AI Regulations and Token Limits Quiz
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Questions and Answers

What is the maximum number of tokens in the context window for the GPT-4 Turbo model?

  • 256,000
  • 2,048,000
  • 65,536,000
  • 8,192,000 (correct)

Which of the following approximates the amount of data equivalent to the information processed by GPT-4 at 32K tokens?

  • 5,000 tweets
  • ~500 Mb of Unicode text
  • 7,500 emails in 30 seconds (correct)
  • 1 year's worth of emails

Which size context window does GPT-3.5 utilize compared to GPT-4's 32K context window?

  • The same as GPT-1
  • Exactly 32,000 tokens
  • Larger than 32,000 tokens
  • Smaller than 32,000 tokens (correct)

How does the context window size affect the type of information processed?

<p>It allows for more extensive data processing. (A)</p> Signup and view all the answers

What is the default token limit for GPT-1?

<p>4,000 (D)</p> Signup and view all the answers

What is one requirement placed on tech companies by the EU AI Act regarding AI-generated content?

<p>They need to label deepfakes and notify users when they are interacting with AI. (D)</p> Signup and view all the answers

What does the EU AI Act require from companies developing AI in high-risk sectors?

<p>They must create technical documentation and publish training data summaries. (D)</p> Signup and view all the answers

What is the main focus of the nonbinding UN AI regulation adopted in March 2024?

<p>To encourage countries to protect human rights and monitor AI risks. (C)</p> Signup and view all the answers

Which AI uses are expected to be banned under the EU AI Act in the future?

<p>AI applications that pose high risks to fundamental rights. (D)</p> Signup and view all the answers

What exemption exists for free open-source AI models under the EU AI Act?

<p>They must share their architectural details and parameters. (B)</p> Signup and view all the answers

Which factor in the BM25 ranking algorithm indicates that more appearances of a search term make a document more relevant?

<p>Term frequency (TF) (C)</p> Signup and view all the answers

What does inverse document frequency (IDF) measure in the context of traditional search?

<p>The importance of a search term based on its occurrences in multiple documents (C)</p> Signup and view all the answers

Which limitation is associated with traditional sparse search methods?

<p>Failure to capture semantic and correlation information (C)</p> Signup and view all the answers

What is a process involved in traditional information retrieval beyond the inverted index?

<p>Document ingestion (B)</p> Signup and view all the answers

In the context of traditional search, which statement best describes field length's impact on relevance?

<p>Shorter fields containing search terms are generally considered more relevant. (C)</p> Signup and view all the answers

What is the primary purpose of retrieval augmentation in language models?

<p>To access additional user data for context (B)</p> Signup and view all the answers

Which of the following represents a traditional element of information retrieval?

<p>Ranking of results based on relevance (B)</p> Signup and view all the answers

What role do rules play in context-building when dealing with multiple users?

<p>They provide explicit guidance on which users to include. (D)</p> Signup and view all the answers

What is a query in the context of information retrieval?

<p>A formal statement of the information need (B)</p> Signup and view all the answers

In information retrieval, what does relevance measure?

<p>How well an object satisfies the information need (C)</p> Signup and view all the answers

What is the primary function of embeddings in information retrieval?

<p>To enhance search results through mathematical representation (D)</p> Signup and view all the answers

Which of the following best describes 'context-building' in relation to language models?

<p>The selection and integration of relevant data into the processing stage. (B)</p> Signup and view all the answers

What challenge arises when dealing with thousands of users in context-building?

<p>Creating rules for user selection can be complex. (D)</p> Signup and view all the answers

What are embeddings primarily used for?

<p>Transforming data into a more useful representation (B)</p> Signup and view all the answers

Which characteristic is true about a good embedding?

<p>Similar items should be positioned closely in the embedding space (B)</p> Signup and view all the answers

What do dense representations in embeddings typically include?

<p>Specific numerical values representing data features (A)</p> Signup and view all the answers

What is an example of what embeddings are NOT?

<p>Representations solely from neural networks (A), Restricted to specific types of input (C), Single modality representations (D)</p> Signup and view all the answers

Why is the concept of embeddings beneficial for AI-powered information retrieval?

<p>They provide a compact representation of diverse data (B)</p> Signup and view all the answers

How does the relationship between search and AI enhance the extraction of information?

<p>They improve the contextual relevance of information (A)</p> Signup and view all the answers

What does embedding relevance imply in the context of AI?

<p>The proximity of embeddings relates to their contextual similarity (A)</p> Signup and view all the answers

What aspect of embeddings might indicate their quality?

<p>Utility for various tasks beyond the initial purpose (B)</p> Signup and view all the answers

What is the primary function of embeddings?

<p>To provide a dense, fixed-size representation of data. (C)</p> Signup and view all the answers

Which embedding is noted as the very first one?

<p>Word2Vec (A)</p> Signup and view all the answers

What does cosine similarity measure in the context of embeddings?

