In this AI, ML, Computer Science run world who better to consult than Hermann Hauser. There is little more elegant an explanation of the future of artificial general intelligence. Come watch his talk, discuss in the forum and check out his book recommendations.
Satya Nadella, CEO of Microsoft, recently shared Microsoft’s artificial intelligence vision. They want to empower people in organizations all over the world by using AI. Some of the examples included: Working with Volvo to make cars less dangerous by analyzing motorists’ faces to ensure they’re alert, verifying the identity of Uber drivers by having them snap a selfie and analyzing it using facial recognition and many more.
It has been ten years since google translate was launched. Today Google announced the Google Neural Machine Translation system (GNMT), which utilises state-of-the-art training techniques to achieve the largest improvements to date for machine translation quality. The system is reducing errors by between 55 and 85% and according to the research, its translation score is only just below that of human translations.
China’s Search Giant Baidu released DeepBench, an open source benchmarking tool for how fast processors train neural networks for machine learning. The benchmark is available here
with some first results from Intel and Nvidia processors running it.
“Python excels when you want high level and your required functionality already exists. If you want to implement a new algorithm with even minor complexity, you’ll likely need another language.” This guide helps you easily build complex data visualizations with JuliaML
A great series by Google consisting of seven videos, teaching you how to train an image classifier with TensorFlow for Poets, writing your first classifier, visualizing a decision-tree and many more topics.
Machine Learning has been the tech industry buzzword for 2016. Every company wants to implement it but often don’t even understand ML and its limitations. Another misconception is that machine learning is the same as AI.
Stuart McClure, CEO of Cylance explains the biggest implications of ML and Big Data for security. According to McClure humans cannot keep up with the current threat landscape, but machines can and there are three core tenets of cybersecurity that ML has the potential to solve: Execution, bypassing and denial of service.
This tutorial in Python builds an algorithmic financial trader! It uses real-world data with a combination of neural nets and self-reinforcement learning; the same broad methods used by Google DeepMind.