There, I said it: I don't mind that industry uses'AI' and'machine learning' to mean the same thing. But is it technically correct? And if we pick some nits, who's really using the term'AI' incorrectly? Academia doesn't consider artificial intelligence (AI) and machine learning (ML) interchangeable, and as an ex-academic I sympathize with their definitions and agree that technically AI is a proper superset of ML which is a proper superset of deep learning. Deep learning (DL) is ML that uses a particular class of algorithms (neural networks) and it's what industry tends to mean when it says AI. How's that for proper? But I also think that most people (and industry) don't particularly care about that distinction and will use language in a less formal way.

AI research platform has experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding. AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in business. OpenAI is a non-profit Artificial Intelligence (AI) research company, associated with business magnate Elon Musk, that aims to carefully promote and develop friendly AI in such a way as to benefit, rather than harm, humanity as a whole. The organization aims to "freely collaborate" with other institutions and researchers by making its patents and research open to the public. The company is supported by over US$1 billion in commitments; however, only a tiny fraction of the $1 billion pledged is expected to be spent in the first few years. The founders are motivated in part by concerns about existential risk from artificial general intelligence.

OpenAI's bots were knocked out of The International – the Dota 2 computer game's annual Olympics – on Thursday after they lost to human pros 2-0 in a best-of-three contest. Dota 2 is a hugely popular online battle strategy game (think Command and Conquer meets Tolkien) played by several hundred thousand people worldwide. The aim is to storm a map, take over bases by destroying towers, and take the ultimate prize: the enemy's "Ancient." Teams are made up of five players known as heroes. There is a pool of more than a hundred different hero types all with their own strengths and weaknesses.

No doubt you've heard: AI is the new frontier, one promising once unimaginable advances in terms of efficiencies, productivity, and scientific discovery. But with AI and machine learning in early stages, there's still much uncharted terrain to navigate--and risks to consider, including the potential for AI to learn undesirable behaviors. The good news is it's more like a human, but the bad news is it's more like a human. Paul Muller sits down with Beena Ammanath, global vice president for big data, artificial intelligence, and innovation at Hewlett Packard Enterprise, and Jim McGregor, founder and principal analyst at Tirias Research, to explore the opportunities and challenges of artifical intelligence, how AI development is different from traditional algorithmic programming, and why we'll likely see the emergence of new hybrid AI models. Are you looking for help with your artificial intelligence deep learning journey? We have a Dummies Guide for that.

In this article, we bring you a list of the Top 10 Deep Learning Github Repositories on a trend that has been sorted by the number of stars. TensorFlow, that, within Google's Machine Intelligence Research organization was originally developed by researchers and engineers working on the Google Brain team is an open source software library used for numerical computation making the use of data flow graphs for the purpose of scalable machine learning. Keras, written in Python is a high-level neural networks API that is capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation that through user friendliness, modularity, and extensibility allows for easy as well as fast prototyping. In short, can be said to be Deep Learning For Humans.

With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box. In this paper it is analyzed how recurrent neural network (RNNs) cope with temporal dependencies by determining the relevant memory time span in a long short-term memory (LSTM) cell. This is done by leaking the state variable with a controlled lifetime and evaluating the task performance. This technique can be used for any task to estimate the time span the LSTM exploits in that specific scenario. The focus in this paper is on the task of separating speakers from overlapping speech. We discern two effects: A long term effect, probably due to speaker characterization and a short term effect, probably exploiting phone-size formant tracks.

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.

Many popular representation-learning algorithms use training objectives defined on the observed data space (which we call "pixel-level"). This may be detrimental when only a small fraction of the bits of signal actually matter at a semantic level. We hypothesize that representations should be learned and evaluated more directly in terms of their information content and statistical or structural constraints. To address the first quality, we consider learning unsupervised representations by maximizing mutual information between part or all of the input and a high-level feature vector. To address the second, we control characteristics of the representation by matching to a prior adversarially. Our method, which we call Deep INFOMAX (DIM), can be used to learn representations with desired characteristics and which empirically outperform a number of popular unsupervised learning methods on classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives catered towards specific end-goals.

Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffers from the noisy labeling problem. In this paper, we propose a novel model for relation classification at the sentence level from noisy data. The model has two modules: an instance selector and a relation classifier. The instance selector chooses high-quality sentences with reinforcement learning and feeds the selected sentences into the relation classifier, and the relation classifier makes sentence level prediction and provides rewards to the instance selector. The two modules are trained jointly to optimize the instance selection and relation classification processes. Experiment results show that our model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level.

Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different scenarios or creating a single model for multiple scenarios have been very rare. In this work it is shown that data of a specific scenario is relevant for solving another scenario. Furthermore, it is concluded that a single model, trained on different scenarios is capable of matching performance of scenario specific models.