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.

Gesture-based interfaces are applications that allow users to control devices using hand and other body parts. Today, they are found in devices used in home automation, shopping, consumer electronics, virtual reality and augmented reality gaming, navigation, and driving, among others. A study reported that the global gesture recognition in the retail market is projected to grow by 27.54 percent from 2018 to 2023. To date, some of the top producers of gestural interface products include Intel, Apple, Microsoft, and Google. According to research titled Hand Gesture Recognition Using Computer Vision, gesture recognition is done in two ways: data glove sensor devices that transform hand and finger motions into digital data, and computer vision which uses a camera. The second method may let humans interact more naturally with machines because it leaves their hands free to move.

Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain, because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. We reformulate the pairwise registration problem as a recursive sequence of successive alignments. For each element in the sequence, a local deformation defined by its position, shape, and weight is computed by our recurrent registration neural network. The sum of all local deformations yield the final spatial alignment of both images. Formulating the registration problem in this way allows the network to detect non-aligned regions in the images and to learn how to locally refine the registration properly. In contrast to current non-sequence-based registration methods, our approach iteratively applies local spatial deformations to the images until the desired registration accuracy is achieved. We trained our network on 2D magnetic resonance images of the lung and compared our method to a standard parametric B-spline registration. The experiments show, that our method performs on par for the accuracy but yields a more compact representation of the transformation. Furthermore, we achieve a speedup of around 15 compared to the B-spline registration.

The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for random graphs drawn from stochastic blockmodels. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance but significantly faster.