Hokkaido University launched the Center for Human-Nature, Artificial Intelligence, and Neuroscience, or CHAIN, in July 2019.

Tech billionaires Jack Ma and Elon Musk can't agree whether artificial intelligence is going to take over the world.

The issues to be addressed include digitalisation, artificial intelligence and other technologies, with particular attention on China and the Pope's …

AI has become the latest bandwagon onto which everyone wants to jump.

Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?

The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.

Abstract: This paper describes neural-fortran, a parallel Fortran framework for neural networks and deep learning. It features a simple interface to construct feed-forward neural networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based parallelism on shared- or distributed-memory machines. First, I describe the implementation of neural networks with Fortran derived types, whole-array arithmetic, and collective sum and broadcast operations to achieve parallelism. Second, I demonstrate the use of neural-fortran in an example of recognizing hand-written digits from images.

I posted here a few weeks back looking for members to join me in putting together an AI research organization. As of today, we've got the groundwork laid for another round of recruitment, so here we go again! We currently have six figures of compute funding and several deep learning experts (as well as domain experts like scientists, doctors, executives, and engineers) on our team. Our goal is to work together to do what we otherwise couldn't alone, and create opportunities for people of all backgrounds to work on research that they are passionate about while publishing work that is valuable to the world. We can provide technical and moral support as well as huge amounts of compute for research projects.