funda18 Flashcards
(24 cards)
What is Artificial Intelligence (AI)
AI is the use of computers and machines to mimic human intelligence, such as reasoning, problem-solving, learning, and decision-making.
The goal of AI is to create systems that can adapt, learn, and respond intelligently to diverse situations.
Generative AI Overview
Generative AI is a subset of AI that creates new content (e.g., text, images, audio, video, or code) by learning patterns and structures from training data.
It uses advanced machine-learning models like transformers and GANs to generate human-like outputs.
Examples include ChatGPT (text generation) and DALL·E (image generation).
How Generative AI Creates Content
Training Phase:
Data Collection: AI models analyze large datasets to learn patterns.
Pattern Learning: Models like deep learning capture relationships within the data.
Model Architecture:
Transformers: Predict sequences (e.g., words or pixels).
GANs: Improve outputs through generator-discriminator collaboration.
Generation Phase:
Input prompt → AI generates content based on learned patterns.
Foundation Models in Generative AI
*Foundation models are large, pre-trained models that can perform a wide range of tasks without needing task-specific training.
*They learn by breaking data into “tokens” (e.g., words or pixels) and recognizing patterns during training.
*These models are often enhanced with plug-in modules to handle specific domains like legal or medical text.
Large Language Models (LLMs)
*LLMs are foundation models designed for human language, capable of analyzing context, recognizing patterns, and generating content.
*They use algorithms to predict the next word or token in a sequence, enabling coherent responses.
*Applications include chatbots, translation, summarization, and creative writing tasks.
Openness of Large Language Models
Open-Source LLMs:
Examples: Llama 2 (Meta) and BERT (Google).
The code and parameters are publicly available, allowing for scrutiny, modification, and community collaboration.
Proprietary Models (e.g., GPT-4):
Only API access is provided for interaction.
The underlying parameters and architecture are not disclosed, making it less transparent.
Generative AI as a Transformative Technology
Impact on Product Development: Enables faster and more efficient creation of products and services.
Examples of Industry Applications:
Healthcare: Accelerates the development of medicines.
High-Tech: Facilitates the creation of media content.
Banking: Improves data analytics capabilities.
*Investment Trends: In the US, 50% of AI investments are focused on generative AI, underscoring its transformative potential.
AI’s Core Strength - Prediction
Advancement: AI excels at prediction, which involves generating new information from existing data.
Examples:
Predicting weather patterns.
Classifying images.
Benefit: Enhances decision-making in uncertain conditions, improving the accuracy and efficiency of processes.
Challenges to Productivity Gains from AI
Real productivity gains require:
Re-engineering business processes to integrate AI effectively.
Leveraging AI to complement human judgment, rather than replace it entirely.
Historical Context:
Robert Solow (1987): Observed that technological advancements often take time to reflect in productivity statistics.
Example: Labor productivity doubled its growth rate between 1995 and 2000 as businesses adapted to computers
Generative AI as a Foundation Model
Learning Process: It learns from vast datasets by analyzing patterns and structures, breaking data into tokens for better understanding.
Versatility: Pre-trained on diverse data, generative AI can perform a wide range of tasks without requiring extensive fine-tuning.
Enhancements: “Plug-in” modules can be added to enhance the model’s attributes, expanding its capabilities further.
AI’s Impact on Productivity Growth
Predictions:
McKinsey forecasts 1.5% productivity growth from AI over the next decade.
Daron Acemoglu estimates a more modest 0.66% increase due to AI’s current limitations.
Reasons for Modest Growth:
AI currently automates easier-to-learn tasks; harder tasks will take longer to adapt.
Less than 5% of US economy tasks (manual labor, social interaction, high-level judgment) can benefit from AI.
Augmentation vs. Automation in AI
Augmentation: AI enhances human capabilities.
Examples:
Da Vinci surgical robots improve surgeons’ precision.
Tokyo taxi drivers use AI for demand prediction, boosting productivity by 14% (especially for less-skilled drivers).
Automation: Replaces human roles in some tasks.
Limited due to the complexity of many economic activities.
Generative AI’s Impact on Work Efficiency
GitHub Copilot Case Study:
Study observed 187,489 developers (2022-2024).
Developers using Copilot increased their focus on coding tasks and decreased involvement in project management.
Efficiency gains driven by:
Autonomous behavior: Developers relied more on their exploration.
Exploration behavior: Experimented with new approaches rather than sticking to existing ones.
Challenges in Protecting AI Innovation
Issues with Patents:
AI relies heavily on software, which is hard to patent.
Rapid innovation cycles make it difficult to establish protections.
Tacit Knowledge:
Hands-on experience in AI development gives pioneer firms an advantage.
Secrecy:
Protecting details like model parameters, training datasets, and fine-tuning methods is critical.
