Game titles produced by AI
The RNN started out knowing nothing about language, video games, or the world. It still knows nothing, but now it generates fake game titles. I read 5,000 of the titles it produced and selected my favorites to share. I felt like I was shopping from a mail order catalog. I think that's the best way to enjoy these titles. Imagine it's 1990. There are a bunch of new games on the market, but there's nowhere to read reviews. Would you spend money on one of these games, sight unseen?...
Capture the Flag: the emergence of complex cooperative agents
Mastering the strategy, tactical understanding, and team play involved in multiplayer video games represents a critical challenge for AI research. Now, through new developments in reinforcement learning, our agents have achieved human-level performance in Quake III Arena Capture the Flag, a complex multi-agent environment and one of the canonical 3D first-person multiplayer games...
A Message From This Week's Sponsor
Data Science Education Built by Data Scientists
Udacity’s Data Scientist Nanodegree program was built with IBM, Starbucks, and Kaggle so that you’ll have what it takes to break into this cutting-edge industry. And you'll get exclusive access to working data scientists at Airbnb, Google, and Slack, so you can craft your career around the exact skills that top companies crave.
Plus, you won’t just be learning data science - you’ll be doing it every step of the way. From machine learning models to natural language processing systems, you’ll build seven projects and a robust Github portfolio.
Start building your data science portfolio - and career - today!
Data Science Articles & Videos
No, Machine Learning is not just glorified Statistics
The purpose of this post isn’t to argue against an AI winter, however. It is also not to argue that one academic group deserves the credit for deep learning over another; rather, it is to make the case that credit is due; that the developments seen go beyond big computers and nicer datasets; that machine learning, with the recent success in deep neural networks and related work, represents the world’s foremost frontier of technological progress...
Global Migration, animated with R
The animation below, by Shanghai University professor Guy Abel, shows migration within and between regions of the world from 1960 to 2015...
Although filled with silly pictures, this work is no joke and I believe it is an important step forward for GANs. In this paper, I argue that standard GAN (SGAN) is missing a fundamental property, i.e., training the generator should not only increase the probability that fake data is real but also decrease the probability that real data is real. This property is fundamental and should have been in the very first GAN...
It’s easier than you think to craft AI tools without typing a line of code
A lot of companies are trying to make it easier to use artificial intelligence, but few are making it as simple as Lobe. The startup, which launched earlier this year, offers users a clean drag-and-drop interface for building deep learning algorithms from scratch. It’s mainly focused on machine vision. That means if you want to build a tool that recognizes different houseplants or can count the number of birds in a tree, you can do it all in Lobe without typing a single line of code...
Are we close to having machines solve TopCoder problems?
With the emergence of deep learning, neural networks started performing almost at a human level in many tasks: visual object recognition, translation between languages, speech recognition, and many other. One area where they haven’t shown anything exciting yet is programming...
Marvel Cinematic Universe Superhero Ranking:
An Emoji Visualisation
As I watch MCU movies and especially the Avengers movies, one question constantly nags me — who is the most powerful MCU superhero? An analysis based on personality AND powers doesn’t mean much when when the superhero is going up against Thanos. Powers trump everything so the best superhero should also be the most powerful superhero. But the bigger question is — do the number of powers matter or does the type of power matter more? Let’s look at the data....
eCommerce Data Science & Machine Learning Analyst - PepsiCo - NYC
Have a strong opinion about Tensorflow lacking an autoregressive dynamic network? So do we!
PepsiCo’s eCommerce Data Science and Analytics group is a team of data scientists, technology specialists, and business innovators who operate within eCommerce to build industry-leading systems and solutions. By focusing on machine learning and automation, the Data Science & Analytics group is pushing the bounds of possibility for PepsiCo and its strategic partners...
Training & Resources
Apply Transforms To PyTorch Torchvision Datasets
Learn how to use the Torchvision Transforms Parameter in the initialization function to apply transforms to PyTorch Torchvision Datasets during the data import process, via a screencast video and full tutorial transcript...
The Emotional Voices Database:
Towards Controlling the Emotion Dimension in Voice Generation Systems
In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way...
Announcing an Easier Way to Build Alexa Skills Using Python
We are excited to announce the Alexa Skills Kit (ASK) Software Development Kit (SDK) for Python (beta). The SDK includes the same features available in our Java and Node.js SDKs, and allows you to reduce the amount of boilerplate code you have to write to process Alexa responses and requests. If you code using Python, you can use the SDK to quickly build and deliver voice experiences using Alexa and the extensive Python support libraries and tools...
Text Mining with R: A Tidy Approach
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective....
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page