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AI Teaches Itself How to Escape! In this video an AI named Albert learns how to escape 5 rooms I’ve designed. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert’s actions are controlled by a Neural Network that’s updated after each attempt in order to try to give Albert more rewards and less punishments over time. Everything in this video (except for the music) was created entirely by myself using Unity. Check the pinned comment for more information on how the AI was trained! Current Subscribers: 0
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Meet Ameca, the world’s most advanced humanoid #robot we met at #ces @The YouTube Tech Guy #ai #skynet #irobot —– Legal: Newegg Inc. provides the information contained herein as an educational service. Although we believe the information in this presentation to be accurate and timely, because of the rapid changes in the industry and our reliance on information provided by outside sources, we make no warranty or guarantee concerning the accuracy or reliability of the content or other material which we may reference. This presentation is provided on an “as is” basis without warranties of any kind, expressed or implied, including but not limited to warranties of title, non-infringement or implied warranties of merchantability or fitness for a particular purpose. This video/audio file is the property of Newegg Inc. Newegg Inc. grants permission to distribute, rebroadcast or copy this file, provided that (1) the below copyright notice appears in all copies (2) is for non-commercial use only and (3) is not modified in any way. Copyright © 2019 Newegg Inc. All rights reserved.
Talking about How Natural Language Processing has progressed with Neural Networks. How have Transformers influenced the game with ELMo, BERT, OpenAI Transformers, XLNet and more! Like and subscribe for more amazing content! REFERENCES [1] Analytics Vidhya Blog on NLP Models: https://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/ [2] NLP’s imageNet moment has arrived – Amazing blog: https://ruder.io/nlp-imagenet/ [3] Techcrunch Blog on hugging face: https://techcrunch.com/2019/12/17/hugging-face-raises-15-million-to-build-the-definitive-natural-language-processing-library/ [4] Original Word2Vec paper in 2013: https://arxiv.org/pdf/1301.3781.pdf [5] Intro to Word2Vec: https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa [6] The paper that introduced Transformer Neural Networks: https://arxiv.org/abs/1706.03762 [7] Elmo Original Paper in 2018: https://arxiv.org/abs/1802.05365 [8] Wonderful illustration of BERT: http://jalammar.github.io/illustrated-bert/ [9] Another Blog with BERT explained: https://www.analyticsvidhya.com/blog/2019/09/demystifying-bert-groundbreaking-nlp-framework/ [10] OpenAI on their new langauge models: https://openai.com/blog/better-language-models/ [11] Neural Machine Translation with LSTM + Attention: https://arxiv.org/abs/1409.0473 [12] XLNet original paper: https://arxiv.org/abs/1906.08237 [13] XLNet paper dissected: https://mlexplained.com/2019/06/30/paper-dissected-xlnet-generalized-autoregressive-pretraining-for-language-understanding-explained/ [14] Huggingface: https://huggingface.co/transformers/ [15] Spacy ml: https://spacy.io/api [16] Neural Machine Translation – How it used to be done with LSTM Networks: https://arxiv.org/abs/1409.0473
After a viral blog post by Andrej Karpathy demonstrated that recurrent neural networks are capable of producing very realistic looking (but fake) text, C source code, and even LaTex, there has been considerable interested in this technology. This video demonstrates the use of LSTM, in Keras/TensorFlow, to generate text based on a sample corpus. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_3_text_generation.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton https://twitter.com/jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton
Resources – https://docs.google.com/document/d/1_bmnn1met7evn1GmRHs_U2ANQ22BsmIJVfYDpVfWZqo/edit?usp=sharing
Healthcare Natural Language API → https://goo.gle/30MbQH7 How to start a Google cloud project – $300 free trial link → https://goo.gle/3FIP6H4 How to get started → https://goo.gle/3HXR7RU In this episode of Healthcare Solutions Spotlight we explore one of Google’s natural language services that helps bring order out of many forms of communication using a process called natural language processing (NLP). This Healthcare-specific NLP API, helps you find, assess, and link the knowledge in your medical documents and utterances. Watch to learn how you can deliver an interconnected experience to your patient using harmonized data insights. Chapters: 0:00 – Intro 0:34 – Natural Language Processing (NLP) 1:08 – Healthcare NLP core features 1:18 – Knowledge extraction 1:24 – Relation extraction 1:34 – Knowledge linking 1:46 – Example of using Healthcare NLP API 2:12 – Vocabulary used to train the models 2:50 – Use cases for customizable apps 4:43 – Enabling the Healthcare NLP API 5:14 – Demo 6:24 – Pairing NLP with additional Googler Services Watch more episodes of Healthcare Solutions Spotlight → https://goo.gle/HealthcareSolutionsSpotlight Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #HealthcareSolutionsSpotlight product: Cloud – API Management – Cloud Healthcare API;
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3CORGu1 This lecture covers many topics within Natural Language Understanding, including: -The Course (10 min) -Human language and word meaning (15 min) -Wordzvec introductions (15 min) -WordZvec objective function gradients (25 min) -Optimization basics (5 min) -Looking at word vectors (10 min for less) 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 00:41 Welcome 01:31 Overview for the lecture 01:56 Lecture Plan & Overview 02:02 Course logistics in brief 02:52 What do we hope to teach in this course? 05:39 Course work and grading policy 07:02 High-level plan for problem sets #ChristopherManning #naturallanguageprocessing #deeplearning
This webinar spotlights the updates and progress since the January 2021 release of U.S. Food and Drug Administration’s Center for Device and Radiological Health’s (FDA CDRH) AI/ML Action Plan. Each panel focuses on one aspect of the Action plan, starting with an overall framework for regulating AI, development of Good Machine Learning Practices, and post-market evaluation of AI/ML Software as a Medical Device (SaMD).
