Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking. Texture synthesis is used to generate a larger image containing the same texture.

Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have changed the way both offline and e-retailers interact with or approach the customer and the way they offer products, particularly in fashion eretail. AI and ML are allowing for critical insights into more accurate personalisation of products and services. Customer experiences can now be data-driven all thanks to insights that we receive through the implementation of AI and ML. These technologies allow pattern recognition of each consumer to match their preferences to the product offerings. Robust recommendation engines based on ML can boost up-sell and cross-selling opportunities – directly affecting the top and bottom line.

The role AI plays today is largely behind the scenes. Other than the occasional industrial robot or self-driving cars, the benefits we see are largely in opaque software features. By working with Novartis, Microsoft has created a much more visual representation of the emerging technology. Project Ada is a giant two-story structure that inhabits building 99 on Microsoft's campus. According to designer Jenny Sabin, it's the first time an architectural structure has been driven by AI in real-time.

Relational data mining is becoming ubiquitous in many fields of study. It offers insights into behaviour of complex, real-world systems which cannot be modeled directly using propositional learning. We propose Symbolic Graph Embedding (SGE), an algorithm aimed to learn symbolic node representations. Built on the ideas from the field of inductive logic programming, SGE first samples a given node's neighborhood and interprets it as a transaction database, which is used for frequent pattern mining to identify logical conjuncts of items that co-occur frequently in a given context. Such patterns are in this work used as features to represent individual nodes, yielding interpretable, symbolic node embeddings. The proposed SGE approach on a venue classification task outperforms shallow node embedding methods such as DeepWalk, and performs similarly to metapath2vec, a black-box representation learner that can exploit node and edge types in a given graph. The proposed SGE approach performs especially well when small amounts of data are used for learning, scales to graphs with millions of nodes and edges, and can be run on an of-the-shelf laptop.

Luminance, which provides Artificial Intelligence software for law firms and in-house teams, is gunning for the Disruptive Technology accolade in the 30th Anniversary Business Weekly Awards. Based at the Maurice Wilkes Building at St John's Innovation Park, Luminance prides itself as being the only platform to bring true artificial intelligence to the legal profession. Its submission says: "By deploying a unique blend of supervised and unsupervised machine learning, the core technology'LITE' is able to read and understand language in a way that is similar to the human brain and then learns from lawyers' interactions with documents as they conduct their review. "Luminance has enhanced the lives of lawyers in 165 countries globally, including 17 of'The Global Top 100', such as Holland & Knight, Slaughter and May and Bird & Bird, as well as each of the Big Four accounting firms." The company's pattern-recognition technology reads, understands and learns from the interaction between lawyers and documents, pinpointing warning signs that would be missed during a manual review.

Healthcare AI is growing at an exponential rate, and is expected to create a potential $150 billion in annual savings for the U.S. healthcare economy by 2026. Machine learning and AI have the power to truly change the cost and quality curve in healthcare. As pattern recognition algorithms become more complex, machines can perform additional tasks without the need for programming – with ever-increasing accuracy. Now is the time to use AI to tackle our industry's financial and administrative processes. Eventually, healthcare AI will be able to reduce physician workload, protect patient safety, and provide diagnostic support.

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts.

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.