Researchers have traditionally recruited native speakers to provide annotations for widely used benchmark datasets. However, there are languages for which recruiting native speakers can be difficult, and it would help to find learners of those languages to annotate the data. In this paper, we investigate whether language learners can contribute annotations to benchmark datasets. In a carefully controlled annotation experiment, we recruit 36 language learners, provide two types of additional resources (dictionaries and machine-translated sentences), and perform mini-tests to measure their language proficiency. We target three languages, English, Korean, and Indonesian, and the four NLP tasks of sentiment analysis, natural language inference, named entity recognition, and machine reading comprehension. We find that language learners, especially those with intermediate or advanced levels of language proficiency, are able to provide fairly accurate labels with the help of additional resources. Moreover, we show that data annotation improves learners' language proficiency in terms of vocabulary and grammar. One implication of our findings is that broadening the annotation task to include language learners can open up the opportunity to build benchmark datasets for languages for which it is difficult to recruit native speakers.

Large language models have taken the public attention by storm – no pun intended. In just half a decade large language models – transformers – have almost completely changed the field of natural language processing. Moreover, they have also begun to revolutionize fields such as computer vision and computational biology. Since transformers have such a big impact on everyone's research agenda, I wanted to flesh out a short reading list (an extended version of my comment yesterday) for machine learning researchers and practitioners getting started. The following list below is meant to be read mostly chronologically, and I am entirely focusing on academic research papers.

Last week Voxel51 hosted the December 2022 Computer Vision Meetup. Our amazing speakers shared insightful presentations, the virtual room was packed, and the Q&A was vibrant! In this blog post we provide the recordings, recap presentation highlights and Q&A, as well as share the upcoming Meetup schedule so that you can join us at a future event. Hope to see you soon! In lieu of swag, we gave Meetup attendees the opportunity to help guide our monthly donation to charitable causes. The charity that received the highest number of votes was Children International.

"The only way to learn a new programming language is by writing programs in it." The world has been evolving at an astonishing rate, and a solid part of the credit for that advancement goes to the application developers. In case you haven't noticed, application development has become all the rage in recent years. Everyone is trying to get in on the application development scene as it offers some of the highest-payingṣ career paths, such as web development, data science, artificial intelligence, and more. But before you start on a career path and create your first application, you need to first pick a programming language.

The artificial intelligence and automation fields are growing at remarkable rates. According to a report from Grand View Research, the global AI industry is currently valued at almost $25 billion, and stands to grow by 46% between 2019 and 2025. Grand View Research also predicts that industrial automation will grow by 8.6% during the same period. As these fields expand, job opportunities are becoming more and more abundant, offering plenty of entry points for those who are not only technically inclined but also inventive and creative. Indeed, experts predict that AI and automation will impact nearly every major industry, which means career prospects are innumerable.

Quantum computing--considered to be the next generation of high-performance computing--is a rapidly-changing field that receives equal parts attention in academia and in enterprise research labs. Honeywell, IBM, and Intel are independently developing their own implementations of quantum systems, as are startups such as D-Wave Systems. In late 2018, President Donald Trump signed the National Quantum Initiative Act that provides $1.2 billion for quantum research and development. TechRepublic's cheat sheet for quantum computing is positioned both as an easily digestible introduction to a new paradigm of computing, as well as a living guide that will be updated periodically to keep IT leaders informed on advances in the science and commercialization of quantum computing. SEE: The CIO's guide to quantum computing (ZDNet/TechRepublic special feature) Download the free PDF version (TechRepublic) SEE: All of TechRepublic's cheat sheets and smart person's guides Quantum computing is an emerging technology that attempts to overcome limitations inherent to traditional, transistor-based computers. Transistor-based computers rely on the encoding of data in binary bits--either 0 or 1. Quantum computers utilize qubits, which have different operational properties.

Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.

It wasn't too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century's Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text. Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation. Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language.

Thousands of data science jobs are going unfilled today as global demand for the talent greatly outstrips supply. Every day, businesses pay the price of the data scientist shortage in missed opportunities and slow innovation. For organizations to realize the full potential of machine learning, data teams have to build hundreds of predictive models a year. For most enterprises, only a fraction of that number is actually achieved due to understaffed data science teams. Databricks can help data science teams be more productive by automating various steps of the data science workflow – including feature engineering, hyperparameter tuning, model search, and deployment – for a fully controlled and transparent augmented ML experience.

Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.