PyGame Learning Environment (PLE) is a learning environment, mimicking the Arcade Learning Environment interface, allowing a quick start to Reinforcement Learning in Python. The goal of PLE is allow practitioners to focus design of models and experiments instead of environment design. PLE hopes to eventually build an expansive library of games. Docs for the project can be found here. A PLE instance requires a game exposing a set of control methods.

There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been shown to be particularly effective when stacked into a deep configuration, achieving state-of-the-art results on a diverse array of problems from language translation to automatic captioning of images and videos. In this post you will get a crash course in recurrent neural networks for deep learning, acquiring just enough understanding to start using LSTM networks in Python with Keras.

Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises' data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data. The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing.

A new study by Kevin Warwick and Huma Shah, published by Taylor & Francis, purports to uncover a flaw in the classic artificial intelligence test developed by Alan Turing. The paper summarizing their findings is called "Taking the fifth amendment in Turing's imitation game." The announcement states, "A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought."

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. One of the key themes for the event emerged from a survey of the attendees.

Machine learning is a branch of artificial intelligence concerned with the design of data-driven programs which autonomously demonstrate intelligent behavior in a variety of domains. Machine learning systems are all around us. When you deposit a check, scan your fingerprint, or post a picture on social media, autonomous algorithms are deployed on the spot to sift through and make sense of your constant interactions with our technology. Machine learning silently underpins the fabric of our digital infrastructure, discriminating spam e-mail and banking fraud, making light-speed transactions in the global financial market, recommending music and films for customers to buy, deciding what search results are relevant to your queries, and countless more of the daily interactions with electronic media that we take for granted. Machine learning is the backbone that powers self-driving cars, content recommendation in social media, face identification in digital forensics, and countless other high-level tasks.

Automation has everyone on the edge. According to a recent report by US-based research firm'HfS Research', India's IT services industry, which employs around 3.7 million people, will lose 6.4 lakh jobs to automation in the next five years. The report further adds that the IT industry worldwide would see a net decrease of 9% in headcount, or about 1.4 million jobs, with countries such as the Philippines, the United Kingdom and the United States also taking hits by 2021. But that is just one part of the story. The wave of'technological unemployment' will have a broader spectrum, say experts.

Every day in Seattle, Aristo takes a mostly multiple-choice fourth-grade science test. Aristo has been taking these tests since 2013, learning from mistakes and filling in knowledge gaps, but has yet to pass one. The Allen Institute for Artificial Intelligence on Lake Union's north shore has been working for three years on the program. Each day, Aristo's memory is wiped clean so it can't cheat by remembering past right and wrong answers. Each week, the tests--but not their difficulty level--are changed.

Up to 40 per cent of current Australian jobs could disappear within the next 10 to 15 years as robots and computers continue their unstoppable advance. They have already replaced humans in workplaces such as factories, supermarkets and airline check-in counters. Hugh Durrant-Whyte, director at the Centre for Translational Data Science at the University of Sydney, said technology was taking on middle-class professions once thought safe from automation -- professions such as law, accountancy and banking. "We always used to think of automation as moving everybody up," he said. "The big difference now is machine learning and artificial intelligence are solving jobs that we thought traditionally were very highly qualified jobs … it's eating out the middle of the job market, rather than the bottom end."

In June on /r/MachineLearning, there were free videos, free books, free courseware, and a quality curriculum made up of free offerings. The word of the month for June is clearly a four letter word starting with'F'. This lot of videos covers a wide range of topics, from general AI, to design issues, to cloud computing, to a variety of machine learning topics and beyond. Microsoft Research has added heavily to these offerings on what seems to be a daily basis since this Reddit post as well. Free knowledge from a top research institute in the field is always welcome.