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Artificial Intelligence 'AI' and additional needs

#artificialintelligence

Something a little more unusual in this blog post as we are going to be exploring how Artificial Intelligence can be a tool that we can use to help and support people, of any age with additional needs or disabilities, in our churches. "Technology has changed the world, bringing knowledge within reach and expanding a range of opportunities. Persons with disabilities can benefit enormously from such advances, yet too many lack access to these essential tools…" So, has anything changed since then? And what does today's Artificial Intelligence, or'AI', offer as technological solutions for disabled people, particularly in our church settings? What can we learn about'AI' together, that can enable us to better serve and support disabled people in our church communities?


Council Post: What Is The Future Of Artificial Intelligence In Photo Editing?

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Ben Meisner is the Founder of the leading online photo editing platform Ribbet.com. Artificial intelligence (AI) may seem like a buzzword of the 21st century, but it entered the human psyche some time ago. A Harvard article on the history of AI points out that science fiction brought the concept into our minds in the first half of the 20th century through characters like the Tin Man in The Wizard of Oz and the humanoid robot impersonating Maria in Metropolis. Mankind is now taking the concept from idea to reality, and today AI has tremendous application in everything from medicine, construction and finance to home appliances, social media and copywriting. It has the unique capability to quickly learn from significant amounts of data, enabling it to tackle some of our most challenging technological issues.


Exclusive Interview with Naren Vijay, EVP of Lumenore

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Organizational intelligence (OI) is the capability of an organization to comprehend and create knowledge relevant to its purpose. In other words, it is the intellectual capacity of the entire organization. Lumenore is a powerful, intuitive, and cloud-based BI and analytics platform that delivers organizational intelligence by sifting data from any business application. Analytics Insight has engaged in an exclusive interview with Naren Vijay, EVP of Lumenore. Lumenore is a powerful, intuitive, and cloud-based BI and analytics platform that delivers organizational intelligence by sifting data from any business application.


Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

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Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers will increase in the coming years due to climate change. Groundwater potential mapping is a valuable tool to underpin water management in the region and, hence, to improve drinking water access. This paper presents a machine learning method to map groundwater potential. This is illustrated through its application in two administrative regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Scaling methods (standardization, normalization, maximum absolute value and max–min scaling) are used to avoid the pitfalls associated with reclassification. Noisy, collinear and counterproductive variables are identified and excluded from the input dataset. A total of 20 machine learning classifiers are then trained and tested on a large borehole database (n=3345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. Maximum absolute value and standardization proved the most efficient scaling techniques, while tree-based algorithms (accuracy >0.85) consistently outperformed other classifiers. The borehole flow rate data were then used to calibrate the results beyond standard machine learning metrics, thereby adding robustness to the predictions. The southern part of the study area presents the better groundwater prospect, which is consistent with the geological and climatic setting. Outcomes lead to three major conclusions: (1) picking the best performers out of a large number of machine learning classifiers is recommended as a good methodological practice, (2) standard machine learning metrics should be complemented with additional hydrogeological indicators whenever possible and (3) variable scaling contributes to minimize expert bias.

Sustainability starts in the design process, and AI can help

MIT Technology Review

Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. "…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity," says Mike Haley, the vice president of research at Autodesk. But there's hope with AI capabilities, Haley continues "This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them." And where "AI and humans come together" is at the start of the process with generative design, which incorporates AI into the design process to explore solutions and ideas that a human alone might not have even considered. "You really want to be able to look at the entire lifecycle of producing something and ask yourself, 'How can I produce this by using the least amount of energy throughout?'" This kind of thinking will reduce the impact of, not just construction, but any sort of product creation on the planet. The symbiotic human-computer relationship behind generative design is necessary to solve those "very complex problems"--including sustainability. "We are not going to have a sustainable society until we learn to build products--from mobile phones to buildings to large pieces of infrastructure--that survive the long-term," Haley notes. The key, he says, is to start in the earliest stages of the design process. "Decisions that affect sustainability happen in the conceptual phase, when you're imagining what you're going to create." He continues, "If you can begin to put features into software, into decision-making systems, early on, they can guide designers toward more sustainable solutions by affecting them at this early stage."


