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Exploring Artificial Intelligence Variants and Their Uses - RTInsights

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The common thread across all AI technologies is the ability to impart human-like decision-making capabilities into applications and systems. Artificial intelligence (AI) refers to the simulation of human intelligence in systems programmed to think like humans and mimic their actions. AI includes a broad range of technologies, including cognitive computing, deep learning, expert systems, machine learning, natural language processing, and IBM Watson. The common thread across these areas, and all of AI, for that matter, is the ability to impart human-like decision-making capabilities into applications and systems. Experts predict AI will be rapidly adopted because they believe it will be a disruptive technology across many industries.


Expert Systems - Artificial Intelligence MCQ Questions - Letsfindcourse

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This section focuses on "Expert System" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations. Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department. Explanation: Expanding is not Capabilities of Expert Systems. Explanation: The components of ES include: Knowledge Base, Inference Engine, User Interface.


More than 1,700 COVID-19 Clinical Trials Registered Worldwide - Expert System

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These are the initial findings from Expert System's Artificial Intelligence platform, Clinical Research Navigator (CRN), which is collecting biomedical research information from official reports and studies published worldwide. Following the launch of its AI-based Clinical Research Navigator (CRN), which is focused on accelerating research on COVID-19, Expert System mined over 620,000 clinical trials, including more than 1,700 trials related to the virus that are taking place around the globe. Clinical landscape is changing rapidly in the context of the current pandemic situation. It is therefore critical to have a global coverage of the trial registries to serve clinical experts with appropriate and effective means to conduct their research on the disease. Expert System analyzed data collected with its Artificial Intelligence CRN platform to gain some insight on key trends correlated to official reports and studies published worldwide.


AI vs. machine learning vs. deep learning: Key differences

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The term AI has been around since the 1950s. In short, it depicts our struggle to build machines that can challenge what made humans the dominant lifeform on the planet: our intelligence. However, defining "intelligence" has turned out to be rather tricky, because what we perceive as intelligent changes over time. Early AIs were rule-based computer programs that could solve somewhat complex problems. Instead of hardcoding every decision the software was supposed to make, the program was divided into a knowledge base and an inference engine.


AI vs. machine learning vs. deep learning: Key differences

#artificialintelligence

The term AI has been around since the 1950s. In short, it depicts our struggle to build machines that can challenge what made humans the dominant lifeform on the planet: our intelligence. However, defining "intelligence" has turned out to be rather tricky, because what we perceive as intelligent changes over time. Early AIs were rule-based computer programs that could solve somewhat complex problems. Instead of hardcoding every decision the software was supposed to make, the program was divided into a knowledge base and an inference engine.


A.I. Shows Promise Assisting Physicians

AITopics Custom Links

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.


Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning

arXiv.org Artificial Intelligence

We study the problem of generating inferential texts of events for a variety of commonsense like \textit{if-else} relations. Existing approaches typically use limited evidence from training examples and learn for each relation individually. In this work, we use multiple knowledge sources as fuels for the model. Existing commonsense knowledge bases like ConceptNet are dominated by taxonomic knowledge (e.g., \textit{isA} and \textit{relatedTo} relations), having a limited number of inferential knowledge. We use not only structured commonsense knowledge bases, but also natural language snippets from search-engine results. These sources are incorporated into a generative base model via key-value memory network. In addition, we introduce a meta-learning based multi-task learning algorithm. For each targeted commonsense relation, we regard the learning of examples from other relations as the meta-training process, and the evaluation on examples from the targeted relation as the meta-test process. We conduct experiments on Event2Mind and ATOMIC datasets. Results show that both the integration of multiple knowledge sources and the use of the meta-learning algorithm improve the performance.


Augmented Q Imitation Learning (AQIL)

arXiv.org Artificial Intelligence

The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback. Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy. This paper proposes Augmented Q-Imitation-Learning, a method by which deep reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.


Milestones in artificial intelligence - ThinkAutomation

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From intelligent personal assistants to home robots, technology once thought of as a sci-fi dream is now embedded into everyday life. But this leap from dream to reality didn't happen overnight. There is no one'eureka' moment in a field as vast as AI. Rather, the technology we enjoy today is a result of countless milestones in artificial intelligence, delivered by countless forgotten people across a countless range of projects. So, let's pay homage to some of that work.