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Ethics & Society: The future of AI: Views from history We hear from Dr Richard Staley, Dr Sarah Dillon, and Dr Jonnie Penn, co-organisers of an Andrew W. Mellon Foundation Sawyer Seminar on the ‘Histories of Artificial Intelligence.’ They share their insights from a year-long study undertaken with a range of international participants on what the histories of AI reveal about power, automation narratives, and how we model and understand climate change. Dr. Sarah Dillon, Reader in Literature and the Public Humanities, University of Cambridge Dr. Richard Staley, Reader in History and Philosophy of Science, University of Cambridge Dr. Jonnie Penn, Researcher at Berkman Klein Center for Internet & Society at Harvard University on LinkedIn #CogX2021 #JoinTheConversation
Stephen Lernout is a serial entrepreneur with an extensive background in Technology Sales and Marketing and has a profound knowledge of the Natural Language Processing (NLP) market. He is also a passionate fan and firm believer in the potential of Artificial Intelligence. He helped establish Miia, an AI company specialized in Natural Language Understanding (NLU) and Computational Linguistics & Semantics. He developed and implemented the company’s business model and focused on building effective, innovative and highly competitive NLU products. Miia merged into Nalantis where he is now responsible for Business Development and Innovation. Within Nalantis, he is also managing Global Partnerships for the HR Bot Unit and he is heading the CityNet Project which is leveraging AI and NLU for Cities and Citizens. Current work: Nalantis bridges the gap between Processing and actually Understanding Language. Using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Upcoming Conference: https://saiconference.com/FTC
Check out “A Brief History of AI”, the first episode of our 4-episode series on Artificial Intelligence with Prof. Dan Cautis from Georgetown University! Part of the Future Horizons communication platform, this episode takes you back to the early days of Artificial Intelligence while highlighting the following matters: 1. Why we should question the hype; 2. AI applications from healthcare, finances and space engineering to defense, smartphones and robotics; 3. The difference between Narrow AI and Artificial General Intelligence; 4. John von Neumann and Alan Turing’s digital computer and the birth of AI; 5. Computationalism and the logic touch in calculations; 6. The leap from number crunching to AI; 7. The Golden Years of AI: the contribution of McCarthy, the ungrounded optimism of Marvin Minsky, Herbert Simon and the likes, and the classical AI paradigm; 8. The early signs of the AI winter; 9. How Machine Learning revived and renewed AI; 10. Moore’s Law and the rise of neural networks as a new field of interest and focus; 11. The paradigm shift towards machine learning; 12. Branches in machine learning: supervised learning, classification, unsupervised learning; clustering, linear regression, prediction, reinforcement learning; 13. Machine learning algorithms and concepts. Follow us for more news on technology & innovation! Facebook https://www.facebook.com/QUALITANCE/ LinkedIn https://www.linkedin.com/company/qualitance/ Twitter https://twitter.com/QUALITANCE
I made an A.I. that teaches itself to drive in the racing game Trackmania, using Machine-Learning. I used Deep-Q-Learning, a Reinforcement Learning algorithm. Again, a big thanks to Donadigo for TMInterface ! Contact : Discord – Yosh#5919 Twitter – https://twitter.com/yoshtm1
http://workersonboard.com Zirtual is hiring full and part time virtual assistants to work from home Apply here https://www.zirtual.com/jobs/zirtual-assistant/ Crowd Source needs freelance writers to submit short articles for weekly pay, http://info.write.com/general-freelance-writing-jobs-2014-start?utm_source=Craigslist&utm_medium=Listing&utm_campaign=Los%20Angeles Get paid $30 a week and up to try a free app for your iPad here https://docs.google.com/forms/d/1C5UIEwtdXX-R5rjPMB8qiyik79iVYU81m3AU5K0c17U/viewform?edit_requested=true Learn how to type from home for free here http://www.typingstudy.com/ Can you make $1,000 a month from home? Read my honest answer to this question here http://www.homebasedmommie.com/2014/05/is-it-possible-to-make-1000-month-from.html#.U2uq2_ldVIE Get $10 to spend from Thredup here http://www.thredup.com/r/NTJKBZ For more ways to earn at home, please go to my official website here http://workersonboard.com
