Wired editor Maria Streshinsky speaks to computer and data science experts Kai-Fu Lee and Fei-Fei Li. We're told constantly that artificial Intelligence (AI) is ever-rising in its ubiquity, seeping into every industry, finding its place in all aspects of the business-- enabling us to work in different ways; in some cases, threatening to take over our roles entirely. Stats such as recruitment firm Robert Walters', which predicts AI will give rise to 133 million new jobs across the globe in the future, can sound vague and far off in a distant future, while things probably haven't seemed to have changed much at our desks. But rest assured, hype aside, the'age of AI' is drawing closer, and the evidence lies in businesses' eagerness to invest in the talent to make it happen. The AI specialist now represents the fastest-growing role in the United States over the last four years.

In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable--hidden inside a so-called black box--how can it be trusted? Among researchers, there's a growing call to clarify how deep learning tools make decisions--and a debate over what such interpretability might demand and when it's truly needed.

That role, A.I. specialist, is the fastest growing U.S. job in terms of number of hires, at least according to LinkedIn, which published its annual emerging jobs report on Tuesday. Hirings for A.I. specialists on the career networking service have grown 74% annually over the past four years, LinkedIn said. But it didn't reveal how many jobs that represents, only that demand for that job role is growing faster than other emerging jobs. What's noteworthy about this year's survey is that last year's top job role, blockchain developer, is absent from the latest list. It highlights how the recent craze over cryptocurrencies and blockchain created a brief demand for blockchain-related jobs, but as the hype died down, so too did demand for people with blockchain skills.

Over the past decade or so, advancements in machine learning have enabled the development of systems that are increasingly autonomous, including self-driving vehicles, virtual assistants and mobile robots. Among other things, researchers developing autonomous systems need to identify ways to integrate components designed to tackle different and yet complementary sub-tasks. For instance, a robot that completes manual tasks in a human user's home should be able to sense objects in its environment while also retrieving information about these objects that can then be used to plan its movements and actions. This process, also known as the "perception-cognition-action" paradigm, is of crucial importance, as it ultimately allows the robot to come up with useful strategies and efficiently complete tasks. So far, most methods to implement this perception-cognition-action paradigm in robots treat these three tasks as almost entirely independent modules that act as black boxes for one another.

Artificial intelligence (AI) started as a concept decades ago. In the early days, only scientific researchers and maybe handfuls of engineers spent time thinking about it. These days, most of us hear about AI daily--a quick Google search of the term yields over 400 million results. But what does AI mean for digital marketers, and how can we use it to create compelling experiences that attract customers? Recent research from WP Engine and Dr. Chris Brauer from The University of London set out to answer that question.

Industry Report "Cognitive Computing Market" provides a clear picture of the Current Market Scenario which includes past and estimated future size with respect to Value and Volume, Technological Advancement, Macro Economical and Governing Factors in the Cognitive Computing market. Cognitive Computing is defined as the technology based on the principle of artificial intelligence, signal processing, machine learning, and natural language processing (NLP) among others technology. It brings human like intelligence for a many business applications which will include big data. Cognitive Computing is a well-known technology basically specialized for processing and analyzing large and unstructured datasets. The major drivers of the cognitive computing market are the advancements in computing platforms like cloud, mobile, and big data analytics which will drive the growth of the market in the forecast period.

Interested in helping the millions of Americans with chronic conditions get better care? OM1 is a leading real-world outcomes and technology company leveraging big clinical data and AI to better understand, compare, and predict patient outcomes. Our products are built to accelerate research, measure and benchmark health outcomes and to personalize patient care. Were looking for Machine Learning Engineers to help design, build, test, deploy, and monitor our platform that seeks to understand clinical text at large scale, as a means to measuring and predicting patient outcomes. Our product-focused team embraces creative, rigorous, innovative approaches and experimentation, while emphasizing high quality code, user-friendliness (data is a first-class product here), and rapid iteration.

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.