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What are agentic AI and agentic operating systems? And what are their potential benefits, drawbacks and longer-term implications?
For further information, the websites and articles included in this video are as follows:
PROTOCOLS:
MCP website: https://modelcontextprotocol.i....o/docs/getting-start
Google announces the A2A protocol: https://developers.googleblog.....com/en/a2a-a-new-era
IBM’s ACP integrated into A2A: https://lfaidata.foundation/co....mmunityblog/2025/08/
A2A Protocol website: https://a2a-protocol.org/latest/
ORCHESTRATION PLATFORMS:
Microsoft AutoGen: https://www.microsoft.com/en-u....s/research/project/a
IBM WatsonX Orchestrate: https://www.ibm.com/products/watsonx-orchestrate
AWS Bedroom AgentCore: https://aws.amazon.com/bedrock/agentcore/
Google Vertex AI Agent Builder: https://cloud.google.com/products/agent-builder
Google Gemini Enterprise https://cloud.google.com/gemini-enterprise
AgentForce: https://www.salesforce.com/uk/agentforce/
Forethought: https://forethought.ai/
Origon AI: https://origon.ai/
AGENTIC OPERATING SYSTEMS:
“Making every Windows PC an AI PC”: https://blogs.windows.com/wind....owsexperience/2025/1
“Experimental Agentic Features” in Windows: https://support.microsoft.com/....en-us/windows/experi
Microsoft Agent 365: https://www.microsoft.com/en-u....s/microsoft-agent-36
MIT Sloan Management Review and BCG article on the agentic enterprise (inc survey): https://sloanreview.mit.edu/pr....ojects/the-emerging-
Apple working on MCP support for agentic AI: https://9to5mac.com/2025/09/22..../macos-tahoe-26-1-be
More videos on computing and related topics can be found at:
http://www.youtube.com/@ExplainingComputers
And more videos on film and other making, plus retro tech, can be found on my Christopher Barnatt channel: http://www.youtube.com/@ChristopherBarnatt
Chapters:
00:00 Titles & Intro
00:48 Agentic AI Basics
07:14 The Good
09:53 The Bad
11:48 The Ugly
13:22 The Future
#AgenticAI #agentic #Aiagent #agent #MCP #A2A #orchestration #ExplainingComputers
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai
August 7, 2025
Guest Lecture:
Gabor Angeli
AI Research Engineer, Resolve AI
Bharat Khandelwal
AI Research Engineer, Resolve AI
Spiros Xanthos
Founder & CEO, Resolve AI
To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu
In this video I will show you methods to efficiently load a custom dataset with images in directories. Depending on how your dataset is structured the method that is the easiest could vary and the most common ways to load is either having structured subfolders, a csv with with annotations or simply all of the images in a single folder. We use the functions image_dataset_from_directory, ImageDataGenerator together with flow_from_directory for subfolders. Pandas togethor with from_tensor_slices when dealing with an annotation csv file and lastly list_files when having all inside a single image folder directory. Hope you find this tutorial useful!
I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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OUTLINE:
0:00 - Introduction
0:29 - Images in subfolders
14:03 - Folder with annotation csv
19:15 - All in single folder
24:53 - Ending
This is my solution to costFunction.m function in Programming assignment 2 from the famous Machine Learning course by Andrew Ng.
Github: https://github.com/AladdinPerz....on/Courses/tree/mast
Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance.
Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks. These machine learning techniques proved to be useful for many tasks beyond image recognition: they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others.
In this episode, an intuitive explanation is given to show the inner workings of deep learning algorithms.
________________________
Original blog post by Christopher Olah (source of many images):
http://colah.github.io/posts/2....014-03-NN-Manifolds-
You can train your own deep neural networks on Andrej Karpathy's website:
http://cs.stanford.edu/people/....karpathy/convnetjs/d
Images used in this video:
Bunny by Tomi Tapio K (CC BY 2.0) - https://flic.kr/p/8EbcEk
Train by B4bees (CC BY 2.0) - https://flic.kr/p/6RzHe4
Train with bunny by Alyssa L. Miller (CC BY 2.0) - https://flic.kr/p/5WPeRN
The knot theory blackboard image was created by Clayton Shonkwiler (CC BY 2.0) https://flic.kr/p/64FYv
The tangled knot image was created by Mikael Hvidtfeldt Christensen (CC BY 2.0) https://flic.kr/p/beYG9D
The thumbnail image is a work of Duncan Hull (CC BY 2.0) - https://flic.kr/p/98qtJB
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Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu
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Checking out a fully custom water-cooled build with 4 A100 40GB cards. An insane powerhouse in a tiny form factor.
Comino Website: https://grando.ai/choose-a-gpu-machine-for-ai-deep-learning/?utm_source=youtube&utm_medium=video&utm_campaign=sentdex#grando-rm
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#comino #watercooling #deeplearning
While the Transformer architecture is used in a variety of applications across a number of domains, it first found success in natural language. Today, Transformers remain the de facto model in language - they achieve state-of-the-art results on most natural language benchmarks, and can generate text coherent enough to deceive human readers. In this talk, we will review recent progress in neural language modeling, discuss the link between generating text and solving downstream tasks, and explore how this led to the development of GPT models at OpenAI. Next, we’ll see how the same approach can be used to produce generative models and strong representations in other domains like images, text-to-image, and code. Finally, we will dive into the recently released code generating model, Codex, and examine this particularly interesting domain of study.
Mark Chen is a research scientist at OpenAI, where he manages the Algorithms Team. His research interests include generative modeling and representation learning, especially in the image and multimodal domains. Prior to OpenAI, Mark worked in high frequency trading and graduated from MIT. Mark is also a coach for the USA Computing Olympiad team.
A full list of guest lectures can be found here: https://www.youtube.com/playli....st?list=PLoROMvodv4r
0:00 Introduction
0:08 3-Gram Model (Shannon 1951)
0:27 Recurrent Neural Nets (Sutskever et al 2011)
1:12 Big LSTM (Jozefowicz et al 2016)
1:52 Transformer (Llu and Saleh et al 2018)
2:33 GPT-2: Big Transformer (Radford et al 2019)
3:38 GPT-3: Very Big Transformer (Brown et al 2019)
5:12 GPT-3: Can Humans Detect Generated News Articles?
9:09 Why Unsupervised Learning?
10:38 Is there a Big Trove of Unlabeled Data?
11:11 Why Use Autoregressive Generative Models for Unsupervised Learnin
13:00 Unsupervised Sentiment Neuron (Radford et al 2017)
14:11 Radford et al 2018)
15:21 Zero-Shot Reading Comprehension
16:44 GPT-2: Zero-Shot Translation
18:15 Language Model Metalearning
19:23 GPT-3: Few Shot Arithmetic
20:14 GPT-3: Few Shot Word Unscrambling
20:36 GPT-3: General Few Shot Learning
23:42 IGPT (Chen et al 2020): Can we apply GPT to images?
25:31 IGPT: Completions
26:24 IGPT: Feature Learning
32:20 Isn't Code Just Another Modality?
33:33 The HumanEval Dataset
36:00 The Pass @ K Metric
36:59 Codex: Training Details
38:03 An Easy Human Eval Problem (pass@1 -0.9)
38:36 A Medium HumanEval Problem (pass@1 -0.17)
39:00 A Hard HumanEval Problem (pass@1 -0.005)
41:26 Calibrating Sampling Temperature for Pass@k
42:19 The Unreasonable Effectiveness of Sampling
43:17 Can We Approximate Sampling Against an Oracle?
45:52 Main Figure
46:53 Limitations
47:38 Conclusion
48:19 Acknowledgements
#gpt3
