For millennia it has been the dividing line in the battle of the sexes. Women may be better at multi-tasking, less susceptible to'man flu' and safer behind the wheel -- but, physically, men are the stronger sex. And no wonder: on average, males are 6in taller than females and have twice the upper-body strength, as well as denser bones, stronger tendons and greater muscle mass. Wonder Woman may be taking cinemas by storm this summer, but it's always Superman, with his rippling abs and bulging biceps, who saves the damsel in distress. However, this could be about to change.

Many marketers anticipate that technologies like augmented reality (AR) and artificial intelligence (AI) will affect their business in the next 12 months, more so than a year prior. That's according to a study by NewBase, a cloud computing and IT managed services company, which polled 1,019 marketers worldwide and asked them which types of technologies they plan to prioritize over the next 12 months. Respondents chose their top 5. In 2017, 30% of respondents planned to prioritize AI in the next 12 months. A year prior, only 13% of respondents said the same.

I provide some additional information on using RStudio in Appendix A. In this section we will discuss how you get data into and out of the Execute R Script module. We will review how to handle various data types read into and out of the Execute R Script module. The complete code for this section is in the zip file you downloaded earlier. We will start by loading the csdairydata.csv

People who suffer a stroke often undergo a brain scan at the hospital, allowing doctors to determine the location and extent of the damage. Researchers who study the effects of strokes would love to be able to analyze these images, but the resolution is often too low for many analyses. To help scientists take advantage of this untapped wealth of data from hospital scans, a team of MIT researchers, working with doctors at Massachusetts General Hospital and many other institutions, has devised a way to boost the quality of these scans so they can be used for large-scale studies of how strokes affect different people and how they respond to treatment. "These images are quite unique because they are acquired in routine clinical practice when a patient comes in with a stroke," says Polina Golland, an MIT professor of electrical engineering and computer science. Using these scans, researchers could study how genetic factors influence stroke survival or how people respond to different treatments.

It was interesting to note the impact of social pressure. In general, people were more comfortable using voice commands when by themselves first, then with friends next, and then not so much with people they don't know very well. As an exception to this, people feel a bit more self-conscious about doing so when in a restaurant by themselves, and the presence of friends made them more likely to use voice commands with their smartphones (it grew from 23.1% to 28.4% of people being likely or very likely to do so). Some of our respondents were even likely or very likely to use voice commands in a public restroom (13%) or in a theater (7.7%).

When we test a theory using data, it is common to focus on correctness: do the predictions of the theory match what we see in the data? But we also care about completeness: how much of the predictable variation in the data is captured by the theory? This question is difficult to answer, because in general we do not know how much "predictable variation" there is in the problem. In this paper, we consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction. We illustrate our methods on the task of predicting human-generated random sequences. Relative to an atheoretical machine learning algorithm benchmark, we find that existing behavioral models explain roughly 15 percent of the predictable variation in this problem. This fraction is robust across several variations on the problem. We also consider a version of this approach for analyzing field data from domains in which human perception and generation of randomness has been used as a conceptual framework; these include sequential decision-making and repeated zero-sum games. In these domains, our framework for testing the completeness of theories provides a way of assessing their effectiveness over different contexts; we find that despite some differences, the existing theories are fairly stable across our field domains in their performance relative to the benchmark. Overall, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness.

We explore the energy landscape of a simple neural network. In particular, we expand upon previous work demonstrating that the empirical complexity of fitted neural networks is vastly less than a naive parameter count would suggest and that this implicit regularization is actually beneficial for generalization from fitted models.

In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to capture the distinguishing information about such local features. To enable efficient learning, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D gradient-weighted class activation maps. An application of this feature identification framework is to recognize difficult-to-manufacture drilled hole features in a complex CAD geometry. The framework can be extended to identify difficult-to-manufacture features at multiple spatial scales leading to a real-time decision support system for design for manufacturability.

Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA (SMIDA) to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns a subspace which has maximum independence with the domain features, so as to reduce the inter-domain discrepancy in distributions. A feature augmentation strategy is also designed to project samples according to their backgrounds so as to improve the adaptation. The proposed algorithms are flexible and fast. Their effectiveness is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision. They can greatly enhance the practicability of sensor systems, as well as extend the application scope of existing domain adaptation algorithms by uniformly handling different kinds of distributional change.

Animals (especially humans) have an amazing ability to learn new tasks quickly, and switch between them flexibly. How brains support this ability is largely unknown, both neuroscientifically and algorithmically. One reasonable supposition is that modules drawing on an underlying general-purpose sensory representation are dynamically allocated on a per-task basis. Recent results from neuroscience and artificial intelligence suggest the role of the general purpose visual representation may be played by a deep convolutional neural network, and give some clues how task modules based on such a representation might be discovered and constructed. In this work, we investigate module architectures in an embodied two-dimensional touchscreen environment, in which an agent's learning must occur via interactions with an environment that emits images and rewards, and accepts touches as input. This environment is designed to capture the physical structure of the task environments that are commonly deployed in visual neuroscience and psychophysics. We show that in this context, very simple changes in the nonlinear activations used by such a module can significantly influence how fast it is at learning visual tasks and how suitable it is for switching to new tasks.