The fundamentals issues in Deep Learning are access to data, processing power and data scientists (i.e. But there are four more fundamental things that set companies apart. In this 10min presentation you will learn what the main challenges in Deep Learning are. For the next part of the tutorial, see a live example of running a TensorFlow MNIST example on Valohai: https://www.youtube.com/watch?v L5CcJ...

Artificial intelligence promises considerable economic benefits, even as it disrupts the world of work. Three priorities will help achieve good outcomes. This new article is by James Manyika and Jacques Bughin of the McKinsey Global Institute. I hope you find it useful. The time may have finally come for artificial intelligence (AI) after periods of hype followed by several "AI winters" over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI's power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation.

Artificial intelligence has a bias problem and it's completely our fault. Last week, Amazon decided to ditch the secret AI tool it was using to source new recruits when it discovered their system was actively favouring male over female candidates. It transpires their network had been trained to vet applicants by observing patterns in CVs submitted over a 10-year period and – as one would expect given the perpetuating male dominance across the tech industry – the vast majority of those applications came from men. This caused the AI to perceive male candidates as preferable, going so far as to actively penalise CVs that even mentioned women. Does this infer that the AI was in some way gender biased?

Deep learning a subset of machine learning comes under the realms of artificial intelligence (AI) and works by gathering huge datasets to make machines act like humans. Deep learning having immense potential uses machine learning to tackle new complex problems like speech, language and image recognition by giving machines the power to learn how features in the data combine into increasingly higher level, abstract forms. The deployment of neural networks has aided deep learning to produce optimized results. Deep learning has immense adaptability, like how Facebook uses deep learning to automatically find friends in an image and suggests the user to tag them. According to a leading source, the deep learning market is expected to exceed $18 billion by 2024, growing at a CAGR of 42%.

Deep learning is an increasingly popular subset of machine learning. Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. The weights are adjusted to find patterns in order to make better predictions. The user does not need to specify what patterns to look for -- the neural network learns on its own.

You can follow any comments to this entry through the RSS 2.0 feed. You can skip to the end and leave a comment. Pinging is currently not allowed. Your email address will not be published.

Variable speed limits (VSL) control is a flexible way to improve traffic condition,increase safety and reduce emission. There is an emerging trend of using reinforcement learning technique for VSL control and recent studies have shown promising results. Currently, deep learning is enabling reinforcement learning to develope autonomous control agents for problems that were previously intractable. In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed. The proposed DRL models use a novel actor-critic architecture which can learn a large number of discrete speed limits in a continues action space. Different reward signals, e.g. total travel time, bottleneck speed, emergency braking, and vehicular emission are used to train the DVSL controller, and comparison between these reward signals are conducted. We test proposed DRL baased DVSL controllers on a simulated freeway recurrent bottleneck. Results show that the efficiency, safety and emissions can be improved by the proposed method. We also show some interesting findings through the visulization of the control policies generated from DRL models.

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.

A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed. Computational stigmergy is used to dynamically increase (or decrease) the strength of a connection, or the activation level, of an artificial neuron when stimulated (or released). This study lays down a basic framework for the derivation of a stigmergic NN with a related training algorithm. To show its potential, some pilot experiments have been reported. The XOR problem is solved by using only one single stigmergic neuron with one input and one output. A static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have been trained to solve the MNIST digits recognition benchmark.

State of the art deep learning models have made steady progress in the fields of computer vision and natural language processing, at the expense of growing model sizes and computational complexity. Deploying these models on low power and mobile devices poses a challenge due to their limited compute capabilities and strict energy budgets. One solution that has generated significant research interest is deploying highly quantized models that operate on low precision inputs and weights less than eight bits, trading off accuracy for performance. These models have a significantly reduced memory footprint (up to 32x reduction) and can replace multiply-accumulates with bitwise operations during compute intensive convolution and fully connected layers. Most deep learning frameworks rely on highly engineered linear algebra libraries such as ATLAS or Intel's MKL to implement efficient deep learning operators. To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries. In this paper we introduce a work flow to quickly generate high performance low precision deep learning operators for arbitrary precision that target multiple CPU architectures and include optimizations such as memory tiling and vectorization. We present an extensive case study on low power ARM Cortex-A53 CPU, and show how we can generate 1-bit, 2-bit convolutions with speedups up to 16x over an optimized 16-bit integer baseline and 2.3x better than handwritten implementations.