Doesn't seem like an accident to me.
Here's the archive of the page before the edits: https://web.archive.org/web/20171026233121/https://harc.ycr....
798 karma · joined December 14, 2013
Here's the archive of the page before the edits: https://web.archive.org/web/20171026233121/https://harc.ycr....
Notebooks are pretty sweet, but in the hands of very few. They're definitely growing in popularity, which is great.
But even the "notebook" concept is a very limited "low pass filter" version of the visions of Engelbart, AKay, TN, etc.
The video that AKay has been sharing around in the post than @dang linked to[0] is a great example of what's not-even-close to possible on today's systems.
[0]https://www.youtube.com/watch?v=AnrlSqtpOkw&feature=youtu.be...
He's doing something really interesting with the funding model of practicaltypography, that I believe should be praised.
This class fulfills a communication requirement at MIT, so there are a one-page response/reflections due at each class. These reflections basically just ensure that you have a decent grasp at the main points of the paper, so that you would be ready for a seminar-style discussion in class.
Genesis is pretty interesting. Here's a link to the details: http://groups.csail.mit.edu/genesis/ But in short, it's an attempt to get computers to understand stories — and to discover what is the fundamental difference between the human mind and all other animals (spoiler alert: Winston's group believes it's the ability for humans to take two concepts and merge them into a new concept, indefinitely. AKA: creating "stories"). So the goal of Genesis is story-understanding.
It too, is an incredible class. Here's the schedule & linked papers from last semester: https://courses.csail.mit.edu/6.803/schedule.html
(Disclaimer: Professor Winston is my current advisor)
https://www.youtube.com/watch?v=n83xzu2xH-E
The video was created to poke fun at Michael Dell, when Dell was being "rude", as Jobs put it.
However, as you can see in the section "1960s", many similar comments were predicted 50 years ago. The point I address in this paper is that optimism is easy when things are going well.
This is a great example. Lots of people are saying today that we will have X in 10 years. But in 2006, no one was saying we will have X in 20 years.
I'm not saying you're wrong, but I'm saying it's worth questing why you are so optimistic, and whether it's because we aren't looking at the big picture.
I'd politely disagree that my comment was a sequence of empty statements, but allow me to provide some clarifying details that might help your understanding.
The debate I describe is actually critical. If humans are not fundamentally different, than the field should be able to model more simplistic animals (such as a rat), and slowly build up to a model of human level intelligence. On the other-hand, if there is a fundamental difference between humans and other animals, then simply modeling other animals will not scale, and will leave the field wanting.
I don't think we have to get too specific to debate this. I would point to Winston, Tattersall, Chomsky as three widely respected individuals who present the case that symbolic language (and the uniquely human ability to combine two concepts into a new concept, indefinitely) is the keystone that separates humans from other animals.
In your second criticism, we agree exactly. As you can see in my previous comment, I agree that "commonsense" is an arbitrary term. Here, I was simply providing an example of how debating commonsense is not a useful exercise.
Finally, in my last paragraph I was providing an example of how attempting to use "simple animals" as a basis for modeling human behavior has not effective. The previous commenter said that we haven't reached "lizard level" and pointed to the OpenWorm project. I pointed to a related project, from Brooks, that had a similar mission. It did not work, perhaps, because modeling simple animals (such as a rat or lizard), won't scale to humans. I did not meant to draw a general conclusion (since I said that it is simply another case, not a proof).
As to your final line, let's keep this a lively discussion and not a personal attack.
If not, let's stick to substantive comments on the content rather than grammar or spelling.
But specifically for the horses example: I think it would be pretty hard to defend the case that horses have commonsense, unless you use the definition of commonsense as "basic survival skills." Horses can walk around until they find food, and they can run if they see a fast moving object. But I can't think of any examples that seem like commonsense, in the definition of "sound judgment in practical matters."
When thinking of it from a bottom-up approach, Rodney Brooks[0] comes to mind. He tried to build rat like creatures in the early 90s, with the goal that modeling rat behavior would enable modeling human behavior. However, the results were unsuccessful, and the implementations did not scale well. (Which is another case for the humans-are-fundamentally-different side of the argument)
[0]: https://www.cs.nyu.edu/courses/fall01/G22.3033-012/readings/...
While this is an interesting read (and NTMs are great), it is not particularly relevant to the model I describe or this paper.
I think you are misunderstanding my generality of my paper: I am not discussing a particular method of deep learning. I am using the idea of gradient descent as an metaphor for the field of AI, itself.
As described in the second paragraph of the "Gradient Descent" section, this analogy is not high dimensional. In fact, it is only three dimensional: distance the field is from General AI, time, and a hypothetical "method of attack".
I have a few responses. As an undergrad, I won't assume my background is strong enough for direct opinions, so apologies in advance as my responses are mostly pointers to other people.
Professor Patrick Winston[0] at MIT would argue that the expert systems, the successes of the '80s also about making money. Obviously not as much present day, but that is due to the tech sector infiltrating more areas/markets simply because of the exponential growth processing power. It would be very interesting to compare AI's value-add to the world between 1980 and today when adjusted for the "inflation" of moore's law.
It's true I didn't consider the number of people in the field currently compared to the past, and that's an interesting point.
The instructor of the class that I wrote this paper for, Joscha Bach[1], would argue that real physical world results are not very significant, since the real world can be thought of as a simulation itself.
The idea of "what happens next? What could go wrong?" with self driving cars is interesting. A question arises, should you be able to ask the car, "why did you just stop short?" and receive an answer? This is a question Gerald Sussman has been discussing recently[2]. If we do want systems that can explain their behavior, then they must speak in human-language and therefore must be symbolic at some level. In general, the idea of "Common Sense" seems too much of (what Minksy would describe as) a suitcase word[3] -- because the definition is so abstract, it's not worthy of debate without defining the term.
Great call on Hillis, thanks! I completely mixed up Symbolics and Thinking Machines. Fixed!
[0]: http://people.csail.mit.edu/phw/ [1]: http://cognitive-ai.com/ [2]: https://vimeo.com/151465912 [3]: https://alexvermeer.com/unpacking-suitcase-words/
It's definitely worth reading more about Minksy's models. I have a lot of references in-line, but here are a few more starting points. The near-final-draft version his book Emotion Machines is available for free on his MIT site[0] and WashPo gave it a very positive review[1].
It would be interesting to investigate some criticisms of the model, though I haven't found any that specifically target the 6 layer model.
[0]: http://web.media.mit.edu/~minsky/Introduction.html [1]: http://www.washingtonpost.com/wp-dyn/content/article/2006/12...
Also, if anything is confusing, I'd be happy to clarify.
Similar ideas on the problems with code editors / current programming, but Bret Victor's ideas are a little more fleshed out.
Also, if you haven't seen it, it's a pretty funny talk. Brilliant guy.
There are alternative solutions, like Scratch at the MIT Media Lab, which is now available on the iPad to teach programming.
However the vast majority of K12 education is not programming.
It might be more effective to start with iPads outside the classroom, not inside. Inside the classroom they have access to a teacher. No current or near-future technology is going to be able to replace the benefits of a teacher.
But at home, many kids are stuck with boring paper homework assignments and static, old (sometimes 10+ yr) textbooks. If we replaced paper homework with interactive questions, and textbooks with engaging iBooks (or other iBooks like) textbook[1], that could make a real difference.
Apple had created great interactive textbook creation software, and it seems great paired with iTunes U. However, there don't seem to be great books coming from it. Apple probably should have made many demos (instead of just 1) to give educators and the public an idea of the potential interactive homework and textbooks could have on student engagement.