Webinar Replay: How to Adapt Foundation Models To Your Own ML Tasks To Reach 99% Accuracy - Part 3

Описание: Have you ever wondered what kind of mistakes foundation models can make? Today, we're delving into the intriguing world of AI models like SAM and GPT to uncover their limitations and areas of improvement.

Discover SAM, Meta's Segmentation Model. While SAM excels in background image segmentation, it leans towards foreground masks in intricate scenes, affecting shadow detection. It also grapples with objects merging seamlessly with backgrounds, like camouflaged subjects. See its challenges with birds on a seaweed-covered beach. 🧠🔍

SAM's hurdles extend further. It struggles to label images in practical domains like medicine and industry, where specialized knowledge is essential. Identifying clothing defects or interpreting remote sensing data poses difficulties. 🌐📷

Shifting to GPT, its impressive NLP skills come with a caveat. Not every task suits it perfectly. "ChatGPT Jack of All Trades and Master of None" paper assesses GPT across 25 NLP tasks. It scores 56 against the state-of-the-art's 73. Remember, GPT isn't always the high-accuracy choice. 📚🤖

Read the full webinar Digest right here:

Chapters:

0:00 - What Causes Mistakes
00:18 - 1 - Training Data
01:36 - 2 - Objective Function
01:50 - Supervised Learning
02:34 - GPT (Self-Supervised Learning)
03:32 - 3 - Lack of Reasoning Capabilities
04:04 - 4 - Prompt Engineering


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