Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the metrics are effective for comprehensive, efficient, and reliable SG evaluation. To address the issues, we establish HAUSER, a holistic and automatic evaluation system for the SG task, which consists of five criteria from three perspectives and automatic metrics for each criterion. Through extensive experiments, we verify that our metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics.

AI technology has been catapulted into popular discourse in recent months with the rise of natural language processing like ChatGPT. Computer vision, though, is even older. It is used in Roomba vacuums and surgical settings, according to Kris Hauser, a computer science professor at the University of Illinois at Urbana-Champaign whose research specializes in open-world robotics. But this is one of the first AI robots to be used in the consumer beauty space, Hauser said.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Automakers spend tens of millions of dollars and thousands of hours each year trying to come up with the next popular automotive design, but what if they could do for a fraction of the cost in minutes? That's what General Motors and the MIT Sloan School of Management tried to find out in a recent study. Sloan marketing professor John Hauser told Fox News Digital that a neural network was trained with data collected from General Motors clinics by inputting images and the scores given by attendees. "These were actually evaluations from consumers in theme clinics of what they thought was an aesthetic image," Hauser said.

From curb appeal in real estate to smooth edges on smartphones, consumers gravitate toward products that are pleasing to the eye. This is especially true in the automotive industry, where product aesthetics have been linked to roughly 60% of purchasing decisions. "People buy cars based on aesthetics. Styling can make a difference," saidJohn R. Hauser, a professor of marketing at MIT Sloan. Styling is also expensive: Carmakers invest more than $1 billion to design the average car model and up to $3 billion for major redesigns.

The industry thrives on its futuristic image, worships boy-CEOs and renders the past obsolete at a frightening pace. Even in the eight years I've sat on the Guardian's technology desk, the field I cover is frequently unrecognisable from what it was when I started – a world where self-driving cars were just around the corner, where virtual reality was an impressive technology that had failed to catch on with normal people, and where the world was starting to tire of the like-clockwork appearance of a new iPhone every 12 months. Well, fine, but some things really have changed in that time. Just before I started at the paper, the Guardian broke the news that the NSA had been spying on Americans – and the rest of the world – through the tech sector, with more revelations to come thanks to the whistleblowing efforts of Edward Snowden. It was the first sign that the lustre had started to come off the sector, an inkling of what was to follow a few years later as the "techlash" saw first Facebook, then the rest of the industry, fall from grace.

One of the most important objectives in motion planning is finding a feasible path from the starting point to a goal without collision with obstacles. The obstacles are either closed doors, which can be opened and removed by the robot, or are obstacles that cannot be passed which can be ignored by the robot with a penalty. Usually, there is no feasible path for some navigation. Recently, some researchers have focused on finding a path for the robot by minimizing the number of removed obstacles. For instance, in Stilman and Kuffner's paper [Stilman and Kuffner, 2005], the robot is able to move the obstacles around and clear its movement space.

"AI models do not need to be interpretable to be useful." Interpretability in machine learning goes back to the 1990s when it was neither referred to as "interpretability" nor "explainability". Interpretable and explainable machine learning techniques emerged from the need to design intelligible machine learning systems and understand and explain predictions made by opaque models like deep neural networks. In general, the ML community is yet to agree on a definition for explainability or interpretability. Sometimes it is even called understandability.

Regardless of the industry that you're in, it is harder than ever to find truly new customer insights. Research budgets are smaller, the low-hanging fruit has already been picked so you need to dig deeper to find new insights, and traditional research can be expensive and time-consuming. But artificial intelligence, or machine learning, is changing the game, according to John Mitchell, president and managing principal at Applied Marketing Science, a Waltham, MA-based research and marketing firm that helps its clients better understand and incorporate the voice of the customer into product development. Between social media, online customer reviews, and customer service calls, companies already have billions of user-generated content (UGC). "Consumers are freely volunteering insights about products and services at the moment of truth," Mitchell told BIOMEDevice Boston attendees on Tuesday. The problem is that sifting through all of that to find valuable product development insights is simply too much for one human reader to process on their own, Mitchell said.

Gauging consumer needs is essential in marketing. Focus groups, interviews and surveys are currently the most common means of gathering this data. But the process can be time-consuming and expensive. The advent of machine learning technology and artificial intelligence (AI) has sparked interest in using the technology to yield valuable insights into consumer wants. Researchers at MIT devised a method of efficiently identifying customer needs from user-generated content (UCG) with machine learning, according to a study published in Marketing Science.

According to Business Insider, the top jobs of the future will be in technology and healthcare. Either way, success will require empathy. Imagine you're part of a team designing a technology solution to help stop the spread of infectious diseases in hospitals. The Centers for Disease Control and Prevention estimate that two million patients get an infection while in the hospital each year, and 99,000 of them die as a result, so your job could have a serious impact! For Daniel Duarte, Head of Innovation and Customer Experience at SAP Labs Latin America, this is a real task.