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Machine learning in detecting frequency-following responses

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To improve the efficiency and timeliness in frequency-following response (FFR) testing, the purpose of this study was to investigate the capabilities of machine learning in the detection of FFRs. Continuous brain waves were recorded from 25 Chinese adults in response to a pre-recorded Mandarin monosyllable \yi2\ with a rising frequency contour. A total of 8000 artifact-free sweeps were recorded from each participant. Continuous brain waves sub-averaged from the first sweep up to the first 500 sweeps were considered FFR absent, whereas brain waves sub-averaged from the first sweep up to the last 1000 sweeps were considered FFR present. Six response features (Frequency Error, Slope Error, Tracking Accuracy, Spectral Amplitude, Pitch Strength and Root-Mean-Square Amplitude) were extracted from each recording and served as key predictors.


Global Big Data Conference

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GPT-3, the latest incarnation of artificially intelligent natural-language systems, knows how to write -- and write and write and write. For a taste of what it can (and cannot) do, here are three examples of its verbosity. In each case, we gave the system a short prompt (in italics) and let it roll. First we asked it to write about itself. Then, playing off a suggestion from a start-up called Sudowrite, which has spent months testing GPT-3, we asked the system to write a Modern Love column.


Human Component in Machine Learning

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With automation becoming increasingly popular in the field of machine learning, one may wonder if the role of humans in machine learning will become non-essential at some point. When building a machine learning model, it's important to remember that the model must produce meaningful and interpretable results in real-life situations. This is where the human experience comes in. A human (qualified data science professional) has to examine the results produced by algorithms and computers to ensure that the results are consistent with real-world situations before recommending a model for deployment. With automation in machine learning, humans are still indispensable to make the connection between data, algorithms, and the real world.


Navigate the road to Responsible AI

#artificialintelligence

Find out how to implement AI responsibly--join our free webinar Responsible AI in Practice on December 15 to learn about fairness, AI in the law, and AI security from experts. The use of machine learning (ML) applications has moved beyond the domains of academia and research into mainstream product development across industries looking to add artificial intelligence (AI) capabilities. Along with the increase in AI and ML applications is a growing interest in principles, tools, and best practices for deploying AI ethically and responsibly. In efforts to organize ethical, responsible tools and processes around a common collective, a number of names have been bandied about, including Ethical AI, Human Centered AI, and Responsible AI. Based on what we've seen in industry, several companies, including some major cloud providers, have focused on the term Responsible AI, and we'll do the same in this post.


New dating app for bald people aims to help destigmatize hair loss

FOX News

'Labor of Love,' hosted by'Sex and the City' star Kristin Davis, follows a woman as she searches for love and motherhood. Love is blind -- and bald too, thanks to an app catering to singles with hair loss. Bald Dating, a new matchmaking site that launched Monday, is aiming to make it easier for singles with hair loss to find "the one" without focusing on hair follicles. The website is being promoted using the tagline "heads & personalities shine." Now there's a niche dating app aiming to connect bald singles with partners who have an affinity for it.


Zapata raises $38 million for quantum machine learning

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Zapata Computing has raised $38 million for its quantum computing enterprise software platform. The figure, which brings its total funding to over $64 million, will be put toward Zapata's core mission: "Delivering quantum advantage for customers through real business use cases." Quantum computing leverages qubits (unlike bits that can only be in a state of 0 or 1, qubits can also be in a superposition of the two) to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Unlike most quantum computing startups that build the hardware, Zapata is focused on the algorithms and software that sit on top. Based in Boston, Zapata has one product: its hardware-agnostic Orquestra quantum computing platform.


Driverless cars and smart cities: the amazing future summarised in 5 online talks

#artificialintelligence

This article is part of KrASIA's partnership with Web Summit. The last 12 months have seen decisive change in the way we spend our free time. Mobility solutions are becoming increasingly popular, with driverless vehicles popping up across the world, while our urban spaces are evolving into smart city projects. Web Summit's lifestyle content covers it all. What CNN calls "Europe's largest tech event" gathers experts from the industries that play vital roles in our lifestyles.


Normal Distribution and Machine Learning

#artificialintelligence

Normal Distribution is an important concept in statistics and the backbone of Machine Learning. A Data Scientist needs to know about Normal Distribution when they work with Linear Models(perform well if the data is normally distributed), Central Limit Theorem, and exploratory data analysis. As discovered by Carl Friedrich Gauss, Normal Distribution/Gaussian Distribution is a continuous probability distribution. It has a bell-shaped curve that is symmetrical from the mean point to both halves of the curve. A continuous random variable "x" is said to follow a normal distribution with parameter μ(mean) and σ(standard deviation), if it's probability density function is given by, This is also called a normal variate.


Artificial Intelligence and Human Lives: Looking forwards 2025-2070

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In the darkest days of a dark year it's good to think about our possible futures together. This talk is about wealth, power, and intelligence, and how we are communicating these due to the digital transformation. Is there a chance for a positive digital future, and if so what would it look like? Joanna Bryson is Professor of Ethics and Technology at the Hertie School of Governance in Berlin, Germany. She holds degrees in psychology and artificial intelligence from the University of Chicago (BA), the University of Edinburgh (MSc and MPhil), and Massachusetts Institute of Technology (PhD).


A Complete Neural Network Algorithm from Scratch in Python

#artificialintelligence

The Neural Network has been developed to mimic a human brain. Though we are not there yet, neural networks are very efficient in machine learning. It was popular in the 1980s and 1990s. Recently it has become more popular. Probably because computers are fast enough to run a large neural network in a reasonable time.