We have the largest annotated dataset for the construction industry ever assembled with all of its real world attributes: dirty, unexplored, and rich. This role is for you if you want hands-on experience with ML on image, speech, and video data. We are looking for someone excited to design, train, apply and evaluate the latest deep learning models on customer data within our cloud based research and production environments. The goal is to generate an automated assessment of job site safety risks and feed the data to a predictive pipeline that will help our clients better manage their workforce and ultimately save lives. Most of our programming is done in Python3 using AWS resources.

Hitachi Vantara, the digital infrastructure and solutions subsidiary of Hitachi, is applying new artificial intelligence technologies to help airlines and airports solve some of their greatest challenges. The Silicon Valley-based company's technology aims to eliminate travel delays caused by congestion at check-in, security, retail and aircraft boarding areas. Hitachi Vantara has developed solutions that combine computer vision with video analytics and machine learning to help airline and airport staff identify when and where congestion and delays are occurring and take action to address them. The solution is already being piloted by a major US airline, and Hitachi Vantara hopes to implement the technology at more US airports in time for the 2020 holiday season. The Lumada Video Insights solution uses lidar to produce a 3D visual model of the airport and the movement of travelers, equipment and luggage in real time.

The innovation and rate of growth in artificial intelligence (AI) and high-performance computing (HPC) applications are staggering. Autonomous cars, fraud detection, business intelligence, affinity marketing, personalized medicine, Alexa, Siri, smart cities and the Internet of Things (IoT) are just a few of the commercial and consumer-driven applications exploding in use on a global scale. Underlying these offerings are dense computing platforms and IT infrastructures that require highly specialized data center environments in order to perform reliably and scale efficiently. Legacy on-premise or colocation data centers, built years ago to support general-purpose computing servers, are becoming obsolete in certain markets due to the intense power and cooling requirements of the HPC and AI servers. These servers are built upon processing-intensive components such as graphics processing unit (GPU) cards and dual central processing unit (CPU) architectures.

When I look back to where technology was in 2010, it's astounding to think about how much has changed -- and how so many of those advancements were fueled by open source. Ten years ago, AI was not a part of our everyday lives, most developers hadn't even heard of containers or microservices, blockchain was little more than an idea, and serverless was a far-off dream. Now these technologies, built on open source projects and the communities that surround them, are shaping how developers do their jobs and how people interact with technology on a daily basis. In this blog post, I talk about some of the trends that have shaped the past decade as we look forward to what 2020 -- and the next decade -- has in store for us. Before 2010, the concepts of containers and microservices were merely ideas.

Earlier this year, Talent Garden hired Spain's Lorena Pérez as its new chief people officer. With more than two decades of experience in HR, Pérez plans to use her expertise to scale Talent Garden in Europe while coming up with ways to retain and motivate the company's talent. She discussed the importance of conserving company culture while a company scales, and some of the ways she uses technology to improve her work and efficiency in HR. I come from the HR world, of course! I've been working for multinational companies, mainly creating and developing HR and people departments. I have more than 20 years of experience now, with my first relevant experience being in a communications company in the tech sector.

Through intelligent automation, optimum use of artificial intelligence (AI) and an unrivaled understanding of the latest training and professional development techniques, Sitel Group brings a new generation of super contact center associates and with it a new level of customer trust. Often when the subject of artificial intelligence (AI) comes up for discussion within the context of customer experience, the emphasis is on efficiency – how AI is reducing contact center costs or deflecting calls that under other circumstances would have resulted in a low-value direct interaction with a contact center associate. This type of conversation is too confining and fails to recognize the true potential of AI, especially when applied to empower frontline CX professionals. This isn't to say leveraging technology to optimize self-service is a bad thing. Customers demand the ability to resolve simple everyday issues at the moment they occur; and to find the answer to a simple question at the moment they need to ask it, without joining a voice queue and negotiating an IVR.

It's never a bad time to be an engineer--or to have people skills. LinkedIn's third annual US emerging jobs report has identified the 15 fastest-growing jobs, as well as the skills and cities most associated with them. This year the company found that the number of artificial intelligence and data science roles continue to expand across nearly every industry. For the first time, robotics has made an appearance on the list, and at least five roles in the ranking include the word "engineer" in the title. But it's not just high-tech roles that have seen a lot more hiring action in the past five years, which is how far back LinkedIn looks to measure the emergence of roles based on user profile data and hiring growth trends.

A new Professional Intelligence Report released by Data Bridge Market Research on Global Artificial Intelligence in Medical Imaging Market which describes the basic characteristics of the industry and market statistics. The influencing Factors of the report is growth of this market includes authorized regulations with respect to the usage of the information, availability of highly reliable products in the market, and increase in operational efficiency. The latest advancement in technology, policies, Artificial Intelligence in Medical Imaging market business plans, possibilities for growth and risks to the sector are being included. The report's two key sections are defined, namely market revenue in (USD$ Billion) and market size. The report provides both, qualitative and quantitative research of the Global Artificial Intelligence in Medical Imaging market, as well as integrates worthy insights into the rational scenario and favored development methods adopted by the key contenders.

Idea submissions are now being accepted for the 2020 Nittany AI Challenge. Student teams with a wide variety of skill sets are invited to submit their ideas for using artificial intelligence to improve the world by providing solutions for problems within the areas of education, health, sustainability, climate change and humanitarian challenges. Teams selected to participate in the three-phase process will be assisted in developing a minimal viable product (MVP) and have opportunities to receive prizes from a pool of $50,000. The deadline for idea submissions is Jan. Brad Zdenek, innovation strategist for the Nittany AI Alliance, said students do not have to be coders to participate, as each team has a need for various skill sets.

"The attention around AI tends to focus on the latest technologies," Iansiti argues, "but the firms that are thriving have harnessed the subtle, inherent power of AI to break down traditional operational constraints, capture new value, and accelerate growth and innovation." What sets AI-driven firms apart is their ability to avoid the inefficiencies and bottlenecks that plague growth when complexity -- primarily caused by humans -- outstrips organizational capacity. These firms strive to construct a model for operational execution that does not require human intervention (ideally, no real-time "human bottlenecks"). In the new digital operating model, most operational tasks circumvent humans entirely. The ultimate aim is to automate and digitize as many operational processes as possible to take advantage of digital reliability and scalability.