Goto

Collaborating Authors

A beginner's guide to AI: The difference between video game AI and real AI

#artificialintelligence

Artificial intelligence, in the form we discuss here at Neural, includes machine learning systems like the core neural networks behind Alexa, Siri, and …


Coming to grips with actual false positive and false negative rates - Ai

#artificialintelligence

While $12.7 billion of this figure goes to another merchant when a customer is turned away, it must to be noticed that false declines "are also making for a less efficient digital economy". This is because "$7.6 billion of potential spending never came about as the shopper lost interest. In the same report, a senior industry executive pointed out that re-visiting risk appetite is vital. Also, a "lot of sins can be hidden in the name of #fraud prevention, because fraud teams aren't always incentivised to have a very rigorous statistical measure of false positives and false negatives". "Many companies just don't want to get on the MasterCard and Visa chargeback programmes, and that's the guiding principle.


AI is messing with our minds

#artificialintelligence

I have repeatedly been challenged to “prove” that human-level artificial intelligence is impossible. My position is that the burden of proof lies on the opposite side.  No serious evidence has been given in support of the thesis that human-level AI is possible. There certainly is no reason to expect that it would be. And there are very good reasons to believe it will never be possible, in any case for an AI system based on a digital computer – more precisely: mathematically equivalent to a Turing machine. Nevertheless, many people nowadays regard it as virtually self-evident that computers will sooner or later become as intelligent as human beings – and, thereafter, become much more intelligent. The futurist Ray Kurzweil boosts his media ratings regularly with predictions to this effect.


Data Annotation- Types, Tools, Benefits, and Applications in Machine Learning

#artificialintelligence

In this article, we have mentioned what data annotation or labeling is, and what are its types and benefits. Besides this, we have also listed the top tools used for labeling images. The process of labeling texts, images, and other objects help ML-based algorithms to improve the accuracy of the output and offer an ultimate user experience. A reliable and experienced machine learning company holds expertise on how to utilize these data annotations for serving the purpose an ML algorithm is being designed for. You can contact such a company or hire ML developers to develop an ML-based application for your startup or enterprise. Read More: How does Machine Learning Revolutionizing the Mobile Applications?


Models Trained to Keep the Trains Running

#artificialintelligence

Steady advances in machine vision techniques such as convolutional neural networks powered by graphics processors and emerging technologies like neuromorphic silicon retina "event cameras" are creating a range of new predictive monitoring and maintenance use cases. We've reported on several, including using machine vision systems to help utilities monitor transmission lines and towers linked to wildfires in California. Now, AI software vendor Ignitarium and partner AVerMedia, an image capture and video transmission specialist, have expanded deployment an aircraft-based platform for detecting railway track obstructions. The AI-based visual "defect detection" platform incorporates Ignitarium's AI software implemented on Nvidia's edge AI platform used to automatically control onboard cameras. The system is designed to keep cameras focused on the track center during airborne inspections.


AI-Labeling Crowdsourcing Platforms

#artificialintelligence

Artificial intelligence (AI) is widely used in today's business such as for data analytics, natural language processing, or process automation. The emergence of artificial intelligence is based on decades of research for solving difficult computer science tasks and is now rapidly transforming business model innovation. Companies that are not considering artificial intelligence will be vulnerable to those companies that are equipped with artificial intelligence technology. While companies like Google, Amazon, and Tesla have already innovated their business models with artificial intelligence, medium and small caps have limited budgets for putting much effort into setting up such capabilities. One high-effort task in creating artificial intelligence services is the pre-processing of data and the training of machine learning models.


Samir Bagga posted on LinkedIn

#artificialintelligence

Are you just starting out programming and wondering where to learn Machine Learning? Check out my website https://lnkd.in/e2nHYsG This website teaches you Machine Learning in seven easy and concise lessons built for programming beginners. It takes you from what to download to program Machine Learning algorithms all the way to programming Neural Networks on your own computer. Not only that, but you can also learn applications of Machine Learning in the real world, like how Spotify creates personalized playlists for you, and how they are programmed.


Student Group Spotlight: CAIS++ Showcases "AI for Social Good" Projects

#artificialintelligence

As the student branch of the USC Center for Artificial Intelligence in Society, CAIS++ members work to promote the development of AI applications for …


Get Ready to Experience AI Technology at Your Digital Workplace

#artificialintelligence

Artificial Intelligence is gradually evolving the idea of our day to day lifestyle. No wonder, the style of working is also an integral part of our daily life. The concept of "agile workplace" used to be a far-fetched dream for humans. The invention of technology has made everything possible for us. Chatbots, AI-enabled Robots had never been invented if the AI technology was not available.


AI: the smart money is on the smart thinking - PMLiVE

#artificialintelligence

AI could also have a transformative effect on clinical decision-making through the utilisation of the huge levels of genomic, biomarker, phenotype, behavioural, biographical and clinical data that is generated across the health system. Bayer and Merck & Co provide a perfect example of this. They have developed an AI software system to support clinical decision-making of chronic thromboembolic pulmonary hypertension (CTEPH) – a rare form of pulmonary hypertension. The software helps differentiate patients from those suffering with similar symptoms that are actually a result of asthma and chronic obstructive pulmonary disease (COPD), and therefore diagnose CTEPH more reliably and efficiently. The CTEPH Pattern Recognition Artificial Intelligence obtained FDA Breakthrough Device Designation in December 2018.