In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long …

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The Roman startup has in fact developed a deep learning system that learns from readers the elements of success of a story capable of going viral.

To help solve this problem, developers are now merging blockchain and Artificial Intelligence (AI) technologies to create an automated and …

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Over the last few years, neural network derived word embeddings became popular in the natural language processing literature. Studies conducted have mostly focused on the quality and application of word embeddings trained on public available corpuses such as Wikipedia or other news and social media sources. However, these studies are limited to generic text and thus lack technical and scientific nuances such as domain specific vocabulary, abbreviations, or scientific formulas which are commonly used in academic context. This research focuses on the performance of word embeddings applied to a large scale academic corpus. More specifically, we compare quality and efficiency of trained word embeddings to TFIDF representations in modeling content of scientific articles. We use a word2vec skip-gram model trained on titles and abstracts of about 70 million scientific articles. Furthermore, we have developed a benchmark to evaluate content models in a scientific context. The benchmark is based on a categorization task that matches articles to journals for about 1.3 million articles published in 2017. Our results show that content models based on word embeddings are better for titles (short text) while TFIDF works better for abstracts (longer text). However, the slight improvement of TFIDF for larger text comes at the expense of 3.7 times more memory requirement as well as up to 184 times higher computation times which may make it inefficient for online applications. In addition, we have created a 2-dimensional visualization of the journals modeled via embeddings to qualitatively inspect embedding model. This graph shows useful insights and can be used to find competitive journals or gaps to propose new journals.

… is on track to solve extraordinarily complex business problems through enhanced optimization, machine learning, and simulation. Make no mistake …

Artificial intelligence, machine learning, deep learning, blockchain … But the question is that if artificial intelligence and robots replace humans in …

Artificial intelligence (AI) is crucial to combat the spread of misinformation and propaganda on the internet. AI is able to analyze and quantify an enormous amounts of information generated daily on a scale that's impossible for humans, ultimately, it's up to us to be part of the process of fact-checking to inform people worldwide. In 2021, the risk of a global pandemic suddenly became a new reality for all and everybody. We are systematically fed by biased, mis- and dis- information. The big tech digital platforms such as Google, Facebook, Baidu or Tencent should do more to tackle fake news and cyber propaganda.

Sierra Leone's minister of education and chief innovation officer David Moinina Sengeh is a man of many talents. He's using mobile phone technology to improve daily life, he invented a way to make a prosthetic limb with a computer-assisted technique and he's a singer and rapper and a clothing designer, too. Sierra Leone's minister of education and chief innovation officer David Moinina Sengeh is a man of many talents. He's using mobile phone technology to improve daily life, he invented a way to make a prosthetic limb with a computer-assisted technique and he's a singer and rapper and a clothing designer, too. David Moinina Sengeh is not your typical education minister.