As part of the AI & Cultural Heritage event, IAS Visiting Fellow Ahmad Elgammal delivers his talk ‘AI and Art, from the Micro Level to the Macro Level’ In this talk, Elgammal will present results of recent research activities at the Art and Artificial Intelligence Laboratory at Rutgers University. We investigate perceptual and cognitive tasks related to understanding human creativity in visual art. In particular, we study problems related to art styles, influence, and iconography. We develop computational models that aim at providing answers to questions about what characterizes the sequence and evolution of changes in style over time. He will talk about how AI can help analyze art in new ways, at the micro level and macro level. #LboroAI #AI #CulturalHeritage #Archives #Museums #Libraries For more information about the IAS, please visit – https://www.lboro.ac.uk/research/ias
Future Digital Twin 2021 Panel Discussion: How to achieve maximum value from the Digital Twin when implementing Machine Learning, Machine Vision and AI – The future of the Digital Twin – Adopting Machine learning to revolutionise the current model – The role of cognitive artificial intelligence – Human v Machine and Physical understandings v Mathematical insights – How AI intelligence plays a central role and maintaining Digital Twin Integrity – Best practices for reducing Digital Twin project risks – Lessons learned in the product lifecycle Speakers: Andrea Course, Venture Principle, Shell Julian Zec, Senior Program Manager Optimisation, Maersk Drilling Michiel Van Haersma Buma, VP of Customer Success, Akselos Debasis Bisoi, VP President Manufacturing, Digital and IoT Solutions, Tech Mahindra Dr. Robello Samuel, Chief Technology Advisor and Technology Fellow, (Well Engineering, Automation and Science, Halliburton Moderator: David Hartell, Managing Director, Stellae Energy Ltd
Real-time Facial Emotion Detection from Facial Expressions Asset is an open source software component that is developed at the Open University of the Netherlands. This work has been partially funded by the EC H2020 project RAGE (Realising an Applied Gaming Eco-System); http://www.rageproject.eu/; Grant agreement No 644187. This software component has the following advantages: 1. This real-time emotion detection asset is a client side software component that can detect emotions from players’ faces. 2. You can use it for instance in games for communication training or conflict management. Or for collecting emotion data during play-testing. 3. The software detects emotions in real-time and it returns a string representing six basic emotions: happiness, sadness, surprise, fear, disgust, and anger. It can also detect the neutral face. 4. The presence of multiple players would not be a problem as the software component can detect multiple faces and their emotions at the same time. 5. As inputs it may use the player’s webcam stream. But, it can also be used with a single image file, or with a recorded video file. 6. The emotion detection is highly accurate: the accuracy is over 83%, which is comparable with human judgment. 7. The software is written in C-Sharp. It runs in Microsoft Windows 7, 8, and 10, and it can be easily integrated in many game engines, including, for instance Unity3D. 8. This software uses the Apache-2 open source license, which means that you can use it for free, even in commercial applications. 9. The real-time [More]
Facebook Offensive Security Engineer Amanda Rousseau aka “Malware Unicorn” uses the power of Twitter to answer common questions about hacking. As an offensive security engineer, Amanda has seen just about everything when it comes computer hacking. What exactly is the difference between a black hat and a white hat hacker? Is there such thing as a red hat hacker? What’s the point of malware, is it just to be annoying? Are people who start DDoS attacks actually hackers? Amanda answers all these Twitter questions, and much more! Amanda is an Offensive Security Engineer on the Red Team at Facebook and previously worked as a Malware Researcher at Endgame, FireEye, and the U.S. Department of Defense Cyber Crime Center. Follow her on Twitter at: https://malwareunicorn.org/#/about Still haven’t subscribed to WIRED on YouTube? ►► http://wrd.cm/15fP7B7 Get more incredible stories on science and tech with our daily newsletter: https://wrd.cm/DailyYT Also, check out the free WIRED channel on Roku, Apple TV, Amazon Fire TV, and Android TV. Here you can find your favorite WIRED shows and new episodes of our latest hit series Tradecraft. ABOUT WIRED WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture. Cybersecurity Expert Answers Hacking Questions From Twitter | Tech Support | WIRED Amanda is an Offensive Security Engineer on the Red Team at Facebook and previously worked as a Malware Researcher at Endgame, FireEye, and the U.S. Department of Defense Cyber Crime Center. Follow her on Twitter at: [More]
Professionisti Italiani a Boston presents a discussion on Artificial Intelligence (A.I.), where it stands and where it’s headed with CEO and Co-Founder of Neurala, Inc. Massimiliano Versace.
