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Welcome to the New AWS AI Blog

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Re: Welcome to the New AWS AI Blog

#51

Post by mza »

boulos wrote: post #47
nielmalhotra wrote: post #44

Quick slightly unrelated question: Does anyone have a comparison of using Google cloud services vs AWS for machine learning? I'm planning to pick one, and I was leaning towards Google Cloud Services because of the TensorFlow support and the fact that Google is big on ML, making it likely that it's something that Google will support and be good at. With this blog post, I'm not sure.

(Disclosure: I work on Google Cloud). Both providers offer you raw VMs with GPUs and such so you can run popular machine learning frameworks yourself by hand. After that the three providers diverge a bit, and I've not seen a good writeup myself. Roughly: - Google has both a hosted TensorFlow (Cloud ML) as well as specific, pre-trained models you can simply use (Cloud Vision, Cloud Speech, etc.). For an easy to use in…

GM for AI at AWS here: actually - we like TensorFlow quite a bit, too.

We provide a machine image with TF, MXNet and others pre-installed, along with Keras, CPU and NVIDIA divers, and other libraries for deep learning. We just added Ubuntu support too:

https://aws.amazon.com/blogs/ai/the-aws-deep-learning-ami-no...

Re: Welcome to the New AWS AI Blog

#52

Post by itchyjunk »

dominotw wrote: post #41
jmngomes wrote: post #40

"Mark Cuban recently talked about it as the most important technology to ramp up on, to avoid becoming a “dinosaur”" I wonder if this impresses this blog's audience, or does exactly the opposite...

does cuban actively invest in A.I startups? wondering what makes him a quotable authority on the topic.

I followed the link to his interview where he says his 2 biggest investments are Netflix and amazon, maybe that why? TV personality known for investment -> he's invested in amazon -> claims amazon is one of the best tech company followed closely by google, facebook -> people will want to go with amazon ? just making guesses based on that interview article.

Re: Welcome to the New AWS AI Blog

#53

Post by ranman »

neom wrote: post #30

Has anyone used Rekognition? We're thinking about pumping traffic cam feeds into it in cities for vehicle counting but don't want to waste time if it's junk.

If you want to email me I can connect you with the rekognition team: randhunt at Amazon dot com.

You cannot currently count objects other than faces and bounding boxes for non face objects are not currently provided.

For the time being existing solutions in the opencv/tensorflow/MXNet realm might provide more of the result you're looking for.

Re: Welcome to the New AWS AI Blog

#55

Post by ranman »

ge96 wrote: post #21
kiliankoe wrote: post #15

Earlier quoted context omitted.

The link to the RSS feed is right down at the bottom ;)

I did see that, but that RSS feed (atom?) I don't think I've ever used that in my life. I'll Google and see what/how to use it. I see it everywhere though. edit: I did see that they have a podcast, nice, something to listen to while I walk 2 hours in the middle of the night.

If you want email updates try something like blogtrottr

Re: Welcome to the New AWS AI Blog

#56

Post by starik36 »

IanCal wrote: post #32
starik36 wrote: post #2

I am a complete noob to the AI space but I was wondering whether the following is possible (in AWS). I have a million scanned images of court documents. Some are briefs, some are motions, some are court orders, etc... Given that I have images and their types, could I "train" the AI with these million documents to recognize a new image that might come in?

People keep recommending things and approaches, but I'm really not clear on what it is you're actually trying to solve, so most of the responses might not help. > Given that I have images and their types, could I "train" the AI with these million documents to recognize a new image that might come in? Do I understand this right: 1. You have lots of documents as images, and their type (brief, motion, court order) 2. Yo…

> Do I understand this right

That is exactly right.

As far as quality, if I classify the document wrong today (or fail to classify it), it's not that big of a deal to the system as a whole - but users will be "very" annoyed and have to correct it.

> Are the documents typically very structurally different? Could I probably tell them apart without wearing glasses? Or are they largely the same, but with nuanced differences in the text?

The documents are structurally reasonably different, but not "very" different. If a trained human looks at it, they would be able to tell them apart. There are exceptions, of course. For instance, if the attorney doesn't follow accepted convention, but those are rare.

