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01:31

Embedding Invisible Codes in Objects for Augmented Reality

InfraredTags is a system for fabricating objects with embedded codes that are only visible to infrared cameras. These codes can be used for purposes such as metadata or interaction with devices through augmented reality. Read the technical paper: https://groups.csail.mit.edu/hcie/files/research-projects/infraredtags/2022-CHI-InfraredTags-paper.pdf YouTube Source for this AI Video
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14:26

Recitation 0C | PyTorch Tensor Fundamentals (2/2)

Carnegie Mellon University 11785 Introduction to Deep Learning | Spring 22 Notebook link: https://colab.research.google.com/drive/1CaznJifYMeKo4jH7tR_paCwWEikZGR1i?usp=sharing Cheatsheet link: https://drive.google.com/file/d/1gixq11ix4bTfNT_2SdafJcAFNQyx7k91/view?usp=sharing Combining Tensors: • Concatenate – 0:23 • Stack – 4:35 • Padding – 6:13 Mathematical Operations: • Point-wise/Element-wise – 7:47 • Reduction – 10:12 • Comparison – 11:21 • Vector/Matrix – 13:17 YouTube Source for this AI Video
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11:23

The Terrifying Truth Behind Meta's New Supercomputer

Meta has just revealed their AI Supercomputer which is surpassing any of its competitors in terms of capabilities and performance. Meta AI Research is using data from sites such as Facebook and Instagram to train and improve its models in the hopes of controlling and influencing its users and for other future secret projects. What […]
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01:20:00

Trends in Machine Learning & Deep Learning with Zachary Lipton – #556

Today we continue our AI Rewind 2021 series joined by a friend of the show, assistant professor at Carnegie Mellon University, and AI Rewind veteran, Zack Lipton! In our conversation with Zack, we touch on recurring themes like “NLP Eating AI” and the recent slowdown in innovation in the field, the redistribution of resources across […]
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09:43

Recitation 0b | Fundamentals of Numpy – Accessing and Modifying Data (2/8)

Carnegie Mellon University 11785 Introduction to Deep Learning | Spring 22 Notebook link: https://drive.google.com/file/d/110pcUc9fCXnJmLUodtB6_u10cyjNFLg0/view?usp=sharing Reference Material : Numpy User Guide https://numpy.org/doc/stable/user/index.html Indexing • Creating a Dummy Array – 0:19 • Indexing Single Element – 0:37 Slicing – 1:37 • Slicing Along a Batch – 1:51 • Slicing Along Rows and Columns – 2:27 • Slicing […]
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03:53

SingularityNET: 2021 Year End Update – Engineering and Development Team, Anand Rajamani

Blog: https://blog.singularitynet.io/part-1-singularitynet-2021-year-end-update-and-a-sneak-peak-at-q1-2022-72110777b757 The Engineering and Platform team of highly talented developers are focused on shaping and enhancing the SingularityNET Platform to become the global AI mind. The team is continually building and augmenting the systems and infrastructure that underpin the ecosystem’s technology products and services. Note: For those unfamiliar, here are working definitions of SingularityNET’s […]
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01:35:00

How to #BeADeepLearner

Event Agenda: – 20 mins: Event kickoff + Opening speech by Andrew Ng – 60 mins: speaker introduction + Q&A on Discourse: How to participate in Q&A: – Join our community on Discourse to post questions to our speakers and discuss with others. – After logging in, go to the Ask Me Anything category (https://community.deeplearning.ai/c/deep-learning-specialization/events-office-hour/beadeeplearner-a-live-ama-event/236) […]
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08:06

SingularityNET: 2021 Year End Update – UX UI Design Team, Greg Kuebler

Blog: https://blog.singularitynet.io/part-1-singularitynet-2021-year-end-update-and-a-sneak-peak-at-q1-2022-72110777b757 The SinguliarityNET UX/UI team leads product design to create a user centric culture across the SingularityNET Platform and ecosystem of projects. They design intuitive and inviting interfaces for ecosystem websites, portals, and platforms. Thus allowing users to interact seamlessly with our systems and products. —- SingularityNET is a decentralized marketplace for artificial intelligence. […]
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04:29

SingularityNET: 2021 Year End Update – Blockchain and Smart Contract Team, Sridhar Kolapalli

