How does datafication, the reduction of the complexity of the world to data values, threaten the Rule of Law? Why should we focus on the regulation of Artificial Intelligence (AI) rather than on ethics? Could human agency be superseded by algorithmic decision-making? And: has the Age of Algorithmic Warfare arrived? In a thought-provoking Sixth Annual T.M.C. Asser Lecture, Prof. Andrew Murray, a leading thinker on information technology and regulation, discusses the challenges that Artificial Intelligence and Big Data pose for human agency and the Rule of Law.
#Reinforcement Learning Course by David Silver# Lecture 1: Introduction to Reinforcement Learning #Slides and more info about the course: http://goo.gl/vUiyjq
For downloading the AI Tool go to : www.eduvance.in/downloads For downloading the datasets go to : www.tinyurl.com/ai-course-datasets http://www.eduvance.in http://www.facebook.com/Eduvance http://www.instagram.com/eduvance #eduvance, #artificialintelligence, #machinelearning, #robot, #images, #image classification, #visualrecognition, #dataset, data, #course, #student #training, #online course, #elearning, #aisoftware, #prediction, #mimodel, #aimodel, #features, #output, #classification, #regression, #gui mode, #programming mode, #python, #python programming, #algorithm, #confusion #matrix, #mse, #r2
For downloading the AI Tool go to : www.eduvance.in/downloads For downloading the datasets go to : www.tinyurl.com/ai-course-datasets http://www.eduvance.in http://www.facebook.com/Eduvance http://www.instagram.com/eduvance #eduvance, #artificialintelligence, #machinelearning, #robot, #images, #image classification, #visualrecognition, #dataset, data, #course, #student #training, #online course, #elearning, #aisoftware, #prediction, #mimodel, #aimodel, #features, #output, #classification, #regression, #gui mode, #programming mode, #python, #python programming, #algorithm, #confusion #matrix, #mse, #r2
Social Impact of AI (Artificial Intelligence) and IoT (internet of Things) – Keynote by Ahsan Zaman – Oxford University Lectures – Impact Investing at Oxford University Poverty Conference 2016. Presented on July 5th, 2016 at Said Business School, University of Oxford, UK. Presented by Ahsan Zaman, WORLD MEDIA ONLINE, CEO #impinv #impactinvesting Social Impact Investing and Social Impact of Technology.
Toby Walsh, AI expert and “rock star” of Australia’s digital revolution talks about machines behaving badly.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jsZydn Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html
What are Network Models? What is Client Server Model ? What is Peer to Peer Model? What is Hybrid Model?
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information http://web.stanford.edu/~cgpotts/ Consulting Assistant Professor Bill MacCartney Senior Engineering Manager, Apple https://nlp.stanford.edu/~wcmac/ To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision. Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
The College of Natural & Agricultural Sciences at the University of California, Riverside is proud to present the 2022 Science Lecture Series entitled Big Data Science. The third of this four-part is Tuesday, April 19, with Dr. Mark Alber, UC Riverside Distinguished Professor of Mathematics, with a presentation on Computational Modeling and Digital Twin of a Patient. Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. This technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where clinicians have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. Multiscale computational modeling is a successful strategy to integrate multiscale, multiphysics data and uncover biological mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution In this lecture, Dr. Alber will demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that can provide new insights into disease mechanisms, help identify new targets and patient specific treatment strategies, and inform decision [More]
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3n7saLk Professor Christopher Manning & PhD Candidate Abigail See, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule
Online Lecture Series, Techfest, IIT Bombay is back with another highly inspiring leader, Andrew Ng! When it comes to rising fields like machine learning, artificial intelligence and computer vision, Andrew Yan-Tak Ng is one of the names you hear first. Being the co-founder of Coursera and deeplearning.ai, he has taught millions of eager learners worldwide through his online courses. He is also the mastermind behind Google Brain, a deep learning research team at Google which combines open-ended machine learning research with information systems and large-scale computing resources. Professor of Computer Science and Electrical Engineering at Stanford University, he has undertaken several research projects related to data mining and machine learning. His work has earned him several awards and he has gifted the world of technology with hundreds of his published papers. Watch Prof. Andrew Ng talk about his life journey and experience, the future of AI and it’s impact on the society! . This is a recorded lecture being streamed live . . . Give us a like on Facebook – https://www.facebook.com/iitbombaytechfest/ Subscribe to our channel – https://www.youtube.com/channel/UCech6f3osmQ_s54OsIZQDgA Follow us on Twitter : https://twitter.com/Techfest_IITB Follow us on Instagram: https://www.instagram.com/techfest_iitbombay/?hl=en
