What advancements has AI made in the past three years?

AI has made impressive advancements in just three years, with Chat GPT now able to perform tasks such as graduating from MIT and generating high-quality recipes, thanks to better models, improved data, and increased computing power.

Is human input necessary for AI projects?

Yes, human input is crucial for the success of AI projects. The speaker collaborates with a team to build a customized AI model that uses GPT4 and multimodal mini GPT4 APIs to answer questions, highlighting the importance of human collaboration.

How does AI perform on tests and exams?

AI's test performance is influenced by the amount and quality of training data. It relies more on memory than mastery, explaining its success on tests based on background knowledge and its failure on tests requiring novel thinking.

Can AI be trusted in high-stakes scenarios?

AI can make unpredictable choices when faced with new situations, making it hard to trust the system, especially in high-stakes scenarios. Trust in AI is still a challenge due to its lack of adaptability and reasoning capabilities.

Is AI more intelligent than humans?

AI is more intelligent than humans in certain applications, such as improving internet accessibility. However, it still lacks the adaptability, reasoning, and understanding capabilities of humans, indicating that it has a long way to go before surpassing human thinking.

The Limitations of AI in Passing Tests

Name: The Limitations of AI in Passing Tests
Uploaded: 2023-09-01 08:07:47
Description: While AI has made impressive advancements, it still lacks the adaptability, reasoning, and understanding capabilities of humans, highlighting the need for human input and collaboration in AI projects.

This article is a summary of a YouTube video "why AI can't pass this test" by Answer in Progress

TLDR While AI has made impressive advancements, it still lacks the adaptability, reasoning, and understanding capabilities of humans, highlighting the need for human input and collaboration in AI projects.

Key insights

🤔

AI's rapid advancements in just three years, from generating incomplete recipes to teaching humans how to cook, highlight the power of better models, high-quality data, human fine-tuning, and improved computing power.
🐱

The machine prioritizes the lives of cats, highlighting the potential bias and limitations of AI systems.
💡

The decision to work with Thomas was based not only on his ideas for building the project, but also on his approach to addressing the video's main question.
😮

The AI's score of 4% in learning from experience highlights its limitations and raises questions about its ability to truly understand and apply knowledge.
🤔

The AI's inability to correctly identify the impossibility of ice cubes floating in water highlights its lack of understanding of basic physics.
🧠

The most challenging questions for AI are those that are surprising or very different from the training data, indicating the need for AI to handle novel situations and think beyond its pre-existing knowledge.
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The long tail problem in AI can lead to unpredictable and potentially destructive behavior, making it difficult to trust the system when the stakes are high.
🧠

To truly test AI's understanding, we need exams that go beyond what it has seen before and probe its comprehension of the material.

Q&A

What advancements has AI made in the past three years?

— AI has made impressive advancements in just three years, with Chat GPT now able to perform tasks such as graduating from MIT and generating high-quality recipes, thanks to better models, improved data, and increased computing power.
Is human input necessary for AI projects?

— Yes, human input is crucial for the success of AI projects. The speaker collaborates with a team to build a customized AI model that uses GPT4 and multimodal mini GPT4 APIs to answer questions, highlighting the importance of human collaboration.
How does AI perform on tests and exams?

— AI's test performance is influenced by the amount and quality of training data. It relies more on memory than mastery, explaining its success on tests based on background knowledge and its failure on tests requiring novel thinking.
Can AI be trusted in high-stakes scenarios?

— AI can make unpredictable choices when faced with new situations, making it hard to trust the system, especially in high-stakes scenarios. Trust in AI is still a challenge due to its lack of adaptability and reasoning capabilities.
Is AI more intelligent than humans?

— AI is more intelligent than humans in certain applications, such as improving internet accessibility. However, it still lacks the adaptability, reasoning, and understanding capabilities of humans, indicating that it has a long way to go before surpassing human thinking.

Timestamped Summary

🤖

00:00
AI has made impressive advancements, but there are concerns about labor exploitation and resource consumption, and OpenAI tested GPT4 on reading comprehension and task completion challenges to assess its learning abilities.
📺

02:55
The speaker decides not to build their own AI and instead uses TruthfulQA and an IQ test to compare intelligence scores between themselves and an AI, with the video sponsored by fiverr, a platform for expert freelancers in AI.
🤝

04:42
Human input is essential for successful AI projects, as demonstrated by the speaker's collaboration with a team to build a customized AI model using GPT4 and multimodal mini GPT4 APIs, and their recommendation to work with a fiverr freelancer for AI services.
😫

06:34
The speaker is frustrated with tests and worries about being perceived as stupid, mentioning a test involving identifying silly or impossible aspects of an image.
😬

08:45
The speaker admits to performing poorly on tests measuring adaptability, deriving information, and understanding, but the AI performed even worse on reasoning and learning from experience.
🤔

10:02
The AI's lack of knowledge about physics, specifically its inability to understand why ice cubes float in water, highlights the inconsistency in its intelligence compared to humans.
🤔

12:10
AI's test performance is influenced by the amount and quality of training data, relying more on memory than mastery, which explains its success on tests based on background knowledge but failure on tests requiring novel thinking.
🤔

14:24
AI can be unpredictable and hard to trust in new situations, but it has the potential to surpass human thinking in certain applications with more training and the ability to generalize and learn new skills.