<p>The angle between two vectors. (C)</p> Signup and view all the answers

What is a key advantage of using OpenAI embeddings?

<p>They offer options for both efficiency and performance. (A)</p> Signup and view all the answers

How are embeddings generally represented?

<p>As dense vectors containing numerical values. (B)</p> Signup and view all the answers

Which of the following is not identified as a use case for embeddings?

<p>Generating random numbers for security purposes. (D)</p> Signup and view all the answers

What is a necessary step for calculating nearest neighbor similarity using embeddings?

<p>Store embeddings as an array of vectors. (B)</p> Signup and view all the answers

What is the purpose of the exercise regarding training an embedding for web pages?

<p>To learn which features a web page provides. (C)</p> Signup and view all the answers

Flashcards

EU AI Act - What is it?

The EU AI Act was approved by the EU in March 2024 and came into effect in May 2024. This law introduces rules for the development and use of artificial intelligence (AI) systems in the European Union.

EU AI Act - High Risk Uses

The EU AI Act bans AI uses that could pose a significant risk to people's rights, such as in healthcare, education, and law enforcement.

EU AI Act - Transparency

Under the EU AI Act, AI companies must be transparent and provide information about their AI systems. For example, they must make it clear when someone is interacting with a chatbot or other AI, and they must label any AI-generated content.

EU AI Act - AI for High-Risk Areas

AI companies that develop AI models for high-risk sectors, like healthcare or critical infrastructure, must comply with specific requirements under the EU AI Act, including documenting their model development process and the data used for training.

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UN AI Resolution

The UN adopted a global AI resolution in March 2024, encouraging countries to protect human rights, personal data, and manage potential risks related to AI. It is non-binding but still considered an important step in regulating AI globally.

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Context Window

The maximum amount of text data a language model can process at once. Larger context windows allow models to remember more information from previous conversations.

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GPT-4 Turbo Context Window

GPT-4 Turbo, a powerful language model, boasts a massive context window of 128,000 tokens. This allows it to handle and understand large amounts of information, like long documents or extended conversations.

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Token

A 'token' is a unit of text used by language models. It can be a single word, punctuation mark, or even part of a word. The context window size is measured in the number of tokens.

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128,000 Tokens in Action

GPT-4 Turbo's 128,000-token context window enables it to process approximately 7,500 emails or about 500 MB of text data, equivalent to a year's worth of tweets or a college novel.

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Contextual Understanding

The ability of a language model to remember and utilize information from previous interactions or context. Context windows are crucial for enabling this ability.

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Traditional information retrieval

A type of search that uses indexes to quickly find relevant documents based on the presence or absence of specific words.

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Boolean search

A search technique that uses Boolean operators (AND, OR, NOT) to narrow down results by combining search terms.

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BM25

A ranking algorithm that considers term frequency (how often a word appears in a document), inverse document frequency (how common a word is across all documents), and field length (the number of words in a document) to determine a document's relevance.

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Search engines are more than just inverted indexes

A search engine comprises various components beyond just indexes, including document ingestion, processing, transaction handling, scaling, relevance ranking, and more.

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Limitations of sparse traditional search

Traditional search methods have limitations because they can't understand the meaning of words (semantics) or the relationships between words (correlation).

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Retrieval Augmentation

The process of retrieving information from a larger data source to enhance the understanding of a specific query.

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Retrieval Augmented Generation (RAG)

A method that combines information retrieval with language models to generate more accurate and informative responses.

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Information Retrieval

The ability to access and retrieve information from a large body of knowledge.

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Embeddings

Numerical representations of text that capture the semantic meaning of words and sentences.

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Embedding Techniques

The process of transforming text into numerical representations that capture its meaning.

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Scaling Language Models

The ability to process large amounts of information and adapt to different situations.

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Computational Cost

The cost associated with running language models, which increases with larger context windows and increased complexity.

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What are Embeddings?

Representing data in a dense, compact, and fixed-size format often learned through machine learning.

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Why are embeddings important for information retrieval?

Embeddings condense information about data points, making it efficient to find similar items.

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What are "Deep Multimodal Embeddings"?

Embeddings are not restricted to one type of data. They can represent text, images, audio, etc., even in combination.

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What makes a good embedding?

Similar data points are located closer together in the embedding space.

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How do embeddings improve search?

Embeddings are used in search engines to find relevant results quickly.

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How do embeddings drive AI-Powered Information Retrieval?

Embeddings enable AI systems to analyze and understand complex data to provide better information.

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How do embeddings go beyond naive nearest neighbor search?

Beyond simply finding the nearest neighbor, embeddings enable finding more relevant results based on complex relationships between data points.

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What are embedding databases?

Embeddings can be used to create specialized databases designed to store and retrieve information based on their relationships.

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What is an embedding?

A compact, learned numerical representation of data, often used for tasks like search, recommendation, or document similarity.

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What is the goal of an embedding?

Embeddings represent data in a way that algorithms can easily understand and compare. They are useful for tasks that involve finding similar items or understanding relationships between different pieces of information.