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Depe55 Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule
The slides for this presentation are available here: shorturl.at/rvBLZ The first in The Turing Lecture mini-series exploring the role of AI and data science in our lives post-lockdown. COVID-19 has precipitated a major experiment for the UK’s education system that may change the way we teach and learn forever, but what role can and should AI play in this transformation? Professor Luckin will discuss the current stage of AI’s application in education and the ways in which AI has supported teachers and learners during the pandemic. Professor Luckin will also look towards the future and consider how AI could be used to support a COVID-compliant transformation for our education system – a transformation that seeks to enable all learners to achieve their full potential. Throughout the lecture examples from different AI systems will be presented to illustrate what is happening in the present and what could happen in the future. We will also hear recommendations as to how our education system can become ‘AI ready.’ — Rose Luckin is Professor of Learner Centred Design at the UCL Knowledge Lab in London. Her research involves the design and evaluation of educational technology using theories from the learning sciences and techniques from artificial intelligence (AI). She has a particular interest in using AI to open up the ‘black box’ of learning to show teachers and students the detail of their progress intellectually, emotionally and socially. Rose is also Director of EDUCATE, a London hub for educational technology startups, researchers and educators to [More]
October 28, 2021 The Tanner Humanities Center was proud to host Shoshana Zuboff for the Obert C. Tanner Lectures on Artificial Intelligence and Human Values, a special series of the Tanner Lectures program. This lecture series on artificial intelligence will also take place at University of California Berkeley, University of Cambridge, University of Michigan, University of Oxford, Stanford University, and Yale University during the 2021-22 and 2022-23 academic years. Zuboff is the author of three books, each of which signaled the start of a new epoch in technological society. In the late 1980s she foresaw how computers would revolutionize the modern workplace. Writing before the invention of the iPod or Uber, she predicted the rise of digitally-mediated products and services tailored to the individual. She also warned of the individual and societal risks. Now her masterwork, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, synthesizes years of research to reveal a world in which technology users are neither customers, employees, nor products. Instead, she argues they are the raw material for new procedures of manufacturing and sales that define an entirely new economic order—a surveillance economy. She invites alternative approaches. Zuboff is the Charles Edward Wilson Professor Emerita at Harvard Business School and a former Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard Law School.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3nd2ZH2 Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule
In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, quantization, and other techniques for accelerating inference, as well as parallelization, mixed precision, and other techniques for accelerating training. We discuss specialized hardware for deep learning such as GPUs, FPGAs, and ASICs, including the Tensor Cores in NVIDIA’s latest Volta GPUs as well as Google’s Tensor Processing Units (TPUs). Keywords: Hardware, CPU, GPU, ASIC, FPGA, pruning, weight sharing, quantization, low-rank approximations, binary networks, ternary networks, Winograd transformations, EIE, data parallelism, model parallelism, mixed precision, FP16, FP32, model distillation, Dense-Sparse-Dense training, NVIDIA Volta, Tensor Core, Google TPU, Google Cloud TPU Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. [More]
In Lecture 12 we discuss methods for visualizing and understanding the internal mechanisms of convolutional networks. We also discuss the use of convolutional networks for generating new images, including DeepDream and artistic style transfer. Keywords: Visualization, t-SNE, saliency maps, class visualizations, fooling images, feature inversion, DeepDream, style transfer Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences between CPUs and GPUs. Keywords: CPU vs GPU, TensorFlow, Keras, Theano, Torch, PyTorch, Caffe, Caffe2, dynamic vs static computational graphs Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University https://stanford.io/3eJW8yT Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanford.edu/
ACHLR ‘The Ethics of Artificial Intelligence: Moral Machines’ Public Lecture Learn more: https://www.qut.edu.au/law/research
Lecture 10 introduces translation, machine translation, and neural machine translation. Google’s new NMT is highlighted followed by sequence models with attention as well as sequence model decoders. ——————————————————————————- Natural Language Processing with Deep Learning Instructors: – Chris Manning – Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/