Horticulture NZ's conference for 2019 was held over two days at Mystery Creek in Hamilton from 31 July, under the theme of'Our Food Future'. Mark attended on behalf of PlantTech and contributed to the discussion about the emerging use of technology in horticulture and how the widespread application of drones, robotics and advancing imaging would become easier and more affordable over time. The pace of change has been described as being similar to the principle of Moore's Law, which states that the speed and capability of computers can be expected to double every 18 months. It would appear that these developing technologies are on a very similar trajectory. With the increasing demand for food, there's a great deal of interest in how the industry can respond to that challenge without creating further pressure on the environment.

Top Stories, Tweets Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford's Law and why is it important for data science? Top KDnuggets tweets, Jul 31 - Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing News Exploratory Data Analysis Using Python Meetings The slow, startling triumph of Reverend Bayes - John Elder's 2019 Keynote at PAW in London Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme Academic Postdoctoral position (2 years) in multivariate analysis and deep learning PhD student position in computational science with focus on chemistry Monash University: Research Fellow - Computer Vision [Melbourne, Australia] Image of the week 12 NLP Researchers, Practitioners, Innovators to Follow Learn how to do Machine Learning on Graphs; Follow these 12 amazing leaders in NLP; Read the explanation of Deep Learning for NLP, including ANNs, RNNs and LSTMs; Understand what is Benford's Law and why is it important for data science; Find the 6 key concepts in Andrew NG Machine Learning Yearning; and more. Knowing Your Neighbours: Machine Learning on Graphs 12 NLP Researchers, Practitioners & Innovators You Should Be Following Deep Learning for NLP: ANNs, RNNs and LSTMs explained! What is Benford's Law and why is it important for data science?

The demand for artificial intelligence has grown significantly in the last few years due to the advantages it provides. Rising use of big data, growing demand for greater transparency and visibility into supply chain data and processes, and increasing adoption of AI for improving consumer services and satisfaction are some of the other factors driving the demand in this market. Moreover, growing applicability of AI in various industries has further augmented the demand in this market. The global artificial intelligence in supply chain market could be classified on basis of technology, application, end-user industry, and offerings. The end-user industry category can further be segmented into manufacturing, aerospace, automotive, retail, consumer packaged goods, healthcare, food and beverages, and others.

Almost two years ago, Dennis Degray sent an unusual text message to his friend. "You are holding in your hand the very first text message ever sent from the neurons of one mind to the mobile device of another," he recalls it read. Degray, 66, has been paralysed from the collarbones down since an unlucky fall over a decade ago. He was able to send the message because in 2016 he had two tiny squares of silicon with protruding metal electrodes surgically implanted in his motor cortex, the part of the brain that controls movement. By imagining moving a joystick with his hand, he is able to move a cursor to select letters on a screen.

Countless studies have shown not only that gender bias is real but also that it has significant repercussions. The pattern of diminishing representation on the higher rungs of the career ladder in STEM fields also holds true broadly in the corporate world: 56% of university graduates are women, yet women represent only 38% of the total workforce, 26% of the managerial ranks, 15% of executive-level positions, and 5% of the CEO ranks. How, then, will AI affect gender diversity in the leadership pipeline? AI has the potential to mitigate the corporate gender and leadership gaps by removing bias in recruiting, evaluation, and promotion decisions; by helping improve retention of women employees; and, potentially, by intervening in the everyday interactions that affect employees' sense of inclusion. Biased data is a source of risk.

The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these "smart objects" to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies. For example, the smart refrigerator in your kitchen (at home) can send you an alert (or notification) on your smartphone (while you are leaving office) when you're out of milk or gas. Your wearable or smartwatch can warn you if there is something wrong with your pulse or heart-rate. Additionally, all this information gets recorded. Later, the software after looking at the data can provide you information like: you are likely to run of milk on Wednesday, run out of gas in two weeks, or likely to get a heart attack in three months (so, time for a check-up and take precautions).

Recently, Dynamic Business attended the 2-day Gartner ReImagine HR event to hear the new trends, reports and insights from HR industry leaders and share them with our readers. HR leaders from all over Australia came together to uncover and discuss the latest HR and leadership trends, best practice, challenges and opportunities. One of those discussions was from Jonathan Tabah, Director at Gartner, about what employees really think about AI, and we've got all the best bits wrapped up for you below. The reality is, whether you or your employees are aware of it or not, we are already using lots of examples of AI in our everyday lives. Do you use Google Maps to figure out how to get somewhere?

Automation and innovation in the work within business is necessary for the reinvention of the system landscapes. The same is possible with the machine learnings together with the help of the artificial intelligence platform. The industries in the recent time are in the tremendous need of the artificial intelligence platform to increase automation, machine interaction and to save time. Furthermore, problem-solving, social intelligence and general intelligence can also be achieved with the help of the artificial intelligence platform. The artificial intelligence platform market is expected to grow during the forecast period due to growth in adoption of cloud based application and services.

Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.

The existing image feature extraction methods are primarily based on the content and structure information of images, and rarely consider the contextual semantic information. Regarding some types of images such as scenes and objects, the annotations and descriptions of them available on the web may provide reliable contextual semantic information for feature extraction. In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web. Specifically, we propose a new method which consists of two consecutive steps to extract our semantic features. For each image in the training set, we initially search the top $k$ most similar images from the internet and extract their annotations/descriptions (e.g., tags or keywords). The annotation information is employed to design a filter bank for each image category and generate filter words (codebook). Finally, each image is represented by the histogram of the occurrences of filter words in all categories. We evaluate the performance of the proposed features in scene image classification on three commonly-used scene image datasets (i.e., MIT-67, Scene15 and Event8). Our method typically produces a lower feature dimension than existing feature extraction methods. Experimental results show that the proposed features generate better classification accuracies than vision based and tag based features, and comparable results to deep learning based features.