Stanford, CA, June 16, 2022: CARPL.ai, a technology platform that connects Artificial Intelligence (AI) applications... CARPL - the world's first testing and deployment platform for radiology automation has recently been... Accelerating model validation and clinical adoption of AI solutions built by Thomas Jefferson University using... CARPL is the world's first end-to-end platform for the development, testing and deployment of medical imaging AI Incubated at India's leading diagnostics provider, CARPL works with 40 HCPs, AI Developers and Med Tech Companies

Researchers have created a taxonomy and outlined steps that developers can take to design features in machine-learning models that are easier for …

The NPV is calculated by a machine–learning (ML) proxy model trained to approximate the NPV that would be calculated from a reservoir simulator run.

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Many people are familiar with some subsets of artificial intelligence (AI) such as machine learning and natural language processing, but don't know there are four types of artificial intelligence. Here we dive into what you need to know about the four types of AI--reactive, limited memory, theory of mind, and self-aware artificial intelligence. TYPES: Reactive Machines: The most basic but still quite useful artificial intelligence is called reactive AI because it reacts to existing conditions, as its name suggests. A famous example of reactive AI is Deep Blue, the supercomputer created by IBM in the 1980s that ultimately competed and won a chess match against reigning world champion Garry Kasparov, is a notable example of reactive AI.

Artificial intelligence is growing at record-breaking speed, literally. Thanks to exponential development, AI has made its way to the Guinness book of world records. Below is a list of records in the AI domain. What started as a simple Bot Camp program became a world record for the largest artificial intelligence programming lesson. Capital One Services LLC hosted this camp as part of its Future Edge DFW initiative in Dallas, Texas, USA, on April 17 2019.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How to grow skills as an AI Engineer / Researcher? In 10 years of working as an AI researcher, here are three lessons I've learnt along the way: ❶ Learn more about data pipeline to have better control of your data. Most of the time, AI researchers solve well-defined problems with well-curated datasets. This tends to form an illusion that the model is solving everything and you may overlook the data lineage and the data governance issues. Don't get too comfortable about the well-curated datasets in the lab!

We have seen how COVID-19 mounted pressure on businesses to fast-track their digital transformation journeys by months and, in several cases, by years. The arrival of the pandemic made them reconsider technologies they had at their fingertips – artificial intelligence (AI) in particular – and harness them to boost productivity, address supply chain issues, and seamlessly deliver products and services. Organizations have realized the indispensability of integrating AI into their digital strategy and this article will focus on addressing common AI adoption challenges. Artificial Intelligence is a revolutionary technology that saves time, energy, and money. It is no longer confined to science textbooks or science-fiction fantasies; it has countless applications in the real world.

On tomato plants, the flowering truss is a group or cluster of smaller stems where flowers and fruit develop, while the growing truss is the most extended part of the stem. Because the state of the growing truss reacts sensitively to the surrounding environment, it is essential to control its growth in the early stages. With the recent development of information and artificial intelligence technology in agriculture, a previous study developed a real-time acquisition and evaluation method for images using robots. Furthermore, we used image processing to locate the growing truss to extract growth information. Among the different vision algorithms, the CycleGAN algorithm was used to generate and transform unpaired images using generated learning images. In this study, we developed a robot-based system for simultaneously acquiring RGB and depth images of the growing truss of the tomato plant. The segmentation performance for approximately 35 samples was compared via false negative (FN) and false positive (FP) indicators. For the depth camera image, we obtained FN and FP values of 17.55 ± 3.01% and 17.76 ± 3.55%, respectively. For the CycleGAN algorithm, we obtained FN and FP values of 19.24 ± 1.45% and 18.24 ± 1.54%, respectively. When segmentation was performed via image processing through depth image and CycleGAN, the mean intersection over union (mIoU) was 63.56 ± 8.44% and 69.25 ± 4.42%, respectively, indicating that the CycleGAN algorithm can identify the desired growing truss of the tomato plant with high precision. The on-site possibility of the image extraction technique using CycleGAN was confirmed when the image scanning robot drove in a straight line through a tomato greenhouse. In the future, the proposed approach is expected to be used in vision technology to scan tomato growth indicators in greenhouses using an unmanned robot platform.

Tiffany Ceasor works for Microsoft in Seattle, where she is in the process of transitioning from a data scientist role to becoming a software development engineer with a focus on data science. She is a student at Florida Atlantic University, working towards her PhD in computer science with a specialism in applied artificial intelligence. Tiffany also studied at Florida Atlantic for her bachelor's degree in international public health and public health before taking her master of science in management information systems and business analytics there too. "Diversity in thought, culture, personality, life experiences and beliefs are what make the world an incredible, dynamic and beautiful place. In technology it is especially important because, as artificial intelligence advances, it is imperative to prevent eminent biases from being trained into models that will in turn potentially determine the future of the way we do things."

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