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) …
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.
Artificial Intelligence, Machine Learning, Deep Learning, Smart Devices, terms that we are constantly bombarded with in the media, making us believe that these technologies are capable of doing anything and solving any problem we face. Nothing is further from reality!! According to the European Commission, "Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal."1. AI encompasses multiple approaches and techniques, among others machine learning, machine reasoning and robotics. Within them we will focus our reflection on machine learning from data, and more specifically on Intelligent Data Analysis aimed at extracting information and knowledge to make decisions. Those data (historical or streaming) that are stored by companies over time and that are often not put into value.
'OK, Dad, this is an incredible essay on the effects of grief and grey morality in a postapocalyptic society," says the eldest child, AKA the millennial. "It's got proper female characters, progressive takes on sexuality and tonnes of rain." "They've made a video game of The Handmaid's Tale?" And both games have the best ending ever." Now she has my interest. Video game endings fascinate me, because my generation started out with arcade games that didn't have them.
When voice actor Heath Miller sits down in his boatshed-turned-home studio in Maine to record a new audiobook narration, he has already read the text through carefully at least once. To deliver his best performance, he takes notes on each character and any hints of how they should sound. Over the past two years, audiobook roles, like narrating popular fantasy series He Who Fights With Monsters, have become Miller's main source of work. But in December he briefly turned online detective after he saw a tweet from UK sci-fi author Jon Richter disclosing that his latest audiobook had no need for the kind of artistry Miller offers: It was narrated by a synthetic voice. Richter's book listing on Amazon's Audible credited that voice as "Nicholas Smith" without disclosing that it wasn't human. To Miller's surprise, he found that "Smith" voiced a total of around half a dozen on the site from multiple publishers--breaching Audible rules that say audiobooks "must be narrated by a human."
The 35th conference on Neural Information Processing Systems (NeurIPS2021) featured eight invited talks. In the last of our series of round-ups, we give a flavour of the final presentation. Radhika's research focusses on collective intelligence, with the overarching goal being to understand how large groups of individuals, with local interaction rules, can cooperate to achieve globally complex behaviour. Each individual is miniscule compared to the massive phenomena that they create, and, with a limited view of the actions of the rest of the swarm, they achieve striking coordination. Looking at collective intelligence from an algorithmic point-of-view, the phenomenon emerges from many individuals interacting using simple rules.
Fisheries collect millions upon millions of fish eggs, protecting them from predators to increase fish yield and support the propagation of endangered species -- but an issue with gathering so many eggs at once is that those infected with parasites can put healthy ones at risk. Jensorter, an Oregon-based startup, has created AI-powered fish egg sorters that can rapidly identify healthy versus unhealthy eggs. The machines, built on the NVIDIA Jetson Nano module, can also detect egg characteristics such as size and fertility status. The devices then automatically sort the eggs based on these characteristics, allowing Jensorter's customers in Alaska, the Pacific Northwest and Russia to quickly separate viable eggs from unhealthy ones -- and protect them accordingly. Jensorter is a member of NVIDIA Inception, a program that nurtures cutting-edge startups revolutionizing industries with advancements in AI, data science, high performance computing and more.
In early collections, most customers will pay within a couple of days when nudged by a friendly reminder, such as text messaging. On the other hand, customers under financial stress should be spoken to sooner rather than later, so that there is sufficient time to resolve the problem and prevent accounts from rolling to later stages of delinquency. Ideally, minimal operational effort is spent on customers that are likely going to pay, so that expensive debt collection resources can be focused on those customers where agent intervention makes a difference. This is a perfect opportunity for digital debt collection. With digital debt collection, this goal is much easier to achieve.