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Council Post: Educating The Next Generation Of Data Scientists

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In recent years, artificial intelligence has become mainstream. Today, more enterprises in more industries are looking to technology like AI for a competitive edge and to help them manage the overwhelming volumes of data they both generate and collect. A recent study by IBM put the "global AI adoption rate" at 35% and found that 44% of global organizations are currently working on embedding AI into their operations. But despite this continued uptick in AI adoption, one element sure to put the brakes on many initiatives is the global skills shortage. Successful AI requires competent data scientists.


Manager, Machine Learning Engineering

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


Machine learning vs data science: Differences, similarities, and future (2022) - Dataconomy

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The much-awaited comparison is finally here: machine learning vs data science. The terms "data science" and "machine learning" are among the most popular terms in the industry in the twenty-first century. These two methods are being used by everyone, from first-year computer science students to large organizations like Netflix and Amazon. The fields of data science and machine learning are related to the use of data to improve the development of new products, services, infrastructure systems, and other things. Both correspond to highly sought-after and lucrative job options.


CSforALL Urges Greater Focus on AI and Data Science

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If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science. But anyone who is serious about expanding computer science education--a list that includes Fortune 500 company CEOs and policymakers on both sides of the aisle--should be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data. That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more. That imperative is at the heart of a list of recommendations by CSforALL, an education advocacy group presented last month at the International Society for Technology in Education's annual conference. Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education.


How AI is changing the way we learn languages

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! When we think about AI and voice recognition, we typically think of one of two suboptimal scenarios. The first is your Amazon Alexa sitting at home, possibly eavesdropping on your everyday conversations and feeding advertising algorithms so you buy the right kind of lawn mower. The second scenario is clunky transcription software, auto-subtitling our videos and TV shows, often to inaccurate (and humorous) effect. In reality, though, there are some deeply exciting developments happening in the AI voice recognition space right now.


Why Computer Science Classes Should Double Down on AI and Data Science

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If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science. But anyone who is serious about expanding computer science education--a list that includes Fortune 500 company CEOs and policymakers on both sides of the aisle --should be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data. That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more. That imperative is at the heart of a list of recommendations by CSforALL, an education advocacy group presented last month at the International Society for Technology in Education's annual conference. Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education.


There Are Too Few Women in Computer Science and Engineering

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Only 20 percent of computer science and 22 percent of engineering undergraduate degrees in the U.S. go to women. Women are missing out on flexible, lucrative and high-status careers. Society is also missing out on the potential contributions they would make to these fields, such as designing smartphone conversational agents that suggest help not only for heart attack symptoms but also for indicators of domestic violence. Identifying the factors causing women's underrepresentation is the first step towards remedies. Why are so few women entering these fields?


The Times view on artificial intelligence: Computer Literacy

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In Stanley Kubrick's film 2001: A Space Odyssey, the on-board computer known as HAL calmly tells the astronaut Dave Bowman that it will not open the doors of the space pod to allow him entry. HAL has mistakenly identified Dave as a threat to the mission and addresses him directly, idiomatically and politely. It is a terrifying scene, symbolising the power of artificially intelligent machines that have acquired language and reason. New research suggests to some that the ability of humans to speak to machines may soon arrive. Linguists researching Jingulu, an aboriginal language spoken by just a few people in the Australian outback, suggest that it has certain characteristics that make it easily translatable into AI commands.


7 Best Intermediate Data Science Courses

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Are you looking for Best Intermediate Data Science Courses? If yes, then this article is for you. In this article, you will find the 7 Best Intermediate Data Science Courses. To gain data science skills, there are numerous courses available. So, without wasting your time, let's start finding the Best Intermediate Data Science Courses.


A 75-year-old Harvard grad is propelling China's AI ambitions

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At a time when the US and China are divided on everything from economics to human rights, artificial intelligence is still a point of particular friction. With the potential to revolutionise everything from food production and health care to financial markets and surveillance, it's a technology that sparks both optimism and paranoia. One of the field's most influential figures is Andrew Chi-Chih Yao, whose education and professional life have straddled the world's two biggest economies. China-born and Harvard-trained, Yao is his country's only recipient of the Turing Award, computer science's equivalent of a Nobel Prize. After almost 40 years in the US, he returned to China in 2004.