Control Statements – for loop Slides: https://tinyurl.com/gectpython
🔥Java Placement Course : https://www.youtube.com/watch?v=yRpLlJmRo2w&list=PLfqMhTWNBTe3LtFWcvwpqTkUSlB32kJop 🔥Telegram: https://t.me/apnikakshaofficial 🔥Instagram: https://www.instagram.com/dhattarwalaman/ My YouTube Gear 😉: https://docs.google.com/document/d/1pyTJVmed-rHFXNqQodOTYr7z-EhH37s8pvtYpR0vMR4/edit?usp=sharing
Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning. Our three-day workshop stems on what we identify as the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning. We invite the leaders in these fields to bolster new collaborations and to look for new angles of attack on the mysteries of deep learning. Day 2 | 4:00 PM–5:30 PM | Suriya Gunasekar, TTI-C; Aleksander Madry, MIT; Soledad Villar, NYU Slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/AI-Institute-Geometry-of-Deep-Learning-2019-Day-2-Session-4-SLIDES.pdf AI Institute “Geometry of Deep Learning” 2019 event page: https://www.microsoft.com/en-us/research/event/ai-institute-2019/
In this live stream, I explain the math behind gradient descent from my previous linear regression video. I also begin exploring the content from Session 4 of the “Intelligence and Learning” course. I build a simple Perceptron example in Processing. This video is part of the third and fourth session of my ITP “Intelligence and Learning” course (https://github.com/shiffman/NOC-S17-2-Intelligence-Learning) Edited videos: Maths of Gradient Descent: https://youtu.be/jc2IthslyzM Perceptron: https://youtu.be/ntKn5TPHHAk Timestamps: 24:00 – Maths for Gradient Descent 1:39:59 – Coding Challenge: Perceptron Simple Perceptron code examples: p5.js: https://github.com/shiffman/The-Nature-of-Code-Examples-p5.js/tree/master/chp10_nn/NOC_10_01_Perceptron Processing: https://github.com/shiffman/The-Nature-of-Code-Examples/tree/master/chp10_nn/NOC_10_01_SimplePerceptron 🚂 Website: http://thecodingtrain.com/ 👾 Share Your Creation! https://thecodingtrain.com/guides/passenger-showcase-guide 🚩 Suggest Topics: https://github.com/CodingTrain/Suggestion-Box 💡 GitHub: https://github.com/CodingTrain 💬 Discord: https://thecodingtrain.com/discord 💖 Membership: http://youtube.com/thecodingtrain/join 🛒 Store: https://standard.tv/codingtrain 🖋️ Twitter: https://twitter.com/thecodingtrain 📸 Instagram: https://www.instagram.com/the.coding.train/ 🎥 Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH 🎥 Intro to Programming: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6Zy51Q-x9tMWIv9cueOFTFA 🔗 p5.js: https://p5js.org 🔗 p5.js Web Editor: https://editor.p5js.org/ 🔗 Processing: https://processing.org References: Session 3 of Intelligence and Learning: https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression Session 4 of Intelligence and Learning: https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week4-neural-networks Nature of Code: http://natureofcode.com/ 3Blue1Brown’s Youtube channel: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw My Gradient Descent video: https://youtu.be/L-Lsfu4ab74 Perceptron on Wikipedia: https://en.wikipedia.org/wiki/Perceptron Books: Frank Rosenblatt’s paper on the Perceptron: http://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf Make Your Own Neural Network: http://amzn.to/2rDkbt4 Calculus Made Easy: http://amzn.to/2sBvlvQ Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For more Intelligence and Learning videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YJ3XfHhT2Mm4Y5I99nrIKX For my Nature of Code videos: https://www.youtube.com/watch?v=6vX8wT1G798&list=PLRqwX-V7Uu6YVljJvFRCyRM6mmF5wMPeE&index=1 For More Live Streams: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bxnFR6no70vlxxuxDEzflz Help us caption & translate this video! http://amara.org/v/7SDJ/ Help us caption & translate this video! http://amara.org/v/aMfe/ 📄 Code of Conduct: https://github.com/CodingTrain/Code-of-Conduct
Designer and architect Neri Oxman is leading the search for ways in which digital fabrication technologies can interact with the biological world. Working at the intersection of computational design, additive manufacturing, materials engineering and synthetic biology, her lab is pioneering a new age of symbiosis between microorganisms, our bodies, our products and even our buildings. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world’s leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design — plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
