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[P] Need suggestions/tips for scraping Twitter data

Hey guys, I just started my Masters Program in AI and our prof threw an assignment at us: Find an IEEE paper and recreate the results… Okay.. I have 0 background in any of this stuff but here goes.

I’ve got one on NLP (with the code, but without the dataset) and I’m trying to scrape twitter data. I researched that there was a python script which allows you to do this, however it requires that you have Twitter Dev Permissions. I made a Dev request, made an App and got Consumer API Keys and Access Token Keys. However, my permissions are set as read and write only. If I want to scrape tweets (with certain #) is read and write enough access for me to export to a CSV file to later use to train the model?

I’ve posted on /r/MLQuestions but haven’t gotten a response there so I’m hoping I’ll have better luck here. Hope someone with more experience can shed some light on the topic.

Thank you!

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[D] AutoML: good/bad/ugly

I’m considering using this next quarter but looking for some honest reviews from those who have already started using.

What do you like about it the most? What do you hate about it/are struggling with? What’s good/bad/ugly about AutoML you have come across?

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[D] Keeping track of latest research

How do you monitor the progress made in your subfields of interest? I’m still studying and I’m not yet a researcher; my only experience was with Arxiv and I had a couple of problems: and there are a couple but when I delved deeper in Adversarial ML for a project I noticed two problems:

  1. There’s no notification system. I’d like to know when a new interesting paper get publish in the field I’m interested in but, at least on Arxiv, there’s no option for that.

  2. I’m not sure if that’s an issue with the field I was studying at the time (Adversarial ML) or if it’s a general issues but I happened to read papers of researchers claiming great descoveries for later being discredited by established authors. That was quite disappointing other than a waste of time.

Are those common problem? How do you keep yourself updated?

I found a partial solution in using twitter lists to group famous researchers and look at what they post but it’s not really optimal.

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Food’s Flying Off the Shelves: Focal Systems Brings AI to Grocery Stores

We’ve all chosen the self-checkout stand over the human cashier, thinking it’ll take less time.

But somehow, things take a terrible turn. The barcodes aren’t scanning, there’s a pop-up scolding you for not placing the product in the bagging area (though you did, of course), and an employee is coming over to fix the chaos.

It would’ve taken less time to go to the cashier.

Focal Systems is applying deep learning and computer vision to automate portions of retail stores to streamline operations and get customers in and out more efficiently, without the pitfalls of the traditional self-checkout.

CEO Francois Chaubard sat down with AI Podcast host Noah Kravitz to talk about how the company is changing retailers.

As labor costs increase, the traditional solution is twofold: automation and human staff reduction. But Chaubard explains that self-checkout systems don’t actually compensate for fewer employees. Instead, “you get more out-of-stocks, because you’ve got less people,” he says.

Focal Systems started by applying AI to a different area of the store: shelves. Chaubard notes that, for store employees, one of the first tasks every day is checking what items are out of stock, and “knowing that answer takes about four hours a day.”

To prevent this, Focal Systems installs small, inexpensive cameras throughout the store, with a focus on high-moving areas like the soda aisle. The cameras “take an image once every half hour” and produce a chart that notes either “in” or “out.”

“Every single hour that you don’t have a product on the shelf is lost sales,” Chaubard emphasizes. This aspect of Focal Systems alerts employees that they need to restock, and helps identify common “out-of-stock hours” so that stores can recognize the pattern and avoid it altogether.

This shelf camera system is already in 11 major retailers across the world.

The other component to Focal Systems is the Focal Scan. While barcode scanning takes three seconds an item, on average, Focal Systems installs a camera on top of the conveyor belt. “You’re just using deep learning and computer vision to detect amongst a hundred thousand different SKUs in 0.1 seconds with 99.9 percent precision recall,” Chaubard explains.

The cashier can just focus on bagging, reducing the total time of the transaction by 60 percent.

Chaubard thinks that the future holds even more automation, but only where it would be cheaper than human labor. “People are hard to beat in certain tasks,” he laughs.

Visit Focal Systems’ website for more information and to find videos of their technology in action.

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The post Food’s Flying Off the Shelves: Focal Systems Brings AI to Grocery Stores appeared first on The Official NVIDIA Blog.

Artist’s NVIDIA-Powered AI Images of New York City Blanket Manhattan Landmark

NVIDIA-developed AI — and NVIDIA GPUs — plays a starring role in the opening this month in New York City of a permanent venue for new media art.

“Machine Hallucination” made its debut this month in the 6,000-square-foot Chelsea Market boiler room — an expansive space beneath the Manhattan landmark’s main concourse.

Created by Turkish media artist and director Refik Anadol and his studio, the installation uses six custom media servers powered by NVIDIA Quadro GPUs.

“People have never seen something like this before. It’s not  hyperbole to say it’s the future of cinema,” said Anadol.

These servers — paired with 18 4K projectors — splash stunning digital images based on some of New York’s most iconic architecture across the cavernous space’s brick walls and terracotta ceiling.  

The story behind the immersive spectacle involves even more NVIDIA technology.

Starting with more than 110 million images of New York — collected, prepared and sorted by an NVIDIA DGX Station — Anadol modified an NVIDIA-developed deep learning algorithm known as a StyleGAN.

“We wrote a latent space browser, a custom program to work with StyleGAN that has the capacity to animate every layer of the neural network and be able to choose latent coordinates to narrate our AI. Basically, we put a camera inside StyleGAN and allowed us to navigate latent space purposefully,” he said.

The exhibit — the inaugural exhibition for ARCTECHOUSE in New York — runs through Nov. 17. Admission is $24..

The post Artist’s NVIDIA-Powered AI Images of New York City Blanket Manhattan Landmark appeared first on The Official NVIDIA Blog.

[D] Instance weighting with soft labels.

Suppose you are given training instances with soft labels. I.e., your training instances are of the form (x,y,p), where x ins the input, y is the class and p is the probability that x is of class y.

Some classifiers allow you to specify an instance weight for each example in the training set. The idea is that a misprediction for a particular example is penalized proportionality to its weight, so instances with high weight are more important to get right and instances with a low weight are less important.

When examples are of the form (x,y,p), it’s clear that the class probabilities could be used as instance weights. A simple way to do this is to weight the loss for each instance by its probability, as suggested here:

Does anyone know of a paper/book where this simple weighting approach is discussed? I can’t find references on this simple idea.

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[P] LatentPainter. Paint with ideas instead of pixels!

Hey guys! Here’s a fun tool I hacked together by abusing spatialized adaptive instance normalization. You paint directly with the latent Z into different layers of the network (basically StyleGAN with some model surgery). The results are pretty cool! : link to the video

Have a lot of features planned including a “color picker” that’s just an encoder to encode the latent Z of an arbitrary image into your latent space so you can paint with it. Also going to be exposing sliders for which layers you’re painting on (earlier layers will be the coarse features like facial structure and later layers will be more textural).

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.