Abstract: We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents.

Abstract: Near the entire university faculty directors must select some qualified professors for respected courses in each academic semester. In this sense, factors such as teaching experience, academic training, competition, etc. are considered. This work is usually done by experts, such as faculty directors, which is time consuming. Up to now, several semi-automatic systems have been proposed to assist heads. In this article, a fully automatic rule-based expert system is developed.

Artificial intelligence has been used to generate artificial images since the 1950s, when American computer scientist Harold Cohen made artworks using autonomous software programs of his own design. In the 1960s, British artist Peter Blake used a computer to generate patterns for his 1967 work with Eduardo Paolozzi, demoing that computers could be used to create works with acreen-printing machine. In the 1970s, American artist Charles Csuri used a computer to generate drawings of plant forms that were made into silk screens and used in a number of his works. In the 1990s, American artist Michael Brewster used a computer to generate images of women that were used in his paintings. Generative art can be defined as art that is created by means of a system, where the artist uses a set of rules or algorithms to create the work.

A survey in 2013 by Vincent C. Müller and Nick Bostrom asked hundreds of scientists when they believe machines will achieve artificial general intelligence (AGI), meaning human-level intelligence. The median years for 10, 50, and 90 percent probability of reaching AGI were 2022, 2040, and 2075, respectively. But, there are still many challenges to reaching human-level intelligence. The first is domain limitation. Today's artificial intelligence primarily applies a mathematical approach that can solve a finite set of statements for a finite set of terms described under a finite set of rules.

The first wave of AI is represented by expert knowledge or criteria developed in law or other authoritative sources and encoded into a computer algorithm, which is referred to as an expert system. Second-wave AI technology is based on machine learning, or statistical learning, and includes natural-language processing (e.g., voice recognition) and computer-vision technologies, among others. In contrast to first-wave systems, second-wave systems are designed to perceive and learn. Second-wave AI systems have nuanced classification and prediction capabilities but no contextual and minimal reasoning capabilities. Examples of second-wave systems include voice-activated digital assistants, applications that assist healthcare workers in selecting appropriate treatment options or making diagnoses, and self-driving automated vehicles. Third-wave AI technologies combine the strengths of first- and second-wave AI and are also capable of contextual sophistication, abstraction, and explanation. An example of third-wave AI is a ship that can navigate the sea without human intervention for a few months at a time while sensing other ships, navigating sea lanes, and carrying out necessary tasks. Strong AI, or artificial general intelligence (AGI), as a human-level and human-like AI, in which a machine will have human-like cognitive capability, remains a significant technical challenge, or rather a wild goose chase. The world is still in the realm known as'weak' or'narrow' AI, in which AI is optimised for specific, narrow tasks such as speech recognition, performing repetitive functions, chess-playing, image generation, self-driving cars, or weaponized AI. #machinelearning #artificialintelligence #nlp #AItechnology

The first wave of AI is represented by expert knowledge or criteria developed in law or other authoritative sources and encoded into a computer algorithm, which is referred to as an expert system. Second-wave AI technology is based on machine learning, or statistical learning, and includes natural-language processing (e.g., voice recognition) and computer-vision technologies, among others. In contrast to first-wave systems, second-wave systems are designed to perceive and learn. Second-wave AI systems have nuanced classification and prediction capabilities but no contextual and minimal reasoning capabilities. Examples of second-wave systems include voice-activated digital assistants, applications that assist healthcare workers in selecting appropriate treatment options or making diagnoses, and self-driving automated vehicles. Third-wave AI technologies combine the strengths of first- and second-wave AI and are also capable of contextual sophistication, abstraction, and explanation. An example of third-wave AI is a ship that can navigate the sea without human intervention for a few months at a time while sensing other ships, navigating sea lanes, and carrying out necessary tasks. Strong AI, or artificial general intelligence (AGI), as a human-level and human-like AI, in which a machine will have human-like cognitive capability, remains a significant technical challenge, or rather a wild goose chase. The world is still in the realm known as'weak' or'narrow' AI, in which AI is optimised for specific, narrow tasks such as speech recognition, performing repetitive functions, chess-playing, image generation, self-driving cars, or weaponized AI. #machinelearning #artificialintelligence #nlp #AItechnology

One of the ultimate goals of Artificial Intelligence is to learn generalised and human interpretable knowledge from raw data. Existing neuro-symbolic approaches partly tackle this problem by using manually engineered symbolic knowledge to improve the training of a neural network. In the few cases where symbolic knowledge is learned from raw data, this knowledge lacks the expressivity required to solve complex problems. In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that solves complex problems, defined in terms of these latent concepts. The novelty of our approach is a method for biasing a symbolic learner to learn improved knowledge, based on the in-training performance of both neural and symbolic components. We evaluate NSIL on two problem domains that require learning knowledge with different levels of complexity. Our experimental results demonstrate that NSIL learns knowledge of increased expressivity than what can be learned by the closest neuro-symbolic baseline systems, whilst outperforming them and other pure differentiable baseline models in terms of accuracy and data efficiency.

Snorkel's Data-centric Foundation Model Development suite will let a non-programmer use a text prompt with natural language queries to get a neural net to automatically label data. The output labels become a means to supervise the training of a downstream machine learning program, which might be a simple classifier. In the last big upsurge in artificial intelligence, in the late '70s and '80s, a popular approach took hold known as expert systems, programs that contained rules for tasks based on human knowledge typed into the computer. Expert systems ultimately failed because they both proved too hard to codify -- what can an expert truly articulate about what they know? In an intriguing twist on that earlier approach, Snorkel, a three-year-old AI startup based in San Francisco, Wednesday unveiled tools to, as they put it, bring the human domain expert back into the driver's seat when it comes to developing neural networks.

Fox News national correspondent Bryan Llenas has the latest as Pennsylvania senate nominees make last plea to voters ahead of Tuesday's midterm elections. The balance of power in Washington, D.C., may not be settled when Election Day comes to an end – or the day after, or the day after that, depending on how things go in Pennsylvania. The Senate race between Democrat John Fetterman and Republican Dr. Mehmet Oz remained a toss-up in the final Fox News Power Rankings before Election Day, and litigation over absentee ballots could draw the process out well beyond November 8. The state's supreme court ruled that undated and incorrectly dated absentee ballots had to be set aside, uncounted, and federal litigation related to this remains ongoing. During a press conference on Monday, Pennsylvania Acting Secretary of State Leigh M. Chapman confirmed that voters worried that they already made mistakes may not be able to fix them.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The last decade has seen a significant rise in the application of AI and machine learning in the field of medicine.