Why do Boston Dynamics Robots dance? And Could Elon Musk be right that superhuman AI is only around five years away?

Boston Dynamics' Dancing Robots:
https://youtu.be/fn3KWM1kuAw

Interview with the GPT-3 AI:
https://youtu.be/PqbB07n_uQ4

PWC report on how automation will impact jobs:
https://www.pwc.co.uk/services..../economics/insights/

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Ever wondered how your favorite technology, or the one you're working on came to be? What and how it changed over the years? These are some of the questions our new series, FlashbackFriday answers. Today, we'll be delving into the history and important milestones of the Machine Learning.


If you are interested to learn more about Machine Learning, check out our Machine Learning playlist here: https://www.youtube.com/watch?v=ukzFI9rgwfU&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy

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In this video, we will build a Vision Language Model (VLM) from scratch, showing how a multimodal model combines computer vision and natural language processing for vision QA. GitHub below ↓

Want to support the channel? Hit that like button and subscribe!

GitHub Link of the Code
https://github.com/uygarkurt/VLM-PyTorch

Dataset
https://huggingface.co/dataset....s/uygarkurt/simple-i

Implement and Train ViT From Scratch for Image Recognition - PyTorch
https://youtu.be/Vonyoz6Yt9c

Implement Llama 3 From Scratch - PyTorch
https://youtu.be/lrWY4O5kUTY

Fine-Tune Visual Language Models (VLMs) - HuggingFace, PyTorch, LoRA, Quantization, TRL
https://youtu.be/3ypHZayanBI

ViT (Vision Transformer) - An Image Is Worth 16x16 Words (Paper Explained)
https://youtu.be/8phM16htKbU

What should I implement next? Let me know in the comments!

00:00 VLM Explanation
04:42 Downloading Dataset
06:39 Imports
10:09 Hyperparameters
12:37 Data Processing
16:03 ViT Definition
19:05 VLM Implementation
41:40 Sample Inference
45:20 Data Loaders
52:46 Training Loop
57:50 Inference

References
https://github.com/AviSoori1x/seemore

Buy me a coffee! ☕️
https://ko-fi.com/uygarkurt

In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed.

Leave me a comment if you have new topics I should be discussing.

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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YouTube Timestamps
0:00 – 0:20 Welcome
0:21 – 0:50 Introduction to the concept of Agentic AI
0:51 – 3:50 Explanation of how Agentic AI works
3:50 – 4:08 Advertisement plug-in
4:08 – 5:20 Example of using compound LLM’s
5:21 – 5:55 Why you should use a compound LLM approach
5:57 – 6:45 Best way to train and use LLM’s for optimal outcome
6:45 – 7:40 How to think of LLM as agents
7:41 – 10:05 Not every agent needs to be an LLM
10:06 - 10:50 Possibility of having an orchestrator agent
10:51 – 12:50 How to use these agents
12:51 – 13:26 Closing remarks

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ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe. InterSystems Corporation is ranked by Gartner, KLAS, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.

Website: https://www.intersystems.com/
Youtube: https://www.youtube.com/@InterSystemsCorp
LinkedIn: https://www.linkedin.com/company/intersystems/
Twitter: https://twitter.com/InterSystems

#agenticai #aiagents #LLM

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 18, 2025
This lecture covers career advice and a guest speaker.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Guest Speaker
Laurence Moroney
Best-selling AI author and award-winning researcher

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

A lot of people think an AI agent is just a large language model wrapped in a chat interface. But when you move from a local demo to a real application, that assumption breaks down completely. A true agent is a production software system that can reason over a goal, retrieve context, maintain state, and trigger real actions through APIs.

In this video, I break down Agent AI System Design from a builder's perspective. We cover the exact architecture required to make single and multi-agent systems reliable, cost-aware, and safe enough to connect to real external tools. You will learn how to properly route models to save costs, structure your tool contracts, separate workflow state from long-term memory, and implement critical approval gates. By the end, you will have a practical mental model for building production-grade systems that users can actually rely on.

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Our workshop is filling up very quickly. We currently have a limited-time 50% discount, so if you have been wanting to understand one of the fastest-growing roles in AI and learn how to build customer-facing AI solutions, now is the best time to register.

Register here: https://maven.com/aishwarya-sr....inivasan/ai-for-forw

👉 You can also check out our one-hour-long FREE Agentic AI System Design masterclass: https://maven.com/p/4c948b/age....ntic-ai-system-desig

CHAPTERS
0:00 The Demo vs. Production Reality
1:49 What is an Agent AI System?
2:12 Single Agent vs. Multi-Agent Systems
3:57 Building Block 1: The Model Layer and Routing
5:39 Building Block 2: Tool Contracts and Boundaries
7:37 Building Block 3: Memory vs. Workflow State
9:57 Building Block 4: Orchestration and Control Flow
12:23 Building Block 5: Trace-Level Evaluations
15:16 Building Block 6: Approval Gates and Policy Controls
17:03 Production Principle 1: Reliability and Fallbacks
18:42 Production Principle 2: Cost and Latency
20:42 Production Principle 3: Context and Retrieval Design
22:20 Production Principle 4: Observability, Security, and Privacy
25:33 Go Deeper with Gen Academy

📚 Resources to Watch Next
* AI Agents Explained: https://youtu.be/TZMdEg1ZoIo?si=xolifmjybYclpNZ-
* Single Agent vs. Multi-Agent Systems: https://youtu.be/-zBbij9rrEI?si=n0B9kG0XH9U0CQeV
* Deep Dive on AI Evaluations: https://youtu.be/_Er8Hao_gmQ?si=KNKjGEJrS7dkU6RF

Subscribe for deep dives into production-grade AI engineering, architecture breakdowns, and practical career strategies. Drop your system design questions in the comments below. I read and answer as many as I can!

Next: Your first AI Employee: https://youtu.be/jYbzKYwdy24

In this video, I break down what Agentic AI really is, beyond the hype and buzzwords. While most people think of AI as just chatbots like ChatGPT, the next evolution - Agentic AI - is quietly transforming how we interact with technology.

I explain in simple terms how Agentic AI systems can actually take initiative, complete complex tasks autonomously, and solve problems without constant human guidance. You'll understand the key differences between reactive AI tools and truly agentic systems that can plan, adapt, and achieve goals.

Whether you're a developer, business leader, or just curious about AI's future, this concise explanation gives you the foundation to understand why Agentic AI represents such a significant leap forward in artificial intelligence.

#AgenticAI #ArtificialIntelligence #AIExplained #FutureOfAI #AgentBasedAI #AITechnology #TechTrends #MachineLearning #BusinessAI #AIAgents