For mobile robots to operate autonomously in general environments, perception is required in the form of a dense metric map. For this purpose, we present the stochastic triangular mesh (STM) mapping technique: a 2.5-D representation of the surface of the environment using a continuous mesh of triangular surface elements, where each surface element models the mean plane and roughness of the underlying surface. In contrast to existing mapping techniques, a STM map models the structure of the environment by ensuring a continuous model, while also being able to be incrementally updated with linear computational cost in the number of measurements. We reduce the effect of uncertainty in the robot pose (position and orientation) by using landmark-relative submaps. The uncertainty in the measurements and robot pose are accounted for by the use of Bayesian inference techniques during the map update. We demonstrate that a STM map can be used with sensors that generate point measurements, such as light detection and ranging (LiDAR) sensors and stereo cameras. We show that a STM map is a more accurate model than the only comparable online surface mapping technique$\unicode{x2014}$a standard elevation map$\unicode{x2014}$and we also provide qualitative results on practical datasets.

An app uses a smart phone camera to detect leukocoria, a pale reflection from the back of the eye. It can be an early sign of disease. Here it appears light brown compared the healthy eye. An app uses a smart phone camera to detect leukocoria, a pale reflection from the back of the eye. It can be an early sign of disease.

It's karaoke-rehearsal time at Knollwood Military Retirement Community, a 300-bed facility tucked away in a leafy corner of northwest Washington, D.C. Knollwood resident and retired U.S. Army Colonel Phil Soriano, 86, has hosted the facility's semi-monthly singalongs since their debut during a boozy snowstorm happy hour in 2016. For the late August 2019 show, he'll share emcee duties with a special guest: Stevie, a petite and personable figure who's been living at Knollwood for the last six weeks. Soriano wants to sing the crowd-pleasing hit "YMCA" while Stevie leads the crowd through the song's signature dance moves. But Stevie is a robot, and this is harder than it sounds. "We could try to make him dance," says Niamh Donnelly, the robot's lead AI engineer, though she sounds dubious. She enters commands on a laptop.

On February 6, 2018, Elon Musk's SpaceX launched the Falcon Heavy rocket, the largest ever, from NASA's Kennedy Space Center. Its cargo was a Tesla Roadster, which is now orbiting the sun somewhere between Mars and the asteroid belt. Between Elon Musk's numerous companies and passion projects (SpaceX, Tesla, Solar City, the Hyperloop, the Boring Company), and the quickly proceeding advances in VR/AR/MR, genetics/cloning, blockchain, AI, 3D printing, and other fields, someone who was in a coma since 1998 and just woke up yesterday would be forgiven for thinking they had jumped a hundred years into the future instead of a mere 20. But then this person would actually get up and go out into the real world and see that mostly everything else is the same, aside from more traffic on the roads, more people in general, most of whom now carry miniature computers with them wherever they go that are more powerful than any desktop from the 20th century. Born in apartheid-era South Africa, he lived the first 16 years of his life in various towns, including Pretoria, moving back and forth between divorced parents.

The pace and scale of medical and scientific innovation is transforming the biopharma industry. The need for better patient engagement and experience is spurring new business models. Data generated, captured, analysed and used in real time by innovative medical devices is biopharma's new currency. A key differentiator for companies is the extent to which they are able to generate insights and evidence from multiple data sources. Consequently, digital transformation is a strategic imperative. This report outlines how artificial intelligence-enabled technologies will impact the biopharma value chain and accelerate biopharma's digital transformation. Although there is a high level of innovation in the industry, biopharma companies are facing a complex and challenging environment due to increased competition and R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny. As we have shown in our series of reports on'Measuring the return from pharmaceutical innovation', these factors are contributing to an alarming decline in the projected return on investment that large biopharma companies might expect to achieve from their late-stage pipelines, threatening their long-term futures.1 Digital transformation could provide a lifeline to biopharma research and development (R&D) and help reverse this trend. Digital transformation will also impact beyond R&D, as companies look to improve their operational performance, productivity, efficiency and cost-effectiveness across the entire biopharma value chain (see figure 1). Digital transformation will also impact business models, the development of new products and services, and how companies engage with health care professionals, patients and other customers. Ultimately, digital transformation is the next step in the evolution of biopharma companies.

The energy industry can free up more time for high value activities and improve safety performance by adopting data-driven digitisation, according to a world-leading artificial intelligence (AI) expert. In a keynote speech at this year's OPITO Global Conference on 6th November in Kuala Lumpur, Dr Ayesha Khanna, founder and CEO of Singapore headquartered ADDO AI will describe how AI and machine learning are delivering huge benefits in other sectors. Celebrating its 10th anniversary, OPITO Global is the only international event focusing on energy industry safety and competency. Industry leaders and experts will share their insights on Safety 4.0, exploring how technology is helping to improve safety, health and wellbeing. Dr Khanna has provided strategic advice to international corporations and governments and last year she was described by Forbes magazine as one of South East Asia's most ground-breaking female entrepreneurs.

Marcus stops by the coffee shop on his way to work as a diplomat at the U.S. Embassy. As he exits, ready to cut across Pylimo Street, a man approaches him. In accented English, the man says that he's lost and motions to his phone. Marcus looks down at the phone and sees a video of a man embracing a woman for a kiss. But the woman is not his wife.

Initially conceived as a technology that could mimic human intelligence, AI hasn't quite achieved this feat but significant progress has been made.

There is some debate recently as to whether data is the new oil [1] or not [2]. Whatever the case, acquiring training data for our machine learning work can be expensive (in man-hours, licensing fees, equipment run time, etc.). Thus, a crucial issue in machine learning projects is to determine how much training data is needed to achieve a specific performance goal (i.e., classifier accuracy). In this post, we will do a quick but broad in scope review of empirical and research literature results, regarding training data size, in areas ranging from regression analysis to deep learning. The training data size issue is also known in the literature as sample complexity.

When data scientists in Chicago, Illinois, set out to test whether a machine-learning algorithm could predict how long people would stay in hospital, they thought that they were doing everyone a favour. Keeping people in hospital is expensive, and if managers knew which patients were most likely to be eligible for discharge, they could move them to the top of doctors' priority lists to avoid unnecessary delays. It would be a win–win situation: the hospital would save money and people could leave as soon as possible. Starting their work at the end of 2017, the scientists trained their algorithm on patient data from the University of Chicago academic hospital system. Taking data from the previous three years, they crunched the numbers to see what combination of factors best predicted length of stay.