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AI in hiring might do more harm than good

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The use of artificial intelligence in the hiring process has increased in recent years with companies turning to automated assessments, digital interviews, and data analytics to parse through resumes and screen candidates. But as IT strives for better diversity, equity, and inclusion (DEI), it turns out AI can do more harm than help if companies aren't strategic and thoughtful about how they implement the technology. "The bias usually comes from the data. If you don't have a representative data set, or any number of characteristics that you decide on, then of course you're not going to be properly, finding and evaluating applicants," says Jelena Kovačević, IEEE Fellow, William R. Berkley Professor, and Dean of the NYU Tandon School of Engineering. The chief issue with AI's use in hiring is that, in an industry that has been predominantly male and white for decades, the historical data on which AI hiring systems are built will ultimately have an inherent bias.


Regularizing Deep Neural Networks

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Lets discuss Regularizing Deep Neural Networks. Deep neural nets with an outsized number of parameters are very powerful machine learning systems. However, overfitting may be a significant issue in such networks. Making it hard to affect over-fitting by associating the predictions of the many different large neural nets at test time, big networks similarly are slow to use. Dropout might be a technique for addressing this problem.


La veille de la cybersécurité

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Today, I continue my top-AI-stocks video series. If you are new to this series, it covers my top 12 artificial intelligence stocks focused on growth and disruptive innovation. I have done my best to find the highest-growth companies in a variety of sectors with disruptive growth trends. Last time, I shared my favorite chatbot stock. In today's video, I am covering an unknown business that Square (NYSE:SQ) acquired in 2020 that focuses on artificial intelligence and machine learning.


Beginner's Guide To Neural Networks.

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Neural networks, additionally called man-made semantic networks or substitute neural networks (SNNs), are a part of artificial intelligence and go to the heart of deep knowing formulas. Their name and also structure are influenced by the human brain, simulating the manner in which biological nerve cells signal to each other. Artificial neural networks (ANNs) are consisted of a node layers, including an input layer, several hidden layers, and also a result layer. Each node, or synthetic neuron, connects to an additional and also has a connected weight and threshold. If the outcome of any specific node is above the defined limit worth, that node is triggered, sending data to the next layer of the network.


Introduction to NLP Deep Learning Theories

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Deep learning models rely on numerical vectors to'understand' the input words. We can think of the numerical vectors as high dimensional features representing the input words. In this high dimensional space, words are located close together or far away from each other. Word representation is built by finding the proper numerical vector representations for all the words in a given corpus. The quality of word representation relies on the corpus. This can be easily understood in the way that two human beings can have a different understanding of the same word, depending on whether he likes to spend time reading the modern newspaper or Shakespeare's literature. Besides, the quality of word representation heavily relies on the methods to find numerical vector representations for all the words. There are several methods to generate word representation by learning from the words' context.


La veille de la cybersécurité

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Deep neural networks have been responsible for much of the advances in machine learning over the last decade. These restrictions not only raise infrastructure costs but also complicate network implementation in resource-constrained contexts like mobile phones and smart devices. Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time. The goal of neural network pruning is to convert a large network to a smaller network with equivalent accuracy. Here in this article, we will discuss the important points related to neural network pruning. The major points to be covered in this article are listed below.


Artificial Intelligence is Critical Enabler of the Energy Transition

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The World Economic Forum has published a new study on how artificial intelligence (AI) can be used to accelerate a more equitable energy transition and build trust for the technology throughout the industry. As the impacts of climate change become more visible worldwide, governments and industry face the urgent challenge of transitioning to a low-carbon global energy system. Digital technologies – particularly AI – are key enablers for this transition and have the potential to deliver the energy sector's climate goals more rapidly and at lower cost. Written in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German Energy Agency, Harnessing Artificial Intelligence to Accelerate the Energy Transition reviews the state of play of AI adoption in the energy sector, identifies high-priority applications of AI in the energy transition, and offers a road map and practical recommendations for the energy and AI industries to maximize AI's benefits. The report finds that AI has the potential to create substantial value for the global energy transition.


I Tried The 40$ Amazon Echo Dot 4 For My Apartment

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You might remember the fist versions of assistants like siri, but you have to admit that in the last 10 years they had a big leap of development. Now not only phones have the abilities to support you daily through talking, but also speakers that can do things like playing music, teaching, shopping and other similar things. Since I needed a new music Speaker and i am a fan of simplicity and easiness I bought the 40$ Amazon Echo Dot 4 with my coupons that were left from Christmas. The day the package shipped I was nervous and happy at the same time. Since this was my first Smart Speaker, i didn't know what to expect but i was purely happy to finally receive a new gadget that could change my life for good.


How AI Will Transform Software Development

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While artificial intelligence (AI) is already effectively assisting human developers at every level of the development process, software development will only get better as it is about to undergo a huge change. Artificial intelligence is revolutionizing the way developers work, resulting in significant productivity, quality and speed increases. Everything -- from project planning and estimation to quality testing and the user experience -- can benefit from AI algorithms. AI will undoubtedly impact how developers create applications and how users interact with them in the modern environment. As organizations become more interested in AI technologies, artificial intelligence will certainly affect the future of software development.


Why companies should use AI for fraud management, detection

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Several use cases for AI in fraud detection and management were discussed in the report. For example, AI can improve the accuracy of transaction monitoring. The analysts described how financial services provider FIS worked with Brighterion, an AI company owned by Mastercard, to improve its anti-money laundering capabilities. The provider now uses AI to vet risk when onboarding new vendors, for example. In other use cases, AI can improve the efficiency of fraud investigations by streamlining and prioritizing alerts.