Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- How to Make A.I. That’s Good for People
“I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.”
- China to bar people with bad ‘social credit’ from planes, trains
China said it will begin applying its so-called social credit system to flights and trains and stop people who have committed misdeeds from taking such transport for up to a year.
- Your Data Is Crucial to a Robotic Age. Shouldn’t You Be Paid for It?
Should Facebook pay us for our puppy pictures?
- Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use
In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.
- When an AI finally kills someone, who will be responsible?
Legal scholars are furiously debating which laws should apply to AI crime.
- On Twitter, the lure of fake news is stronger than the truth
An analysis of 4.5 million tweets shows falsehoods are 70 percent more likely to get shared
- AI has a hallucination problem that’s proving tough to fix
Companies are rushing to infuse everything with artificial intelligence, driven by big leaps in the power of machine learning software. But the deep-neural-network software fueling the excitement has a troubling weakness: Making subtle changes to images, text, or audio can fool these systems into perceiving things that aren’t there.
- Reddit and the Struggle to Detoxify the Internet
How do we fix life online without limiting free speech?
- Inside the Chinese lab that plans to rewire the world with AI
Alibaba is investing huge sums in AI research and resources—and it is building tools to challenge Google and Amazon.
- Myanmar: UN blames Facebook for spreading hatred of Rohingya
‘Facebook has now turned into a beast’, says United Nations investigator, calling network a vehicle for ‘acrimony, dissension and conflict’
- Facebook Really Is Spying on You, Just Not Through Your Phone’s Mic
How to limit the amount of data Facebook and advertisers are collecting about you
- Suspending Cambridge Analytica and SCL Group from Facebook
“We are suspending Strategic Communication Laboratories (SCL), including their political data analytics firm, Cambridge Analytica, from Facebook. Given the public prominence of this organization, we want to take a moment to explain how we came to this decision and why.”
- What is AI, really?
A cultural and practical introduction for designers
- A startup is pitching a mind-uploading service that is “100 percent fatal”
Nectome will preserve your brain, but you have to be euthanized first.
- Semantic Image Segmentation with DeepLab in TensorFlow
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications.
- How I implemented iPhone X’s FaceID using Deep Learning in Python.
Reverse engineering iPhone X’s new unlocking mechanism.
- Marketing “Dirty Tinder” On Twitter
“Checking further, I noticed that some of the accounts either followed, or were being followed by other accounts with similar traits, so I decided to write a script to programmatically “crawl” this network, in order to see how large it is.”
- The Role of Luck in Life is Still Misunderstood
The paper Talent vs Luck: the role of randomness in success and failure, by A. Pluchino. A. E. Biondo, and A. Rapisarda, has been receiving a lot of positive reception lately…
- Now we know why Siri was so dumb for so long
It’s no secret that Siri is way behind other voice assistants like the Google Assistant and Amazon’s Alexa when it comes to comprehension and total number of skills.
- Microsoft using AI to match human performance in translating news from Chinese to English
A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person.
- Using Google Cloud AutoML to classify poisonous spiders
“In this post I will show you how I was able, in just a few hours, to create a custom image classifier that is able to distinguish between different types of poisonous Australian spiders. I didn’t have any data when I started and it only required a very basic understanding of machine learning related concepts.”
- Introduction to Recurrent Neural Networks in Pytorch
“This tutorial is intended for someone who wants to understand how Recurrent Neural Network works, no prior knowledge about RNN is required. We will implement the most simple RNN model – Elman Recurrent Neural Network.”
- Some π-ography with Julia
“It was the more famous Leonhard Euler and his use of the π symbol later in the century that did more to establish the Greek letter’s primary mathematical meaning for posterity”
- Sequence Tagging with Tensorflow
bi-LSTM + CRF with character embeddings for NER and POS
Create graphics with a hand-drawn, sketchy, appearance
- Pandas on Ray
Make Pandas faster by replacing one line of your code
- Take screenshots of math equations and paste the extracted Latex
Finally a smart application of computer vision for us researchers
- DALEX: Descriptive mAchine Learning EXplanations
In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction. DALEX is a set of tools that help to understand how complex models are working.
- The Building Blocks of Interpretability
“Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them and the rich structure of this combinatorial space.”