Technical workshop
Simone Scardapane - Rome ML & Data Science Meetup
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922573/1497377723-12444023-ghost-global-AI-banner-GLO.jpg)
Challenge #2
Make news real again!
Your challenge is to build a model which will make it easier and more efficient to identify what really is fake news and what is not (+ everything in between). You model should be able to determine a level of credibility, content authenticity, and limit the viral spread of fake content, including fake images.
Sample dataset: https://www.kaggle.com/mrisdal/fake-news
The dataset contains text and metadata from 244 websites [...] Each website was labeled according to the BS Detector [...] There are (ostensibly) no genuine, reliable, or trustworthy news sources represented in this dataset (so far), so don't trust anything you read.
Another example dataset: http://www.fakenewschallenge.org/
Stance Detection involves estimating the relative perspective (or stance) of two pieces of text relative to a topic, claim or issue. [...] we have chosen the task of estimating the stance of a body text from a news article relative to a headline. Specifically, the body text may agree, disagree, discuss or be unrelated to the headline.
Baseline system: bag-of-words + standard classifier
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922673/1-eUedufAl7_sI_QWSEIstZg.png)
Text Classification using Neural Networks [machinelearning.co]
Advanced concepts: vector space models
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922704/nce-nplm.png)
Vector representations of words [TensorFlow]
Advanced concepts: bidirectional recurrent networks
Neural Networks, Types, and Functional Programming [colah.github.io]
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922818/RNN-bidirectional.png)
Additional topics
- Handling metadata information
- Fake images
- Additional data sources
- Bag of words meets bag of popcorns [Kaggle tutorial]
- Natural language understanding [IBM Bluemix]
Challenge #3
Emotional AI
Your challenge is to build a model (from scratch or on top of an existing model) that is smart enough to recognize various emotions through voice or facial expressions.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922906/front_page.png)
Convolutional neural networks
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922909/Screen-Shot-2015-11-07-at-7.26.20-AM.png)
Understanding convolutional neural networks for NLP [WildML.com]
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922919/pasted-from-clipboard.png)
Google Cloud ML - Recognizing emotions
Microsoft cognitive services
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922924/pasted-from-clipboard.png)
Google Video Intelligence API
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922937/google_cloud_video_intelligence_api.jpg)
Example dataset 2
![](https://s3.amazonaws.com/media-p.slid.es/uploads/726665/images/3922947/framework.png)
Another dataset:
Good luck to all teams!
Global AI Hackathon - Technical workshop
By Simone Scardapane
Global AI Hackathon - Technical workshop
Technical workshop for the Global AI Hackathon series (Rome edition).
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