Large language models (LLMs) have demonstrated impressive reasoning ability in various language-based tasks. Despite many proposed reasoning methods aimed at enhancing performance in downstream tasks, two fundamental questions persist: Does reasoning genuinely support predictions, and how reliable is the quality of reasoning? In this paper, we propose a framework \textsc{SCORE} to analyze how well LLMs can reason. Specifically, we focus on self-contradictory reasoning, where reasoning does not support the prediction. We find that LLMs often contradict themselves when performing reasoning tasks that involve contextual information and commonsense. The model may miss evidence or use shortcuts, thereby exhibiting self-contradictory behaviors. We also employ the Point-of-View (POV) method, which probes models to generate reasoning from multiple perspectives, as a diagnostic tool for further analysis. We find that though LLMs may appear to perform well in one-perspective settings, they fail to stabilize such behavior in multi-perspectives settings. Even for correct predictions, the reasoning may be messy and incomplete, and LLMs can easily be led astray from good reasoning. \textsc{SCORE}'s results underscore the lack of robustness required for trustworthy reasoning and the urgency for further research to establish best practices for a comprehensive evaluation of reasoning beyond accuracy-based metrics.

There's a special bond that's forged by the vulnerability of sitting -- dripping wet -- as a hairdresser snips and shears your wayward locks. A trusted hairdresser or barber has the power to make or break your day -- even month -- with a few flourishes, all while deftly discussing everything from politics to family gossip. Service robots have already come for our grocery stores and construction sites, and now a new hair brushing robot designed by engineers at MIT could be the first step toward automating hairdressers as well. While robots and algorithms may outdo humans when it comes to efficiency, Michelle Shell, a visiting professor of operations and technology management at Boston University whose research focuses on humans' emotional response to technology, tells Inverse that automating these very human jobs could have repercussions not only for customers, but employees as well. Built like a bodybuilder, the thick robotic arm of RoboWig is adorned by a tiny, delicate hairbrush that is designed to sweep gently through users' hair -- at least in theory.

The possibilities clog our news feeds, create interesting conversations, and give tech leaders inspiration to explore solutions. What will this mean for us as humans? Could this impact all of society? With all the questions being asked, only one thing is absolutely clear. We're about to enter one of the biggest transformations our society has witnessed in the last century - if not millennium.

Google Home and Amazon's Echo are the most famous, but a whole raft of these gadgets is preparing to flood the market. One of the most advanced will likely come from Baidu, the Chinese tech giant that, like Google, began as a search engine and now has its tendrils in all sorts of digital and physical spaces. Andrew Ng, Baidu's chief AI scientist, calls these devices "conversational computers," and he's a key reason any of them have learned to talk in the first place. A former AI researcher at Stanford, Ng is best known for spearheading the Google Brain initiative, an ambitious artificial-intelligence project that helped advance Silicon Valley's understanding of deep-learning techniques. Instead of being programmed to respond to specific actions, a deep learning system is fed massive amounts of data from which it is able to discern patterns over time, loosely mimicking how the human mind absorbs information. Ng's system at Google famously figured out what a cat looks like after scanning millions of online images.

David Bruemmer is the co-founder, president and CTO of 5D Robotics. One might ask why we would ever want to create robots that can do human work when we have so many people who need jobs. The goal of robotics should not be to replace humans with robots, but rather to improve productivity and safety, removing humans from harm's way and enabling them to focus on things that humans should be doing. We can agree that humans shouldn't be carrying heavy loads, exposing themselves to radiation or finding land mines. What is less clear is the gray area, where the line that divides human and robot competency is becoming blurred.