The first large-scale, crowdsourced study that monitors how Meta tracks people across the internet By: Surya Mattu, Angie Waller, Simon Fondrie-Teitler, and Micha Gorelick Introduction Have you ever shopped for a product online only to see the item appear ad nauseam in your Facebook or Instagram feeds? This is often the result of the Meta...
Category: Science
5 Data Processing Challenges Resolved for Data Scientists
Data processing is an essential step in analyzing large volumes of data and get the right insights to make key business decisions. Not only are data scientists expected to find ways to wade through the vast volume of data that businesses are receiving every day, but they also have to put it together in ways...
Edge Computing + Serverless Computing = Ephemeral Computing
What is Edge Computing? The concept of Edge Computing defines it as a networked computing component that resides at the periphery of the network. As such it functions as an interface between the network and the physical world. The “edge” can be composed of devices like cameras, sensors or actuators. Edge Computing is based on...
The Power of Experiments
Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you’ve probably been an unwitting participant in a variety of experiments also known as randomized controlled trials designed to test the impact of different online experiences. Once an esoteric tool for academic research, the randomized controlled trial...
Why is A/B testing important?
A/B testing is essential to identify strengths and weaknesses of your app or service to improve it. A/B testing is a variable-experimentation technique to compare two versions of the same thing to see which one performs better. For example, you can test two different landing pages, two different subject lines, or two different offers. The...
There are fundamental Problems how we train AI
The current machine learning training and testing process is not rigorous enough to ensure that the models being trained will work in the real world. During the training process many different models can be produced that all perform equally well when tested in lab settings and differ only in small, arbitrary ways. The differences stem...
Here’s why Artificial Intelligence is so power-hungry
Artificial intelligence is getting more expensive to develop, and the cost is growing faster than the energy efficiency of the models. This is because they are trained many times with different structures, and the best one is selected. For example a model called Bidirectional Encoder Representations from Transformers (BERT) used 3.3 billion words from English books...
LAL – Little Activity Language
lal is a lightweight Service composition and execution language that is tailored towards the requirements of mobile infrastructures. lal has a considerable small set of abstractions that are easy to understand for people – in comparison with workflow languages like BPEL or YAWL. The latter are used to specify each step that is necessary towards...
Intel, MIT and Georgia Tech Improve Machine-Programming Code Similarity System
A team of researchers from Intel, MIT and Georgia Tech has published a tool that has the potential of helping developers to write code automatically. The tool, called machine inferred code similarity – or MISIM for short – is able to get the intends of pieces of code. It does so by “looking” at the...
Prepare for Artificial Intelligence to Produce Less Wizardry | WIRED
It has become very expensive to train modern networks; in fact it has become so expensive, that some companies are choosing not to use AI methods at all. A new research paper by Neil Thompson et. al. argues that it is, or will soon be, impossible to keep increasing computational power at rate needed to...
