This tutorial will explain some practical tips about how to train a neural machine translation system. It is partly based around examples using the lamtram toolkit. Note that this will not cover the theory behind NMT in detail, nor is it a survey meant to cover all the work on neural MT, but it will show you how to use lamtram, and also demonstrate some things that you have to do in order to make a system that actually works well (focusing on ones that are implemented in my toolkit). This tutorial will assume that you have already installed lamtram (and the cnn backend library that it depends on) on Linux or Mac. Then, use git to pull this tutorial and the corresponding data. The data in the data/ directory is Japanese-English data that I have prepared doing some language-specific preprocessing (tokenization, lowercasing, etc.). Machine translation is a method for translating from a source sequence F with words f_1, ..., f_J to a target sequence E with words e_1, ..., e_I. This usually means that we translate between a sentence in a source language (e.g.

Science fiction authors have always dreamed up cities of the future. When it comes to seeing those dreams realized, though, artists and filmmakers have heavily focused on what Western cities might look like. There are, however, exceptions to that rule. Olalekan Jeyifous is a Nigerian-born artist currently based in Brooklyn. With a background in architecture, much of his work is focused on urban environments and buildings.

After more than 10 years since the release of "Robocop" the dreams of film creators are likely to become true. Of course, the humanity has not devised hi-tech cyborgs like the main character of the film. However, today complicated robots maintain public order and security in different parts of the world. Here you can get yourself acquainted with "robocops" which help police in real world. This year after annual Republican Convention the police of Cleveland allowed a robot named Griffin to patrol the streets.

Data volumes are soaring to previously unimaginable heights. More data has been created in the past two years than in the entire history of humanity. It is predicted that, by 2020, each person on the planet will account for the creation of an average of 1.7 megabytes of new data every second. Scalable AI solutions could help address humanity's biggest challenges. Drawing meaningful insight from such vast amounts data is beyond our capabilities as humans, but perhaps not those of machines.

Still think the Great Pyramid is an ancient tomb? Some experts in deep learning may beg to differ. They have discovered that a particular application of deep learning methodology applied to the ancient structures on the Giza Plateau, including the Great Pyramid, may in fact show that it is a three dimensional process diagram. "We were using a deep learning methodology that helps us identify and categorize unknown symbols and processes. We thought it would be fun to use it on something everyone knows. We were amazed to find that the Giza Plateau may in fact be a three dimensional process diagram."

Microsoft's attempt to use machine learning to improve on the Duckworth-Lewis method in cricket has been dismissed by the current custodian of the system. For the uninitiated, the Duckworth-Lewis method, or D/L method for short, is used to calculate the score that the team batting second in a limited overs cricket match needs to reach if the match is affected by rain. For example, if the team batting first scores 400 in 50 overs for five wickets, but rain reduces the second team's innings to 40 overs, the D/L method may put forward a score of 300. It was invented by statisticians Frank Duckworth and Tony Lewis and was first used in 1997 in a match between England and Zimbabwe. It is now regularly used at matches at all levels.

This is a tough task, I was in this precarious situation trying to explain to my younger son. I had a curated list of top 10 frequently used Machine Learning algorithms, but the key was to do a backward mapping of these Machine Learning techniques to solve problems which are of interest and relevance to my son. As a starting point to the conversation I asked him, list down your decision making points, meaning there may be many situations when you had to make decisions but you may not have all the information. I took those and matched it to the machine learning algorithms while explaining the core concept behind the problem solving. A classifier is a machine learning technique that takes a bunch of data and attempts to predict which class the new data belongs to. Sure, remember sometime back you were asking me who you want to invite to your birthday party and whether they will accept your invitation or not! Now, assume that you have got a data set about all the other 29 kids in your class. The information contains their hobby, kind of books they read, do they share their tiffin or not, are they friendly, in your last birthday did they come and did they bring nice gifts, etc. Now, given these information, you want to predict whether your classmates will accept your invitation or not.

One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction. Ex Machina, a Hollywood blockbuster made on a 15 million budget, tells the story of a programmer who is invited by his employer, the eccentric billionaire Nathan Bateman who built a fictional search engine called Blue Book, to administer the Turing test to an android with artificial intelligence, which essentially determines whether a computer can trick a human into believing she is having a conversation with another human. Everything in our online life is indexed. Every idle tweet, status update, or curious search query feeds the Google database.

This is the last part of our special series on Artificial Intelligence (AI). If you are catching it for the first time you can check out our early posts here, here and here where we build on the bigger idea behind AI. In this series, I have seized moments to highlight just how quickly AI is unfolding. The interesting bit is that the developments silently running in the background, like computer processes, for the most part, are away from everyday headlines but it's still a big deal. From my point of view, judging by the number of AI startups that have come up and are being acquired, we are in the middle of an AI gold rush right now which some have characterized as the AI spring . This hurry is also the reason behind key backers and AI enthusiasts such as Elon Musk, the CEO of Tesla, have been a sounding alarm.