If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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We tend to think of machine and deep learning AI as consistent, logical, and unwavering, but surprisingly, that isn't always the case. Bias is the source of many AI failures. So why, and how, does bias happen in AI models? The simple answer is that bias exists in these models because they're created by humans. Let's take a look at three types of AI bias that can plague AI models – sample bias, measurement bias, and prejudice bias – and how developers can eliminate these biases with more thorough AI model training.
Figure 1: Training instability is one of the biggest challenges in training GANs. Despite the existence of successful heuristics like Spectral Normalization (SN) for improving stability, it is poorly-understood why they work. In our research, we theoretically explain why SN stabilizes GAN training. Using these insights, we further propose a better normalization technique for improving GANs' stability called Bidirectional Scaled Spectral Normalization. Generative adversarial networks (GANs) are a class of popular generative models enabling many cutting-edge applications such as photorealistic image synthesis.
Did you miss a session from the Future of Work Summit? PSA, for the uninitiated, is software that is typically used by professional services companies to help plan, manage, and measure their projects' performance through centralizing processes such as project management, time-tracking, invoicing, resource planning, business intelligence, and more -- while automating many of the manual work involved. RMM, meanwhile, is software that is typically installed locally by managed service providers (MSPs) and IT professionals so they can oversee systems and devices remotely. The problem that SuperOps.ai is ultimately trying to solve is that MSPs are facing growing complexities in demands from their customers, which is exacerbated by the multitude of different tools and platforms that they use and -- a problem that is impacting most industries -- a shortage of technical talent. Replacing a patchwork of PSA and RMM tools and plugins that were not designed with integration in mind, SuperOps.ai
Pazzi is the world's first autonomous pizza robot. By building an autonomous restaurant, Pazzi Robotics is revolutionizing the fast-food industry. With increasing public health and safety concerns, quest for quality and traceability, and labor constraints, the definition of "Fast Food of the Future" is changing. Additionally, Pazzi is dramatizing robotics to change its approach. The show cooking experience is providing an emotional approach to the dining experience while providing autonomy to the consumers (from ordering to collecting their meal). It has been pioneering fast-food automation since its start in 2013 providing the most advanced tech.
Eric McCarter remembers the first time he operated a forklift truck in France--while sitting behind a desk in California. McCarter used the forklift to move a stack of pallets into a waiting truck using a setup resembling a video gaming rig. He sat behind a steering wheel and pedals that transmitted commands to the forklift thousands of miles away; large screens offered views in front, behind, and to the sides of the vehicle. The vehicle relies on limited artificial intelligence to avoid obstacles and safely come to a stop if the connection between France and the US were to fail. But the AI isn't yet clever enough to let the robotic forklift navigate on its own through an unfamiliar warehouse or take on a new task.
For cartographers and cartophiles, Harold Fisk's 1944 maps of the lower Mississippi River are a seminal work. The centerpiece of his report was 15 maps showing the meandering Mississippi and its historical floodplains stretching from Missouri to southern Louisiana. More than seven decades later, Daniel Coe, a cartographer for the Washington Geological Survey, wanted to re-create Fisk's maps with greater accuracy and a new aesthetic. Coe had the advantage of hyperprecise U.S. Geological Survey (USGS) data collected using lidar, a system of laser pulses sent from aircraft to measure topography. The lasers detect the river's shape along with everything around it--every house, tree, and road.
Note that the Buckeye Flats location (a) contains greater acoustic activity, a result of the nearby rapid flowing stream that produced considerable geophonic sounds. The inset (b) graphs the same data but with Buckeye Flats removed. These values (b) reflect mostly biophony. Sycamore Creek contained the greatest acoustic activity of these three. The fall contains the greatest activity although there was no consistent pattern across sites. Photos of each landscape are provided in (c).
Tech companies in the U.S. and the U.K. haven't done enough to prevent bias in artificial intelligence algorithms, according to a new survey from Data Robot. These same organizations are already feeling the impact of this problem as well in the form of lost customers and lost revenue. DataRobot surveyed more than 350 U.S. and U.K.-based technology leaders to understand how organizations are identifying and mitigating instances of AI bias. Survey respondents included CIOs, IT directors, IT managers, data scientists and development leads who use or plan to use AI. The research was conducted in collaboration with the World Economic Forum and global academic leaders.
Did you miss a session from the Future of Work Summit? During the pandemic, a growing number of manufacturers have begun to pilot -- or fully embraced -- AI in their organizations. While technical and human roadblocks threaten to slow adoption, manufacturers are deploying AI across a range of maintenance, quality assurance, and production processes. Ninety-three percent of enterprises believe that AI will be a pivotal technology to drive growth and innovation in the manufacturing sector, according to Deloitte. And manufacturing companies are expected to spend $13.2 billion on AI software, hardware, and services in 2025, up from $2.9 billion in 2018.