For most people who talk to our technology -- whether it's Amazon's Alexa, Apple Siri or the Google Assistant -- the voice that talks back sounds female. Some people do choose to hear a male voice. Now, researchers have unveiled a new gender-neutral option: Q. "One of our big goals with Q was to contribute to a global conversation about gender, and about gender and technology and ethics, and how to be inclusive for people that identify in all sorts of different ways," says Julie Carpenter, an expert in human behavior and emerging technologies who worked on developing Project Q. The voice of Q was developed by a team of researchers, sound designers and linguists in conjunction with the organizers of Copenhagen Pride week, technology leaders in an initiative called Equal AI and others. They first recorded dozens of voices of people -- those who identify as male, female, transgender or nonbinary.

Expert System is making enhancements to Cogito, its Artificial Intelligence platform that understands textual information and automatically processes natural language, delivering key updates in the areas of knowledge graphs, machine learning, and RPA. Cogito 14.4 enables users to more easily customize its Knowledge Graph of approximately 350,000 concepts connected by 2.8 Million relationships and lets them import targeted knowledge from any sources (such as company repositories Wikipedia or Geonames) in only a few clicks, enabling the platform to resolve references to real-world entities (such as people, companies, locations) and to link them to knowledge repositories by using standardized identifiers. Cogito 14.4 also extends its Natural Language Processing (NLP) extraction pipeline with a new active learning workflow that accelerates machine-learning-based analytics projects. Through an intuitive web application, Cogito 14.4's active learning workflow enables end-users to visualize the quality of extraction and provide feedback to the engine, which iteratively retrains the engine to reach the user's quality goals, thus reducing the amount of manual annotation needed Cogito 14.4 includes a Robotic Process Automation (RPA) connector that extends the use of RPA bots into process automation leveraging knowledge (and not only structured data) as well as requiring human-like judgement. The Cogito RPA Connector leverages deep contextual understanding to extract precise data from unstructured business documents.

The internet is full of lies. That maxim has become an operating assumption for any remotely skeptical person interacting anywhere online, from Facebook and Twitter to phishing-plagued inboxes to spammy comment sections to online dating and disinformation-plagued media. Now one group of researchers has suggested the first hint of a solution: They claim to have built a prototype for an "online polygraph" that uses machine learning to detect deception from text alone. But what they've actually demonstrated, according to a few machine learning academics, is the inherent danger of overblown machine learning claims. In last month's issue of the journal Computers in Human Behavior, Florida State University and Stanford researchers proposed a system that uses automated algorithms to separate truths and lies, what they refer to as the first step toward "an online polygraph system--or a prototype detection system for computer-mediated deception when face-to-face interaction is not available."

Nuro has partnered with Fry's Food Stores to utilize its autonomous vehicles to deliver groceries in Scottsdale. Supermarket giant Kroger said it soon will end a pilot program in which more than 2,000 grocery deliveries were made in self-driving vehicles from a store in Scottsdale, Arizona. The program, launched last August, featured deliveries in autonomous vehicles from robotics company Nuro from the Kroger-owned Fry's store at 7770 E. McDowell Road for customers in ZIP code 85257. The companies described it as the nation's first program featuring deliveries to the general public from fully unmanned vehicles. Wednesday will mark the final day of deliveries.

In the early part of the 20th century, a group of German experimental psychologists began to question how the brain acquires meaningful perceptions of a world that is otherwise chaotic and unpredictable. To answer this question, they developed the notion of the "gestalt effect"--the idea that when it comes to perception, the whole is something other than the parts. Sine then, psychologists have discovered that the human brain is remarkably good at perceiving complete pictures on the basis of fragmentary information. A good example is the figure shown here. The brain perceives two-dimensional shapes such as a triangle and a square, and even a three-dimensional sphere.

Legal experts warn people's online photos are being used without permission to power facial-recognition technology that could eventually be used for surveillance. Legal experts warn people's online photos are being used without permission to power facial-recognition technology that could eventually be used for surveillance. Said New York University School of Law's Jason Schultz, "This is the dirty little secret of [artificial intelligence] training sets. Researchers often just grab whatever images are available in the wild." IBM recently issued a set of nearly 1 million photos culled from the image-hosting site Flickr, and programmed to describe subjects' appearance, allegedly to help reduce bias in facial recognition; although IBM said Flickr users can opt out of the database, deleting photos is almost impossible.

During surgery, the da Vinci robot is docked over the patient and the instruments still typically enter through the abdomen, through much smaller incisions than a traditional laparotomy, which opens up the belly. The surgeon sits at a nearby control panel in the operating room where they can maneuver cameras and instruments with a range exceeding the human hand.

Quantum computing and artificial intelligence are both hyped ridiculously. But it seems a combination of the two may indeed combine to open up new possibilities. In a research paper published today in the journal Nature, researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. The team says that future quantum computers should allow machine learning to hit new levels of complexity. As first imagined decades ago, quantum computers were seen as a different way to compute information.

It's always cool to see lionfish while snorkeling or scuba diving. They're spectacular-looking, and because they're covered in flamboyant spines, they're usually secure enough in their invincibility that they'll mostly just sit there and let you get close to them. Lionfish don't make for very good oceanic neighbors, though, and in places where they're an invasive species and have few native predators (like most of the Atlantic coast of the United States), they do their best to eat anything that moves while breeding almost continuously. A single lionfish per reef reduced young juvenile fish populations by 79 percent in only a five-week period. Many species were affected, including cardinalfish, parrotfish, damselfish, and others.

Machine learning and quantum computing have their staggering levels of technology hype in common. But certain aspects of their mathematical foundations are also strikingly similar. In a paper in Nature, Havlíček et al.1 exploit this link to show how today's quantum computers can, in principle, be used to learn from data -- by mapping data into the space in which only quantum states exist. One of the first things one learns about quantum computers is that these machines are extremely difficult to simulate on a classical computer such as a desktop PC. In other words, classical computers cannot be used to obtain the results of a quantum computation.