Why AI is Harder Than We Think (Machine Learning Research Paper Explained)

#aiwinter #agi #embodiedcognition The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This paper examines the reasons for the repeated periods of overconfidence and identifies four fallacies that people make when they see rapid progress in AI. OUTLINE: 0:00 - Intro & Overview 2:10 - AI Springs & AI Winters 5:40 - Is the current AI boom overhyped? 15:35 - Fallacy 1: Narrow Intelligence vs General Intelligence 19:40 - Fallacy 2: Hard for humans doesn't mean hard for computers 21:45 - Fallacy 3: How we call things matters 28:15 - Fallacy 4: Embodied Cognition 35:30 - Conclusion & Comments Paper: https://ift.tt/3vCZlYL My Video on Shortcut Learning: https://youtu.be/D-eg7k8YSfs Abstract: Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense. Authors: Melanie Mitchell Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

5 Fiber-Like Tools That Can Now Be 3D-Printed!

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/2ScsVpj 📝 The paper "Freely orientable microstructures for designing deformable 3D prints" and the Shadertoy implementation are available here: - https://ift.tt/3u7gedJ - https://ift.tt/3gQQrCL 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Is Simulating Wet Papers Possible? 📃💧

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/2QZYke4 📝 The paper "A moving least square reproducing kernel particle method for unified multiphase continuum simulation" is available here: https://ift.tt/32S9MLx 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Tune any musical instrument with ML5 and Crepe - Made with TensorFlow.js

Today on Made With TensorFlow.js we’re joined by Michelle Sun, an interaction designer, who solved a problem she had - never having a guitar tuner nearby when she needed one. Learn how Michelle created a system to tune any instrument (even your voice) live in the web browser using a pitch detection model known as Crepe without the need for any specialist hardware. Learn more and try it out: Guitar tuner → https://goo.gle/3dylLD3 ​ Snake game → https://goo.gle/3cMHMPk ​ Ukulele version → https://goo.gle/2R0emom ​ ML5 pitch detection → https://goo.gle/2OeIcEs ​ Crepe pitch tracker → https://goo.gle/2QX86xv​ Want to be on the show? Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show. Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

Argumentation and Machine Learning: A Tutorial @ IJCAI 2019

Argumentation technology is a rich interdisciplinary area of research that, in the last two decades, has emerged as one of the most promising paradigms for commonsense reasoning and conflict resolution in a great variety of domains to the point that it is used in actual commercial research project such as IBM Debater. In this tutorial PhD students, early stage researchers, and machine learning experts will be introduced to (1) argumentation technology with concrete practical examples; (2) current state-of-the-art approaches of argumentation technology that leverage machine learning, from argument mining to automatic algorithm selection; and (3) current state-of-the-art approaches to machine learning that leverage argumentation technology, for instance for explainability of results coming from deep models.

I Cooked A Recipe Made By A.I.

#gpt3 #airecipe #cooking We went to the store and bought a set of completely random ingredients and had OpenAI's GPT-3 come up with a recipe, which we then cooked and ate. Our Rules: 1. All Vegan 2. Follow the recipe as closely as possible 3. We must finish our plates The Recipe: 1. Boil the potatoes and carrots. 2. In the meantime, prepare the VEGAN minced meat, or use pre-cooked soy meat. 3. Then fry the VEGAN butter, add the garlic, and the mushrooms, and stir for 2 minutes. 4. Add the soy cream, stir and cook for three minutes. 5. Add the pickles, tomatoes, and beans, stir and simmer for five minutes. 6. Cut the bread in small squares and fry in the vegan butter until golden brown. 7. Cut the limes into cubes and squeeze the juice into the bean mixture. 8. Add the soy sauce, parsley, salt, pepper, cumin, cilantro, and dried figs. Stir, and add the kale. 9. Pour the bean mix into a blender. 10. Bake for 5 minutes in the oven at 180C. 11. Cut the sweet potatoes in cubes, and add to a pot with the remaining butter. Add the red beans mixture. 12. Cut the bell pepper into cubes and add to the pot. 13. Add the VEGAN minced meat, and cook in the oven at 180C for 10 minutes. 14. Add the avocado. 15. Add the chickpeas. 16. Add the chocolate. 17. Serve on bread with mustard and pommegrenade on top. OUTLINE: 0:00 - The Plan 2:15 - Ingredients 4:05 - What is GPT-3? 6:10 - Let's cook 12:25 - The Taste Test GPT-3 on Wikipedia: https://ift.tt/2TRCMim GPT-3 Paper: https://ift.tt/2Xdo3Ac Jonas' Scholar: https://ift.tt/3b0J1sX Edit by Ryan Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Week 13 -Codeboxx- AI & Machine Learning

