Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is through advancements in a related task known as slot filling. In this task, given an entity query in form of [Entity, Slot, ?], a system is asked to fill the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection. The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models. In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard. Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning. We release the source code and pre-trained models.

The unprecedented success of deep learning (DL) makes it unchallenged when it comes to classification problems. However, it is well established that the current DL methodology produces universally unstable neural networks (NNs). The instability problem has caused an enormous research effort -- with a vast literature on so-called adversarial attacks -- yet there has been no solution to the problem. Our paper addresses why there has been no solution to the problem, as we prove the following mathematical paradox: any training procedure based on training neural networks for classification problems with a fixed architecture will yield neural networks that are either inaccurate or unstable (if accurate) -- despite the provable existence of both accurate and stable neural networks for the same classification problems. The key is that the stable and accurate neural networks must have variable dimensions depending on the input, in particular, variable dimensions is a necessary condition for stability. Our result points towards the paradox that accurate and stable neural networks exist, however, modern algorithms do not compute them. This yields the question: if the existence of neural networks with desirable properties can be proven, can one also find algorithms that compute them? There are cases in mathematics where provable existence implies computability, but will this be the case for neural networks? The contrary is true, as we demonstrate how neural networks can provably exist as approximate minimisers to standard optimisation problems with standard cost functions, however, no randomised algorithm can compute them with probability better than 1/2.

Fusion Apes is a NFT collectible project re-imagining classic ape silhouettes in a fusion-inspired art style. 4,200 collectible NFTs individually drawn using the power of neural networks.

I'd love the thank my friends who gave me permission to use their handsome faces in the name of artificial intelligence science! We can definitely tell that this fine gentleman has brown eyes. On the other hand, the model is pretty certain that this individual has blue eyes with a probability greater than 90%. We have a correct prediction but a not very confident probability of 69% (and that's no coincidence). Finally, we try it on me… not so handsome and no so great prediction confidence.

According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally, killing 17.9 million people in 2019 [1]. The WHO risk models identified many different variables as risk factors for CVDs, including the key predictor variables: age, blood pressure, body mass index, cholesterol, and tobacco use. Historically, this potpourri of factors made CVDs almost impossible to predict with any meaningful accuracy. A new study by Kondeth Fathima and E. R. Vimina [2], published in Intelligent Sustainable Systems Proceedings of ICISS 2021, used Deep Neural Networks (DNNs) with four Hidden Layers (HDs) to predict CVDs with an impressive 99% accuracy. Neural network models have come to the forefront in recent years, gaining popularity because of their exceptional prediction capabilities.

Comparing brains to computers is a long and dearly held analogy in both neuroscience and computer science. It's not hard to see why. Our brains can perform many of the tasks we want computers to handle with an easy, mysterious grace. So, it goes, understanding the inner workings of our minds can help us build better computers; and those computers can help us better understand our own minds. Also, if brains are like computers, knowing how much computation it takes them to do what they do can help us predict when machines will match minds.

Deep learning implements structured machine learning algorithms by making use of artificial neural networks. These algorithms help the machine to learn by itself and develop the ability to establish new parameters with which help to make and execute decisions. Deep learning is considered to be a subset of machine learning and utilizes multi-layered artificial neural networks to carry out its processes, which enables it to deliver high accuracy in tasks such as speech recognition, object detection, language translation and other such modern use cases being implemented every day. One of the most intriguing implementations in the domain of artificial intelligence for creating deep learning models has been the Boltzmann Machine. In this article, we will try to understand what exactly a Boltzmann Machine is, how it can be implemented and its uses.

DropBlock is available on glasses in my computer vision library! Today we are going to implement DropBlock in PyTorch! DropBlock introduced by Ghiasi et al is a regularization technique specifical crafter for images that empirically works better than Dropout. By why Dropout is not sufficient? Dropout is a regularization technique that randomly drops (set to zeros) parts of the input before passing it to the next layer.

Artificial intelligence in filmmaking might sound futuristic, but we have reached this place. Technology is already making a significant impact on film production. Today, most of the outperforming movies that come under the visual effects category are using machine learning and AI for filmmaking. Significant pictures like'The Irishman' and'Avengers: Endgame' are no different. It won't be a wonder if the next movie you watch is written by AI, performed by robots, and animated and rendered by a deep learning algorithm.

Our mushy brains seem a far cry from the solid silicon chips in computer processors, but scientists have a long history of comparing the two. As Alan Turing put it in 1952: "We are not interested in the fact that the brain has the consistency of cold porridge." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. Today, the most powerful artificial intelligence systems employ a type of machine learning called deep learning. Their algorithms learn by processing massive amounts of data through hidden layers of interconnected nodes, referred to as deep neural networks. As their name suggests, deep neural networks were inspired by the real neural networks in the brain, with the nodes modeled after real neurons--or, at least, after what neuroscientists knew about neurons back in the 1950s, when an influential neuron model called the perceptron was born.