Science and psychic phenomena11/14/2023 What is notable here is that the software made this test possible with just a few hours of investigator time in contrast to previous image classification approaches which would have required a specifically defined classification scale and a large number of research participants. While there was considerable overlap in the resulting classifications, the software successfully grouped four of the five “Hate” images together with a unique descriptor not found in any of the other images, nor previously considered by the investigator. Traditionally, FieldREG data are analyzed by looking for deviations from randomness however, the current exploratory study employed image concept recognition software (Clarifai, Inc.) to classify the images based on their visual attributes. The data, collected using FieldREG software (Psyleron, Inc.), were then processed though Windbridge Institute custom visualization software that converted them into complex 3D images. In a recent exploratory study, machine vision recognition software was used to classify and extract details from a set of images that were created from random event generator (REG) data that were collected from 10 sessions in which meditators focused on feelings of “Love” (five sessions) and “Hate” (five sessions). With these new tools come new potential methods for examining psi-related data sets. Google, Inc.’s TensorFlow, IBM’s Watson, and Microsoft’s Computational Network Toolkit along with a wide range of powerful APIs (application program interfaces) now make machine learning highly accessible. In addition, many of the companies and universities that are driving this development effort have started to release their machine learning tools as either low cost or open source software. The past few years have brought major advancements to the field of machine learning, including new developments in deep learning (the implementation of complex, multilayered neural networks) and advanced applications such as natural language processing, computer vision, medical diagnosis, user preferences, and image recognition and feature extraction. Machine learning algorithms iteratively learn from data, allowing computers to find connections in data without the need for them to be explicitly programmed. With the advent of Big Data (data sets so large or complex that they render traditional information technology tools and techniques ineffective), data analysts have turned to Artificial Intelligence and machine learning in order to find meaning in and make predictions from data. (Published in the Journal of Scientific Exploration, Vol. It is shown here that rather than being based on any kind of substantial evidence, the criticisms that Reber and Alcock put forth in support of this counterargument are instead based on a combination of narrow personal opinion, unfounded assumption, and superficial rhetoric, leaving their claims unsound and ultimately unconvincing. At the heart of their rebuttal, Reber and Alcock seek to make the counter-argument that this evidence cannot be meaningful because psi phenomena are "impossible," appearing to violate four fundamental principles of physics. A critical commentary is offered on a skeptical rebuttal made by Arthur Reber and James Alcock in the July/August 2019 issue of Skeptical Inquirer (), which came in response to an article by Etzel Cardeña (published in the mainstream journal American Psychologist in 2018) that reviewed the extensive evidence from parapsychological experiments which seems to collectively offer support for the existence of psychic (or psi) phenomena.
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