Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Word embedding is a technique that treats words as vectors whose relative similarities correlate with semantic similarity. This technique is one of the most successful applications of unsupervised learning. Natural language processing (NLP) systems traditionally encode words as strings, which are arbitrary and provide no useful information to the system regarding the relationships that may exist between different words. Word embedding is an alternative technique in NLP, whereby words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size, and the similarities between the vectors correlate with the words' semantic similarity. For example, let's take the words woman, man, queen, and king.

I am unable to train a network using my pretrained wording embeddings as weights for input layer to LSTM. My word2vec embedding is trained on a larger corpus and the training corpus is a subset of it. My vocabulary for the task consists of some word2vec_vocab additional words in corpus. Test data: one hot vector with 1 at position w.r.t corpus(I am not using full dict for mapping, thus position indices differ.) Problem: Model overfitting on training data with increasing loss on validation set.

This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision. In a step toward overcoming this hurdle, researchers at Google's DeepMind -- the company that developed the Go-playing computer program AlphaGo -- announced earlier this week the creation of a neural network that can not only learn, but can also use data stored in its memory to "logically reason" and make inferences to answer questions. DeepMind's new system -- called a Differentiable Neural Computer (DNC) -- combines deep learning, wherein it can learn from examples and make sense of complex input it has never received before, with an external memory, which, as the DeepMind researchers Alexander Graves and Greg Wayne explain in a blog post, allows it to "store knowledge quickly and reason about it flexibly." In order to achieve this, the researchers first trained the neural network using randomly generated map-like structures -- a process that allowed the DNC to learn how to store connections between various parts in its external memory. After this, when it was confronted with a new map, the DNC was able to provide answers that were not explicitly stated in the data set.

With the increasing volume of interactions with customers over social media channels, KLM Royal Dutch Airline is the first airline to test how artificial intelligence could assist customer service agents. "We have 100,000 mentions a week on social media," says Tjalling Smit, senior vice president of Digital at KLM Royal Dutch Airlines. "We handle around 15,000 customer service cases a week and we answer our customers 24/7 in 10 different languages." As social channels proliferate, KLM makes sure it is present where its customers live online. "We were the first airline to allow customers to get their boarding passes and flight confirmation through Facebook Messenger," says Smit. KLM is piloting DigitalGenius' GPU-accelerated AI system that is integrated directly into KLM's Customer Relationship Management tool, and provides a layer of deep learning and artificial intelligence to service agents in real-time.

Machine Learning Deep learning start-up MetaMind has published details of a system that is more accurate than other language processing methods. The company is developing technology designed to be capable of a range of different artificial-intelligence tasks. Google and Facebook, are investing huge sums into the research and development of improved artificial intelligence algorithms for processing language. Around the world, various research groups are making steady progress toward improving a computer's language skills especially using recent advances in machine learning. "The insight--and it's almost trivial--is that every task in NLP is actually a question-and-answer task."

And – why we aren't ready for Superintelligence, DeepMind created an AI with memory, Facebook's ideas for VR and more! Last weekend we saw Cybathlon, the world's first "bionic Olympics", where disabled athletes assisted with exoskeletons, prosthetic robotic hands or brain-computer interfaces competed in a series of challenges. This article from BBC describes the games and lists all the winners. Some amputees want to have a prosthetic limb that can do a bit more or just looks better.Waterproof, dustproof, customized to client's skin color, matching to the owner's tattoos. And there are companies that are ready to help them for an appropriate price.

Scientists at Google have created an artificial intelligence program that can compute problems requiring strategic reasoning, The Guardian reports. The algorithm, part of an emerging field called deep learning, is able to master tasks independently using external memory, similar to the way humans work through a new recipe, according to the study published in Nature. In this case, it was able to figure out on its own the quickest route between stops on the London Underground and reassess if the destination was overshot. This could pave the way to more efficient virtual assistant applications, which might be bad news for Apple's sassy sidekick.

News[N] Intel will add deep-learning instructions to its processors (lemire.me) I don't understand why you would bother though, I thought the massive parallel nature of GPUs was a major reason as to why they are used for deep learning, not solely because of the instructions.

What is driving the'robot age' and how can businesses leverage the capabilities being produced? Artificial intelligence is one of the 21st century's dominant fields of innovation. So it's no surprise that cutting-edge robots and other advanced smart machines fall under the rapidly expanding Internet of Things, which is projected to reach 25 billion devices by 2020. Every day we're reading headlines on machines getting'smarter' and robotics transforming a variety of industries, but what's driving this'robot age' and how can businesses successfully integrate and leverage this advanced automation? It's clear that artificial intelligence (AI) is a new industrial revolution, one that's driving the rise of robotics. But AI won't just be an industry – it will be part of every industry.