Technology billionaire Elon Musk has quit the board of the research group he co-founded to look into the ethics of artificial intelligence. In a blog post, OpenAI said the decision had been taken to avoid any conflict of interest as Mr Musk's electric car company, Tesla, became "more focused on AI". He has been one of AI's most vocal critics, stressing the potential harms. Mr Musk will continue to donate to and advise the group. In 2014, Mr Musk said AI was humanity's biggest existential threat.

Elon Musk is stepping down from the board of a $1 billion AI organisation he helped create. On Tuesday, OpenAI announced that the billionaire entrepreneur is exiting his role at the company to avoid potential conflicts of interest with his work at Tesla. Founded in 2015 with $1 billion in funding, OpenAI is a non-profit organisation focused on research about artificial intelligence technology and on examining its potential social implications and safety risks. It has developed AI capable of playing video games like "Dota 2," and earlier this week co-authored a report looking at how artificial intelligence technology could be abused for malicious purposes, from drone attacks to fraudulent videos. In a blog post published Tuesday, OpenAI said Musk was leaving its board, but will continue to provide funding and advice. "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon," the post said.

Two Imperial undergraduate students have launched a new multidisciplinary Machine Learning Society. Undergraduates Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) have set up the Imperial College Machine Learning Society to get students involved in and inspired by technology that's going to change the world. Here they tell us more about what inspired them, what happened on launch day and their plans for the future. Image above: Antonia Creswell teaches the audience about the history of machine learning, specifically deep learning. Harry: We really wanted to emphasise the interdisciplinary potential of machine learning – it's not just for computing students, or postgraduates – we're keen to give everyone, particularly undergrad students, the opportunity to get involved.

Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there. Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has "learned", it can classify, with a certain probability of accuracy, passing vehicles by their make and model.

Introduction Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library. In the last few weeks, I have been dabbling a bit in PyTorch.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

Long Short Term Memory networks, usually called "LSTMs", were introduced by Hochreiter and Schmiduber. These have widely been used for speech recognition, language modeling, sentiment analysis and text prediction. Before going deep into LSTM, we should first understand the need of LSTM which can be explained by the drawback of practical use of Recurrent Neural Network (RNN). So, lets start with RNN. Being human, when we watch a movie, we don't think from scratch every time while understanding any event.

Is it not odd to anyone that Deep Learning uses only real numbers? Or perhaps, it would be even odder if Deep Learning uses complex numbers (note: the kind with imaginary numbers). One viable argument is that it is highly unlikely that the brain uses complex numbers in its computation. However, you can make the argument also that the brain doesn't perform matrix multiplication or perform chain rule differentiation. Besides, Artificial Neural Networks (ANN) have a cartoonish model of actual neurons.

Artificial intelligence is modeled to some extent on the human brain; and there's a deep problem with this approach. Machine learning is a subset of artificial intelligence (AI) where computer programs automatically learn from data without explicit programming. Inspired in part by the human biology, deep learning is a machine learning method that deploys layers of artificial neurons, called nodes, in an artificial brain called a neural network. Neuroscientists and psychologists have yet to fully understand how the human brain works. Similarly, there's a big problem with deep learning; scientists do not really know exactly how deep learning reaches its decisions.

Curated list of my reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world. Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.