Pandas Tutorial || Series in pandas || Machine Learning || SILAN Software

#silansoftwarebbsr #machinelearningusingpython #machinelearningtraining pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers two data structures like Series and DataFrame and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. SILAN SOFTWARE: JAVA, Python, AI & Machine Learning, Deep Learning, Data Science, Data Analytics Training, Development & Research Center at Bhubaneswar, Odisha, India founded by Er.Trilochan Tarai(Ex.TCS). We provide Academic Training, Industrial Training, Corporate Training & Internship. you can mail your requirements to info@silansoftware.com Please like, share and subscribe our channel for knowing more and more updates. www.silansoftware.com Call to : 0674-2361252 https://www.facebook.com/SilanSoftware/ https://twitter.com/SilanTechnology https://www.linkedin.com/in/silantechnology/

9 Years of Progress In Cloth Simulation! 🧶

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3sLsIGo 📝 The paper "Homogenized Yarn-Level Cloth" is available here: https://ift.tt/3ewynuC 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Pandas Tutorial || Series in pandas || Machine Learning || SILAN Software

#silansoftwarebbsr #machinelearningusingpython #machinelearningtraining pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers two data structures like Series and DataFrame and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. SILAN SOFTWARE: JAVA, Python, AI & Machine Learning, Deep Learning, Data Science, Data Analytics Training, Development & Research Center at Bhubaneswar, Odisha, India founded by Er.Trilochan Tarai(Ex.TCS). We provide Academic Training, Industrial Training, Corporate Training & Internship. you can mail your requirements to info@silansoftware.com Please like, share and subscribe our channel for knowing more and more updates. www.silansoftware.com Call to : 0674-2361252 https://www.facebook.com/SilanSoftware/ https://twitter.com/SilanTechnology https://www.linkedin.com/in/silantechnology/

This AI Makes Beautiful Videos From Your Images! 🌊

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/32xqqQs 📝 The paper "Animating Pictures with Eulerian Motion Fields" is available here: https://ift.tt/3llrNZq GPT-3 website layout tweet: https://twitter.com/sharifshameem/status/1283322990625607681 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/32zPwOF Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Node-Red: Visual coding for ML on Raspberry Pi and beyond - Made with TensorFlow.js

This time on Made With TensorFlow.js we’re joined by Paul Van Eck, a software developer with IBM who shows how to use Node-RED, an open source visual programming tool that even supports machine learning with TensorFlow.js and can even deploy to a Raspberry Pi and more. Watch as Paul uses this system to keep his cat off the table, open his garage door when the correct car is recognized by its number plate, and more! Take command of the physical world with TensorFlow.js and Node-Red in this episode! Happy hacking. Node-RED and TensorFlow.js tutorial → https://goo.gle/3mdA5EG​ Node-RED and TensorFlow.js video → https://goo.gle/31GU0Cx​ Want to be on the show? Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show. Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)

#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE: 0:00 - Intro & Overview 4:50 - View Synthesis Task Description 5:50 - The fundamental difference to classic Deep Learning 7:00 - NeRF Core Concept 15:30 - Training the NeRF from sparse views 20:50 - Radiance Field Volume Rendering 23:20 - Resulting View Dependence 24:00 - Positional Encoding 28:00 - Hierarchical Volume Sampling 30:15 - Experimental Results 33:30 - Comments & Conclusion Paper: https://ift.tt/2J0gzJ7 Website & Code: https://ift.tt/3gGztDX My Video on SIREN: https://youtu.be/Q5g3p9Zwjrk Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x,y,z) and viewing direction (θ,ϕ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Saving Machine Learning Models | Pre Trained Model Formats