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Word2Vec

Word2Vec is one of the first embedding methods, focusing on understanding relationships between words.

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Sentence Transformers

Sentence transformers are used to create embeddings of entire sentences, allowing for comparisons and understanding of sentence similarity.

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CLIP

CLIP allows you to represent both text and images as embeddings, leading to a richer understanding of relationships between visual and textual information.

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OpenAI Embeddings

OpenAI embeddings provide fast and efficient representations of text data and are easy to integrate with your projects.

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Nearest Neighbor Similarity

A method to find similar items by comparing their embeddings. Essentially, measuring how alike their numerical representations are.

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Embedding Databases

When you have a large amount of data, traditional nearest neighbor search can be computationally expensive. Embedding databases optimize this process, making it faster and more scalable.

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Study Notes

EU AI Act

  • EU approved the AI Act in March 2024, effective May 2024 within the EU.
  • Some AI use cases will be banned later due to high risk to fundamental rights, such as in healthcare, education, and law enforcement.
  • Tech companies will be required to label deepfakes and AI-generated content and notify users when interacting with AI systems, like chatbots.
  • Citizens can complain if harmed by AI.
  • A new European AI Office will coordinate compliance, implementation, and enforcement.
  • AI companies will need to be more transparent in high-risk sectors, including critical infrastructure and healthcare.
  • Companies developing large language models must create and maintain technical documentation detailing model development, copyright compliance, and training data summaries.
  • Free open-source AI models sharing every detail of their development (architecture, parameters, and weights) are exempt from many AI Act obligations.

UN AI Regulation

  • UN adopted its first global AI resolution in March 2024.
  • Proposed by the US and co-sponsored by China and over 120 other nations.
  • Encourages countries to safeguard human rights, protect personal data, and monitor AI risks.
  • The resolution is non-binding, but still important.

AIDA Canada's AI Act

  • Artificial Intelligence and Data Act (AIDA) proposed in June 2022 by the Canadian government.
  • AIDA aims to ensure responsible AI development in Canada and promote Canadian firms' values in global AI.
  • AIDA's regulations will largely apply to "high-impact AI systems," similar to the EU AI Act's "high-risk" category.

AI Problems & Solutions

  • Lack of Factuality, Reliability of Results:
    • AI models can generate fluent but incorrect, toxic, or undesirable outputs (hallucination).
    • Potential solutions involve requiring models to cite sources (e.g., through models like GPT-01, Bing search, or Perplexity.ai). Strategies include improving model calibration (“knowing what they know”) and better context provision.
  • Lack of Robustness:
    • AI models perform less efficiently on new applications, domains, or languages.
    • Possible solutions include model engineering and prompting for specific tasks. Custom models for specific domains can be created or fine-tuned with domain-specific datasets (e.g. Meta Galactica in science, and/or Google Med-PaLM in medicine or BloombergGPT in finance.

LLM APIs & Implementation

  • Popular LLM APIs include OpenAI, Anthropic, and AWS Bedrock.
  • OpenAI APIs offer models like GPT-4 and GPT-4-mini, with tasks including completion, fine-tuning, and function calling. Anthropic APIs offer Claude 3 for completion tasks. AWS Bedrock has models like Titan and Llama.
  • OpenAI API calls to use the ChatGPT model are easy, similar to REST API invocations.

Local LLM Execution

  • Open-source communities offer collections of ChatGPT-like chatbot LLMs that can run locally on computers.
  • Reasons for running LLMs locally include offline mode, privacy/security, and cost savings.
  • The GPT4All ecosystem lets one install LLMs on their computer and try various models (GPT-J, LLaMA, MPT, Replit, Falcon, and StarCoder).

Libraries for LLM Applications

  • Language models can be chained together using LangChain, a popular Python library.
  • Streamlit helps build ChatGPT-like web interfaces.
  • Hugging Face offers tools for pre-processing, training, fine-tuning, and deployment of language models.
  • Vector databases (e.g., Pinecone, ChromaDB, Milvus) store and manage embedding data.

General notes from the slides

  • AI-based features can be integrated into web applications.
  • Issues like context window limitations, needing more information relevant to the query, and the need for appropriate tools to access external information require consideration.
  • Information retrieval (IR) is a process using resources within a collection of resources to meet an information need, including full-text searches.
  • Embedding indexes are data structures that let you perform approximate nearest neighbor searches. They are useful but have limitations.
  • Tools can be used as elements in a larger chain. Agents use LLM tools more automatically.
  • LLMs are more useful when fed with external data, potentially via tools and agents.
  • Retrieval Augmented Generation (RAG) is a practical approach to allow LLMs to access external data, enabling better, more robust answers.

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Description

Test your knowledge on AI regulations, token limits for different models, and the impact of context window sizes in language processing. This quiz covers fundamental aspects of the EU AI Act and various AI models like GPT-4 and GPT-3.5. Challenge yourself to see how well you understand these crucial topics in artificial intelligence.

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