Waveform: The MKBHD Podcast

How Does AI Actually Work?

Oct 24, 2023

01:10:10

Snipd AI

Danu Mbanga, Director of Generative AI Solutions at Google, explains the differences between AI, machine learning, and deep learning. They discuss the transformative effects of AI in various industries such as media, healthcare, and financial services. The speaker explores the concept of generative AI and its foundation, and the future of AI including specialized AIs and the issue of AI hallucinating. They also discuss the emergence of new cognitive strengths in AI systems and the importance of structure and design patterns in building large models.

AI Summary

Highlights

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

AI is a collection of tools and technologies that provide human cognitive capabilities to computers, with generative AI being a subset that aims to incubate generative AI solutions into production-grade applications for companies.

Deep learning, a subset of machine learning that uses neural networks, has become popular due to its ability to process large amounts of data and maintain performance, and the transformer architecture has played a significant role in processing large amounts of data and maintaining structure.

Generative AI, powered by the transformer architecture, allows for the creation of specific artifacts such as images, text, or audio, and its versatility has practical applications in various industries, reducing the time and effort required for innovation and creative exploration.

Deep dives

The Evolution of AI and Generative AI

AI is a collection of tools and technologies that provide human cognitive capabilities to computers. Generative AI is a subset of AI that aims to incubate generative AI solutions into production-grade applications for companies. This involves identifying patterns within the AI ecosystem, packaging these patterns into applications or open-source capabilities. Machine learning is a subset of AI that utilizes mathematical and statistical techniques, while deep learning is a subset of machine learning that uses neural networks. Deep learning has become popular in machine learning due to its ability to process large amounts of data and maintain performance. The transformer architecture, introduced in 2017, played a significant role in processing large amounts of data and maintaining structure. Emergent properties of AI include multi-tasking, in-context learning, and reasoning.

Accelerating Application Development with Chatbots

01:02

Defining Intelligence and the Possibilities of AGI

04:00

Foundations of AI Image Generation

01:03

Introduction

2min

Integrating Generative AI into Production Grade Applications

21min

Layers of AI and its Impact in Various Industries

6min

Using Bard for API building and generative AI

18min

The Future of AI: Specialized AIs and AI Hallucination

2min

Exploring the Potential of AI and Cognitive Strengths

18min

Achievements, Projects, and the Need for Structure in AI

3min

This week, we have a special episode for you! There has been so much talk about AI over the last year or two, but not a lot of explanations. What is AI? What is the difference between AI and Machine Learning? How do they work? David sat down with Danu Mbanga, Director of Generative AI Solutions at Google, to get to the bottom of it all. This talk switches between a general overview of AI and an in-depth discussion about the meaning of intelligence. Danu has years of experience in this field so we hope you learn as much as we did! Enjoy.

Links:

Attention Is All You Need Paper: https://bit.ly/attentionisallyouneed

IBM k-nearest neighbors: https://ibm.co/3S6hdtm

Follow Danu Mbanga:

Threads: https://www.threads.net/@devchiral

X: https://twitter.com/dmbanga

Shop the merch:

https://shop.mkbhd.com

Instagram/Threads/X:

Waveform: https://twitter.com/WVFRM

Waveform: https://www.threads.net/@waveformpodcast

Marques: https://www.threads.net/@mkbhd