Blog: https://blog.singularitynet.io/part-1-singularitynet-2021-year-end-update-and-a-sneak-peak-at-q1-2022-72110777b757 SingularityNET’s outstanding blockchain team creates secure and efficient smart contracts for the SingularityNET ecosystem (Platform, Marketplace, and Spin-Off Project blockchain interactions). The team designs contracts that minimize friction and maximize blockchain capability. This team’s function is growing increasingly important and complex as SingularityNET becomes truly multi-chain. —- SingularityNET is a decentralized marketplace for […]
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38:12

Speaking of intelligence – DeepMind: The Podcast (Season 2, Episode 2)

Hannah explores the potential of language models, the questions they raise, and if teaching a computer about language is enough to create artificial general intelligence (AGI). Beyond helping us communicate ideas, language plays a crucial role in memory, cooperation, and thinking – which is why AI researchers have long aimed to communicate with computers using […]
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13:54

Recitation 0e | Google Colab

A quick overview of Google Colab Google Form (for Kaggle usernames): https://forms.gle/pRMRCKgGb9rjtpTJ8 Notebook: https://colab.research.google.com/drive/1A9kQk1oWaR489jncwGUDJz65gy1mM8ZC#scrollTo=UTwAlE6o2YU6 Colab pricing: https://colab.research.google.com/signup YouTube Source for this AI Video
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05:15

Two Minute Papers: Opening The First AI Hair Salon! 💇

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper “SketchHairSalon: Deep Sketch-based Hair Image Synthesis” is available here: https://chufengxiao.github.io/SketchHairSalon/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji […]
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39:15

A breakthrough unfolds – DeepMind: The Podcast (Season 2, Episode 1)

In December 2019, DeepMind’s AI system, AlphaFold, solved a 50-year-old grand challenge in biology, known as the protein-folding problem. A headline in the journal Nature read, “It will change everything” and the President of the UK’s Royal Society called it a “stunning advance [that arrived] decades before many in the field would have predicted”. In […]
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09:15

IS TESLA OVER? Tesla's FSD Competition is Heating Up

Tesla’s Full Self Driving Software has been called the most impressive AI Software and years ahead of its competitors, but that is likely about to change with more and more Tesla Competitors coming out with their own FSD Software. Elon Musk has claimed that Level 5 Full Self Driving is about to be released in […]
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41:29

Solving the Cocktail Party Problem with Machine Learning, w/ ‪Jonathan Le Roux – #555

Today we’re joined by Jonathan Le Roux, a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL). At MERL, Jonathan and his team are focused on using machine learning to solve the “cocktail party problem”, focusing on not only the separation of speech from noise, but also the separation of speech from speech. In […]
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33:20

Self-Driving Cars – Lecture 11.2 (Object Tracking: Filtering)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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18:37

[ML News] ConvNeXt: Convolutions return | China regulates algorithms | Saliency cropping examined

#mlnews #convnext #mt3 Your update on what’s new in the Machine Learning world! OUTLINE: 0:00 – Intro 0:15 – ConvNeXt: Return of the Convolutions 2:50 – Investigating Saliency Cropping Algorithms 9:40 – YourTTS: SOTA zero-shot Text-to-Speech 10:40 – MT3: Multi-Track Music Transcription 11:35 – China regulates addictive algorithms 13:00 – A collection of Deep Learning […]
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07:18

Intro to Machine Learning (ML Zero to Hero – Part 1)

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself. But what does ML actually look like? In part one of Machine Learning Zero to Hero, AI Advocate Laurence […]
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08:29

Recitation 0b | Fundamentals of Numpy – Installing and Initializing arrays (1/8)

Carnegie Mellon University 11785 Introduction to Deep Learning | Spring 22 Notebook link: https://drive.google.com/file/d/110pcUc9fCXnJmLUodtB6_u10cyjNFLg0/view?usp=sharing Reference Material : Numpy User Guide https://numpy.org/doc/stable/user/index.html Numpy Installation – 0:33 Importing Numpy – 1:08 • Randomization Seed – 1:32 Initialization – 2:00 • Empty Array – 2:11 • Zeros Array – 2:26 • Ones Array – 2:41 • Full Array […]
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12:54

Self-Driving Cars – Lecture 11.1 (Object Tracking: Introduction)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video