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Rich's Tongfamily

Podcast

OK, if you aren’t using Apple Podcast, Spotify or any of the others, you can listen to all my podcasts in this view. Just click on the latest at the top. Or if you want to listen to any of the previous podcasts, there’s a link for each below. Have fun! This is using the JetPack big version player with all the subscribe buttons and showing the latest Podcast.

Youtube Channel

Here is the video cast from the Video Podcast channel (thanks to a special Smash Balloon, YouTube Feed plugin which you can embed with a shortcode

Rich Tong

The Excellent Adventures of Tong with co-hosts Paul and Deon. Covering current tech news and analysis. As well as regular updates on all things Apple, Smart Home, Smart Everyday Carry, Hime Theater, Linux, Startups, Robotics and Artificial Intelligence

Rich Tong
The London Stock Exchange Group had a Tech Summit on 21 October 2024, as part of that we had an AI Hub breakout with 40 technologies and business leaders focused on what is up next technical with AI in the Enterprise (Video: Zoom to Final Cut Pro to Compressor as Apple Devices 4K HEVC 10-bit, HLG, Dolby Vision 8.4)
RT6. TNE3. LSEG Tech Summit AI Hub 2024-10-21
There's been a hiatus mainly because we've been shipping products, but more because I lost all my skills at making new videos. I was stuck for a long time on Drop Zones and doing a better intro and outro, but that's a digression for Final Cut Pro nerds. (Note that the ST1a version has a bug fix for the intro video, but is otherwise identical to ST1.) In this episode, we cover the latest trends as of August 8, 2024, on AI and the latest trends. The big news has been the shipment of so many Large Language Models (LLMs) and what it means for AI which is way more choice and confusion. Plus the emergence of a much better set of tools that are much smaller as well called Small Language Models (SLM sometimes) and also Agents that are chopping the problem into many small pieces. I also wanted to introduce Steven to the mix, we are going to have a rotating set of co-hosts and solo episodes as well so we can get the content out on time and not take 6 weeks for post productions. Thanks to the new intro and outro, that should be easier. I'm playing with these a bunch, but check out https://tongfamily.com and https://tne.ai where I hang out a bunch! Spotify podcast version at https://podcasters.spotify.com/pod/show/richt/episodes/ST1--August-Update-on-AI-2024-08-28-e2pcufa and Youtube at https://youtu.be/gqGD5_LxLL0
ST1a. AI Predictions Diamonds 1-3 - 2024-08-08 [4K HEVC HDR PQ 10-bit]
Note: The ST1 version is identical, the intro video has an adjusted icon size and it is shorter. I just couldn't take not having this bug fix. There's been a hiatus mainly because we've been shipping products, but more because I lost all my skills at making new videos. I was stuck for a long time on Drop Zones and doing a better intro and outro, but that's a digression for Final Cut Pro nerds. In this episode, we cover the latest trends as of August 8, 2024, on AI and the latest trends. The big news has been the shipment of so many Large Language Models (LLMs) and what it means for AI which is way more choice and confusion. Plus the emergence of a much better set of tools that are much smaller as well called Small Language Models (SLM sometimes) and also Agents that are chopping the problem into many small pieces. I also wanted to introduce Steven to the mix, we are going to have a rotating set of co-hosts and solo episodes as well so we can get the content out on time and not take 6 weeks for post productions. Thanks to the new intro and outro, that should be easier. I'm playing with these a bunch, but check out https://tongfamily.com and https://tne.ai where I hang out a bunch! Spotify podcast version at https://podcasters.spotify.com/pod/show/richt/episodes/ST1--August-Update-on-AI-2024-08-28-e2pcufa and Youtube at https://youtu.be/gqGD5_LxLL0
ST1. AI Predictions Diamonds in the Rough. 2024-08-08
Alex Tong's Thesis the last half excerpt
Alex Tong's Thesis Defense Excerpt - Screen Recording 2021 08 25 at 12 34 44 PM
This is another sort of nerdy side note. If anyone is still watching, this section is just to give intuition on the basics of the hardware. There are lots of assumptions about GPUs and CPUs that I wanted to make sure people understood. But the basics are that CPUs are tuned for lots of branches and different workflows, while GPUs are tuned for lots of the same things like matric math. And because they are so fast, most of the job of the computer folks is "feeding the beast". That is caching the most frequently used information so they don't have to wait. There are some errors I think in the levels of the CPU and GPU performance particularly in the cache performance as it is not very clear how this works and the results of course vary depending on the models of processors, so these are all approximations. Put in comments better sources. I have all the sources listed in a spreadsheet that is part of this. We are happy to send this to anyone who wants the source data. I'll fix these errors in later editions (as I'm obsessive that way). Also, I'm quite proud of the HDR mix, using the latest OBS settings, producing in HDR in Final Cut Pro, and adjusting the video scope levels helps. The audio is a little hot and I'm sorry about that, I'll turn it down next time, I stoo much time in the red. My Scarlett needs to about 1 O'clock and it works. See https://tongfamily.com/2024/03/08/pod-rt3-ai-hardware-introduction/ for details and https://podcasters.spotify.com/pod/show/richt/episodes/RT3--AI-Hardware-Introduction-e2gr9ad for the podcast version Chapters: 00:00 AI Hardware Introduction 00:42 Computer Engineering in Two Slides 05:40 It's 165 Years to a Disk Access?!! 14:12 Intel Architecture CPU 17:03 What's all this about NVidia 25:24 And now for something completely Different, Apple 29:45 Introduction summary 2024-03 -08. Shot as UHD HEVC HDR PQ 10 bit using OBS Studio and Final Cut Pro © 2024. Iron Snow Technologies, LLC . All Rights Reserved.
RT3. AI Hardware Introduction
OK, we are not experts nor PhDs so most of this is probably not technically correct, but the math is so complicated and the concepts so complicated, that we thought it would be good to just get some intuition on what is happening. So this is a quick talk that summarizes readings from so many different sources about the history of AI from the 1950s all the way to January 2024 or so. It is really hard to keep up but much harder without some intuition about what is happening. We cover expert systems, the emergence of neural networks, Convolution Neural Networks, and Recurrent Neural Networks. The Attention is All You Need paper led to Transformers and then finally some intuition on how such a simple idea, that is training on things can lead to emergent and unexpected behaviors, and finally, some intuition on how Generative images work. Also I apology is that there is a lot of brushing over, over the section on embeddings is not quite right, particularly for visual transformers, we will correct that, but the concept of embeddings is still important there, just not quite correctly explained. Sorry! Chapters: 00:00 AI History 09:07 Neural Networks Weights 11:18 Convolutional Neural Networks (CNNs) 16:07 Recurrent Neural Networks (RNNs) 17:57 Embedding Vectors are the key! 23:25 Attention is all you need Transformers 27:36 But it's too Simple! Emergence is surprising 33:04 What emerges inside a Transformer? 43:01 One Model to Rule Them All 47:54 Works for Image generation too
DT6. AI Intro and Intuitions 2024-03-07
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