▬▬▬▬▬▬ Sources ▬▬▬▬▬▬ https://developers.google.com/protocol-buffers https://apple.github.io/coremltools/coremlspecification/ https://onnx.ai https://www.hdfgroup.org/solutions/hdf5/ https://pytorch.org/tutorials/beginner/saving_loading_models.html https://www.tensorflow.org/tutorials/keras/save_and_load https://spark.apache.org/docs/latest/ml-pipeline.html https://scikit-learn.org/stable/modules/model_persistence.html ▬▬▬▬▬▬ O T H E R DataScience Dialogue Sessions 🎥 ▬▬▬▬▬▬ ♂DataScience Dialogue - Open Source GPU DataScience #1 https://youtu.be/NJQXWT8c0sA​​ ♂DataScience Dialogue - AutoML and DataRobot Demo #2 https://youtu.be/p59hx6-ZwW0​​ ♂DataScience Dialogue - Machine Learning & NLP Best Practices #3 https://youtu.be/0fxwiv8ZX70 ▬▬▬▬▬▬ O T H E R P L A Y L I S T 🎥 ▬▬▬▬▬▬ 🎥 Django Tutorial - Health Insurance Claim Project https://youtube.com/playlist?list=PLw945x_O7SHqoyFNXQSnI4CgH6tVpRUcb 🎤 Good Tech Talks Playlist https://youtube.com/playlist?list=PLw945x_O7SHo6iaXWxPzApDaEQtjo3Pzy 📱▬▬▬▬▬▬ S O C I A L N E T W O R K S 🎭 ▬▬▬▬▬▬ 🎣 Facebook : https://www.facebook.com/Iampythoner/​​​​ 🎣 Instagram : https://www.instagram.com/iampython/​​​​ 🎣 Twitter: https://www.twitter.com/iampythoner/​​​​ 🎣 LinkedIn : https://www.linkedin.com/company/iamp​​​​... 🎣 Telegram : https://t.me/iampythoner​​​​ #datascience #machinelearning #ai

Tutorial on Microsoft Azure Machine Learning Studio (AutoML-Regression)

Created by Microsoft AI for Earth Project: AI Monitoring Coal-fired Power Plant Emission from Space 0:00 Initiate a Automated ML run 0:12 Create a dataset from local files 1:40 Configure run 1:52 Select a compute cluster (create one if none exists) 3:00 Select machine learning task (classification/regression/time series prediction) 3:36 Monitor the status of the experiments 4:53 Check the completed experiment, view the trained models and identify the best model 5:31 View the explanation of the best model (why the model achieves the best performance, reveal the most important features in the decision making) 7:08 Deploy the best model as a service 8:32 The deployed REST service endpoint's swagger description 8:49 Use the embedded Test form to call the service 9:47 Use the Consume function to call the service using client code (Python)

OmniPhotos: Casual VR Photography!

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3mWzCHs 📝 The paper "OmniPhotos: Casual 360° VR Photography" is available here: https://ift.tt/2Qbl6Q7 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

[30 days] AI MACHINE LEARNING & DATA SCIENCE INTERNSHIP @ FAANG

In this video, we talk about what you should expect for Artificial Intelligence (AI) and Machine Learning, Data Science Interviews. We also come up with an example study plan to help you prepare for your interviews. This can help you crack interviews at top companies like facebook, microsoft, apple, google, netflix etc.

Low Code AI - Tutorial 1 - Computer Vision With Bubble.io and AWS (Rekognition, Lambda, API Gateway)

Learn to build AI-powered Web Applications writing only a few lines of code! We build an application that can count the number of cats in a user-uploaded photo :) We use the following tools: - Rekognition, AWS's Machine Vision API, to process the image- AWS Serverless tools (Lambda and API Gateway) to handle the API processing- and Bubble.io to build the user interface. We hope this video can show you how quickly and easily you can get started building apps with machine learning capabilities, and as a gentle introduction to using Bubble with AWS. There is no need to be a professional programmer to understand the content of this tutorial, but if you have coded before, particularly in Python, you will probably find the steps outlined in this video quite straightforward. If you like this content, let us know! We are planning a series of similar videos, using other AWS Machine Learning APIs, so leave a comment if there is a specific API you would be interested in seeing. Thanks for watching! (Note - You may want to watch the video at 2x speed. I'm a slow talker at baseline, and this was recorded fairly late in the evening :s ) ------------------------------------------- Some of our favourite resources to learn Bubble: Coaching No Code Apps - https://www.youtube.com/watch?v=6OKVj1A9OcY Matthew Neary - https://www.youtube.com/watch?v=llIJJagQk88 ------------------------------------------- LaunchableAI - Helping Makers, Founders, and Businesses launch AI-powered products and businesses.

