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5 Ways AI Voice Synthesis Will Change Our Lives

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Researchers use generative adversarial networks (GANs) and other machine learning techniques to manipulate audio and visual scenes that may result in deepfake videos. In principle, with sufficient training data, AI voice synthesis can generate voice skins for anybody. It's crucial that you do not embrace the perspective that deception is the main point of voice-modeling technologies. It isn't, and we've discussed this at length in our article about ethical voice cloning.


How banks are using AI to retain customers

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We all want to improve customer retention. If we keep customers happy they stay longer, take up more products, and tell more people about the experience. Here are 3 ways in which AI is helping banks improve their customer retention. AI Interaction analytics examines the specific language used by customers, in web chat or email for example, so you can manage the interaction appropriately and efficiently. It removes the need for manual review of each incoming query and enables you to handle them effectively from the outset.


Is Fine Art the Next Frontier of AI?

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In 1950, Alan Turing developed the Turing Test as a test of a machine's ability to display human-like intelligent behavior. "Are there imaginable digital computers which would do well in the imitation game?" In most applications of AI, a model is created to imitate the judgment of humans and implement it at scale, be it autonomous vehicles, text summarization, image recognition, or product recommendation. By the nature of imitation, a computer is only able to replicate what humans have done, based on previous data. This doesn't leave room for genuine creativity, which relies on innovation, not imitation.


Eta Introduces TENSAI Flow for Machine Learning in Low Power IoT Devices

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Eta Compute, a machine learning company, recently announced its new TENSAI Flow software, which is designed to complement the company's existing development resources and enable design from concept to firmware in IoT and low power edge devices. "Neural network and embedded software designers are seeking practical ways to make developing machine learning for edge applications less frustrating and time-consuming," said Ted Tewksbury, CEO, Eta Compute. Now, designers can optimize neural networks by reducing memory size, the number of operations, and power consumption, and embedded software designers can reduce the complexities of adding AI to embedded edge devices, saving months of development time." "In order to best unlock the benefits of TinyML we need highly optimized hardware and algorithms. Eta Compute's TENSAI provides an ideal combination of highly efficient ML hardware, coupled with an optimized neural network compiler," said Zach Shelby, CEO, Edge Impulse. "Together with Edge Impulse and the TENSAI Sensor Board this is the best possible solution to achieve extremely low-power ML applications." It includes a neural network compiler, a neural network zoo, and middleware comprising FreeRTOS, HAL and frameworks for sensors, as well as IoT/cloud enablement. "Google and the TensorFlow team have been dedicated in bringing machine learning with the tiniest devices.


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

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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

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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

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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

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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

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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

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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.