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How to Choose a Major for Artificial Intelligence: Degree Research Guide

#artificialintelligence

Artificial intelligence (AI) offers plenty of opportunities in the job market, as many AI companies try to solve real-world problems through this field of practice. AI's growth also comes with a wide range of options available to find the best majors for artificial intelligence. When it comes to what degree in artificial intelligence should you pursue, keep reading to learn how to choose a major for artificial intelligence and know the possible AI career paths that are open to you after graduating. A career in artificial intelligence provides tech professionals with competitive pay, job security, and continuous learning and development. The Bureau of Labor Statistics (BLS) reports that the average annual salary for computer and AI professionals is $126,830.


Is diversity the key to collaboration? New AI research suggests so

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As artificial intelligence gets better at performing tasks once solely in the hands of humans, like driving cars, many see teaming intelligence as a next frontier. In this future, humans and AI are true partners in high-stakes jobs, such as performing complex surgery or defending from missiles. But before teaming intelligence can take off, researchers must overcome a problem that corrodes cooperation: humans often do not like or trust their AI partners. MIT Lincoln Laboratory researchers have found that training an AI model with mathematically "diverse" teammates improves its ability to collaborate with other AI it has never worked with before, in the card game Hanabi. Moreover, both Facebook and Google's DeepMind concurrently published independent work that also infused diversity into training to improve outcomes in human-AI collaborative games.


Google Has a Plan to Stop Its New AI From Being Dirty and Rude

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Silicon Valley CEOs usually focus on the positives when announcing their company's next big thing. In 2007, Apple's Steve Jobs lauded the first iPhone's "revolutionary user interface" and "breakthrough software." Google CEO Sundar Pichai took a different tack at his company's annual conference Wednesday when he announced a beta test of Google's "most advanced conversational AI yet." Pichai said the chatbot, known as LaMDA 2, can converse on any topic and had performed well in tests with Google employees. He announced a forthcoming app called AI Test Kitchen that will make the bot available for outsiders to try.


The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution

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When Hugging Face first announced itself to the world five years ago, it came in the form of an iPhone chatbot app for bored teenagers. It shared selfies of its computer-generated face, cracked jokes and gossiped about its crush on Siri. It hardly made any money. The viral moment came in 2018--not among teens, but developers. The founders of Hugging Face had begun to share bits of the app's underlying code online for free.


Men who pose topless on Tinder are seen as less competent and more promiscuous, study reveals

Daily Mail - Science & tech

While dating apps were once seen as taboo, they're now one of the main ways that singletons find love around the world. But if you have a profile on a dating app, a new study may encourage you to reassess which pictures you include. Researchers from the University of Colorado have revealed that men who pose topless on Tinder are seen as less competent and more promiscuous. The first dating app can be traced back to 1995 when Match.com was first launched. The website allowed single people to upload a profile, a picture and chat to people online.


Researchers use artificial intelligence to predict road user behavior - Actu IA

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For an autonomous car to drive safely, being able to predict the behavior of other road users is essential. A research team at the Massachusetts Institute of Technology's CSAIL, along with researchers at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in Beijing, have developed a new ML system that could one day help driverless cars predict in real time the upcoming movements of nearby drivers, cyclists and pedestrians. They titled their study, " M2I: From Factored Marginal Path Prediction to Interactive Prediction." Qiao Sun, Junru Gu, Hang Zhao are the IIIS members who participated in this study while Xin Huang and Brian Williams represented MIT. Humans are unpredictable, which makes predicting road user behavior in urban environments de facto very difficult.


Ethics And Conversational Assistants

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It is utopian to rule out any form of anthropomorphism when addressing a conversational assistant because of the use of language as a vector of exchange. Designers, therefore, must limit these shortcomings with the implementation of these design rules, thus reducing the risks of deception and dependency, and giving confidence in these systems.


Nearest-neighbor missing visuals revealed

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The unsupervised K- Nearest Neighbour (KNN) algorithm is perhaps the most straightforward machine learning algorithm. However, a simple algorithm does not mean that analyzing the results is equally simple. As per my research, there are not many documented approaches to analyzing the results of the KNN algorithm. In this article, I will show you how to analyze and understand the results of the unsupervised KNN algorithm. I will be using a dataset on cars.


Artificial Intelligence: Its benefits and challenges - Clover Infotech

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Artificial Intelligence was first popularized by a small group of scientist gathered at the Dartmouth College in the United States in 1956. Since then, AI has advanced considerably and is powering many real-world applications ranging from facial recognition to language translators and virtual assistants such as Siri and Alexa. Still, we are far from witnessing AI-powered robots emulating humans. So far that is confined to the Sci-Fi movies. However, AI has created quite a stir in the business world with its many benefits and challenges.


Methods Included

Communications of the ACM

Although workflows are very popular, prior to the CWL standards, all workflow systems were incompatible with each other. This means that users who do not use the CWL standards are required to express their computational workflows in a different way each time they use another workflow system, leading to local success but global unportability. The success of workflows is now their biggest drawback. Users are locked into a particular vendor, project, and often a specific hardware setup, hampering sharing and reuse. Even non-academics suffer from this situation, as the lack of standards, or their adoption, hinders effective collaboration on computational methods within and between companies.