Economics of Artificial Intelligence Conference, Fall 2021 https://www.nber.org/conferences/economics-artificial-intelligence-conference-fall-2021 DATE: September 23-24, 2021 How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program AUTHOR(S): Sukwoong Choi, Massachusetts Institute of Technology Namil Kim, Harbin Institute of Technology Junsik Kim, KAIST Hyo Kang, University of Southern California DISCUSSANT(S): Ananya Sen, Carnegie Mellon University Amalia R. Miller, University of Virginia and NBER
This talk and related interactive demonstration aim to introduce our efforts in developing an Open data hub and Open-source simulation and optimization framework based on virtual tracks and transportation mesh networks. We will demonstrate how to deliver rapid prototyping of SCDT and enable smarter multimodal policy decisions. We will also discuss a cross-resolution modeling approach to enable consistency from discrete traffic simulation to continuous fluid queue-based model. The proposed open-data-based and open-source enabled framework also intends to create a user community of thought leaders in this emerging area across different geographically distributed communities. Within the Open-SCDT context, large-scale agent-based simulator and GMNS based modeling tools aim to engage citizens with local transportation planning by making it as easy as possible to imagine how changes might affect a person’s commute. It could be used by city authorities to interactively communicate proposed and ongoing projects, by the general public to explore and submit ideas for improving their community, and by advocacy groups to educate people about options for reducing automobile dependency.
CIS Digital Twin Days 2021 | 15 Nov. 2021 | Lausanne Switzerland Prof. Karen E. Willcox, Director, Oden Institute for Computational Engineering and Sciences, University of Texas, Austin Predictive Digital Twins: From physics-based modeling to scientific machine learning Abstract A digital twin is an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. Key to the digital twin concept is the ability to sense, collect, analyze, and learn from the asset’s data. To make digital twins a reality, many elements of the interdisciplinary field of computational science, including physics-based modeling and simulation, inverse problems, uncertainty quantification, and scientific machine learning, have an important role to play. In this work, we develop a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset-twin system as a set of coupled dynamical systems, evolving over time through their respective state-spaces and interacting via observed data and control inputs. The abstraction is realized computationally as a dynamic decision network. Predictive capabilities are enabled by physics-based reduced-order models. We demonstrate how the approach is instantiated to create, update and deploy a structural digital twin of an unmanned aerial vehicle. cis.epfl.ch
Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the ASS presents a novel dimension reduction method which aims to improve the accuracy and the efficiency of speaker’s age estimation systems based on speech signal. Two different age estimation approaches were studied and implemented; the first, age-group classification, and the second, precise age estimation using regression. These two approaches use the Gaussian mixture model (GMM) supervectors as features for a support vector machine (SVM) model. When a radial basis function (RBF) kernel is used, the accuracy is improved compared to using a linear kernel; however, the computation complexity is more sensitive to the feature dimension. Classic dimension reduction methods like principal component analysis (PCA) and linear discriminant analysis (LDA) tend to eliminate the relevant feature information and cannot always be applied without damaging the model’s accuracy.
