The American Bureau of Shipping recently collaborated with Google Cloud and software engineers SoftServe to use artificial intelligence (AI) models to detect levels of corrosion and marine coatings breakdowns on brown- and bluewater vessels. The pilot project is aimed at developing image recognition software tools that can examine early signs of degradation in hull structures, to avoid unsafe working conditions, unscheduled maintenance and resulting operational downtime. The effort demonstrated how AI can support early detection of structural anomalies that are usually found through traditional, visual inspections. The project was focused on corrosion and coatings failures, but ABS engineers believe the new tools could also be used to detect stress fractures and larger hull deformations. These AI techniques -- in tandem with advanced data algorithms -- could be used to analyze images over time to understand the trends in corrosion and asset fatigue that would support a transition to more efficient class and maintenance regimes for everything from workboats to offshore structures.

Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes have been developed for this purpose. However, in contrast to how humans categorize objects, these approaches have not yet made use of any taxonomical organization of class labels. With such an approach, for instance, we may see why a chimpanzee is classified as a chimpanzee, but not why it was considered to be a primate or even an animal. In this work we introduce a model that uses hierarchically organized prototypes to classify objects at every level in a predefined taxonomy. Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy. The hierarchical prototypes enable the model to perform another important task: interpretably classifying images from previously unseen classes at the level of the taxonomy to which they correctly relate, e.g. classifying a hand gun as a weapon, when the only weapons in the training data are rifles. With a subset of ImageNet, we test our model against its counterpart black-box model on two tasks: 1) classification of data from familiar classes, and 2) classification of data from previously unseen classes at the appropriate level in the taxonomy. We find that our model performs approximately as well as its counterpart black-box model while allowing for each classification to be interpreted.

Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine's "magic bullet" phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an "AI revolution," one that will finally result in high-value care. While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action.

Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.

In just four years, SenseTime went from being an academic project to become the world's most valuable artificial intelligence (AI) company with a current valuation of $4.5 billion. Based in China, the company has a portfolio of 700 clients and partners, including the Massachusetts Institute of Technology (MIT), Qualcomm, Honda, Alibaba, Weibo, and more. They use their proprietary artificial intelligence and machine vision technology to drive its success and "redefine human life as we know it." With the number of core technologies, products, and services SenseTime offers, it's hard to believe it's such a young company. Here are just a few ways SenseTime uses artificial intelligence to "power the future." SenseTime developed several AI technologies including face, image, object and text recognition; medical image and video analysis; remote sensing; and autonomous driving systems.

Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.

In personalised decision making, evidence is required to determine suitable actions for individuals. Such evidence can be obtained by identifying treatment effect heterogeneity in different subgroups of the population. In this paper, we design a new type of pattern, treatment effect pattern to represent and discover treatment effect heterogeneity from data for determining whether a treatment will work for an individual or not. Our purpose is to use the computational power to find the most specific and relevant conditions for individuals with respect to a treatment or an action to assist with personalised decision making. Most existing work on identifying treatment effect heterogeneity takes a top-down or partitioning based approach to search for subgroups with heterogeneous treatment effects. We propose a bottom-up generalisation algorithm to obtain the most specific patterns that fit individual circumstances the best for personalised decision making. For the generalisation, we follow a consistency driven strategy to maintain inner-group homogeneity and inter-group heterogeneity of treatment effects. We also employ graphical causal modelling technique to identify adjustment variables for reliable treatment effect pattern discovery. Our method can find the treatment effect patterns reliably as validated by the experiments. The method is faster than the two existing machine learning methods for heterogeneous treatment effect identification and it produces subgroups with higher inner-group treatment effect homogeneity.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

The Best Machine Learning Course to Learn 2019(view) – This online course explains a detailed introduction to Machine Learning, data mining, and statistical pattern recognition. The course will also cover several case studies and applications which are very helpful, so that students also get to know how to apply learning algorithms to creating smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. I would like to recommend this course to any students who is interested in learning machine learning as it is amazing and has been great help to students. It has Flexible schedule and deadlines. The best way to complete it in 7 hours per week.