Machine learning, deep learning, and Artificial Intelligence (AI) are buzzwords that everyone is talking about. These terms often seem to be used interchangeably which creates lots of misconceptions in people's understanding. Hence, the need for why it is important to dispel the myth that these concepts are synonymous and understand the difference between the three. Both machine learning and deep learning help discover latent patterns in data, but they involve dramatically different techniques and coverage. Machine learning and deep learning are both subsets of AI.

Resiliency and scalability of contact centers are the key factors to Business Continuity Planning. Contact centers built on legacy systems are expensive to maintain and do not scale well to handle increasing customer anxiety. Contact center operations face dips in agent productivity due to legacy technology and skill gaps. To overcome these business challenges, organizations must implement a flexible solution and leverage automation to enhance operational scalability. Join our upcoming webinar with guest speaker, Analyst Kate Leggett of Forrester Research to learn how to leverage a full suite of AI technologies, including Natural Language Processing and machine and deep learning to create a smarter, and more scalable contact center.

In a technical paper quietly released earlier this year, IBM detailed what it calls the IBM Neural Computer, a custom-designed, reconfigurable parallel processing system designed to research and develop emerging AI algorithms and computational neuroscience. This week, the company published a preprint describing the first application demonstrated on the Neural Computer: a deep "neuroevolution" system that combines the hardware implementation of an Atari 2600, image preprocessing, and AI algorithms in an optimized pipeline. The coauthors report results competitive with state-of-the-art techniques, but perhaps more significantly, they claim that the system achieves a record training time of 1.2 million image frames per second. The Neural Computer represents something of a shot across the bow in the AI computational arms race. According to an analysis recently released by OpenAI, from 2012 to 2018, the amount of compute used in the largest AI training runs grew more than 300,000 times with a 3.5-month doubling time, far exceeding the pace of Moore's law. Video games are a well-established platform for AI and machine learning research.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." The workshop marked the official beginning of AI history. But as the two-month effort--and many others that followed--only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it.

Bit by bit, byte by byte, artificial intelligence has been working its way into public consciousness and into everyday computer use. Artificial intelligence and deep learning have been deeply woven into more and more aspects of end-user computing. Smartphones and other mobile devices use AI as well. Up until now, the artificial intelligence work has been done in the cloud, but a new approach to software design aims to arm mobile devices with real artificial-intelligence capability. "A mobile device is very resource-constrained," explained William & Mary computer scientist Bin Ren.

We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same number of training epochs as dense models. Dynamic Sparse Training achieves state of the art performance compared with other sparse training algorithms on various network architectures. Additionally, we have several surprising observations that provide strong evidence to the effectiveness and efficiency of our algorithm. These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures. Despite the impressive success that deep neural networks have achieved in a wide range of challenging tasks, the inference in deep neural networks is highly memory-intensive and computationintensive due to the over-parameterization of deep neural networks. Network pruning (LeCun et al. (1990); Han et al. (2015); Molchanov et al. (2017)) has been recognized as an effective approach to improving the inference efficiency in resource-limited scenarios. Traditional pruning methods consist of dense network training followed with pruning and fine-tuning iterations. To avoid the expensive pruning and fine-tuning iterations, many sparse training methods (Mocanu et al., 2018; Bellec et al., 2017; Mostafa & Wang, 2019; Dettmers & Zettlemoyer, 2019) have been proposed, where the network pruning is conducted during the training process. However, all these methods suffer from following three problems: Coarse-grained predefined pruning schedule.

To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation of the weighted inputs, FBN normalizes the intermediate state of the summation. We propose a novel light-weight network based on FBN, called Finet. At training time, the convolutional layer with FBN can be seen as an inverted bottleneck mechanism. FBN can be fused into convolution at inference time. After fusion, Finet uses the standard convolution with equal channel width, thus makes the inference more efficient. On ImageNet classification dataset, Finet achieves the state-of-art performance (65.706% accuracy with 43M FLOPs, and 73.786% accuracy with 303M FLOPs), Moreover, experiments show that Finet is more efficient than other state-of-art light-weight networks.

The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors). There is a wide range of applications within the field; for instance, assisted living, security surveillance, and intelligent transportation. In HAR, the development of Activity Recognition models is dependent upon the data captured by these devices and the methods used to analyse them, which directly affect performance metrics. In this work, we address the issue of accurately recognising human activities using different Machine Learning (ML) techniques. We propose a new feature representation based on consecutive occurring observations and compare it against previously used feature representations using a wide range of classification methods. Experimental results demonstrate that techniques based on the proposed representation outperform the baselines and a better accuracy was achieved for both highly and less frequent actions. We also investigate how the addition of further features and their pre-processing techniques affect performance results leading to state-of-the-art accuracy on a Human Activity Recognition dataset.

Recently, with the development of deep learning, end-to-end neural network architectures have been increasingly applied to condition monitoring signals. They have demonstrated superior performance for fault detection and classification, in particular using convolutional neural networks. Even more recently, an extension of the concept of convolution to the concept of kervolution has been proposed with some promising results in image classification tasks. In this paper, we explore the potential of kervolutional neural networks applied to time series data. We demonstrate that using a mixture of convolutional and kervolutional layers improves the model performance. The mixed model is first applied to a classification task in time series, as a benchmark dataset. Subsequently, the proposed mixed architecture is used to detect anomalies in time series data recorded by accelerometers on helicopters. We propose a residual-based anomaly detection approach using a temporal auto-encoder. We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.