We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs by training a neural network with a single hidden layer of nonlinear gates. Our main finding is that GD starting from a randomly initialized network converges in mean squared loss to the minimum error (in 2-norm) of the best approximation of the target function using a polynomial of degree at most $k$. Moreover, the size of the network and number of iterations needed are both bounded by $n^{O(k)}$. The core of our analysis is the following existence theorem, which is of independent interest: for any $\epsilon > 0$, any bounded function that has a degree-$k$ polynomial approximation with error $\epsilon_0$ (in 2-norm), can be approximated to within error $\epsilon_0 + \epsilon$ as a linear combination of $n^{O(k)} \mbox{poly}(1/\epsilon)$ randomly chosen gates from any class of gates whose corresponding activation function has nonzero coefficients in its harmonic expansion for degrees up to $k$. In particular, this applies to training networks of unbiased sigmoids and ReLUs.

The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles are deployed on urban roads a minimum safety level must be assured. Since the autonomous car technology is still under development there is no standard methodology to evaluate such systems. It is important to completely understand the technology that is being developed to design efficient means to evaluate it. In this paper we assume safety-critical systems reliability as a safety measure. We model an autonomous road vehicle as an intelligent agent and we approach its evaluation from an artificial intelligence perspective. Our focus is the evaluation of perception and decision making systems and also to propose a systematic method to evaluate their integration in the vehicle. We identify critical aspects of the data dependency from the artificial intelligence state of the art models and we also propose procedures to reproduce them.

The Internet of Things empowers a high level of interconnectivity between smart phones, sensors and wearable devices. These technological developments provide unprecedented opportunities to rethink about the future of machine learning and artificial intelligence: Centralized computational intelligence can be often used for privacy-intrusive and discriminatory services that create'filter bubbles' and undermine citizens' autonomy by nudging [11, 27, 15]. In contrast, this paper envisions a more socially responsible design for digital society based on decentralized learning and collective intelligence formed by bottomup planetary-scale networks run by citizens [17, 16]. In this context, the structural elements of decentralized deep learning processes play a key role. The effectiveness of several classification and prediction operations often relies heavily on hyperparameter optimization [24, 46] and on the learning structure, for instance, the number of layers in a neural network, the interconnectivity of the neurons, the activation or deactivation of certain pathways i.e. dropout regularization [44], can enhance learning performance.

Bidirectional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers. 1 Introduction Neural models have become the dominant approach in the NLP literature. Compared to handcrafted indicator features, neural sentence representations are less sparse, and more flexible in encoding intricate syntactic and semantic information. Among various neural networks for encoding sentences, bidirectional LSTMs (BiLSTM) (Hochreiter and Schmidhuber, 1997) have been a dominant method, giving state-of-the-art results in language modelling (Sundermeyer et al., 2012), machine translation (Bahdanau et al., 2015), syntactic parsing (Dozat and Manning, 2017) and question answering (Tan et al., 2015). Despite their success, BiLSTMs have been shown to suffer several limitations.

We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of $5$ feedback requests for every input.

Ask critics of police face recognition why they're so skeptical and they'll likely cite unreliability as one factor. Unfortunately, that caution appears to have been warranted to some degree. South Wales Police are facing a backlash after they released data showing that their face recognition trial at the 2017 Champions League final misidentified thousands as potential criminals. Out out of 2,470 initial matches, 2,297 were false positives -- about 92 percent. The police unit pinned the results on both "poor quality images" from Interpol and UEFA and the novelty of the technology.

Artificial intelligence (AI) is seen as both a boon and a threat. It uses our personal data to influence our lives without us realising it. It is used by social media to draw our attention to things we are interested in buying, and by our tablets and computers to predict what we want to type (good). It facilitates targeting of voters to influence elections (bad, particularly if your side loses). Perhaps the truth or otherwise of allegations such as electoral interference should be regarded in the light of the interests of their promoters.

Every year an estimated 20 million people die from heart disease, but now, hot on the heels of an artificial intelligence (AI) system that can predict death, a team of researchers from the University of Nottingham, the same university that's found a way to regrow teeth from stem cells, have developed a machine learning algorithm that can predict an individuals likelihood of having a heart attack, or a stroke, better than any doctor. Over the past few decades the American College of Cardiology and the American Heart Association (ACC-AHA) has developed a series of guidelines to help doctors evaluate a patient's cardiovascular risk, based on eight factors that include age, cholesterol level and blood pressure. You might think that's already good enough – but Stephen Weng and his team wanted to make it even better so they built four computer learning algorithms and fed them data from over 380,000 patients. Firstly, the new system used 295,000 records to build its internal predictive models, and then it used the remaining 85,000 records to test and refine them, and the result? After all, you don't want to be told you're likely to have a heart attack if you aren't – doctors aren't beasts you know… Translating all of that into normal language what this all means is that out of the 85,000 records it analysed the new model could have saved 355 lives, but interestingly the AI system identified a number of risk factors and predictors not covered in the existing guidelines, like severe mental illness and the consumption of oral corticosteroids.

New research, from the University of Plymouth, U.K., indicates that a social robot can deliver a'helpful' and'enjoyable' motivational interview. This artificial intelligence equipped machine could be used to help counsel humans with a wide variety of problems, supplanting psychologists across a number of areas. Of course the use of a machine in place of a qualified psychologist remains many years away and even when the technology reaches an acceptable level of competency, revealing all to a machine may not be to everyone's taste. Nonetheless, the advances made in a recent study show how close artificial intelligence is coming to offering a counseling service. The focus has been on the "motivational interview".

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.