The growth of the artificial intelligence (AI) industry worldwide -- and Canada specifically -- has revealed that its female researchers face many of the same challenges as they do in the other science, technology, engineering and math (STEM) fields. Underrepresentation, lower hiring rates and limited professional opportunities are all ongoing barriers. However, that may be changing, according to a new study published in the Journal of Informetrics. The authors present an analysis of gender patterns that evolved in the AI field over two decades, from 2000 to 2019. They used social network analysis, natural language processing, statistical analysis and machine learning to examine the space women occupy and the nature of their work in this ever-evolving and increasingly diverse field.
It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. It is found that, social media is one of the most powerful tool from where we can analyze the text and estimate the chances of suicidal thoughts. Using nlp we can analyze twitter and reddit texts monitor the actions of that person. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide.
Artificial Intelligence (AI), as one of the leading technological trends, continues to grow in popularity among marketers and sales professionals, and has evolved into an essential tool for brands seeking to provide a hyper-personalized, exceptional customer experience. AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software is now available, bringing AI to the enterprise without the high costs previously associated with the technology. On the basis of exclusive interactions with leaders in the BFSI sector, Nidhi Shail Kujur of Elets News Network (ENN) explores how with constantly evolving technologies, the banking and financial services industry promises to exceed customer expectations. The banking industry is undergoing significant change, particularly with the spread of customer-centricity. We live in a world where the majority of people have access to the internet.
On Friday, Elon Musk announced he was pausing his $45bn purchase of Twitter because he had only just discovered some of the accounts on the site were fake. But that's not the strangest thing that has happened to the beleaguered social media platform this week. Because on Tuesday the current top brass, perhaps trying to demonstrate their vision for the site, released a Super Nintendo-style browser game that recaps Twitter's private policy. The platform unveiled Twitter Data Dash, which plays like a vintage side-scrolling platformer that's been draped with a healthy dose of disinformation anxiety. You take control of a blue-hued puppy named Data and are tasked with retrieving five bones hidden in each of the game's day-glo urban environments.
There are so many great applications of Artificial Intelligence in daily life, by using machine learning and other techniques in the background. AI is everywhere in our lives, from reading our emails to receiving driving directions to obtaining music or movie suggestions. Don't be scared of AI jargon; we've created a detailed AI glossary for the most commonly used Artificial Intelligence terms and the basics of Artificial Intelligence. Now if you're ready, let's look at how we use AI in 2022. Artificial intelligence (AI) appears in popular culture most often as a group of intelligent robots bent on destroying humanity, or at the very least a stunning theme park. We're safe for now because machines with general artificial intelligence don't yet exist, and they aren't expected to anytime soon. You can learn the risk and benefits of Artificial Intelligence with this article.
In a previous blog post we had a look at how we can set up our very own GPT-J Playground using Streamlit, Hugging Face, and Amazon SageMaker. With this playground we can now start experimenting with the model and generate some text, which is a lot of fun. But eventually we want the model to actually perform NLP tasks like translation, classification, and many more. In this blog post we will have a look how we can achieve that using different parameters and particular prompts for the GPT-J model. This blog post will build on this previous blog post and this Github repo and it is assumed that you have already built your own GPT-J playground.
Meta AI, the research arm of Facebook’s parent company, studies how the human brain processes language. Researchers are looking at how the brain and AI language models respond to the same spoken or written sentences. The work is part of Meta AI’s broader focus on human-level AI that learns with little to no human supervision. Meta AI is working with NeuroSpin (CEA), a Paris-based research center for innovation in brain imaging, and the French National Institute for Research in Digital Science (INRIA). Meta AI and NeuroSpin are studying how language models are trained to predict the next word in a sentence. Such prediction based on partially observable inputs is at the core of AI self-supervised learning. Researchers are creating an original neuroimaging dataset to further hone their study. King said that neuroscientists could help improve AI models by studying long-range forecasting capability more in-depth. He said that enhancing algorithms with such capability can help them become more correlated with the brain. He added that progress requires bringing together the disciplines of neuroscience and AI.
In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings.
Go through the Machine Learning news these days, and you will see Transformers everywhere (watch this video IBM Technology for a quick overview to the idea). Since their introduction, Transformers have taken the world of Deep Learning by storm. While they were traditionally associated with Natural Language Processing, Transformers are now being used in Computer Vision Pipelines too. Just in the last few weeks, we have seen the use of Transformers in some insane applications in Computer Vision. Thus, it seemed like Transformers would replace Convolutional Neural Networks (CNNs) for generic Computer Vision tasks. Researchers at Facebook AI however have something to add.