-- Probabilistic Neural Network (PNN) is a feed-forward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit. Probabilistic Neural Network (PNN) in comparison to the other neural network types have a faster training phase where the training data is used for computing the probability density functions (PDF) of each data category.

Abstract-- The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%. The use of the resistive switching memories in crossbar provides an option to build analog computing neural networks to implements dot product operation [1].

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.

AI is a buzzword across nearly all industries, and there's lots of talk about how it can transform customer experience. But how can AI play a role in customer experience, and what does it actually look like when it's put into action? The possibilities may be endless, but many of them boil down to three main ideas. First, customer service chatbots and virtual assistants can turn everyday tasks into simple commands. We're already surrounded by these virtual assistants in the form of Amazon Alexa, Google Home, Apple's Siri and more.

Slice Labs Inc., a U.S. company offering cloud-based on-demand insurance, has partnered with XL Catlin to offer an on-demand cyber insurance product for U.S. small and medium-sized business (SMBs) built on the Slice Insurance Cloud Services (ICS) platform. The ICS platform will include services designed to help clients best prepare for cyberattacks. The subscription and usage-based cyber product will be available on-demand for SMBs. The product will use artificial intelligence to deliver real-time alerts to customers so they can better manage their risk postures. The ICS platform delivers through a monthly subscription without contracts or implementation costs.

AI systems already display vision, language and controlled motor skills, but researchers are looking to answer the question: 'When will we have robots that can do housework, communicate in natural language conversations and defend themselves against discrimination?' At the global artificial intelligence conference IJCAI-ECAI 2018 held in Stockholm, Sweden, AI experts and research students from top universities around the world came together to discuss the state of AI as it stands today, and where we are headed in the not-so-distant future. "There won't be a Big AI Bang where complete AI systems suddenly surround us in the next year," says Christian Guttmann, Executive Director of the Nordic Artificial Intelligence Institute, "Instead, we will see more and more AI features being included in our products and services." Most researchers are in agreement – artificial intelligence will not become ubiquitous in a day. "The truth is, that despite tremendous advances in AI technologies, we are still far from having robot maids," according to Joyce Chai, Director of the Language and Interaction Research Group at Michigan State University.

With all of the buzz around regtech, it's easy to forget that banks have leveraged technology for compliance and reporting for decades. But thanks to recent developments in data architecture, artificial intelligence and more, regtech is on the rise, and it's evolving into something a lot more sophisticated. The definition of regtech is simple. According to New-York-based analytics firm CB Insights, regtech is "technology that addresses regulatory challenges and facilitates the delivery of compliance requirements." Regtech can be as simple as using an Excel spreadsheet for financial reporting or as complex as using adaptive algorithms to monitor markets.

Airborne data collecting platforms like the RQ-4 Global Hawk have a problem: the usefulness of the data they collect is limited by how fast and how well it can be analyzed. US military intelligence gathers a lot of data, but in order to make the data useful for a decision making process, the Air Force needs a "sensing grid that fuses together data," C4ISRNET reported Wednesday. AI will help the force interpret that fused data. The AI will harvest information from airborne systems in development such as Gremlin drones, which the US military portrays as a swarm of small drones that take off from an aircraft mid-flight and are recovered by the same aircraft. "How do I get the data so I can fuse it, look at it and then ask the right questions from the data to reveal what trends are out there?" Lt. Gen. VeraLinn Jamieson said in a July 31 interview with the news outlet.

Artificial intelligence is already helping marketers succeed in a number of ways, from efficiently engaging website visitors via chatbots to conducting predictive analytics campaigns. We've asked some marketing experts, and they came up with four distinct, yet equally exciting ways that AI will change content marketing in 2019 and beyond. According to Peter Mikeal, Head of Marketing Strategy at NC.-based Small Footprint, AI will fundamentally change the landscape of marketing as we know it. "The age-old statement in marketing, "50 percent of our marketing efforts work, we just don't know which 50%", will become a fable of the past," he said. He noted that as AI matures it will eliminate many of the legacy marketing jobs that are task-oriented and will create new ones to manage the data and make swift pivots to increase lead generation and revenue.