One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning to data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.

Will self-driving cars be able to cope with highly dangerous roads? Let's talk about dangerous roads. In a moment, I'll provide you with a recently published list of the presumed Top Ten most dangerous roads in the world. For some of you, the odds are that you'll be happy that you've never had a cause to try and traverse these bad-to-the-bone roads, while others of you are probably going to put these alarming roads on your bucket list of places you have to go and give a whirl someday. Do you prefer roads that are calm, easy to navigate, and present little or no qualms?

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The following paper by Gupta and Roughgarden--"Data-Driven Algorithm Design"--addresses the issue that the best algorithm to use for many problems depends on what the input "looks like." Certain algorithms work better for certain types of inputs, whereas other algorithms work better for others. This is especially the case for NP-hard problems, where we do not expect to ever have algorithms that work well on all inputs: instead, we often have various heuristics that each work better in different settings. Moreover, heuristic strategies often have parameters or hyperparameters that must be set in some way. The authors present a theoretical formulation and analysis of algorithm selection using the well-developed framework of PAC-learning to analyze fundamental learning questions.

The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. Although there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well. We interpret our results in the contexts of learning greedy heuristics, instance feature-based algorithm selection, and parameter tuning in machine learning. Rigorously comparing algorithms is hard. Two different algorithms for a computational problem generally have incomparable performance: one algorithm is better on some inputs but worse on the others. The simplest and most common solution in the theoretical analysis of algorithms is to summarize the performance of an algorithm using a single number, such as its worst-case performance or its average-case performance with respect to an input distribution. This approach effectively advocates using the algorithm with the best summarizing value (e.g., the smallest worst-case running time). Solving a problem "in practice" generally means identifying an algorithm that works well for most or all instances of interest. When the "instances of interest" are easy to specify formally in advance--say, planar graphs, the traditional analysis approaches often give accurate performance predictions and identify useful algorithms.

This article aims to tell the story of how we put words into computers. It is part of the story of the field of natural language processing (NLP), a branch of artificial intelligence.a

According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered. The inclusivity of AI systems refers to whether they are effective for diverse user populations.

This is a crazy idea," the review read. Closing my laptop lid, I added in my mind "and ... it will never work," as a lump welled in my throat. What we were proposing to do was simple yet ambitious. Using functional magnetic resonance imaging, we might better understand what goes on in the minds of programmers as they read and understand code. We had performed pilot experiments with a neurobiologist, had promising results, and encouraging words from colleagues and reviewers.

Future homes will employ potentially hundreds of Internet of Things (IoT) devices whose sensors may inadvertently leak sensitive information. A previous Communications Inside Risks column ("The Future of the Internet of Things," Feb. 2017) discusses how the expected scale of the IoT introduces threats that require considerations and mitigations.2 Future homes are an IoT hotspot that will be particularly at risk. Sensitive information such as passwords, identification, and financial transactions are abundant in the home--as are sensor systems such as digital assistants, smartphones, and interactive home appliances that may unintentionally capture this sensitive information. IoT device manufacturers should employ sensor sensor permissioning systems to limit applications access to only sensor data required for operation, reducing the risk that malicious applications may gain sensitive information. For example, a simple notepad application should not have microphone access.

Machine vision coupled with artificial intelligence (AI) has made great strides toward letting computers understand images. Thanks to deep learning, which processes information in a way analogous to the human brain, machine vision is doing everything from keeping self-driving cars on the right track to improving cancer diagnosis by examining biopsy slides or x-ray images. Now some researchers are going beyond what the human eye or a camera lens can see, using machine learning to watch what people are doing on the other side of a wall. The technique relies on low-power radio frequency (RF) signals, which reflect off living tissue and metal but pass easily through wooden or plaster interior walls. AI can decipher those signals, not only to detect the presence of people, but also to see how they are moving, and even to predict the activity they are engaged in, from talking on a phone to brushing their teeth.