Last week, on the third floor of a small building in San Francisco's Mission District, a woman scrambled the tiles of a Rubik's Cube and placed it in the palm of a robotic hand. The hand began to move, gingerly spinning the tiles with its thumb and four long fingers. Each movement was small, slow and unsteady. But soon, the colors started to align. Four minutes later, with one more twist, it unscrambled the last few tiles, and a cheer went up from a long line of researchers watching nearby.

According to the Centers for Disease Control and Prevention (CDC), an estimated 50 million adults in the U.S. suffered from chronic pain in 2016, and according to the Substance Abuse and Mental Health Services Administration (SAMHSA), an estimated 10.3 million people in the U.S. ages 12 and older misused opioids in 2018. As such, the National Institutes of Health (NIH) have announced the awarding of $945 million in research grants to tackle the national opioid crisis through NIH HEAL Initiative (Helping to End Addiction Long-term Initiative). The UC San Francisco Department of Radiology and Biomedical Imaging is pleased to announce that one such project is the Back Pain Consortium (BACPAC) Research Program of which Sharmila Majumdar, PhD, vice chair for Research, is a part of. At this time, chronic low back pain is one of the most common forms of chronic pain in adults, and current treatments are ineffective, leading to increased use of opioids. This research will also lay the foundation for NIH funded research at the newly established Center for Intelligent Imaging, using artificial intelligence fueled algorithms for fast image acquisition, data analysis, quantitative sensory assessments, brain imaging, and biomechanical evaluation of the spine.

Researchers from the Allen Institute for AI and University of California, Irvine, have released AllenNLP Interpret, a toolkit for explaining the results from natural-language processing (NLP) models. The extensible toolkit includes several built-in methods for interpretation and visualization components, as well as examples using AllenNLP Interpret to explain the results of state-of-the art NLP models including BERT and RoBERTa. In a paper published on arXiv, the research team described the toolkit in more detail. AllenNLP Interpret uses two gradient-based interpretation methods: saliency maps, which determine how much each word or "token" in the input sentence contributes to the model's prediction, and adversarial attacks, which try to remove or change words in the input while still maintaining the same prediction from the model. These techniques are implemented for a variety of NLP tasks and model architectures.

BELLEVUE, WA – September 17, 2019 -- Today, Skylum has announced two major new features coming to Luminar 4, set to be released this fall. AI Skin Enhancer and Portrait Enhancer will enable photographers to further develop and improve their portraits. These tools use machine learning to speed up the process, but contain detailed controls for even the most demanding photo editor. Previously, photographers would have to spend time selectively editing their photographs, manually adjusting various tools through selections and masking. With Luminar 4, these tedious tools are a thing of the past.

On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects. A 5-feet 7-inch tall white robot, basically a spindly arm affixed with a claw of the sort customarily found in an arcade vending machine, glided around the kitchen on its two Segway wheels. Following the command of a research scientist sitting at a nearby computer, the robot grabbed a Cheez-It box on the counter and extended its limb to gently place the snacks inside a cabinet. "What's deceptive is that what's simple to us in the kitchen is challenging for a robot," said University of Washington Computer Science and Engineering Professor Dieter Fox, who also serves as the lab's senior director of robotics research. The Silicon Valley-based technology company opened the robotics lab last fall to harness the UW's talent in a sector where Seattle plays a central role. Still, paranoia around the capabilities of AI technology persist.

And, I wanted to start with a personal story to tell you why it's so meaningful for me to be here. So, it was a long time ago, I was in my early 20's, I was living in Manhattan, and I was living life in the fast lane at a high-stress job. I was working a lot of hours, and I was doing things in my personal life that you do when you're in the early 20s and you think you're invincible. So, I ended up hitting the wall and I hit it really hard both physically and mentally and ended up taking extended time off just to recover and regroup. Coincidentally, one of the books I read at that time which was a tipping point for me was a book called The Mind-Body Connection by John Sarno. Have any of you read that book? Okay, a few of you, if you haven't read it, I highly recommend it. It was a turning point for me and at that time I started meditating every day, I started working out every day. Both things that I do every day to this day. And it's really helped my helped turn my life around. And I share that story with you to tell you that the work that you do, I am deeply, deeply grateful for, so thank you. And it's not just me, as a MINDBODY community, we have an impact on a lot of people around the world. In the past year, all of you have touched thirty-eight point three million lives around the world. I think you deserve a round of applause for that.

The governmenbt of Australia is subsidizing the study of responsible, ethical, and inclusive autonomous decision-making technologies. The Australian government is providing AU$31.8 million to the Australian Research Council to study responsible, ethical, and inclusive autonomous decision-making technologies. The Center of Excellence for Automated Decision-Making and Society, which will be based at the Royal Melbourne Institute of Technology (RMIT), will house researchers who will work with experts from seven other Australian universities, as well as 22 academic and industry partner organizations in Australia, Europe, Asia, and the U.S. The global research project aims to ensure machine learning and decision-making technologies can be used safely and ethically. Said RMIT researcher Julian Thomas, "Working with international partners and industry, the research will help Australians gain the full benefits of these new technologies, from better mobility, to improving our responses to humanitarian emergencies."

Fueled by improvements in speech recognition, machine learning, better algorithms, cloud processing, and more powerful computing devices, the quality of machine translations is improving. Learning another language has never been a simple proposition. It can take months of study to absorb the basics and years to become fluent. Of course, there's the added headache that learning a language doesn't help if a person encounters one of the world's other 7,000 or so languages. "There has always been a need for human translators and interpreters," says Andrew Ochoa, CEO of translation technology firm Waverly Labs.

Pepe the robot was wall-mounted near a handwashing station. It prompted children to wash their hands and provided positive reinforcement. The hand-shaped robot, dubbed'Pepe', is the product of a collaboration between researchers from the University of Glasgow in Scotland and Amrita Vishwa Vidyapeetham University in India. Pepe was mounted to the wall above a handwashing station at the Wayanad Government Primary School in Kerala, which has around 100 pupils aged between five and 10. A small video screen mounted behind Pepe's green plastic exterior acted as a'mouth,' allowing researchers to tele-operate the robot to speak to the pupils and draw their attention to a poster outlining the steps of effective handwashing.

Dimensionality reduction is a technique for reducing the number of variables of data samples and has been successfully applied in many fields to make machine learning algorithms faster and more accurate, including the pathological diagnoses of gene expression data [26], the analysis of chemical sensor data [16], the community detection in social networks [27], the analyses of neural spike sorting [1], and others [22]. Due to their dependence on label information, dimensionality reduction methods can be divided into supervised and unsupervised methods. Typical unsupervised dimensionality reduction methods are the principal component analysis (PCA) [12, 15], the classical multidimensional scaling (MDS) [4], the locality preserving projections (LPP) [11], and the t-distributed stochastic neighbor embedding (t-SNE) [28]. To make use of prior knowledge on the labels, we focus on supervised dimensionality reduction methods. Supervised dimensionality reduction methods map data samples into an optimal low-dimensional space for satisfactory classification while incorporating the label information. One of the most popular supervised dimensionality reduction methods is the linear discriminant analysis (LDA) [3], which maximizes the between-class scatter and reduces the within-class scatter in a low-dimensional space.