DETECTING FAKE NEWS

Online disinformation, or fake news intended to deceive, has emerged as a major societal problem. Currently, fake news articles are written by humans, but recently-introduced AI technology based on Neural Networks might enable adversaries to generate fake news. Our goal is to reliably detect this “neural fake news” so that its harm can be minimized.

To study and detect neural fake news, we built a model named Grover. Our study presents a surprising result: the best way to detect neural fake news is to use a model that is also a generator.

Learn more and try Grover by clicking the link below.

https://t.me/ai_python_en/1416

Google open-sources soccer reinforcement learning simulator
Article:
https://lnkd.in/eKw5Zie
Code:
https://lnkd.in/eiT9Z5j

#MachineLearning #ArtificialIntelligence #DataScience

✴️ @AI_Python_EN

An easy to follow and inspirational Blog about #PyTorch internals.
https://lnkd.in/efSEwpP

✴️ @AI_Python_EN

How Twitter and #MachineLearning (KDE + LDA) help to predict Crime?

📘 predict Crime

✴️ @AI_Python_EN

Lex Fridman (DeepTweets: Generating Fake Tweets with Neural Networks Trained on Individual Twitter Accounts)

I fine-tuned GPT-2 neural net on people's tweets to create #AI versions of them. Surprisingly realistic and at times profound. Here's a real tweet about tunnels from Elon Musk rewritten by AI versions of Just Bieber, Kanye West, and Katy Perry.
Details: https://lnkd.in/eaWkeqg

✴️ @AI_Python_EN

We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


✴️ @AI_Python_EN

Simulation of fan emotions during a basketball game:
https://flowingdata.com/2019/06/07/simulation-of-fan-emotions-during-a-basketball-game/

✴️ @AI_Python_EN

Free website design workshop in Boston
https://www.facebook.com/events/2424424517786811/?ti=icl

✴️ @AI_Python_EN

If you've read job descriptions in data lately you are probably confused. Are you a data scientist, machine learning engineer, or research scientist? Instead of title matching, try asking yourself these questions:

1. Can you use statistics to answer questions about a situation that is new to you? Meaning, is your comfort with stats solid enough that you can bring it to bear appropriately depending on scenario?

2. Can you explain why a particular model performs well in a scenario, rather than just noting it does well? Meaning, do you understand the inner workings of models to tune and make sense of why they do what they do?

3. If someone mentions time and space complexity to you, does it make sense? In a big data world, thinking carefully about load of a particular algorithm is extremely important. This matters particularly for MLE and science positions.

4. Can you build something new? Maybe there isn't a perfect algorithm for what you want. Maybe the package in R doesn't exist. Can you make it happen if you need to?

5. Do you know what it means to put something into production? Do you have examples of how you've succeeded or failed with this?

These questions are not all encompassing, but they point to some of the key skillsets you'll need.

#datascience #analytics #data

✴️ @AI_Python_EN

Uncertainty in big data analytics: survey, opportunities, and challenges

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0206-3

#BigData #statistics #NLP

✴️ @AI_Python_EN

Comparing Google's AI Speech Recognition To Human Captioning For Television News
https://www.forbes.com/sites/kalevleetaru/2019/06/09/comparing-googles-ai-speech-recognition-to-human-captioning-for-television-news/amp/

✴️ @AI_Python_EN

Why 2019 is the year of Knowledge Graphs?
✔️#Knowledgegraph became a centerpiece of #Accentur and #Microsoft ’s toolkits.

✔️Knowledge graph lessons from Google, #Facebook, #eBay, #IBM.

✔️Graph algorithms and analytics by #Neo4j, #Nvidia and #AWS.

More about the why?
https://lnkd.in/g87BTrH

💥Great resources to get some hands-on experience:

✅ Implementing Knowledge Graphs in #Enterprises:
https://lnkd.in/ghisXMw

✅ How #Google’s Knowledge Graph Updates Itself:
https://lnkd.in/gayCpPw

✅ Extracting knowledge from knowledge graphs using #Facebook #Pytorch BigGraph.
https://lnkd.in/gHgj6AH

✅ The Data Fabric for #MachineLearning : #DeepLearning on Graphs. By Favio Vazquez
https://lnkd.in/gsCnTTM

✅ Why Knowledge Graphs Are Foundational to #ArtificialIntelligence
https://lnkd.in/g5WVARe

Absolutely essential for data scientists to upskill themselves, Knowledge Graphs are coming...
#datascience #AI

✴️ @AI_Python_EN

Announcing fellowships for open-source developers

https://medium.com/palantir/fellowships-for-open-source-developers-3892e6b75ee1

✴️ @AI_Python_EN

The Deep Learning Toolset — An Overview

http://bit.ly/2R2eUGh

#artificialintelligence, #bigdata, #businessanalytics, #datascience, #deeplearning, #ds #datascience, #iot, #machinelearning, #neuralnetworks #AI

✴️ @AI_Python_EN

Google at ICML 2019

http://ai.googleblog.com/2019/06/google-at-icml-2019.html

✴️ @AI_Python_EN