More than 400 million people worldwide speak Spanish as their native language. However, those people have been left behind by tech companies who make products based on speaking and listening, like Google Home and Amazon Echo. On Tuesday, the former remedied the situation. Google announced in a company blog post Tuesday morning that its Home line of smart-speaker products would listen and speak Spanish, starting immediately. According to Google, it is as easy as going to the preferences section of the Google Home app and changing the digital assistant's language to Spanish.

The first networked electronic mail message was sent by Ray Tomlinson of Bolt Beranek and Newman in 1971. This year, according to market research firm Radicati Group, 3.8 billion email users worldwide will send 281 billion messages every day. You may feel like a substantial number of them end up in your inbox. And yet, some observers say email is dying. It's so'last century', they say, compared to social media messaging, texting, and powerful new collaboration tools.

In the 1970s, when Microsoft and Apple were founded, programming was an art only a limited group of dedicated enthusiasts actually knew how to perform properly. CPUs were rather slow, personal computers had a very limited amount of memory, and monitors were lo-res. To create something decent, a programmer had to fight against actual hardware limitations. In order to win in this war, programmers had to be both trained and talented in computer science, a science that was at that time mostly about algorithms and data structures. The first three volumes of the famous book The Art of Computer Programming by Donald Knuth, a Stanford University professor and a Turing Award recipient, were published in 1968–1973.

Ryan Calo's "law and Technology" Viewpoint "Is the Law Ready for Driverless Cars?" (May 2018) explored the implications, as Calo said, of " ... genuinely unforeseeable categories of harm" in potential liability cases where death or injury is caused by a driverless car. He argued that common law would take care of most other legal issues involving artificial intelligence in driverless cars, apart from such "foreseeability." Calo also said the courts have worked out problems like AI before and seemed confident that AI foreseeability will eventually be accommodated. One can agree with this overall judgment but question the time horizon. AI may be quite different from anything the courts have seen or judged before for many reasons, as the technology is indeed designed to someday make its own decisions.

Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.

When webpages influence choice: Effects of visual primes on experts and novices.

Artificial Intelligence can seem like it is improving at an often alarming rate but the reality is often less glamorous.

The security control of underground pipelines is a crucial task for the security. In this pitch we will analyze how we were able to apply AI and in particular training Neural Networks to anticipate dangerous situations based on images. We will go through the whole process from data gathering and cleaning to tuning of the model. DISUMMIT 2018: AI demystified This is the event for all European data professionals who are passionate about Data Innovation, Digital Transformation and Artificial Intelligence. The Data innovation summit is a full-day event taking place each year in Brussels.

A new age has arrived, bringing us novel, exciting, and sometimes scary things to explore, work around, and live with. Late in the 18th century, the world began to industrialize. It all started with the dawn of steam power and the power loom. These innovations drove growth in the coal, iron, textile, and railroad industries, completely changing the way goods were manufactured. The second industrial revolution, starting late in the 19th century, brought the expansion of petroleum, steel, and electricity, again toppling business models and society at large.

On April 12, at offices of the healthcare incubator MATTER at the Merchandise Mart in downtown Chicago, as well as streamed live online, Andrew Burt, chief privacy officer and legal engineer at the data management company Immuta, delivered a lecture entitled "Regulating Artificial Intelligence: How to Control the Unexplainable" in which he focused on the ethical, legal, and regulatory issues surrounding the deployment of machine learning systems. Sponsored by all three UChicago Graham School Professional Masters degree programs--Biomedical Informatics (MScBMI), Analytics (MScA), and Threat and Response Management (MScTRM)--the catalyst for the occasion was Sam Volchenboum, MD, PhD, MS, director of the Center for Research Informatics at UChicago, and faculty director for the BMI program, whose encounter with Burt at a recent South by Southwest conference led to an exchange of ideas he saw as immediately relevant to the Graham School programs. "As a physician, I'm seeing the use of machine learning algorithms all over the hospital and all over medicine," Dr. Volchenboum said. "We just plow ahead with developing our models and our predictions. But it wasn't until I spoke with Andrew that I really stopped and thought about the implications of these algorithms and how they can be used in both good and also bad ways. It was an eye-opening experience and I've been really excited about bringing Andrew here to talk ever since."