Created by John Elder 1.5 hours on-demand video course In this course I'll teach you how to make graphical user interfaces for Python using TKinter, and how to connect those apps to the ChatGPT Artificial Intelligence API. You'll be surprised just how quickly you can create some pretty cool looking apps! You'll be able to type questions to ChatGPT straight from your app, and receive a response that is output to the screen of your app. Finally, I'll discuss how to connect to ChatGPT with an API Key, query the engine, and parse the responses in the correct way. If you've seen ChatGPT recently and want to learn how to use it programmatically, then this is the course for you!
Bhopal (Madhya Pradesh): A workshop on Artificial Intelligence and Deep Learning was held at Maulana Azad National Institute of Technology (MANIT) in the city recently. The Department of Computer Science and Engineering and Department of Electronics and Communication Engineering had organised the five-day online workshop. The focus was on understanding the application of techniques like regression and classification over databases acquired from different sources. The topics of the workshop were artificial intelligence, Machine Learning techniques, soft computing and deep learning techniques. Hands-on training and practice sessions were conducted to gain confidence on techniques, their demonstration and implementation.
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UW System officials want the state to approve $38 million to modernize classrooms on campuses around the state. This 116 year old classroom in Agriculture Hall on UW-Madison's campus is used for large enrollment classes in key areas like biochemistry, nutritional science, math and economics. Our guest explains how the advancement of AI is forcing a reckoning in the academic and art worlds. Find out how to Support WPR.
MIT senior Rachel Chae and alumnus Sihao Huang '22 have been selected to join the 2023 class of Marshall Scholars and will begin graduate studies in the U.K. next fall. Funded by the British government, the Marshall Scholarship provides up to 50 scholarships for exceptional American students to pursue advanced study in any field at any university in the U.K. MIT's endorsed Marshall candidates are advised and supported by the distinguished fellowships team, led by Associate Dean Kim Benard in Career Advising and Professional Development. They are also mentored by the MIT Presidential Committee on Distinguished Fellowships, co-chaired by professors Will Broadhead and Tamar Schapiro. "Working with this year's Marshall applicants has been as rewarding and humbling as ever," says Broadhead. "These amazing students engage in a months-long exercise in critical introspection and personal growth, supported by the expert mentorship provided by Kim Benard and her team in the Distinguished Fellowships Office and by the dedicated faculty, staff, and graduate students who serve on the Distinguished Fellowships Committee. We on the committee have been inspired by all of this year's fellowship applicants and are especially pleased to congratulate Rachel and Sihao, whose wisdom, good humor, and future-minded optimism will serve them well as they take their richly deserved places in this year's class of Marshall Scholars."
Research led by Monash University, RMIT and the University of Adelaide has developed an accurate method of controlling optical circuits on fingernail-sized photonic integrated circuits. The development, published in the prestigious international journal Optica builds on the work by the same team who recently created the world's first self-calibrated photonic chip. Photonics, or the use of light particles to store and transmit information, is a burgeoning field, supporting our need to create faster, better, more efficient and more sustainable technology. Programmable photonic integrated circuits (PICs), offer diverse signal processing functions within a single chip, and present promising solutions for applications ranging from optical communications to artificial intelligence. Whether it's downloading movies or keeping a satellite on course, photonics is radically changing the way we live, revolutionising the processing capability of large scale equipment onto a chip the size of a human fingernail.
In 1988, The first successful CNN was implemented by the Computer Scientist Yann LeCun where CNNs were used for scanning the bank checks and recognizing characters from them. Have you ever wondered how object detection works and aids in designing self-driving cars, or how it performs medical image classification for disease detection? A Convolutional neural network is neuro-scientifically connected with the human brain. The design of CNN is inspired by the visual cortex of human brain. This visual cortex processes a large amount of data when we look at the image.
This article provides a comprehensive overview of the main ethical issues related to the impact of Artificial Intelligence (AI) on human society. AI is the use of machines to do things that would normally require human intelligence. In many areas of human life, AI has rapidly and significantly affected human society and the ways we interact with each other. It will continue to do so. Along the way, AI has presented substantial ethical and socio-political challenges that call for a thorough philosophical and ethical analysis. Its social impact should be studied so as to avoid any negative repercussions. AI systems are becoming more and more autonomous, apparently rational, and intelligent. This comprehensive development gives rise to numerous issues. In addition to the potential harm and impact of AI technologies on our privacy, other concerns include their moral and legal status (including moral and legal rights), their possible moral agency and patienthood, and issues related to their possible personhood and even dignity. It is common, however, to distinguish the following issues as of utmost significance with respect to AI and its relation to human society, according to three different time periods: (1) short-term (early 21st century): autonomous systems (transportation, weapons), machine bias in law, privacy and surveillance, the black box problem and AI decision-making; (2) mid-term (from the 2040s to the end of the century): AI governance, confirming the moral and legal status of intelligent machines (artificial moral agents), human-machine interaction, mass automation; (3) long-term (starting with the 2100s): technological singularity, mass unemployment, space colonisation. This section discusses why AI is of utmost importance for our systems of ethics and morality, given the increasing human-machine interaction. AI may mean several different things and it is defined in many different ways. When Alan Turing introduced the so-called Turing test (which he called an'imitation game') in his famous 1950 essay about whether machines can think, the term'artificial intelligence' had not yet been introduced. Turing considered whether machines can think, and suggested that it would be clearer to replace that question with the question of whether it might be possible to build machines that could imitate humans so convincingly that people would find it difficult to tell whether, for example, a written message comes from a computer or from a human (Turing 1950). The term'AI' was coined in 1955 by a group of researchers--John McCarthy, Marvin L. Minsky, Nathaniel Rochester and Claude E. Shannon--who organised a famous two-month summer workshop at Dartmouth College on the'Study of Artificial Intelligence' in 1956. This event is widely recognised as the very beginning of the study of AI.
Currently, SNIascore can classify what are known as Type Ia supernovae, or the "standard candles" in the sky. A machine learning algorithm developed by astronomers at the California Institute of Technology (Caltech) autonomously classified 1,000 supernovae using data from the Zwicky Transient Facility (ZTF) sky survey instrument at Caltech's Palomar Observatory. The SNIascore algorithm hit that milestone 18 months after classifying its first supernova, in April 2021. The algorithm is intended to help the ZTF team by processing data from the hundreds of thousands of transient events ZTF detects every night. SNIascore currently has the ability to classify Type Ia supernovae that astronomers use to measure the universe's expansion rate.