Three-year olds are known for a long list of bad habits: biting other kids, throwing toys at their mom and answering every question with "no." Despite those irrational behaviours, they are also smart. Show a three-year-old girl a van, a truck and a car, and she will quickly learn to identify the three types of vehicles. Digvir Jayas, vice-president of research at the University of Manitoba and grain storage expert, said computers aren't as smart as three- year olds, at least when it comes to computer vision and identifying objects. But scientists are now teaching computers to think like a three-year old, so the machines can see the differences between one object and another.
Russia's Kalashnikov arms manufacturer (part of state hi-tech corporation Rostec) has developed certain expertise in creating military systems with artificial intelligence that implies weapons' self-learning, Industrial Director of the Rostec Conventional Armament, Ammunition and Special Chemistry Cluster Sergei Abramov said on Wednesday. The Rostec official spoke at the conference tiled: "Digital Industry of Industrial Russia 2018." The need, prospects and expediency of creating and adapting artificial intelligence systems for military use do not evoke any doubts and all the leading countries are taking efforts for introducing artificial intelligence into existing and future weapons, he said. "The developers of military hardware consider this task largely as an engineering effort: to create the corresponding software and hardware meeting the Defense Ministry's requirements. In this regard, we should note the expertise accumulated by the Kalashnikov Group that has achieved certain successes in this sphere," Abramov said, without specifying the weapon systems.
This deep learning method lets the algorithm build from a single layer of information so that the understanding of a deeper network of data is possible. Through clustering, for example, neural networks of true or false questions that can represent numerical data. Asked repeatedly, this method allows machines to learn all the answers to the questions and turn them into new rules that evolve when new data appears. New data means an added layer of information with subsequent increase of intelligence and learning from mistakes.
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at https://github.com/artetxem/vecmap
Machine learning is a collection of computer methodologies or algorithms that predict outcomes based on collating information from previous choices. These learning systems are adaptive, constantly evolving from new examples, and capable of determining internal parameters to recognize the nature of new data. ML acquires knowledge through the analysis of previous behaviors and/or experimental data, e.g. a learning dataset. Smart technology and AI applications and programs collect a vast amount of data which can then be analyzed to predict outcomes. Information obtained using machine learning methods are by far the most dependable way to predict results and construct reliable models, particularly if some data is unknown or unobtainable.
Some jobs are simply too dangerous for humans. Near the top of that list is nuclear cleanup -- so to keep us mere homo sapiens safe, we're sending in robots. To help improve the bots' decision making skills, scientists at the University of Lincoln have won a grant to develop artificial intelligence systems for self-learning robots deployed at nuclear sites. The team received £1.1 million ($1.5 million) from the UK Engineering and Physical Sciences Research Council to develop machine learning AI. The algorithms will help robots handle tasks like decommissioning, waste handling and site monitoring.
Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. The FastText model was first introduced by Facebook in 2016 as an extension and supposedly improvement of the vanilla Word2Vec model. Based on the original paper titled'Enriching Word Vectors with Subword Information' by Mikolov et al. which is an excellent read to gain an in-depth understanding of how this model works. Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub and claims to have the following.
We introduce a novel view to understand how dropout works as an inexplicit ensemble learning method, which do not point out how many and which nodes to learn a certain feature. We propose a new training method named internal node bagging, this method explicitly force a group of nodes to learn a certain feature in training time, and combine those nodes to be one node in inference time. It means we can use much more parameters to improve model's fitting ability in training time while keeping model small in inference time. We test our method on several benchmark datasets and find it significantly more efficiency than dropout on small model.
Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec. However the technique is different and training is performed on an aggregated global word-word co-occurrence matrix, giving us a vector space with meaningful sub-structures. This method was invented in Stanford by Pennington et al. and I recommend you to read the original paper on GloVe, 'GloVe: Global Vectors for Word Representation' by Pennington et al. which is an excellent read to get some perspective on how this model works. We won't cover the implementation of the model from scratch in too much detail here but if you are interested in the actual code, you can check out the official GloVe page.
Patients have had heart failure (HF) for centuries, and it is estimated that more than 37 million people worldwide are currently affected.1 Despite being a complex clinical syndrome, contemporary clinical descriptors lag far behind its nuanced scientific understanding. In fact, current classifications used clinically and in trials rely heavily on incomplete descriptors such as left ventricular ejection fraction (LVEF) cut points, stratifying patients simply as those with "reduced" or "preserved" LVEF: HFrEF and HFpEF.2 There is increasing recognition that such classifications are discordant with the current understanding of HF and may impair our ability to personalize risk assessment and treatment. The emphasis on LVEF is particularly notable as prior studies have shown only modest differences in long‐term survival among patients with "reduced" as compared with "preserved" LVEF.3, 4 Still further, numerous promising therapies have failed to demonstrate benefit in clinical trials where inclusion was based almost exclusively on LVEF.5 Despite this, recent guidelines have recommended even further subclassification of HF according to LVEF, with the introduction of HF with "midrange ejection fraction" as a distinct clinical entity.6