Mathias Ekman from Microsoft speaks about the future of Life science powered by AI and Genomics. #pharma #ai #science #genomics #conference #dubrovnik #pharmaceuticals #panel #future #learning #talks #keynote Get your tickets for this NEXT 2021 event now: https://nextpharmasummit.com
Explore an overview of the developments in the machine learning ecosystem at Google that can be applied across your ML powered apps, from hobby projects to cutting-edge research. Speaker: Nadav Eiron (VP, Engineering) Watch all Google’s Machine Learning Virtual Community Day sessions → https://goo.gle/mlcommunityday-all Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow #MLCommunityDay product: TensorFlow – General; event: ML Community Day 2021; fullname: Nadav Eiron; re_ty: Publish;
Elon Musk Artificial Intelligence Neuralink are three of the most discussed topics in the world of technology. Elon Musk has been warning us about the dangers of AI for a longtime. .Many widely regarded scientist, like Stephen Hawking, Steve Wozniak and Bill Gates, have already expressed their concerns that super-intelligent AI could go out of control and be an existential threat to humans. And Elon musk thinks Neuralink is going to help us in the future to solve the AI problem. By the outstanding performance of Neuralink in recent days we already know Neuralink is going to change the world in future. But what is Neuralink going to do in terms of saving Humanity from Artificial Intelligence? It is going to play a massive role actually.  Watch the video till the end.  And Subscribe to ProSitas to watch wonderful videos on Science, Technology and History:- https://www.youtube.com/channel/UCr2espOXs-iL24IbVJlvd7Q?sub_confirmation=1  Some Clips used in the video  —————————————————————- Elon Musk interview with Jonathan Nolan:- https://youtu.be/kzlUyrccbos Elon Musk interview with Jack Ma :- https://youtu.be/f3lUEnMaiAU Elon Musk interview with Joe Rogan :- https://youtu.be/ycPr5-27vSI ————————————————————— Music used in this video  ————————————————————— Fragments – AERØHEAD https://soundcloud.com/aerohead Creative Commons — Attribution-ShareAlike 3.0 Unported — CC BY-SA 3.0 Free Download / Stream: https://bit.ly/al-fragments Music promoted by Audio Library https://youtu.be/O7PzeUTESAA —————————————————————
Presented by: Keith Galli In the past year, massive developments have been made in the natural language processing field. Improvements in areas such as question answering, machine translation, and sentiment analysis have opened up doors to utilize NLP more effectively than ever before. In this tutorial we will perform a brief overview of the field of NLP and look at the Python libraries that allow us to utilize different techniques and models. We will start with simple, traditional approaches to NLP that will provide us baseline for our models. As we progress in the tutorial we will look at some more advanced concepts that can give quick boosts to model performance. We will end by introducing state-of-the-art language models and how we can incorporate them into applications that we build. Tutorial resources:https://github.com/keithgalli/pycon2020
Want to build your own peer-to-peer video chat app? WebRTC is a technology that creates a realtime connection between browsers where users can exchange audio/video streams https://fireship.io/lessons/webrtc-firebase-video-chat/ 00:00 WebRTC Explained 02:01 Build your own Video Chat 3:37 Code setup 04:34 Peer Connection & Webcam 06:49 Offer Signaling 09:45 Answer Signaling Source Code https://github.com/fireship-io/webrtc-firebase-demo Useful Resources WebRTC Docs https://webrtc.org/ Codelab https://webrtc.org/getting-started/firebase-rtc-codelab Signaling https://developer.mozilla.org/en-US/docs/Web/API/WebRTC_API/Signaling_and_video_calling #webdev #js #100SecondsOfCode Install the quiz app 🤓 iOS https://itunes.apple.com/us/app/fireship/id1462592372?mt=8 Android https://play.google.com/store/apps/details?id=io.fireship.quizapp Upgrade to Fireship PRO at https://fireship.io/pro Use code lORhwXd2 for 25% off your first payment. My VS Code Theme – Atom One Dark – vscode-icons – Fira Code Font
In this video, we will learn How to extract text from a pdf file in python NLP. Natural Language Processing (NLP) is the field of Artificial Intelligence, where we analyse text using machine learning models. Text Classification, Spam Filters, Voice text messaging, Sentiment analysis, Spell or grammar check, Chatbot, Search Suggestion, Search Autocorrect, Automatic Review, Analysis system, Machine translation are the applications of NLP. This notebook demonstrates the extraction of text from PDF files using python packages. Extracting text from PDFs is an easy but useful task as it is needed to do further analysis of the text. We are going to use PyPDF2 for extracting text. You can download it by running the command given below. We have used the file NLP .pdf in this notebook. The open() function opens a file and returns it as a file object. rb opens the file for reading in binary mode. 🔊 Watch till last for a detailed description 02:43 Importing the libraries 06:21 Reading and extracting the data 09:17 Append write or merge PDFs 13:20 Analysing the output 👇👇👇👇👇👇👇👇👇👇👇👇👇👇 ✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: https://bit.ly/bert_nlp 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, [More]