I BUILT A NEURAL NETWORK IN MINECRAFT | Analog Redstone Network w/ Backprop & Optimizer (NO MODS)

#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The network uses Redstone wire power strengths to carry the signal through one hidden layer, including nonlinearities, and then do automatic backpropagation and even weight updates. OUTLINE: 0:00 - Intro & Overview 1:50 - Redstone Components Explained 5:00 - Analog Multiplication in Redstone 7:00 - Gradient Descent for Square Root Computation 9:35 - Neural Network Demonstration 10:45 - Network Schema Explained 18:35 - The Network Learns a Datapoint 20:20 - Outro & Conclusion I built this during a series of live streams and want to thank everyone who helped me and cheered for me in the chat! World saves here: https://ift.tt/3ddtJlT Game here: https://ift.tt/OzF7s5 Multiplier Inspiration: https://www.youtube.com/channel/UCLmzk4TlnLXCXCHcjuJe2ag Credits to Lanz for editing! Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Do Neural Networks Think Like Our Brain? OpenAI Answers! 🧠

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/3thiApV 📝 The paper "Multimodal Neurons in Artificial Neural Networks" is available here: https://ift.tt/3sP4fQH 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

adversarial.js: Break any neural network live in your browser - Made with TensorFlow.js

Today on Made With TensorFlow.js we’re joined by Kenny Song, an active researcher on security and reliability, where he shows you how to break neural networks in your web browser in real-time by changing inputs, such as pixels in an image, to fool a machine learning model. Watch as he turns a photo of a “dog” which is initially classified correctly to be misclassified as a “hotdog” - even though to you, as a human, the image still looks the same. Learn how he does it in this educational video so you can make your models even more robust to such attacks in the future. Try the adversarial.js demo yourself → https://goo.gle/3cHLcCP​ Get the source for adversarial.js on GitHub → https://goo.gle/39DZNND​ Want to be on the show? Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show! Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

Artificial Intelligence | Machine Learning Full Course | Artificial Intelligence For Beginners |

Learn Artificial Intelligence: https://youtu.be/KmSq57W-Kdw​​​​ Lecture 1 : Introduction To C Programming Language . https://youtu.be/1TRn0W3QNc0​​​​ Basic Structure Of C Program: https://youtu.be/hXzaKOUpRKo​​​​ introduction to data structures and algorithms in Hindi In 10 Min. https://youtu.be/0B4Uv60K8QA​​​​ Basics of object oriented programming language (oop's) in 10 min in Hindi: https://youtu.be/aYkGEiPKKhY​​​​ Learn Data Base Management System : https://youtu.be/Jc6uq4zvCZc​​​​ Learn "How To Make an Login Form" : https://youtu.be/BvOVX4iGHVA​​​​ In This Tutorial You Will Learn Machine Learning Tutorial Subscribe Study Mode YT Channel To Learn More About Trending Technology Machine Learning Artificial Intelligence #ML​ #AI #MachineLearning​ #ArtificialIntelligence #TypesofML​​ #MachineLearning​​ #ArtificialIntelligence​​ #UnsupervisedMachineLearning​​​ #KMeansClustering​​​ #ML​​​ #AI

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

#dreamcoder #programsynthesis #symbolicreasoning Classic Machine Learning struggles with few-shot generalization for tasks where humans can easily generalize from just a handful of examples, for example sorting a list of numbers. Humans do this by coming up with a short program, or algorithm, that explains the few data points in a compact way. DreamCoder emulates this by using neural guided search over a language of primitives, a library, that it builds up over time. By doing this, it can iteratively construct more and more complex programs by building on its own abstractions and therefore solve more and more difficult tasks in a few-shot manner by generating very short programs that solve the few given datapoints. The resulting system can not only generalize quickly but also delivers an explainable solution to its problems in form of a modular and hierarchical learned library. Combining this with classic Deep Learning for low-level perception is a very promising future direction. OUTLINE: 0:00 - Intro & Overview 4:55 - DreamCoder System Architecture 9:00 - Wake Phase: Neural Guided Search 19:15 - Abstraction Phase: Extending the Internal Library 24:30 - Dreaming Phase: Training Neural Search on Fictional Programs and Replays 30:55 - Abstraction by Compressing Program Refactorings 32:40 - Experimental Results on LOGO Drawings 39:00 - Ablation Studies 39:50 - Re-Discovering Physical Laws 42:25 - Discovering Recursive Programming Algorithms 44:20 - Conclusions & Discussion Paper: https://ift.tt/2Bvb7gY Code: https://ift.tt/2PTVTcP Abstract: Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ``wake-sleep'' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws. Concepts are built compositionally from those learned earlier, yielding multi-layered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience. Authors: Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Finally, Video Stabilization That Works! 🤳

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "FuSta - Hybrid Neural Fusion for Full-frame Video Stabilization" is available here: - Paper https://ift.tt/39YdOWs - Code: https://ift.tt/2PIHJLH - Colab: https://ift.tt/3mBUZ0y 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/3mBUZO6 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #stabilization #selfies

