121–130 of 155 posts
"During an earlier conversation, Ng told me that his personal goal is to help bring about an AI-powered society." So is this Elon Musk's arch-nemesis?
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Sure; Doesn't die -- HTML being a specification in name only, there are a lot of really crazy web pages out there that render on browsers but are pathological edge cases. Does a good job of distinguishing 'good' links from 'bad' links on a page. -- Lots of pages have links that should not be followed, some are easy they are rendered in the same color as the background (SEO black hat link juice) and others refer to cr…
Cool, thanks. When you say google and microsoft have this advantage in creating data sets, is it just the massive size of the web indices they are able to compile or do they use their crawlers in specific ways for compiling structured data that would be more useful for certain ML projects than a general web index? Are there any tweaks you'd make to a crawler if you sent it out with the purpose of creating a dataset f…
> ... do they use their crawlers in specific ways for
> compiling structured data that would be more
> useful for certain ML projects than a general
> web index?
Google also post processed crawl data to indicate the spread of flu in their experiment of extracting health data from query logs.
> Are there any tweaks you'd make to a crawler if you
> sent it out with the purpose of creating a dataset
> for a specific AI / ML project, rather than a
> general purpose web index?
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It's still available. I've been taking it for the past month or so. After learning more about him, he's probably the only person in the world that I envy. I resonate with his ideas a lot, but I'm like 1% of what he is. It makes me a bit sad. I'll be taking his new course as well and hopefully one day I will be able to work in the same field as him.
Imposter Syndrome is pretty common in CS/Software dev. I try to recognize it for what it is when I feel it and move along. https://en.wikipedia.org/wiki/Impostor_syndrome
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It's far from challenging. The Stanford one (CS229) is though.
Maybe not if you are someone lucky enough to have gone to Stanford, and are a CS major. When I took the ML Class (I also took the AI Class at the same time, but had to drop out due to personal reasons - but I stayed in on the ML Class and finished it), I hadn't really touched linear algebra since high school. I graduated high school in 1991; Ng's course was 20 years later. I also didn't have any stats or probability…
Having not completed an AI course, I tread lightly.. However, I would guess that this project involves re-implementing an established solution. -- It is work like this that drives me away from such courses; as I can't imagine how creative practices are promoted, instead "correct" techniques are repeatedly hammered in.
> I don't know if any of this will lead anywhere for me career-wise. I'm happy with my current employer, so I expect to stick around here for a while.
Professionals learning to program late in their career typically have a misconception that they're only eligible for entry-level positions in the field of software engineering. Many fail to realize that the 10+ years of experience in their own field can be coupled with their newfound-skill, giving them a background unlike that of many existing professional developers.
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Whenever I see announcements like this, it's very unclear to me what is meant by "AI." Are they talking about basically getting the most out of the current ML/deep learning type systems? If so then I guess building data sets makes sense but it seems more like an uninteresting business strategy than what I think of as pushing AI forward. If, on the other hand, they are talking about making progress on the more traditi…
Don't have anything relevant to say except to make a fun note that we can change your second sentence, keeping it correct, while increasing its ambiguity. If we restate it as: Are they talking about basically getting the most out of the current ML type systems? Since Deep learning is a subset of machine learning, the sentence retains correctness but there are now two equally valid interpretations. Okay, I do have som…
I mentioned this in another comment, but I don't know that we can train these models just fine, a human can get a lot more information out of an audiovisual stream than a rudimentary transcription of recognized speech, objects and/or text.
If we make these models more sophisticated to capture more information (e.g. body language, tone, context), we have to decide how that information is structured and communicated to the "higher level meaning interpretation" stage. No matter what, their output is going to be more rigidly structured and will contain less information than the raw stream. The extent to which the sensory processing model captures the human-recognizable information in the stream is the extent to which you have created an intelligent system.
There is some form of this structuring and reducing happening inside our brains, but we will never get machines to do that if we continue curating structured data sets. We, the researchers, are using our human intelligence to process raw sensory data and put it into a nice format for the AI. They need to be able to do that themselves.
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Looks like Data sets are going to be the moat of AI companies.
Why do you think companies like Google release so many things open source related with AI (papers, models, frameworks)? Because they have the data to train them. In my company 90% of my time is dealing with the dataset, having a good and big dataset is first step to train any algorithm.
Slight side topic: Has anyone gone through the new deeplearning.ai track on Coursera yet? Wondering how difficult it is for someone that can write code, but never had any formal academic training in CS.
I see this question asked very often in the last year or two. I am not an expert on deep learning, nor have I taken the deeplearning.ai track, but am currently learning about the topic.
Some of the resources out there have nice primers. I think you need to be somewhat comfortable with understanding the usual log/exp functions for the very base, understand calculus, with partial derivatives, and be used to linear algebra and matrix operations. Some good understanding of statistics could be useful as well when learning about ML. I don't think that a good background in CS is necessary for this stuff. This has not much to do with programming languages, operating systems, Turing completeness. Maybe having a good base in algorithms could be useful for _implementing_ the libraries to make sure they are optimal.
I was wondering why do people ask this question (or ask about resources on learning about this topic in general), when answers on this are so easily findable online.
I look at announcements like this, and past ones about Ng, and I always marvel at how things have gone since I took and completed his 2011 ML Class... That was one helluva course, challenging and interesting, and fun all at the same time (and so much "concretely" - lol). From what I understand, that course is still available thru Coursera (which Ng booted up after the ML Class experiment; Udacity was Thrun's contribu…
Abu-Mostafa's Learning From Data[0] was more more rigorous, in my experience. It's a full-fledged Caltech course, sometimes taught on EdX concurrently with on-campus sessions. The Ng class was a good introduction, but it was mostly applications, not the mathematics and theory behind them. [0] https://work.caltech.edu/telecourse.html
[1] https://www.edx.org/course/learning-data-introductory-machin...