Yesterday, Elon Musk decided to enter the recent charged Twitter debate on whether artificial general intelligence (AGI), or the ability for AI to …

Developers may use the open-source software to construct intelligent apps that learn from real-time data and adapt using machine learning.

USAID Artificial Intelligence Action Plan outlines how to engage responsibly with AI in our programming and in the countries where we work.

Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information. However, a more interconnected society enables the spread of intentionally false information. To mitigate the negative impacts of fake news, it is essential to improve detection methodologies. This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection by using deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious. The proposed framework was evaluated using four merged datasets containing real and fake news. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) models were trained to utilize both lexical and syntactic features, and their performance was evaluated. The obtained results demonstrate that a multi-policy analysis reliably identifies suspicious statements, which can be advantageous for fake news detection.

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.

Artificial intelligence and machine learning are entering more and more into music production, helping creative to streamline and speed up all the everyday mechanical and tedious processes. With the help of a unique neural network trained with 20TB of data, LALAL.AI is able to extract and separate vocals and instruments from audio files. But let's take a step back to understand what's and how can help artificial intelligence and machine learning in the music industry. Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Because of the infinite fields that the human intelligence can be applied, nowadays artificial intelligence is programmed and developed for specific tasks like language recognition, image recognition, audio recognition, and more.

By converting sounds to images, scientists can use artificial intelligence to quickly find and assess animals' calls, even deep in the ocean.

With artificial intelligence growing ever-more sophisticated, and self-driving cars and autonomous aircraft on the horizon, Waters says it's …

We harness the power of medical images by using the most advanced Artificial Intelligence technologies, to increase the accuracy of diagnosis and …

Median Technologies Launches Imaging Lab, Spearheading the Integration of iBiopsy® Artificial Intelligence Technologies Into iCRO Imaging Services …