Project on "AI Based COVID Pneumonia Classifier Using Machine Learning " #R.M.D.EngineeringCollege

Guide Name :Ms.A.Sowmiya,AssistantProfessor/EIE Student Members :Navya P, Nivetha R S, Priyadharshini K, Vidura S #R.M.D.EnggCollege #R.M.K.groupofInstitutions #rmdeie #EngineeringCollege #studentsproject

AI & Machine Learning Session 3

Artificial Intelligence | Machine Learning Full Course | Artificial Intelligence For Beginners |

Learn Artificial Intelligence: https://youtu.be/KmSq57W-Kdw​​​​ Lecture 1 : Introduction To C Programming Language . https://youtu.be/1TRn0W3QNc0​​​​ Basic Structure Of C Program: https://youtu.be/hXzaKOUpRKo​​​​ introduction to data structures and algorithms in Hindi In 10 Min. https://youtu.be/0B4Uv60K8QA​​​​ Basics of object oriented programming language (oop's) in 10 min in Hindi: https://youtu.be/aYkGEiPKKhY​​​​ Learn Data Base Management System : https://youtu.be/Jc6uq4zvCZc​​​​ Learn "How To Make an Login Form" : https://youtu.be/BvOVX4iGHVA​​​​ In This Tutorial You Will Learn Machine Learning Tutorial Subscribe Study Mode YT Channel To Learn More About Trending Technology Machine Learning Artificial Intelligence #ML​ #AI #MachineLearning​ #ArtificialIntelligence #TypesofML​​ #MachineLearning​​ #ArtificialIntelligence​​ #UnsupervisedMachineLearning​​​ #KMeansClustering​​​ #ML​​​ #AI

PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.

In the recurring debate about bias in Machine Learning models, there is a growing argument saying that "the problem is not in the data", often citing the influence of various choices like loss functions or network architecture. In this video, we take a look at PAIR's AI Explorables through the lens of whether or not the bias problem is a data problem. OUTLINE: 0:00 - Intro & Overview 1:45 - Recap: Bias in ML 4:25 - AI Explorables 5:40 - Measuring Fairness Explorable 11:00 - Hidden Bias Explorable 16:10 - Measuring Diversity Explorable 23:00 - Conclusion & Comments AI Explorables: https://ift.tt/3wAMzLp Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Oh My…Simulating Beautiful Soap Bubbles! 🧼

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "A Model for Soap Film Dynamics with Evolving Thickness" is available here: https://ift.tt/2Ouw5U2 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/3uuzNwm Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

ml5.js: Creative coding with ML for all - Made with TensorFlow.js

In this episode of Made with TensorFlow.js we’re joined by Yining Shi and Bomani Oseni McClendon who are working on the ml5.js library that is built upon TensorFlow.js to try and make machine learning even more usable by everyone. From creative coding to hardware experiments, ml5.js can enable you to do many advanced things with just a few lines of code. Learn more and have a go yourself! Hosted by Jason Mayes, Senior Developer Relations Engineer for TensorFlow.js. ml5.js website → https://goo.gle/31J5jKo​ Beginner’s guide to ML in JS with ml5.js → https://goo.gle/3sHkkIC​ p5.js demo sketch → https://goo.gle/2Ohn3JZ​ Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show! Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

[Live] Building a Neural Network in Minecraft | Part 4

We build a Deep Neural Network in Minecraft No Command Blocks No Mods Multiplier inspiration from here: https://www.youtube.com/watch?v=Wc29p6mgRMo World Save: https://ift.tt/3wCdAyj Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Sentiment Analysis (II) using pre-trained transformers | NLP

In this video we'll learn how to do Sentiment Analysis using pre trained transformers from Hugging-Face Play list of tutorials on Introduction to Natural Language Processing - https://www.youtube.com/watch?v=M48huar8qSk&list=PLKv35vv6eFL-LhjhEFTPGeJudZIMRall1 Read my monthly Machine Learning Posts: ML News Monthly March 2021 - https://sushtend.com/machine-learning/ml-news-monthly-mar-2021/ ML News Monthly February 2021 - https://sushtend.com/machine-learning/ml-news-monthly-feb-2021/ ML News Monthly January 2021 - http://sushtend.com/machine-learning/ml-news-monthly-jan-2021/ WHO AM I: I'm Sushrut Tendulkar working as Sr Consultant - Data Science at Bengaluru. I would want to make videos about AI, ML, Technology and Productivity 🌍 My website / blog - http://www.sushtend.com CONNECT ELSEWHERE: 📉 LinkedIn - https://www.linkedin.com/in/sushtend/ 🐦 Twitter - https://twitter.com/sushtend 📸 Instagram - https://instagram.com/sushtend #NLP #NLProc #MachineLearning #ArtificialIntelligence #DeepLearning #AI