Screening and Panel Discussion on Coded Bias Film, March 29 ACM’s Technology Policy Council and Diversity and Inclusion Council sponsored a free screening and public discussion of the film “Coded Bias” and how those in computer science fields can address issues of algorithmic fairness. The discussion occured on Monday, March 29, 2021 from 2:30-4:00 pm EDT (8:30pm CEST). PANELISTS: Dame Prof. Wendy Hall, Regius Professor of Computer Science, University of Southampton Hon. Bernice Donald, Federal Judge U.S. Court of Appeals for the Sixth Circuit Prof. Latanya Sweeney, Daniel Paul Professor of Government & Technology, Harvard University Prof. Ricardo Baeza-Yates, Research Professor, Institute for Experiential AI, Northeastern University MODERATOR: Prof. Jeanna Matthews, Professor of Computer Science, Clarkson University SPONSORS: ACM Technology Policy Council ACM Diversity & Inclusion Council National Science Foundation ADVANCE Grant Clarkson Open Source Institute (COSI), Clarkson University https://www.acm.org/diversity-inclusion/from-coded-bias-to-algorithmic-fairness
At the dawn of the 20th century, scientists discovered a hidden universe lying inside the atom, giving rise to the revolutionary theory of quantum mechanics. Quantum theory played a profound role in the previous century, giving rise to atomic weapons, medical devices such as the PET scanner, as well as led to the invention of transistors, computers, and smartphones. The 21st century is likely to similarly shaped by scientific advances in the field of artificial intelligence, which designs machines that “think”. Stunning advances in AI and machine learning have given rise to machines that beat the best humans at open-ended quiz competitions, like Jeopardy, or challenging games, like chess and Go. We are beginning to see autonomous cars driven by AI roaming our streets, and AI software now daily analyzes vast amounts of information on the web. The next decades will usher in even more significant advances in AI, where machines are capable of “imagination”, and are able to perform more creative tasks, from creating art and designing novel drugs, to inventing new scientific ideas by reading the scientific literature. Many countries, such as China and Russia, have recognized the importance of AI, and are devoting significant resources to building AI expertise. The arms race in the 21st century is likely to be significantly shaped by advances in AI. Sridhar Mahadevan is the Director of the Data Science Laboratory at Adobe Research in San Jose, California, as well as an adjunct professor at the College of Information and Computer Science [More]
🙋‍♂️ We’re launching an exclusive part-time career-oriented certification program called the Zero to Data Science Bootcamp with a limited batch of participants. Learn more and enroll here: https://www.jovian.ai/zero-to-data-science-bootcamp 🔗 Resources used • Notebook created in the workshop: https://jovian.ai/aakashns-6l3/deep-learning-project-live • Guidelines and datasets for deep learning projects: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/course-project 💻 In this live hands-on workshop, we’ll build a deep learning project from scratch in 2.5 – 3 hours. You can follow along to build your own project. Take our Free Certification Course “Deep Learning with PyTorch: Zero to GANs” to learn the required skills: http://zerotogans.com Here’s an outline of the workshop: 📄 Find an interesting unstructured dataset online (images, text, audio, etc.) ❓ Identify the type of problem: regression, classification, generative modeling, etc. 🤔 Identify the type of neural network you need: fully connected, convolutional, recurrent, etc. 🛠 Prepare the dataset for training (set up batches, apply augmentations & transforms) 🔃 Define a network architecture and set up a training loop ⚡ Train the model and evaluate its performance using a validation/test set 🧪 Experiment with different network architectures, hyperparameters & regularization techniques 📰 Document and publish your work in a Jupyter notebook or blog post 📒 Datasets from the workshop: Chest X-Ray Images (Pneumonia) – https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia Fruits 360 – https://www.kaggle.com/moltean/fruits Flowers Recognition – https://www.kaggle.com/alxmamaev/flowers-recognition Malaria Cell Images Dataset – https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria Intel Image Classification – https://www.kaggle.com/puneet6060/intel-image-classification Best Artworks of All Time – https://www.kaggle.com/ikarus777/best-artworks-of-all-time CelebFaces Attributes (CelebA) Dataset – https://www.kaggle.com/jessicali9530/celeba-dataset Open Datasets – https://github.com/JovianML/opendatasets ⚙ Check out these projects for inspiration: • Blindness [More]
WWDC: Artificial intelligence robotic car from Anki is demoed at Apple keynote