We featured 10 startups in our earlier post. Here are five more startups that are in in the vanguard of turning AI into a value-adding technology for construction sites. APE Mobile is on a mission to make construction easier by using disruptive technology to make the complex simple and arm contractors with the information they need. They are now adding Artificial Intelligence (AI) and Natural Language Processing (NLP) capabilities to bring the user answers when and where they need them. "Ask a question and it gives you answers, instantly.

Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2020. Analysts presented their findings during Gartner IT Symposium/Xpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years. "People-centric smart spaces are the structure used to organize and evaluate the primary impact of the Gartner top strategic technology trends for 2020," said David Cearley, vice president and Gartner Fellow. "Putting people at the center of your technology strategy highlights one of the most important aspects of technology -- how it impacts customers, employees, business partners, society or other key constituencies. Arguably all actions of the organization can be attributed to how it impacts these individuals and groups either directly or indirectly. This is a people-centric approach."

When it comes to vehicles, dials and switches are used to control everything. As the automotive industry evolves, so do its norms. Today, we are rapidly moving towards a world of shared and self-driving cars. Automotive manufacturers implement a range of human-machine interface technologies (HMIs), including voice controls, interior-facing cameras, touch-sensitive surfaces, and smarter, personalized platforms. Voice control is among the most preferred interfaces with the most significant percentage of HMIs since it allows hands-free control and, therefore, less distraction from the road. Other examples include multifunctional controllers, touchscreens, and head-up displays. Autonomous driving has been the central concern of the automotive industry for quite some time. This revolutionary concept wouldn't be possible without the help of Artificial Intelligence.

On today's episode of the podcast, I got to chat with software engineer Jackson Bates who lives and works in Melbourne, Australia. Jackson used to be a high school English teacher, but gradually taught himself to code and landed a pretty sweet gig as a React dev, partly by chance. Today he works part time as a developer, part time as a stay at home dad, and volunteers his time with various open source projects. Jackson grew up in England, and studied English in school. Although going into education seemed a logical choice, he dabbled in other fields - like working at a prison cafeteria - for a while before landing a teaching job.

During a session at Google's I/O 2015 conference headlined by the Advanced Technologies and Projects Group (ATAP), engineers demoed what they called Project Soli, a novel gesture-recognition technology bound for handheld devices. The promise of the tech was that you could interact with things without actually touching them, which ostensibly would open up all manner of new ways of performing tasks. After a little over four years in development, it emerged in the Pixel 4 series as the gesture-detecting Motion Sense. So was it worth the wait? We used the Pixel 4 for a week to put Motion Sense through its paces.

People have more trust in robots than their managers, according to the second annual AI at Work study conducted by Oracle and Future Workplace. The study of 8 370 employees, managers and HR leaders across 10 countries, found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent. Contrary to common fears around how AI will impact jobs, employees, managers and HR leaders across the globe are reporting increased adoption of AI at work and many are welcoming AI with love and optimism. AI is becoming more prominent with 50% of workers currently using some form of AI at work compared to only 32% last year. Workers in China (77%) and India (78%) have adopted AI over two-times more than those in France (32%) and Japan (29%).

In this special guest feature, Glen Rabie, CEO of Yellowfin, discusses how alerts are commonly used as a basic business intelligence tool, but there's a better alternative: AI-driven automated analytics. AI has the power to parse the data behind dashboards and send a signal when significant activity happens. Here are five reasons why AI-driven automated analytics are better than alerts in today's evolving business landscape. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics.

"Alexa, help me find a job at McDonald's." That's how interested job seekers can start an application with the global fast-food company, McDonald's recently announced. Claiming it to be the world's first voice-initiated job application process, the company has launched McDonald's Apply Thru, which works on Amazon Alexa and Google Assistant. The app is currently available in the United States, Australia, Canada, France, Germany, Ireland, Italy, Spain and the United Kingdom and is expected to roll out to other countries in the coming months. Once Alexa or Google Assistant responds, users are asked to provide basic information, such as their name, contact information, job area of interest and location. Potential applicants then receive a text message with a link to the McDonald's careers site to continue their application process.

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often refereed to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of hitherto attempts at handling uncertainty in general and formalizing this distinction in particular. 1 Introduction Machine learning is essentially concerned with extracting models from data and using these models to make predictions.

We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class. Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class. We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity. Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.