AI With Zero Coding | Disease Detection with Google Teachable Machine (Full Project)

Check out the full course here: https://www.udemy.com/course/modern-artificial-intelligence-with-zero-coding/?couponCode=SDSPROMO AI is revolutionizing healthcare and medicine in many areas such as: (1) medical imagery and (2) drug research. AI and more specifically Deep Learning has been proven to be superior to humans in detecting and classifying several diseases using imagery data. Skin cancer could be detected more accurately by Deep Learning than by dermatologists (2018). Human dermatologists detection = 86.6% Deep Learning detection = 95% In this project, we will assume that you work as a consultant and you have been hired by a hospital in downtown Toronto to automate the process of detecting/classifying chest disease. The team has collected X-Ray chest data and approached you to develop a model to detect and classify diseases in less than 1 minute. The goal is to reduce the cost and time of detection. You have been provided with 133 images that belong to 4 classes: Healthy Covid-19 Bacterial Pneumonia Viral Pneumonia I hope you will enjoy this video. Please subscribe to the channel for more videos. Thanks. #AIforeveryone #aiwithzerocoding #AI

CS196 Lecture 19 - AI/ML

Lecturer: William Eustis

Machine Learning with AI

#DSTC #JUNAGADH #TECHNICAL_CAMPUS Stay tuned at : https://bit.ly/2SIn88o Dr. Subhash Technical Campus - Junagadh #ForUs, Education is a Tradition _________________________ ------ Follow Us: Instagram https://instagram.com/Dr.SubhashTech Facebook https://facebook.com/Dr.SubhashTech YouTube https://youtube.com/DrSubhashTech ------ Contact Us : Website : http://drsubhashtech.edu.in Helpline : +918511188222 Our mission is to develop Dr. Subhash Technical Campus as a sustainable educational hub strongly networked with Gujarat Technological University, regional industries, government departments, parents of students, leading citizens and alumni to become one of top five professional institutions in Saurashtra that impart enterprise worthy or employment worthy professional knowledge and skills to every aspiring and deserving youth from Coastal Saurashtra region.

Minecraft Neural Network Test Stream

We build a Deep Neural Network in Minecraft Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

TensorFlow.js Community "Show & Tell" #5

7 new demos from the #MadeWithTFJS global community pushing the boundaries of on device machine learning using JavaScript that can give you superpowers in the browser and beyond. Hosted by Jason Mayes. Want to be on our next show? Use the #MadeWithTFJS tag on social to share your best TensorFlow.js creations, and we may feature you in our next show! Got questions about the show? Ask or connect with Jason on social: Twitter → https://goo.gle/3ePjPbj LinkedIn → https://goo.gle/38QkMfY Links for this session’s presenters: ml5.js website - https://goo.gle/31J5jKo Beginner’s guide to ML in JS with ml5.js - https://goo.gle/3sHkkIC p5.js demo sketch - https://goo.gle/2Ohn3JZ adversarial.js demo - https://goo.gle/3cHLcCP adversarial.js GitHub - https://goo.gle/39DZNND Node-RED and TF.js tutorial - https://goo.gle/3mdA5EG Node-RED and TF.js video - https://goo.gle/31GU0Cx Guitar tuner - https://goo.gle/3dylLD3 Snake game - https://goo.gle/3cMHMPk Ukulele version - https://goo.gle/2R0emom ML5 pitch detection - https://goo.gle/2OeIcEs Crepe pitch tracker - https://goo.gle/2QX86xv 3D In Your Browser - https://goo.gle/2R1jx7v Parallax effect project GitHub - https://goo.gle/3drJm8m MIRNet-TFJS demo - https://goo.gle/2PLP78B MIRNet-TFJS demo GitHub - https://goo.gle/3sQq6aY DepthX - https://goo.gle/3sK9ode Watch past #MadeWithTFJS​ interviews → http://goo.gle/made-with-tfjs ​ TensorFlow.js Community Show & Tell series → http://goo.gle/tf-show-and-tell​ Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

This AI Learned To Stop Time! ⏱

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes" is available here: https://ift.tt/3qjFdsz ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/3wmiQG0 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m