2, 3)

- https://www.coursera.org/courses?languages=en&query=neurosci... (Especially "Medical Neuroscience": https://www.coursera.org/learn/medical-neuroscience)

- https://www.edx.org/course?search_query=neuroscience

Thank you so much for those insightful links!

just toys? https://en.wikipedia.org/wiki/Deep_learning#Applications

Reply on news.ycombinator.com