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An Upgrade to SyntaxNet, New Models and a Parsing Competition
41–50 of 93 posts
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#42Earlier quoted context omitted.
I've been slowly working on my own simple home "Alexa" using mostly CMUSphinx for the voice detection. Honestly my most successful methods involved the least amount of complex NLP. Just simply treating the sentence as a bag of words and looking for "on" or "off" or "change" (and their synonyms) and the presence of known smart objects works extremely well. I could say "Hey Marvin, turn on the lights and TV", or "Hey M…
> I've got the project on hold for now because I can't find a decent, non-commercial way of converting voice to text. I'd really rather not send my audio out to Amazon/Google/MS/IBM Same concern here... so my voice->text method is via android's google voice - forced to offline mode. The offline mode is surprisingly good. Re mis triggers... I also have opencv running on the same android. It only activates the voice re…
> text method is via android's google voice - forced to offline mode. The offline mode is surprisingly good.
I actually tried this at one point with a wall-mounted tablet before trying Sphinx. It is surprisingly good for offline, probably the best offline I've tried yet outside of dedicated software like Dragon. But it doesn't meet my open criteria, so I'm hoping to find something better.
I'll most likely give up on the requirements of it needing to be local and open, and use Sphinx for hotword detection to send the audio out to AWS for processing.
> Re mis triggers... I also have opencv running on the same android. It only activates the voice recognition when I am actually looking directly at the android device (an old phone).
That's an awesome idea :) I haven't gotten around to playing with anything vision based yet. But I've thought of 'simple' projects like that, which would add a lot to the perceived intelligence. Figuring out the number of people in a room would be another useful idea I think. The AI could enter a guest mode when there is more than 1 person in the room, or when it detects faces that aren't mine, or something similar.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#43For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work).
I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of data from a lot of sensors, including images. I have _no idea_ how I would even get started using Tensor Flow, but it would be cool if I could run experiments on environmental conditions and find optimal conditions for this sweet pea cultivar. Maybe I'm talking nonsense. Let me ask a more basic question, how might one log and create data for use with Tensor Flow. How might Tensor Flow be applied to robotic botanical situations?
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#44Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.
> Alexa, light up the kitchen.
Alexa turns the gas stove directly to 'high', and waits.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#45For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work). I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of da…
The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly.
Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496
However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#46I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox ( h…
Plug for Mathematica, which after its installed you can do deep learning on in one or two lines, with GPU support on all three platforms with no setup. Very concise. Getting fairly competitive in features with other high level declarative frameworks as of 11.1 (which was just released today). Very nice visualizations thanks to being in Mathematica. The language is of course closed source, paid software. Many universi…
Two times in my life, I've gotten deeply excited about Mathematica. The first time I wanted to use it for economics homework as an undergraduate. (Don't worry, I did it on paper first.) The second time, I wanted to use it for machine learning, especially NLP. Mainly the knowledge base Mathematica hooks into is what drew me.
The problem in the end is that the customizability of Mathematica ends right where things get interesting. If you want to show people cool little examples, Mathematica is clean and fast, but you can't build anything serious with it. And by "serious", I guess I mean something with few enough constraints to have an identity of its own, rather than being "a thing you can do with Mathematica."
Another limitation is the data input. Someone needs to rethink it.
I could be wrong. I actually want to be wrong, because of the simplicity and power of Mathematica in its scope.
Programming languages/platforms are network goods. IMHO, Mathematica has tried to swim against this fact and has failed.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#47Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.
I've been slowly working on my own simple home "Alexa" using mostly CMUSphinx for the voice detection. Honestly my most successful methods involved the least amount of complex NLP. Just simply treating the sentence as a bag of words and looking for "on" or "off" or "change" (and their synonyms) and the presence of known smart objects works extremely well. I could say "Hey Marvin, turn on the lights and TV", or "Hey M…
This was recently mentioned on HN, but I haven't really looked into it (apparently requires training your own models, but provides prepared scripts to do that for some common datasets): https://github.com/mozilla/DeepSpeech
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#48I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox ( h…
It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.
I disagree - I think you are well-served to compile on your own unless you know you don't need it, e.g. if you are just trying it out to learn how it works.
The standard build uses a "least common denominator" Intel instruction set (SSE4), but the odds are extremely high that the machine on which you're running tensorflow supports 4.2. Building from source allows you to use the most up-to-date instruction set (the default configuration script at https://www.tensorflow.org will do it automatically).
I've seen dramatic (>50%) reductions in processing time on test scripts by building from source. Note that those tests were built primarily for my own education, not for benchmarking. But the speedup was so dramatic that I couldn't help but notice and probe a little deeper. YMMV depending on the particular application, whether you are using GPU computing (I am not), etc.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#49For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work). I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of da…
I agree with the previous post that you should focus on Keras rather than Tensorflow. Understanding Tensorflow is a great skill to have because you get a more appreciative and deeper understanding of the models when you dig deeper. But for most application, especially for a fun side project, Keras should be perfect.
I recommend http://course.fast.ai/ to learn more about the applications of neural networks and how to apply neural networks quickly through python.
Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition
#50> and to allow neural-network architectures to be created dynamically during processing of a sentence or document.
Oh lord, is this the spark that lights the google skynet powder keg