Abstract: Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.

Consider a regression problem of approximating f(x): Rn Rm when we have two datasets: a set of correct answers corresponding to a typical regression dataset, and an additional set of wrong answers. For a wrong answer ( xi, yi), we only know that the output of f( xi) shouldn't be yi . How can we incorporate this knowledge into the regression? We can add to the loss function a term -( yi - yhati)2 rewarding the model for yielding results far away from the negative examples. But then we'd need to somehow clamp/saturate this reward and add regularization to avoid sending the model output to infinity. This adds hyperparameters and doesn't have any clear theoretical justification.

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

Benoît Carré has written songs for some of France's biggest stars: from Johnny Halliday – the French Elvis, who died last year – to chanteuse Françoise Hardy. But this month, the 47-year-old is releasing an album with a collaborator he could never have dreamt of working with. It's called Flow Machines, and it is, arguably, the world's most advanced artificially intelligent music program. Recently, it's often felt like AI is about to take over the music world – that soon, computers will be making our favourite songs. AI has been used to write classical music and Irish folk songs.

Egyptians call them "emperors", and, every night, millions tune in to watch them lecture, entertain and rant their way through hours of television output. However, the very entertainers people love to watch are also widely recognised as by-products of a state of censorship that has become synonymous with Egyptian media - by-products and hosts on the front lines of Abdel Fattah el-Sisi's government's propaganda efforts. The government has created an environment where disbursement of information, unless it is tightly controlled by the government, is all but impossible. "One of the key aspects of these talk shows is the way they whip out a sense of national emergency," says Marwan Kraidy, director at the Center for Advanced Research in Global Communication. "They react in a very emotional, sensationalistic way to very atrocious events. You not only support the government. You bend over backwards, so to speak. So, dissidents, political prisoners are typically vilified, they are portrayed as enemies of the nation. "And if you portray anybody as an enemy of the nation in a time of emergency," continues Kraidy, "what you're saying is'it's okay to jail them, it's okay to beat them up.' And, in some cases, 'it's okay to kill them.'" Most hosts understand that toeing the line may be overlooked - although not recommended - but are very well aware of the consequences that await them should they cross the unspoken red line set out by the Sisi government. Criticism of the president, the military and/or intelligence services are all off limits. One such journalist who didn't heed the general warnings and guidelines was Ibrahim Eissa. "Ibrahim Eissa is sort of the type of muckraking, investigative journalist who's not afraid of speaking truth to power.

The game board was classic CES misdirection--a way to set the IVS booth apart from the convention's 4,000 other exhibitors and to draw journalists like me into learning about a piece of factory machinery.

Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone. The telescope has produced 14 billion data points about 200,000 stars. It has also amassed 35,000 signals indicating possible planets. People alone would not have been able to keep up. Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone.

Since robots and machines have been incorporated into our lives, we have always lived with the question of will the robot rise and take over humans? We see it in films and TV but could it become a possibility one day? The field of Artificial Intelligence (AI) is growing, rapidly. AI and Machine Learning is becoming a part of our lives, and while at the moment there doesn't seem to be much of a threat I want to take a look at whether AI can go too far and if there are already warning signs of this happening. It was only at the beginning of the month that it was reported that Facebook had to shut down its AI bots as they started communicating with each other in their own language.

Chaos: When the present determines the future, but the approximate present does not approximately determine the future.