Complementary Assets in AI Development
Key Assets:
Hardware: 350,000 NVIDIA H100 GPUs ($10 billion for Llama 3).
Data: Requires public, semi-public, copyrighted, or user-generated data for training.
Ethical/Safety R&D: Investments to address regulatory and safety concerns.
Durability:
Technological changes may reduce the importance of computational power but make access to unique data and AI safety critical long-term.
Case Study: Llama’s Open-Source Model
Timeline:
April 2022: Llama released with documentation but without parameter weights.
March 2023: Model weights leaked, spurring rapid innovation in the open-source community.
Impact:
By the end of 2023, 30% of large language models built by startups and universities were based on Llama or its derivatives.
Open Source Benefits:
Fosters a vibrant ecosystem for third-party innovation.
Attracts top talent and enhances the firm’s reputation as a benevolent leader in AI.
AI as a General-Purpose Technology
Similarities to Electricity and the Internet:
AI has broad applicability across industries and society.
Transformative Potential:
AI can augment workers across diverse sectors (e.g., healthcare, education, blue-collar jobs).
Long-term adoption will depend on its integration into systems that complement human judgment rather than replace it.
openess pp
What are the key concepts the authors use to analyze the competitive environment in generative AI?
The authors primarily focus on two concepts from innovation economics: appropriability and complementary assets.
◦ Appropriability refers to whether firms can control the knowledge generated by their innovations.
◦ Complementary assets are the specialized infrastructure and capabilities that firms need to effectively commercialize their innovations and compete in the market
What is the “technology stack” of generative AI, and why is it important?
The generative AI technology stack has multiple layers:
◦ Compute Layer: Includes the hardware and software infrastructure (like GPUs) needed for training and running AI models.
◦ Data and Cloud Storage: The large datasets used for training and the cloud infrastructure to host them.
◦ Foundation Model Layer: The pre-trained AI model itself that provides a general-purpose interface for applications.
◦ Application Layer: The layer that facilitates end-user interaction with the foundation model, which may include specialized applications.
◦ The foundation model layer is a potential chokepoint for innovation and competition because control at this layer can limit entry and innovation at the application level
According to the authors, what is the significance of “openness” in the context of generative AI?
While many computer scientists emphasize openness and transparency (making the code, data, and model details available) as a way to foster competition, the authors argue that it is not enough. They believe that:
◦ Openness in a technical sense doesn’t necessarily lead to a competitive market.
◦ Incumbents can control other key factors, such as complementary assets, to limit competition even if models are technically open.
◦ Discussions about the true meaning of “openness” can be a distraction if the goal is to ensure ongoing competition in generative AI
What are the six key complementary assets that the authors identify as being critical for success in generative AI?
The six complementary assets are:
◦ Compute environment: The hardware and software needed to train and fine-tune models.
◦ Model-serving and inference capabilities: The ability to deploy the model in a production environment and provide outputs to end-users.
◦ Safety and governance procedures: Measures to ensure models are developed and used responsibly.
◦ Benchmarks and metrics: Tools to evaluate the performance of foundation models.
◦ Access to massive quantities of non-public training data: Large and diverse datasets needed for effective model training.
◦ Data network effects: The ability of a model to improve with user engagement and feedback
How do the authors evaluate the role of intellectual property in protecting foundation models?
The authors argue that formal intellectual property rights, like patents, are not especially valuable for protecting foundation models from imitation. They state that:
◦ Much of AI/ML research may fall into the category of “abstract concept” and therefore not be patentable.
◦ The pace of innovation in generative AI may not align well with the pace of the patent system.
◦ Firms can keep critical knowledge proprietary or tacit through other means, such as keeping model weights secret, or through the accumulation of craft knowledge
What was the “Llama leak,” and what did it demonstrate?
The “Llama leak” refers to the accidental release of Meta’s Llama model, including its weights, online. This event demonstrated that:
◦ Even when a model’s inner workings are exposed, it doesn’t necessarily guarantee a more competitive market.
◦ A vibrant open-source community can rapidly build upon open models.
◦ Incumbents might struggle to maintain control of the knowledge at the core of the models they sponsored.
◦ The leak did not prevent leading firms from adopting proprietary models
What are some of the public policy recommendations made in the paper?
The authors propose the following public policy measures:
◦ Establish credible performance benchmarks to help compare foundation models, with a government-sponsored organization similar to the National Renewable Energy Lab (NREL).
◦ Address key legal issues, such as the use of copyrighted data for training purposes and provide guidance on existing laws as they apply to AI.
◦ Encourage the fractionalization of infrastructure to allow smaller firms and researchers to access the resources needed to develop AI models.
◦ Establish platform policies to ensure competition at the application layer of the technology stack and to prevent walled gardens that limit third party application providers
