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An AI-Based Approach to Monitor, Expose, and Counter Anti-Hindu Hate

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MyIndMakers enables the exchange of Global Ideas and Solutions from India. All day news updates related to Business, Hindu, Hinduism, India, Indic, Culture, Travel, Religion, Politics, Foreign Policy, Modi, Swami, BJP, Congress, Trump, Biden, Israel, Jihad, Christianity, China, Japan, Book Reviews, Movie Reviews, Indian Artciles, Blogs, Interviews, Podcasts, Videos, MyIndBook, MyIndMakers, myind.net,Hindumisia, hindumisia.ai, AI-based approach, Deep Learning, Anti-Defamation League, Online Hate Index


A Tidal Shift for AI in Banking

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In a galaxy [not at all] far, far away… was a commonly accepted idea that the bigger banks could hire the best talent, implement state of the art technologies, and ultimately enhance bottom line revenues via improved risk modeling, novel product offerings, etc. This concept is the proverbial'black box' of large banking institutions and how their success has been perceived for many years, which in broad strokes, is fairly accurate. But what has changed in the recent years and why are smaller institutions so keen on getting their hands on this sexy new tech? For starters, those huge banks have made extraordinarily large investments over the past decade in order to continue validation of their claim to the top rungs of industry and to service the largest clients available. However, thanks to their valiant efforts of progressivism, and some well-placed IPO funding rounds of promising AI unicorns, they managed to provide an industry fresh off its second'AI winter' with the funding necessary to inspire entirely new solutions applicable to the broader public. The other important aspect of this boom in a large bank's successful employment of data science applications is attributed to the vast quantities of data amassed at a scale that's exponentially larger than that of smaller banks.


Is artificial intelligence the next tool to fight wildfires?

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The high-tech help could come from an area not normally associated with fighting wildfires: artificial intelligence (AI).


Artificial intelligence in medicine may be able to distinguish between races – Code List – codelist.biz

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According to a study of Massachusetts Institute of Technology (MIT), the iartificial intelligence (AI), can predict the races of patients through …



Why we must rethink AI benchmarks

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. For decades, researchers have used benchmarks to measure progress in different areas of artificial intelligence such as vision and language. Especially in the past few years, with deep learning becoming very popular, benchmarks have become a narrow focus for many research labs and scientists. But while benchmarks can help compare the performance of AI systems on specific problems, they are often taken out of context, sometimes to harmful results. In a paper accepted at the NeurIPS 2021 conference, scientists at University of California, Berkeley, University of Washington, and Google outline the limits of popular AI benchmarks.


How do we keep bias out of AI?

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From the coining of the term back in the 1950's to now, AI has taken remarkable leaps forward and only continues to grow in relevance and sophistication But despite these advancements, there's one problem that continues to plague AI technology – the internal bias and prejudice of its human creators. The issue of AI bias cannot be brushed under the carpet, given the potential detrimental effects it can have. A recent survey showed that 36% of respondents reported that their businesses suffered from AI bias in at least one algorithm, resulting in unequal treatment of users based on race, gender, sexual orientation, religion or age. These instances incurred a direct commercial impact: of those respondents, two-thirds reported that as a result they lost revenue (62%), customers (61%), or employees (43%). And 35% incurred legal fees because of lawsuits or legal action.


Is artificial intelligence the next tool to fight wildfires?

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With wildfires becoming bigger and more destructive as the western part of the United States dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chainsaws and aircraft. The high-tech help could come from an area not normally associated with fighting wildfires: artificial intelligence (AI). Lockheed Martin Space, based in Jefferson County, is tapping decades of experience in managing satellites, exploring space and providing information to the US military to offer more accurate data quicker to ground crews. It is talking to the US Forest Service, university researchers, and a Colorado state agency about how their technology could help. By generating more timely information about on-the-ground conditions and running computer programs to process massive amounts of data, Lockheed Martin representatives say they can map fire perimeters in minutes rather than the hours it can take now.


Guest Blog – Machine Learning In Talent Management - AI Summary

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AI & Machine Learning Applications in the Real World According to the latest trends of AI-based solutions, there is hardly any decisive sector or industry that does not rely on smart algorithms and automation to perform highly advanced tasks that would be impossible for most humans. Many companies use Machine Learning and Artificial Intelligence to identify and sort through the best possible candidates for a position. With a few Machine Learning courses that are specially designed for regular people, without advanced technical knowledge, it's easy to understand why there are so many applications of advanced technologies in the real world. Luckily, this situation can now be avoided by training machine learning algorithms to take over the task. According to a case study performed at Canada's largest bookstore chain (Indigo), the use of AI and machine learning algorithms to screen job candidates and decide who to hire has led to an increase in overall productivity.


ML for Algorithmic Trading, with Stefan Jansen

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Listen to this episode on Anchor FM. Stefan has been a partner in an investment firm where he assisted in building data infrastructure and predictive analytics practice. He accomplished this when data science was only beginning to be taken seriously in the investment industry. You won't want to miss this opportunity to learn from Stefan's experiences. Machines learning from data will continually improve in achieving performance measures.