Melbourne man Milorad "Michael" Trkulja has won his high court battle to sue the search engine Google for defamation over images and search results that link him to the Melbourne criminal underworld. Trkulja said he would continue legal action against Google until it removed his name and photos from the internet. Trkulja, who was shot in the back in a Melbourne restaurant in 2004, successfully argued in the Victorian supreme court in 2012 that Google defamed him by publishing photos of him linked to hardened criminals of Melbourne's underworld. Four years later the Victorian court of appeal overturned the decision, finding the case had no prospect of successfully proving defamation. The high court disputed that ruling in a judgment on Wednesday and ordered Google to pay Trkulja's legal costs.

A good classification method should yield more accurate results than simple heuristics. But there are classification problems, especially high-dimensional ones like the ones based on image/video data, for which simple heuristics can work quite accurately; the structure of the data in such problems is easy to uncover without any sophisticated or computationally expensive method. On the other hand, some problems have a structure that can only be found with sophisticated pattern recognition methods. We are interested in quantifying the difficulty of a given high-dimensional pattern recognition problem. We consider the case where the patterns come from two pre-determined classes and where the objects are represented by points in a high-dimensional vector space. However, the framework we propose is extendable to an arbitrarily large number of classes. We propose classification benchmarks based on simple random projection heuristics. Our benchmarks are 2D curves parameterized by the classification error and computational cost of these simple heuristics. Each curve divides the plane into a "positive- gain" and a "negative-gain" region. The latter contains methods that are ill-suited for the given classification problem. The former is divided into two by the curve asymptote; methods that lie in the small region under the curve but right of the asymptote merely provide a computational gain but no structural advantage over the random heuristics. We prove that the curve asymptotes are optimal (i.e. at Bayes error) in some cases, and thus no sophisticated method can provide a structural advantage over the random heuristics. Such classification problems, an example of which we present in our numerical experiments, provide poor ground for testing new pattern classification methods.

The U.S. Navy is investing real money to integrate artificial intelligence (AI) into the force, requesting $62.5 million in the FY19 Defense Department budget for AI and rapid prototyping. As the technology matures, the Navy needs to adapt by displacing human intelligence in roles for which AI is better suited while being aware of the many roles in which human intelligence will still have an edge. The Navy should identify candidates for automation where, relative to human intelligence, AI is likely to be increasingly fast, agile, or low-cost. But leadership should also understand where AI isn't likely to be applicable and comprehend the implementation difficulties the Navy faces relative to other government and commercial organizations. Discussions around AI need to evolve from "we need it" to "this is how we get it."

This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the ground up. In the one-sided classification paradigm, the objective is to separate elements of the target class from all outliers. These one-sided classifiers are generally chosen, in practice, when there is a deficiency of some sort in the training examples. Sometimes outlier examples can be rare, expensive to label, or even entirely absent. However, this author would like to note that they can be equally applicable when outlier examples are plentiful but nonetheless not statistically representative of the complete outlier concept. It is this scenario that is explicitly dealt with in this research work. In these circumstances, one-sided classifiers have been found to be more robust that conventional multi-class classifiers. The term "unexpected" outliers is introduced to represent outlier examples, encountered in the test set, that have been taken from a different distribution to the training set examples. These are examples that are a result of an inadequate representation of all possible outliers in the training set. It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target. The findings from this research have shown the potential drawbacks of using conventional multi-class classification algorithms when the test data come from a completely different distribution to that of the training samples.

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.

Whether you're aware of it or not, artificial intelligence (AI) has a ubiquitous presence in our lives today – think the personalised playlists on Spotify or the'Recommended for you' lists on Netflix, both of which use AI to curate a selection tailored just for you. Now its presence is being felt in the area of document management, with AI and cognitive computing set to revolutionise the ways in which we store, archive, process and extract information. Here are 5 ways AI is transforming document management systems . Automatic classification and processing - While OCR (optical character recognition) technology allows for text recognition, AI takes this a step further by being able to "read" the information on that document, classify it correctly and automate workflows based on that classification – all at a fraction of the speed a human could. While the system is initially guided by a set of rules, its identification and processing capabilities continue to improve using machine learning, meaning it is able to learn from repeated exposure to documents, as well as from the actions taken by employees upon those documents.

The variety of pedestrians detectors proposed in recent years has encouraged some works to fuse pedestrian detectors to achieve a more accurate detection. The intuition behind is to combine the detectors based on its spatial consensus. We propose a novel method called Content-Based Spatial Consensus (CSBC), which, in addition to relying on spatial consensus, considers the content of the detection windows to learn a weighted-fusion of pedestrian detectors. The result is a reduction in false alarms and an enhancement in the detection. In this work, we also demonstrate that there is small influence of the feature used to learn the contents of the windows of each detector, which enables our method to be efficient even employing simple features. The CSBC overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech dataset. Particularly, our method is more efficient since fewer detectors are necessary to achieve expressive results.

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.

About 10 years ago, it would have been hard to believe that you could ask a Bluetooth speaker for a classic cheese soufflé recipe or take a picture of an object using your phone and find out exactly where to purchase it. These interactions have been primarily realized through advancements in machine learning AI. One of the biggest developments in AI over the past three years has been in the area of voice recognition and natural language processing and we're starting to see advancements in more complex human machine interaction in the form of image/video search. Forward-thinking businesses are already using this new form of machine learning AI image recognition to allow users to search for products using pictures to find the same or similar looks and outfits they stock. However, does this mean intelligent image search is the next big thing? Major search engines have supported a form of'image search' for some time.

In the 2012 Hollywood movie "Act of Valor", US Navy Seals launch a Raven UAV to procure live video streaming for identifying targets prior to a raid. In yet another movie, "Zero Dark Thirty", analysts use FMV/ drone feed to re-create Bin Laden's compound for training US Navy Seals, and later guide them to accomplish their mission. The growing use of image analytics in tracking, detecting, analyzing and predicting outcomes have been effectively used by story writers to piece amazing stories. Image analytics is a technique by which an image is digitally processed for extracting and analyzing data for insightful information. Big data still remains a scary and invincible concept, because of the unmanageable amount of unstructured data present in it.