'Gutfeld!' on Biden's divisiveness

FOX News

'Gutfeld!' panel discusses President Biden's sinking approval ratings and pivot to voting rights. This is a rush transcript of "Gutfeld!" on January 13, 2022. This copy may not be in its final form and may be updated. KAMALA HARRIS, VICE PRESIDENT OF THE UNITED STATES: You can Google it or go into any search engine and find out where free testing and the free testing site is available. If you want to figure out how to get across town to some restaurant you heard is great you usually do Google. And what a glorious Thursday it is. Only 33 percent of adults approve of Biden's job performance which is a record low. And after answering the poll those 33 percent were rushed back to the mental health facility where the pollsters had found them. But his approval is like his body temperature, barely above freezing. It's dropped three points since November. And it beats former President Trump's worst approval rating by one point. Well, at least he can finally say he's better than Trump at something. But it is an amazing comparison. Consider the kind of press that Trump got was a relentless drumbeat of hysterical hate. And yet, Joe, who has none of that from the press is rated worse than Trump. How is it that a genial old man coddled by every media entity on Earth is now less popular than the monster? The media deemed worse than Hitler? Stalin and Maroon Five combined? Yes, it's an amazing feat, but not when you see how bad it's really gotten. He's praying for it all to end. Remember, Americans were sold Joe, his political Xanax, after the crystal meth of Donald Trump. Joe was supposed to be beloved popular or leave from Captain Evil. But even treated with kid gloves we can't stand him, even with the facelift and the hair transplant you can't make him look good.


Computing With Light

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This article will look at a technology for AI inference processing using light rather than electrons from LIghtmatter and combined with traditional CMOS including SRAM memory. This article is based upon an interview with Lightmatter CEO, Nick Harris. The company sees this product being useful for data center inference and perhaps eventually in some AI computation intensive industrial and consumer applications (such as autonomous vehicles). There are widely cited forecasts that project accelerating information and communications technology (ICT) energy consumption increases through the 2020's with a 2018 Nature article estimating that if current trends continue, this will consume more than 20% of electricity demand by 2030. At several industry events I have heard talks that say one of the important limits of data center performance will be the amount of energy consumed.


Landing AI hires vision expert Dechow to correct the Big Data fallacy

#artificialintelligence

The field of deep learning has been suffering from what you might call a Big Data fallacy, the belief that more and more data is always a good thing. It may be time to focus on quality rather than just quantity. "There's a very fundamental problem that a lot of AI faces," said Andrew Ng, founder and CEO of Landing AI, a startup working to perfect the technology for industrial uses, in an interview with ZDNet this week. "A lot of AI is focused on maximizing the number of calories, which works up to a certain point," he said. "And sometimes you do have a lot of data, but when you have a small data set, it's more the quality of the data rather than the sheer volume."


Do You think AI has Risk Factors? Know these 5 Downsides

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The role of AI has modified considerably – from its preliminary creation on the threshold of an enterprise of their innovation labs, to the modern-day while human beings are starting to recognize that it has the ability to convert businesses from the center out. Recently there's been a warning approximately extending its use past simple functionality, and what sort of it could be trusted, which has supposed its use hasn't been pervasive inside businesses. However, now that an increasing number of businesses have dipped their toe into the water and have had their eyes opened as to the advantages it may provide, the technology is ultimately prepared to attain maturity. A key cause for this is to stop customers from also are attaining adulthood in their personal expertise approximately each how they are able to get the fine outcomes from AI, and additionally the rights and wrongs of the usage of it. Now that AI has been in large part demystified, customers have much higher expertise of a way to practice it successfully and correctly, because of this that they're subsequently prepared to undertake it on a much broader foundation and ship its use into the mainstream.


Landing.AI hires vision expert Dechow to correct the Big Data fallacy

ZDNet

The field of deep learning has been suffering from what you might call a Big Data fallacy, the belief that more and more data is always a good thing. It may be time to focus on quality rather than just quantity. "There's a very fundamental problem that a lot of AI faces," said Andrew Ng, founder and CEO of Landing.AI, a startup working to perfect the technology for industrial uses, in an interview with ZDNet this week. "A lot of AI is focused on maximizing the number of calories, which works up to a certain point," he said. "And sometimes you do have a lot of data, but when you have a small data set, it's more the quality of the data rather than the sheer volume."