Re: Welcome to the New AWS AI Blog

#57

Post by neom »

ranman wrote: post #53
neom wrote: post #30

Has anyone used Rekognition? We're thinking about pumping traffic cam feeds into it in cities for vehicle counting but don't want to waste time if it's junk.

If you want to email me I can connect you with the rekognition team: randhunt at Amazon dot com. You cannot currently count objects other than faces and bounding boxes for non face objects are not currently provided. For the time being existing solutions in the opencv/tensorflow/MXNet realm might provide more of the result you're looking for.

Hi Randall, long time no chat! Hope you're doing well. Good to know, it's not really our core product mission right now, so we're not at the point of building something (like a tensorflow implementation). I figured it rekognition has what we needed out of the box we might just clip it on and see how it performs, but wouldn't bother if it's not there yet, I'd imagine over time it will improve. Thanks.

Re: Welcome to the New AWS AI Blog

#58

Post by starik36 »

jononor wrote: post #10
starik36 wrote: post #6

Earlier quoted context omitted.

OCR is my current approach. I am not really happy with it. The quality of OCRing leaves much to be desired, probably due to the documents themselves being haphazardly handled by the court personnel. OCR itself is a pretty CPU intensive activity and takes a significant time to complete for many documents. Thus, I was looking for a more advanced approach.

What do you want to recognize? OCR is a better understood problem than a general neural net, so I think it's likely easier to improve its quality that to superseded the quality with image-based recognition.

Ideally, I would like to get all the information from the page. Phone numbers, who is suing whom, case caption, etc...

With OCR, you get bits and pieces of information, but because I don't know what the type of the document is, it is difficult to determine where, structurally speaking, this information resides on a page.

If I could use AI to determine the type of the doc, I would know the structure of the document and I could then use OCR to pinpoint specific information on the page.

Most court documents are created from templates.

Re: Welcome to the New AWS AI Blog

#59

Post by MrQuincle »

mza wrote: post #51
boulos wrote: post #47

Earlier quoted context omitted.

(Disclosure: I work on Google Cloud). Both providers offer you raw VMs with GPUs and such so you can run popular machine learning frameworks yourself by hand. After that the three providers diverge a bit, and I've not seen a good writeup myself. Roughly: - Google has both a hosted TensorFlow (Cloud ML) as well as specific, pre-trained models you can simply use (Cloud Vision, Cloud Speech, etc.). For an easy to use in…

GM for AI at AWS here: actually - we like TensorFlow quite a bit, too. We provide a machine image with TF, MXNet and others pre-installed, along with Keras, CPU and NVIDIA divers, and other libraries for deep learning. We just added Ubuntu support too: https://aws.amazon.com/blogs/ai/the-aws-deep-learning-ami-no...

The challenge is always to get a lot of training data.

Are there (artificial) datasets that can be used that showcase particular fit for deep learning?

I think there is way to little research in building artificial datasets (using domain knowledge of course).

It might even be possible to run these generative models and have this type of data very soon.

Re: Welcome to the New AWS AI Blog

#60

Post by starik36 »

LeanderK wrote: post #26
bhaavan wrote: post #5

Earlier quoted context omitted.

Principally, yes. However, the approach may be more nuisanced than that. If I were you, I would first pick a character recognition engine (which might have already been well trained) to convert the image to text. Once the text is there, that might serve as a better feature to classify the content. Furthermore, I had recommend converting words in the text to word-embeddings/ vectors using a suitable Glove or Word2Vec…

i don't know, maybe different approaches could be combined. Maybe the layout provides a clue for some types of court documents? You could calculate the probability function of prediction a certain type right (or just use the outputs of the NN, that depends on the problem) as a confidence value and only do the OCR as a last resort. Disclaimer: pretty new to ML

That is exactly the approach I had in mind, except I would use the knowledge of the document type (as determined by AI) to guide OCR to specific sections of the page to get information from it.

But I know next to nothing about AI and ML - that's why I was asking this question.

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