In this episode, Gary discussed the recent HIMSS conference with John Glaser, Ph.D., Executive-in-Residence, Harvard Medical School, Kaveh Safavi, M.D., J.D., Senior Managing Director, Accenture, and Suchi Saria, Ph.D., Founder and CEO, Bayesian Health. They discussed the utility of in-person meetings, as well as the many cutting edge technologies that will be influencing healthcare systems in the near future. For More Info about The Gary Bisbee Show Visit: https://www.thinkmedium.com/programs/the-gary-bisbee-show Suchi Saria, Ph.D., is the Founder and CEO of Bayesian Health, the John C. Malone Associate Professor of computer science, statistics, and health policy, and the Director of the Machine Learning and Healthcare Lab at Johns Hopkins University. She has published over 50 peer-reviewed articles with over 3000 citations and was recently described as “the future of 21st century medicine” by The Sloan Foundation. Her research has pioneered the development of next-generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. At Bayesian Health, Dr. Saria is leading the charge to unleash the full power of data to improve healthcare, unburdening caregivers and empowering them to save lives. Backed by 21 patents and peer-reviewed publications in leading technical and clinical journals, Bayesian leverages best-in-class machine learning and behavior change management expertise to help health organizations unlock improved patient care outcomes at scale by providing real-time precise, patient-specific, and actionable insights in the EMR. Dr. Saria earned her M.Sc. and Ph.D. from Stanford University working with Professor Daphne Koller. She visited Harvard University as an NSF Computing [More]
Charles Powell interrupts @Lex Fridman and Oriol Vinyals, the Lead for deep learning and research director at Deep Mind. Here I give My thoughts on a method I believe will be one way to achieve artificial general intelligence. //This is a double upload because I messed up and uploaded the wrong video that wasn’t fully edited. (I’m still learning) See if you can spot the differences
Oriol Vinyals, who leads DeepMind’s deep learning team, talks about AlphaCode, his group’s code-writing language model, and DeepMind’s winding road toward artificial general intelligence.
Gary Marcus, NYU, Robust.ai Thursday, April 21st, 4:30m PM Towards a Proper Foundation for Artificial General Intelligence Large pretrained language models like BERT and GPT-3 have generated enormous enthusiasm, and are capable of producing remarkably fluent language. But they have also been criticized on many grounds, and described as “stochastic parrots.” Are they adequate as a basis for general intelligence, and if not, what would a better foundation for general intelligence look like? Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. He is the author of five books, including The Algebraic Mind, Kluge, The Birth of the Mind, and The New York Times best seller Guitar Zero, as well as editor of The Future of the Brain and The Norton Psychology Reader. He has published extensively in fields ranging from human and animal behavior to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence, often in leading journals such as Science and Nature, and is perhaps the youngest Professor Emeritus at NYU. His newest book, co-authored with Ernest Davis, Rebooting AI: Building Machines We Can Trust aims to shake up the field of artificial intelligence.
www.predictconference.com Predict is organised by Creme Global. We provide data and models to decision makers. www.cremeglobal.com www.expertmodels.com
Is creativity under attack from the rise of artificial intelligence? Who better to answer that question than Ai-Da, the world’s first artist robot that has made headlines for its incredible paintings and sculptures – not least a portrait of the Queen to celebrate the Platinum Jubilee earlier in 2022. Ai-Da Robot gives evidence at the House of Lords as part of its A Creative Future inquiry, examining potential challenges for the creative industries and looking at how they can adapt as tech advances. #AI #Robot #SkyNews SUBSCRIBE to our YouTube channel for more videos: http://www.youtube.com/skynews Follow us on Twitter: https://twitter.com/skynews Like us on Facebook: https://www.facebook.com/skynews Follow us on Instagram: https://www.instagram.com/skynews Follow us on TikTok: https://www.tiktok.com/@skynews For more content go to http://news.sky.com and download our apps: Apple: https://itunes.apple.com/gb/app/sky-news/id316391924?mt=8 Android https://play.google.com/store/apps/details?id=com.bskyb.skynews.android&hl=en_GB Sky News videos are now available in Spanish here/Los video de Sky News están disponibles en español aquí https://www.youtube.com/channel/skynewsespanol Sky News videos are also available in German here/Hier können Sie außerdem Sky News-Videos auf Deutsch finden: https://www.youtube.com/channel/UCHYg31l2xrF-Bj859nsOfnA To enquire about licensing Sky News content, you can find more information here: https://news.sky.com/info/library-sales