Abstract: Unsupervised learning can be a challenging task due to the absence of an outcome variable. When on top it concerns outlier detection, the scarcity of this type of observations makes the problem even more challenging. This talk will be about applying the isolation forest algorithm in a financial context in order to detect unusual customer behavior.

This is a group for anyone interested in Artificial Intelligence, how it can be used in the enterprise and what benefits it can bring. This group was started to meet others with an interest in AI technology and to share different experiences. The group is aligned with Neurons.AI - The Online Professional Network for AI, but you don't have to be a member of Neurons to join this meetup ... everyone is welcome to these meetings. Don't forget to follow us on Twitter @Neurons_AI (http://twitter.com/Neurons_AI)

Around 47% of the AI companies acquired since 2012 have had VC backing. Corporate giants like Google, IBM, Yahoo, Intel, Apple, and Salesforce are competing in the race to acquire private AI companies, with Ford, Samsung, GE, and Uber emerging as new entrants. Over 250 private companies using AI algorithms across different verticals have been acquired since 2012, with 37 acquisitions taking place in Q1'17 alone. That quarter also saw one of the largest M&A deals: Ford's acquisition of Argo AI for $1B. Baidu has been particularly aggressive in its AI acquisitions in 2017, with 3 M&A deals so far this year, including its acquisition of Amazon Alexa Fund-backed Kitt.ai this quarter.

Harnessing cutting-edge technologies could bring new hope for people with heart and circulatory disease. In the first of a new series, Sarah Brealey learns how artificial intelligence could help treat heart failure. If you're diagnosed with a disease, you probably want to know what the future holds. But with some conditions, it's hard for your doctor to be certain. Pulmonary hypertension is a rare condition that damages the right side of the heart.

"Digital skills, IT skills, all the STEM (Science, Technology, Engineering, and Mathematics) skills are really very important for…the workers of the future," said Stefano Scabbio, president of Northern Europe, Mediterranean, and Eastern Europe at ManpowerGroup, a global staffing firm. In a January survey of 20,000 employers from 42 countries, ManpowerGroup found 86 percent of companies plan to increase or maintain headcount in the next two years due to automation, with the biggest job gains in the IT, customer service and advanced manufacturing sectors. But employers said they struggle to find talent with the right mix of skills, like problem solving, communication, organization and collaboration. "It's important that we educate companies of every size to really understand the importance of re-skilling and skilling people," Scabbio said. There's plenty of debate over just how many jobs will be lost to robots.

We've been hearing for years about the digital transformation of multiple business sectors. And the technology sector is hardly alone in this regard: For the past decade, legacy industries have been making the transition, too,and the result has been massive opportunities for entrepreneurs.

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.

The aim of mathematical psychology is to develop theoretical models of cognitive (and more generally psychological) processes [20]. Its methodology comprises two main aspects. The first one is to establish sensible assumptions about how humans behave in the psychological/conceptual situations under study, which are then translated into specific mathematical descriptions. The second aspect is about confronting human subjects to these conceptual situations in controlled experimental settings, usually consisting of preliminary sessions where participants are instructed about what to do in the experiments, followed by the real situations where the data of their responses is collected, analyzed and then confronted with the models. Generally speaking, a mathematical model will be considered satisfactory if capable of explaining the collected data (like the relative frequencies of outcomes, interpreted as probabilities) and possibly predict their structure (the relations which data obey) [18, 30]. 1

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of microvasculature from structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). OCTA suffers from need of specialized hardware, laborious acquisition protocols, and motion artifacts; whereas our model works directly from standard OCT which are ubiquitous and quick to obtain, and allows unlocking of large volumes of previously collected standard OCT data both in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed and accurate inferences of tissue function from structure imaging.

Appearance changes based on lighting, seasonal, and weather conditions provide a significant challenge for outdoor robots relying on machine learning models for perception. While providing high performance in their training domain, visual shifts occurring in the environment can result in significant deviations from the training distribution, severely reducing accuracy during deployment. Commonly, this challenge is partially counteracted by employing additional training methods to render these models invariant to their application domain [1]. For scenarios where labelled data is unavailable in the target domain, the problem can be addressed in the context of unsupervised domain adaptation [2], [3]. Recent stateof-the-art approaches which address this challenge operate by training deep neural networks within an adversarial domain adaptation (ADA) framework. These approaches are characterised by the optimisation of potentially multiple encoders with the objective to confuse a domain discriminator operating on their output [3], [4], [5] in additional to their main objective. The main intuition behind this framework is that by training the encoder to obtain a domain invariant embedding, we allow the main supervised task to be robust to changes in the application domain.